To help find the right tool, we’ve compared the top five data migration tools to move, transform, and optimize your organization’s data efficiently. Here are our top picks:

1. AWS Database Migration Service: Best for AWS Cloud Migration
2. IBM Informix: Best for Versatile Data Management
3. Matillion: Best for Data Productivity
4. Fivetran: Best for Automated Data Movement
5. Stitch: Best for Versatile Cloud Data Pipelines

Top 5 Data Migration Tools Comparison

Take a look at some of the top data migration tools and their features:

Jump to:

• Key Features of Data Migration Tools
• How to Choose the Best Data Migration Tools for Your Business
• Frequently Asked Questions (FAQs)
• Methodology
• Bottom Line: Choosing the Right Data Migration Tool

AWS Database Migration Service

Best for AWS Cloud Migration

The technology giant Amazon extends data migration services to customers through AWS Database Migration Service. It removes undifferentiated database management tasks to simplify the migration process. This high-performance tool offers the additional advantage of access to other AWS solutions and services. Thus, it is best suited for businesses looking for AWS cloud migration support and features.

Pricing

The AWS Free Tier plan helps users get started with the data migration service for free. See the AWS Pricing Calculator for detailed pricing plans and information.

Features

• Centralized access with AWS Management Console
• Multi-AZ and ongoing data replication and monitoring
• Homogeneous and heterogeneous migration support
• Automated migration planning with AWS DMS Fleet Advisor

Pros

• Simple and easy-to-use service
• Automatic schema assessment and conversion
• Supports migration among 20-plus databases and analytics engines

Cons

• Large-scale data migration can be costly
• Frequent changes in pricing

IBM Informix

Best for Versatile Data Management 

IBM offers data management and migration solutions through an embeddable database: IBM Informix. It is a highly versatile tool that simplifies administration and optimizes database performance. It relies on a hybrid cloud infrastructure. Informix is best for multi-tiered architectures that require device-level processing.

Pricing

IBM Informix Developer Edition is ideal for development, testing, and prototyping and can be downloaded for free. The Informix Innovator-C Edition supports small production workloads and is also freely available. Other editions are available that offer a complete suite of Informix features. Contact the team for their pricing details.

Features

• Real-time analytics for transactional workloads
• High availability data replication (HADR) for mission-critical environments
• Event-driven processing and smart triggers for automated data management
• Silent installation with a memory footprint of only 100 MB

Pros

• Robust processing and integration capabilities
• Minimal administrative requirements
• Native encryption for data protection
• Real-time analytics for fast insights

Cons

• Big data transfers can slow down the platform
• Complex pricing policies

Matillion

Best for Data Productivity

Matillion helps businesses with next-gen ETL (extract, transform, load) solutions for efficient data orchestration. It can automate and accelerate data migration with its universal data collectors and pipelines. With its advanced capabilities, it helps extract full value from a business’s existing infrastructure.

Pricing

Matillion follows a simple, predictable, and flexible pricing model along with free trial versions. It offers Free, Basic, Advanced, and Enterprise editions and pay-as-you-go options. The minimum price for paid plans is $2 per credit. Contact the vendor to speak to an expert for details.

Features

• Change data capture and batch data loading for simplified pipeline management
• Low-code/no-code GUI
• Reverse ETL and prebuilt connectors for easy data sync back
• Drag-and-drop functionality for easier usage

Pros

• Fast data ingestion and integration
• Enterprise assurance
• Post-load transformations
• Customizable configurations

Cons

• High-volume data load can cause crashes
• Support issues
• Needs better documentation

Fivetran

Best for Automated Data Movement

Fivetran offers an efficient platform for data migration. This cloud-based tool relies on a fully-managed ELT architecture that efficiently handles all data integration tasks. It has numerous database replication methods that can manage extremely large workloads.

Pricing

Fivetran offers a 14-day free trial option. It has Free, Starter, Standard, Enterprise, Business Critical, and Private Deployment plans with different features and pricing options. Contact the sales team for specific pricing details.

Features

• More than 300 prebuilt, no-code source connectors
• Quickstart data models for automated transformations
• End-to-end data monitoring with lineage graphs
• Fivetran API for programmatic scaling

Pros

• Flexible connection options for secure deployment
• Advanced role-based access control
• Data catalog integrations for metadata sharing

Cons

• Only cloud-based solutions
• Lacks support for data lakes
• Expensive option for large volumes of data

Stitch

Best for Versatile Cloud Data Pipelines

Stitch offers fully automated cloud data pipelines that can be used without any coding expertise. It helps consolidate data from a vast range of data sources. This enterprise-grade cloud ETL platform is highly trusted for extracting actionable insights.

Pricing

Stitch offers a free trial for two weeks. It follows a transparent and predictable pricing model with no hidden fees. There are three plans: Standard, Advanced, and Premium. The minimum price starts at $100 per month, if billed monthly, or $1,000 if billed annually. Contact the sales team for exact pricing details for each plan.

Features

• 140+ popular data sources
• External processing engines like MapReduce and Apache Spark
• In-app chat support

Pros

• No coding is required
• Centralized, fresh, and analysis-ready data
• Automatically updated pipelines

Cons

• Needs a more friendly user interface
• Customer support issues

Key Features of Data Migration Tools

The primary purpose of using data migration tools is to simplify data transfer across different systems, ensuring integrity and accuracy. Some of the key features they include to accomplish this goal are:

Data Transformation

Data migration tools need to consolidate data from multiple sources, which requires them to have data transformation capabilities. Having a standardized data structure or format across different environments is impossible, but data transformation features can help to make these disparate data sources more manageable and uniform. These tools must optimize data for the destination system, ensuring consistency and coherence. They must also be able to identify inconsistencies or issues and transform data as per target requirements.

Connectors

Data migration tools connect various data sources and targets. Thus, they require various connector modules to help them interact with different systems during a migration. With comprehensive connector coverage, data migration tools can establish a link between the source and targets using required protocols, APIs, or drivers. As a result, data can be efficiently extracted from the source and loaded into the target.

Real-time Analysis

Efficient data migration demands real-time insights for seamless data exchange. Real-time analysis helps in the early detection of errors and accurate data mapping between the source and target. This makes it an essential feature of data migration tools, as it helps with performance monitoring, error detection and prevention, data validation, synchronization, and consistency.

Security and Compliance

Data migrations involve substantial risks like information misuse, unauthorized access, data loss, and corruption. These incidents can lead to severe financial and reputational damages, and may also involve potential legal liabilities. Due to these risks, data migration tools must adhere to strict security and compliance standards to minimize security incidents and other risky outcomes.

Customization

Different businesses have different data requirements. To meet business expectations, data migration tools must offer customization features for changing business requirements. A strong data migration tool will also provide the flexibility and adaptability to help organizations with tailored migration processes.

How to Choose the Best Data Migration Tool for Your Business

Data migrations and similar operations are risky processes, as they involve moving your organization’s sensitive information. Thus, choosing a versatile and reliable tool that ensures a smooth and successful migration is essential.

Here are some key considerations to help select the best data migration tool for specific business needs:

Configuration Type

There are two distinct types of data tool configurations: cloud-based and on-premises. On-premises data tools do not rely on the cloud for data transfer. Instead, they migrate data within the organizational infrastructure, offering full-stack control. These are effective solutions when the business desires to restrict data within its own servers.

Cloud-based data migration tools transfer and store data using cloud platforms on cloud servers. The architecture can be expanded effectively due to the quick availability of resources. These tools also facilitate data migration from on-premises to cloud systems. In addition, they are highly secure and cost-effective.

Enterprise Cloud Migration Services

Choosing enterprise-focused cloud migration services can give you an additional edge. Data migration services that are specifically designed for enterprises can more effectively take care of industry standards and maintain top-notch IT infrastructure. Besides, they offer constant updates based on the latest advancements in technologies and methodologies. They can handle complex business projects with well-designed transformation processes.

Technical Support

When choosing a data migration tool, it is also essential to pay attention to technical support capabilities offered by the vendor. Businesses especially need post-migration support to address any issues. They must also help develop robust backup and recovery strategies to deal with system failures or other potential challenges.

Additional Considerations

There are many different types of data migration, like storage, database, cloud, application, data center, and business process migration. Therefore, you should select the most suitable migration tool based on your business goals and the types of migration you want to complete.

Apart from these aspects, it is also vital that the tool you select integrates efficiently with your current business infrastructure and supports data sources and target systems. This can reduce disruptions and compatibility issues.

Frequently Asked Questions (FAQs)

How Do Data Migration Tools Benefit Businesses?

Data migration tools benefit businesses by streamlining data transfer, storage, and management processes, ensuring accuracy. Since they automate these processes, companies can focus on other essential operational aspects. Also, these tools offer the necessary flexibility and scalability to cater to specific demands.

What Types of Data Can Data Migration Tools Handle?

Data migration tools handle enormous volumes of data in different formats and structures within different systems. They deal with both structured and unstructured data and need to work with databases, enterprise applications, data warehouses, spreadsheets, JSON, XML, CSV, and other file formats.

What Are Open-source Data Migration Tools?

Open-source data migration tools are publicly accessible, typically free-to-use solutions. The source code is available on a central repository and can be customized too. Although they require technically skilled employees for proper implementation and use, community-driven support is a major plus with open-source technology, as you can get assistance from technical experts whenever it’s needed. Therefore, these are ideal options for small-scale projects involving lesser complexities.

Methodology

We implemented a structured research methodology to analyze different data migration tools available in the current marketplace. The research was based on specified evaluation criteria and essential feature requirements.

We evaluated each tool’s real-world performance based on user reviews and performance, as customer satisfaction is crucial. After in-depth analysis with several other criteria, we finally documented the top results for the best data migration tools.

Bottom Line: Choosing the Right Data Migration Tool

Choosing the right data migration tool is crucial for aligning specific business goals. Throughout this article, we explored the top five tools, each with unique strengths. When selecting a data migration solution for your business, consider factors like data complexity, scale, real-time vs. batch processing, security, and compatibility.

Remember, the key to successful data migration lies in aligning your specific business goals with the capabilities offered by your chosen tool. Take the time to evaluate and understand your requirements, consult with stakeholders, and make an informed decision that sets your organization on the path to achieving its desired outcomes.

Also See

Also See Data Migration Trends

Table Of Contents

• Digital Transformation Companies Comparison Table
• Top Digital Transformation Companies
  • Accenture: Best For Reporting Insights
  • Deloitte: Best For Scalability
  • Boston Consulting Group: Best For People-Focused Consultation
  • Cognizant: Best For Company Culture
  • Capgemini: Best For Telecommunications
  • Microsoft Corporation: Best For Industry Cloud
  • McKinsey & Company: Best For Subject Matter Expertise
• Benefits of Working With a Digital Transformation Company
• How To Choose What Digital Transformation Companies Best For You
• Bottom Line: Digital Transformation Companies

Digital Transformation Companies Comparison Table

Top Digital Transformation Companies

The top digital transformation companies all offer feature-rich services at varying prices. For example, a consulting engagement with these companies can help your business transform billing and payment processing, provide better analytics and reporting to gain deeper insights into customer behavior, and improve infrastructure across your organization. In short, digital transformation service providers can help your business keep up with the competitive nature of technology and industry needs.

Your business’s specific needs and budget will determine which company is the right fit, but comparing pricing can be a challenge. It is not uncommon for digital transformation providers not to disclose pricing publicly because it is dependent upon the particular tools and services involved, the complexity and duration of the engagement, and other factors that make it difficult to offer a one-size-fits-all pricing strategy.

When selecting a provider, consider the use cases outlined below to find those that best meet your specific needs and then contact those providers to get cost estimates for the right solutions to address them.

Accenture: Best For Reporting Insights

Accenture’s digital transformation services are part of its Technology Strategy and Advisory Practice, and include assistance with cloud acceleration, data-driven company tools, operating models and innovation, network connection services, and modern architecture using the Accenture Momentum digital transformation tool.

Features

• Real-time Reporting: Accenture’s digital transformation tools track the status of initiatives such as assessing, reviewing, and resolving risks.
• Value Drivers: The company’s digital transformation tools build and visualize value trees to create complete business cases using many details to help with individual initiatives.
• Forward-looking Insights: Accenture Momentum provides reporting dashboards for dynamic views and drill-downs.

Pros

• Holistic evaluations
• Processing oriented systems
• Strong service delivery system and personnel

Cons

• Limited data flexibility
• Limited dynamic frameworks
• Slow delivery timelines

Pricing

Accenture does not list pricing on its website. However, it recently introduced Manage myPrice, its new approach to pricing. For more detailed information, contact sales.

Deloitte: Best For Scalability

Deloitte’s digital transformation capabilities are incorporated within its Strategy and Analytics business as well as its Monitor Deloitte arm. Deloitte aims to help enterprises with the capacity for change to help fuel growth, practice continuous transformation, and harness insights.

Features

• Intelligent Mining: Deloitte assists companies in implementing sustainable transformation by offering the required multidisciplinary skills and industry experience needed.
• Business Scalability: It helps companies realize and refine their digital transformation vision by providing flexibility and offering integrated delivery teams for an agile operation.
• Concept Refinement: Deloitte offers iterative concept refinement and faster income statements through its Deepen, Design, and Prove steps.

Pros

• Excels at integrating technology
• Experts in DevOps and automation
• Helps with multi-cloud setup

Cons

• Limited technology capabilities
• High prices
• Heavy research needed

Pricing

Deloitte does not list prices on its site. Contact the sales team or see the pricing strategy page to learn more, or send a Request for Proposal.

Boston Consulting Group: Best For People-Focused Consultation

Boston Consulting Group (BCG) describes its digital transformation capabilities as focused on people and technology. Its six steps of success include crafting a clear strategy, commitment to leadership from top to middle, putting the best employees for the job in the right place, having an agile government mindset, monitoring and controlling progress, and creating a business-led data platform.

Features

• Digital Skill-Building: BCG’s digital transformation is about evolving employee and business capabilities in such areas as data science, location strategies, and finding the balance between insourcing and outsourcing.
• Operating Model Transformation: BCG’s goal is to work to align governance, processes, and organizational structure with a platform-driven operating model.
• Outcomes Implementation: Digital transformation practices include identifying, prioritizing, and implementing high-impact use cases for your company.

Pros

• Highly regarded consultants
• Wide range of capabilities
• Fees based on agreed-upon Key Performance Indicators (KPIs)

Cons

• Not for small companies
• A slow-paced approach
• No specific tools used

Pricing

The Boston Consulting Group offers a pricing page but does not offer specific pricing. Contact sales for more information.

Cognizant: Best For Company Culture

Cognizant offers digital transformation services through its Digital Strategy umbrella. Its goal is to keep up with customer demands across all sides of a business to ensure relevancy in its infrastructure. Cognizant’s digital transformation services offer insight into transformation, managed innovation, workforce transformation, change adoption, and transformation enablement. The offerings aim to create a positive company strategy that adds agile processes and a digital approach in different departments within the company.

Features

• Transformation Insights: Customers improve their insights through reinvention, offering an agile approach and using execution expertise.
• Managed Innovation: Insight-driven processes develop client solutions with new forms of value to establish, nurture, and grow innovation capabilities.
• Change Adoption: Cognizant’s change adoption offers digital tools to enable growth and agility with a structured approach to help clients with their adoption rate and pace with business change all around them.

Pros

• Strong cloud technology
• Diverse technical services
• Flexible payment model

Cons

• Limited location access
• Weak strategy capabilities
• Limited customer service help

Pricing

Cognizant does not list pricing. However, it does provide a pricing and fees page that offers direct contact and lists available products and services. Contact sales to discuss a payment model.

Capgemini: Best For Telecommunications

Capgemini’s digital transformation services are focused on the telecommunications industry to help accelerate the transformation from communication service providers to digital service providers. For B2C operators, it enables new value digital proposals; for B2B, it offers the ability to rewire and reboot customer experience and operational excellence. For vertical industries, it will help increase revenue and efficiency with the better-connected world.

Features

• Customer-First B2C: Capgemini promotes designing digital and market-facing services to design business models and operations to support connected experiences to provide value.
• B2B Omni-Channel Customer Experience: Capgemini aims to improve customer experience and help transform companies into digital service providers.
• 5G Monetization: The company’s support for vertical industries helps them make the most of revenue and efficiency opportunities in the current digital world.

Pros

• Deep digital expertise
• Flexible approach to digital transformation
• Helps with customer’s self-funded projects

Cons

• Limited global expertise
• Limited strategy capabilities
• Expensive platform

Pricing

Capgemini’s site explains its PriceLab pricing tool but does not provide specific pricing. Contact the company to learn more.

Microsoft Corporation: Best For Industry Cloud

Microsoft is a top tech company whose digital transformation services focus on five industries: financial services, government, health care, manufacturing, and retail. It has led with digital transformation by modernizing platforms with Microsoft 365 and Azure Edge, helping organizations find solutions to fix common issues, giving data and analytics assistance, and helping them further engage with customers.

Features

• Industry Cloud: Azure Cloud helps companies gain agility while accelerating innovation in a specific industry.
• Customer Relationship Management: Microsoft’s digital transformation services help companies reach more customers by outperforming competitors with new innovative digital agility.
• Artificial intelligence (AI) For Digital Transformation: AI benefits digital transformation by increasing intelligent marketing, by adding innovation to the current infrastructure, and with analysis.

Pros

• Wide range of services
• Strong documentation
• Reliable known company

Cons

• Limited navigation help
• Complicated networking
• Limited reporting ability

Pricing

Microsoft is the most transparent provider of the seven when it comes to pricing, and offers estimated pricing based on which tools a customer uses. Use Microsoft’s pricing calculator, which also lists digital transformation products. Contact sales for more information.

McKinsey & Company: Best For Subject Matter Expertise

McKinsey & Company’s digital transformation service, McKinsey Digital, helps businesses add capabilities such as AI and other new technologies to automate operations, encourage digital growth, create unique digital experiences, and improve customers’ digital talent and culture.

Features

• Speed Of Impact: McKinsey Digital aims for a quick impact to generate higher cash flows and is dependent on temporary subject matter experts to fill roles within companies.
• Long-term Sustainability: McKinsey Digital addresses and emphasizes capability building, organizational setup, and improvement to give a continuous impact.
• Emphasis On Innovation: McKinsey Digital offers companies the ability to invest heavily in their IT departments to build completely new infrastructure and have more subject matter experts.

Pros

• Highly regarded company
• Many projects completed
• Pilot testing before expanding to other parts of the company

Cons

• Known to be expensive
• Limited technical capabilities
• Hard-driving attitude

Pricing

McKinsey & Company does not list pricing on its website, but provides a pricing article that explains how customers are charged. Contact sales to get a quote.

For more: 5 Digital Transformation Examples

Benefits of Working With a Digital Transformation Company

Digital transformation supports the business in multiple ways, and a good digital transformation consultant will work with customers to create a framework that does the following:

• Boosts Performance: Digital transformation helps keep up with new technology and improves performance, such as adding automation to complete menial tasks and free up resources for strategic work.
• Improves Company Communication: Digital transformation provides staff with new communication skills and tools, and enables business improvements.
• Enhances Innovation: Digital transformation can provide better access and insights to help innovate new products and services and new ways of doing things.
• Informs Decision-Making: Because digital transformation uses analytic tools, it provides insight into the correct decision on what’s best for a company and its data.

How To Choose a Digital Transformation Company 

There are several criteria to weigh when looking to choose the right digital transformation services provider.

Outline a Strategy

When a business looks into working with a digital transformation company, it should know exactly what it wants from the process. Finding a provider to help refine and execute a digital strategy can be more effective than rushing into an engagement without planning ahead.

Research Providers

Before picking a digital transformation provider, research the company and its customer stories. If it is meeting the needs of similar companies and the case studies sound familiar, it’s more likely to be a good fit for your own needs too.

Inventory Tool Stack

Digital transformation requires measuring the effectiveness of your current infrastructure and tool stack. For example, if a company uses a cloud-based infrastructure, on-site equipment likely will not help during the transformation process—technologies must support each capability. Before taking on a digital transformation project, make an inventory of existing tools to provide a more clear picture of what the transformation will encompass.

Consider Budget

While it can be difficult to compare prices from providers, a company considering a digital transformation project should know what it can spend and what it is willing to invest. Narrow down providers by their ability to meet specific needs, and then work with their sales teams to price out an engagement. Providers can also help organizations calculate the potential ROI of such a project and help with cost/benefit analysis.

Bottom Line: Digital Transformation Companies

When considering a digital transformation provider, it is vital for companies to weigh features and costs against their primary goals, including refining and executing their strategies and frameworks. While each digital transformation consulting firm offers a variety of services, matching them to specific use cases can help companies evaluate them against their specific needs.

For more information on digital transformation: Data Transformation Trends

Meanwhile, the ever-growing value of big data is driving data scientists to increase their access along with their ability to mine and analyze data for insights and information by adapting how data repositories are managed in relation to the type of data they house. While some legacy and on-premises solutions continue to be indispensable, a mass shift to the cloud is proving to be the answer to many of the problems organizations are facing in regards to data volume, compatibility, and accessibility.

Companies of various sizes and industries adapt to progress at different rates and may migrate data for different reasons. The five major trends in data migration in 2023 reflect the industry’s attitude as a whole toward solving specific problems.

1. A Shift Towards Data Lakehouses

Data lakehouses are open data management architectures that combine the flexibility, cost-efficiency, and scale of data lakes with the data management abilities of data warehouses. The result is a unified platform used for the storage, processing, and analysis of both structured and unstructured data. One reason this approach is gaining popularity is a sustained desire to break down data silos, improve quality, and accelerate data-driven decision-making within organizations.

Data lakehouses’ large capacity enables them to handle large volumes of data in real time, making them ideal for live consumer data, Internet of Things (IoT) networks, and physical sensors. Their ability to process data from multiple sources makes it easier for organizations to gain insights from multiple data streams.

Additionally, the centralization of data lakehouses allows for a unified, up-to-date view of data across an entire organization, facilitating inter-departmental collaboration on data-based projects and greatly reducing the costs and complexity of hosting multiple data storage and processing solutions.

2. A Focus on AI and Automation in Governance

Data migration helps organizations keep pace by ensuring their systems are able to accommodate the ever-increasing flow of new data. To simplify the already complex and time-consuming task of data governance, many companies are turning to artificial intelligence (AI)/machine learning (ML) algorithms and automation.

These technologies have revolutionized data migration by allowing organizations and data managers to automate some of the many manual processes it involves. It also enables them to reduce the risk of failures due to human error and execute the migration process more accurately and efficiently. With the help of smart algorithms, organizations can also better gain insights into their data than previously possible while identifying and eliminating data duplicates, which may reduce storage costs and improve performance.

Thanks to the recent boom in AI and ML-based technologies being developed and partially launched by a number of cloud computing giants, including Microsoft and Google, the role of such technologies in the more critical processes of data migration is likely to increase as the models become more and more sophisticated.

3. Expanding Storage Capacity

The world is expected to generate around 120 zettabytes of data in 2023, a nearly 24 percent increase from the prior year. This data is generated from a wide variety of sources, including IoT devices, log files, and marketing research. In this case, bigger is better—many organizations are looking to embrace big data by expanding storage capacities through novel methods of data storage.

One prominent option is cloud storage, which stands out as a scalable, reliable solution that’s also easily accessible over the internet. However, one of the challenges that arises with data migration to the cloud is maintaining security during transit. Organizations must carefully plan their migration strategies—including encryption, backup, and recovery plans—to protect financial and medical data and personal information while it is at risk.

Organizations can also benefit from an increase in agility and compounded value of structured and unstructured data by expanding their overall data storage capacity through flexible and scalable means.

4. Handling Unstructured Data

Most data sources produce semi-structured or unstructured data that cannot be easily organized and categorized. Company mergers and system updates are prominent sources of unstructured data—the initial categorization and structure of the data must be shed in order to fit into a different system. Unstructured data tends to be much larger in volume than structured data carrying the same amount of information and insights.

This poses a problem when migrating data. Not only is the massive volume costly to transfer and secure, both in-transit and at-rest, but it cannot be analyzed or stored in relational databases. However, that doesn’t make it void of value, as many organizations are seeking data science and migration solutions that would help structure incoming data.

Solving the unstructured data problem is a time-sensitive endeavor for many organizations. That’s because situational data quickly loses its value with time and gets replaced by more recent data, often in greater volume.

5. A Move From On-Premises Legacy Systems to Cloud Storage

Most data originates in the cloud, from such sources as digital logs, monitoring devices, customer transactions, and IoT devices and sensors. Many organizations are finding it more efficient to migrate entirely to the cloud rather than remaining split between legacy on-premises systems and cloud storage.

This approach would involve the integration of legacy data and systems with already-present data stored in the cloud, creating a more unified and comprehensive approach to data management and enabling remote access. A move to the cloud would also be accompanied by embracing multi-cloud architectures, allowing companies to optimize costs by working and switching between multiple cloud providers simultaneously.

Moving entirely to the cloud would also facilitate data storage segmentation, enabling data managers to differentiate data by type, purpose, and origin in addition to sensitivity and the level of security it may require. Organizations with data split between legacy and cloud systems may seek to unify the multiple sources in the cloud, enabling them to develop a richer, more holistic view of their data and how they might be able to use it.

Predictions for the Future of Data Migration

Data migration is expected to continue to grow in popularity alongside the exponential growth in the average volume of data produced annually by organizations. As businesses increasingly adopt cloud-based alternatives to everything from computing and processing to hosting software, cloud-based data solutions are likely to follow.

This will spark a wave of innovation, creating modern tools and technologies that aim to simplify the data migration process, ensuring the security and reliability of data in transit. Combined with the latest advancements in AI, ML, and automation, the migration process is likely to become faster, more efficient, and less prone to errors, making data migration as a concept more accessible to startups and emerging businesses who want to shift to the cloud and make the most out of their data.

Data migration involves moving data from one system to another, often to upgrade or replace a legacy system. ETL—which stands for Extract, Transform, and Load—is the process of pulling data from one or more sources, transforming it into a suitable format, and loading it into the target location. The key difference is scale. Data migration is typically used to transfer whole databases while ETL is often used for smaller datasets or parts of a database. Organizations are more likely to use data migration when replacing an outdated system, moving to the cloud, or merging with another company because it allows for better business continuity by moving all of the company’s data wholesale.

How Does Data Migration Work?

At a high level, data migration is simply the process of moving a database from one storage system to another. There are several approaches, including transfering the data directly or exporting it externally and then importing it to the new system. The goal of the process is to ensure all data is retained during the move and that it remains consistent with the new system’s data format.

One of the biggest challenges of data migration comes when moving it from an outdated system to a new system, which can increase the likelihood of data loss or corruption. It’s important to have a migration strategy in place that takes both systems and transfer paths into consideration.

How Does ETL Work?

ETL is a migration process that involves extracting data from its sources, transforming it to fit the specific format of the target system, and loading it into the new system. Done in three separate steps, ETL is often used by smaller organizations or when smaller data sets are required for a hyper-specific purpose, such as annual reports or business intelligence.

The first step is data extraction, which can be done using a variety of methods from querying a database to directly reading a file. Once the data has been extracted, it may or may not need to go through a format transformation process using a series of rules and algorithms. Finally, the transformed data is imported, or loaded, into the target system.

ETL’s advantage is that it allows the transfer of only specific data rather than an entire database, which can save time and resources and reduce the risk of error and inconsistencies. But the process tends to require more manual intervention than data migration and it can interrupt business continuity at times.

What Are Some Popular Tools for Data Migration and ETL?

There are a number of tools available for data migration and ETL, each with its own strengths and weaknesses—here are a few of the most popular.

Microsoft SQL Server Migration Assistant

Microsoft SQL Server Migration Assistant simplifies the process of migrating data from to SQL servers. It supports a variety of database sources, such as Oracle, MySQL, and Access, as well as many data formats, including JSON, hierarchical data, spatial data, and XML data.

Pentaho Data Integration Kettle

Kettle is Pentaho’s free, open-source data migration tool capable of performing ETL processes, data cleaning, and data synchronization tasks. It supports various data formats, including XML data, CSV, and JASON, and can extract data from sources ranging from SQL and NoSQL databases to APIs, applications, and web services.

Informatica PowerCenter

Informatica PowerCenter is a comprehensive data integration solution that combines a wide range of proprietary and open-source data integration strategies and solutions. It enables companies to export, transform, and load data from a variety of sources, but it’s best used for real-time integration.

AWS Database Migration Service

AWS Database Migration Service (DMS) is a cloud-based solution that facilitates the movement of data from old systems to the AWS cloud. It supports a variety of database sources, including Oracle, SQL Servers, MySQL, and PostgreSQL. Also, as a fully-managed service, it ensures minimal downtime and continuous replication as well as automation of scalable features.

Talend Open Studio

Talend Open Studio is a free and open-source data integration tool that combines various data and application integration services, such as ETL, data quality, data profiling, and MDM (Master Data Management). It supports a wide range of data formats and can be used for batch and real-time data migration and integration.

Quest Migration Manager

Quest Migration Manager is a data migration and consolidation solution that facilitates and automates the process of moving data for active directories. It’s best used for the migration and restructuring of user accounts, data, and systems with a minimal impact on business continuity.

Bottom Line: Data Migration vs. ETL

While data migration and ETL may seem identical at first glance, there are a number of differences between the two approaches that better suit them for different tasks. Data migration is a good fit for moving entire databases, while ETL works best for limited or specific data sets. Choosing the right method depends on the volume of the data, the type of migration, and whether the data needs to be reformatted for the new database.

For businesses considering a data warehouse solution, a number of competing providers offer a range of features and prices. This article will compare the top seven solutions and explain the features that differentiate them, making it easier to match them to specific needs.

Table Of Contents

• Top Data Warehouse Providers And Solutions
• Data Warehouse Providers And Solutions Comparison Table
• Amazon Redshift: Best For Deployment Optimization
• Google BigQuery: Best For Serverless Data Warehousing
• IBM Db2 Warehouse: Best For Analytic Workloads
• Azure Synapse Analytics: Best For Code-Free Offering
• Oracle Autonomous Data Warehouse: Best For Integration
• SAP Datasphere: Best For Data Warehouse Templates
• Snowflake: Best For Cloud Data Warehouses
• Key Features Of Data Warehouse Providers And Solutions
• How To Choose What Data Warehouse Provider Best For You
• Frequently Asked Questions (FAQ)
• Bottom Line: Data Warehouse Providers And Solutions

Top Data Warehouse Providers and Solutions

The top seven providers all offer feature-rich data warehousing plans at varying prices. A business’s specific needs will determine which is right for them. When selecting a provider, consider the use cases and costs for each as outlined below.

Data Warehouse Providers And Solutions Comparison Table

Amazon Redshift: Best For Deployment Options

With Amazon’s entry into the cloud data warehouse market, Redshift is an ideal solution for those organizations that have already invested in AWS tooling and deployment. Redshift deploys with Software as a Service (SaaS), cloud, and web-based solutions.

Pricing

Amazon Redshift has a pricing page where users can sign up for a trial period, request a quote, or calculate costs based on needs. Pricing starts at $0.25 an hour and can be configured using various models based on usage.

Features

• Spectrum Feature: This feature allows organizations to directly connect with data stores in the AWS S3 cloud data storage service, reducing startup time and cost.
• Strong Performance: The performance benefits companies from AWS infrastructure and large parallel processing data warehouse architecture for distributed queries and data analysis.
• Integration With AWS Glue: AWS Glue makes it easy to write or autogenerate Extract, Transform, and Load (ETL) scripts in addition to testing and running them.

See all Redshift features at https://aws.amazon.com/redshift/features.

Pros

• Parallel processing capabilities
• Contains network isolation security
• Good documentation

Cons

• Expensive
• Poorly designed user interface
• Unable to restrict duplicate records

For more on AWS: AWS Data Portfolio Review

Google BigQuery: Best For Serverless Technology

Google BigQuery is a reasonable choice for users looking to use standard SQL queries to analyze large data sets in the cloud. It is a serverless enterprise data warehouse that uses cloud, scale, Machine Learning (ML)/Artificial Intelligence (AI), and Business Intelligence (BI).

Pricing

Google BigQuery’s pricing page contains specific information about pay-as-you-go plans and longer-term (one to three year) commitments. The provider offers multiple versions of the platform, including Enterprise Edition and Enterprise Plus Edition. The Standard Edition is a pay-as-you-go plan starting at $0.04 per slot hour and the Enterprise Edition has different plans to help a company find its cloud data warehouse.

Features

• Serverless Technology: Using serverless technology, Google handles the functions of a fully managed cloud service, data warehouse setup, and resource provisioning.
• Logical Data Warehousing Capabilities: BigQuery lets users connect with other data sources, including databases and spreadsheets to analyze data.
• Integration With BigQuery ML: With BigQuery ML machine learning, workloads can be trained on data in a data warehouse.

See all BigQuery features at https://cloud.google.com/bigquery.

Pros

• Works with Google Cloud
• Full SQL query support
• Efficient management of data

Cons

• No user support
• Difficult for beginners in data warehouses
• Difficult user interface

For more information on Google: Google Data Portfolio Review

IBM Db2 Warehouse: Best For Analytic Workloads

IBM Db2 Warehouse is a strong option for organizations handling analytics workloads that can benefit from the platform’s integrated in-memory database engine and Apache Spark analytics engine.

Pricing

IBM offers a free trial for IBM Db2 Warehouse and provides a pricing page where users can ask for a quote and estimate the cost. For the flex one plan, the pricing is $1.23 per instance-hour, $0.99 per VPC-hour, and $850 per a service endpoint dedicated connectivity.

For more information, go to IBM’s pricing page.

Features

• Helpful Integration: IBM Db2 Warehouse integrates an in-memory, columnar database engine, which can be a big benefit for organizations looking for a data warehouse that includes a high-performance database.
• Netezza Technology: Db2 Warehouse benefits from IBM’s Netezza technology with advanced data lookup capabilities.
• Cloud Deployment And On-Premises: Deployment can be done in either IBM cloud or in AWS, and there is also an on-premises version of Db2 Warehouse, which can be useful for organizations that have hybrid cloud deployment needs.

See all Db2 Warehouse features at https://www.ibm.com/products/db2/warehouse.

Pros

• Includes in-memory columnar database
• Cloud deployment options
• Configuration flexibility

Cons

• Expensive
• Limited references online
• Limited buffer pool commands

For more on IBM: IBM: Hybrid Cloud Portfolio Review

Azure Synapse Analytics: Best For Code-Free Offerings

Azure Synapse Analytics, previously known as Azure SQL Data Warehouse, is well suited for organizations of any size looking for an easy on-ramp into cloud-based data warehouse technology, thanks to its integration with Microsoft SQL Server.

Pricing

Azure Synapse Analytics’s pricing page allows customers to request a quote or explore pricing options. For tier one, Azure offers 5,000 units for $4,700; tier two offers 10,000 units for $9,200. For other tier options, refer to the pricing page.

Features

• Dynamic Data Masking (DDM): Azure Synapse Analytics provides a granular level of security control, enabling sensitive data to be hidden on the fly as queries are made.
• Azure Integration: Existing Microsoft users will likely find the most benefit from Azure SQL Data Warehouse, with multiple integrations across the Microsoft Azure public cloud and more importantly, SQL Server for a database.
• Parallel Processing: In contrast to simply running SQL Server on-premises, Microsoft has built on a massively parallel processing architecture that can enable users to run over a hundred concurrent queries.

See more Azure Synapse Analytics features at https://learn.microsoft.com/en-us/azure/synapse-analytics/whats-new.

Pros

• Easy integration
• Some code-free offerings
• Strong data distribution

Cons

• Difficult logging metrics
• Limited diagramming tools
• Limited documentation

For more on Microsoft Azure: Microsoft Azure: Cloud Portfolio Review

Oracle Autonomous Data Warehouse: Best For Integration

For existing users of the Oracle database, the Oracle Autonomous Data Warehouse might be the easiest choice, offering a connected onramp into the cloud including the benefits of data marts, data warehouses, data lakes, and data lakehouses.

Pricing

Oracle’s Autonomous Data Warehouse’s main page offers pricing information as well as a cost estimator for users. The bottom price for Oracle Autonomous Data Warehouse shared and dedicated infrastructures is $0.25 per unit.

Features

• Works With Cloud And Hardware: A key differentiator for Oracle is that it runs the Autonomous Data Warehouse in an optimized cloud service with Oracle’s Exadata hardware systems, which has been purpose-built for the Oracle database.
• Easy Collaboration: The service integrates a web-based notebook and reporting services to share data analysis and enable easy collaboration.
• Strong Integration: While Oracle’s namesake database is supported, users can also migrate data from other databases and clouds, including Amazon Redshift, as well as on-premises object data stores.

See more features at https://www.oracle.com/autonomous-database/autonomous-data-warehouse/.

Pros

• Migration support for other database services
• Purpose-built hardware
• Fast query performance

Cons

• No on-premises solutions
• Limited data connection
• Complicated setup

For more on Oracle: Oracle Data Portfolio Review

SAP Datasphere: Best For Templates

Thanks to the pre-built templates it offers, SAP’s Datasphere might be a good fit for organizations looking for more of a turnkey approach to getting the full benefit of a data warehouse. SAP Datasphere allows data professionals to deliver scalable access to business data.

Pricing

SAP Datasphere’s pricing page lists a free tier and range of flexible pricing options based on needs. The price for capacity datasphere units is $1.06 per unit.

Features

• SAP’s HANA (High-performance Analytic Appliance): The cloud services and database are at the core of Data Warehouse Cloud, supplemented by best practices for data governance and integrated with a SQL query engine.
• Pre-Built Business Templates: Templates can help solve common data warehouse and analytics use cases for specific industries and lines of business.
• Integration with SAP Applications: SAP Datasphere integration means easier access to on-premises as well as cloud data sets.

See more features including a product demo at https://www.sap.com/products/technology-platform/datasphere.html.

Pros

• Inventory controls
• Extract data from multiple sources
• Strategic solutions

Cons

• Difficult for beginners
• Difficult integration
• Limited visual analytics

For more on SAP: SAP Data Portfolio Review

Snowflake: Best For Data Warehouse In The Cloud

Snowflake is a great option for organizations in any industry that want a choice of different public cloud providers for data warehouse capabilities. Snowflake aims to bring development to data, help companies govern data for users, and work globally and cross-cloud.

Pricing

Snowflake’s pricing page links to a quote page and offers a 30-day free trial with $400 of free usage.

Features

• Database Engine: Snowflake’s columnar database engine capability can handle both structured and semi-structured data, such as JSON and XML.
• Cloud Provider Of Choice: Snowflake architecture allows for compute and storage to scale separately, with data storage provided on the user’s cloud provider of choice.
• Virtual Data Warehouse: The system creates what Snowflake refers to as a virtual data warehouse, where different workloads share the same data but can run independently.

See more features at https://www.snowflake.com/en/.

Pros

• SQL-based queries for analytics
• Support for JSON and XML
• Integration with AWS, Azure, and GCP

Cons

• Limited data visualization
• Unable to create dynamic SQL
• Difficult documentation

For more information on Snowflake: Snowflake and the Enterprise Data Platform

Key Features of Data Warehouse Providers and Solutions

Cloud data warehouses typically include a database or pointers to a collection of databases where the production data is collected. Many modern cloud data warehouses also include some form of integrated query engine that enables users to search and analyze the data and assist with data mining.

Other key features to look for in a cloud data warehouse setup:

• Integration or API Libraries
• Data Quality and Compliance Tools
• ETL Tools
• Data Access Tools/Database Searchability
• SQL and NoSQL Data Capabilities

For more features and benefits: Top 10 Benefits of Data Warehousing: Is It Right for You?

How To Choose Which Data Warehouse Provider is Best for You

When looking to choose a cloud data warehouse service, there are several criteria to consider.

Existing Cloud Deployments. Each of the major public cloud providers has its data warehouse that provides integration with existing resources, which could make deployment and usage easier for cloud data warehouse users.

Ability to Migrate Data. Consider the different types of data the organization has and where it is stored. The ability to migrate data effectively into a new data warehouse is critically important.

Storage Options. While data warehouse solutions can be used to store data, having the ability to access commodity cloud storage services can provide lower-cost options.

Bottom Line: Data Warehousing Providers and Solutions

When considering providers and solutions of data warehousing, it’s important to weigh features and cost against your company’s primary goals, including deployment and analytic needs and cloud services.

While each provider and solution offers a variety of features, identifying a company’s own use case can help better evaluate them against a company’s needs.

For more information: 15 Best Data Warehouse Software & Tools

For example, a business might aggregate a pool of 1,000 customers by their locations, by their purchases, or by their demographics—gender and age—to better understand customer behavior and buying decisions.

Aggregated data can be analyzed and reported on to provide insights about particular resources or resource groups, and data aggregation plays a role in both data analytics and Business Intelligence (BI).

Table of Contents

• The Process Of Data Aggregation
• Benefits Of Data Aggregation
• Who Uses Data Aggregation?
• Manual Vs. Automated Data Aggregation
• Top Data Aggregation Tools
• Bottom Line: Data Aggregation

The Process Of Data Aggregation

Data aggregation is done using data aggregation software tools that typically include features for collecting, processing, and presenting aggregate data.  Some perform highly specialized single tasks to meet specific use cases.

Aggregation is done on varying scales, and data can be aggregated over different time frames—for example, a business might gather data from a few hours of website traffic to monitor customer behavior, or from five years to show long-term trends.

Data aggregation is a three-step process:

1. Collection: Data aggregation tools extract data from one or multiple sources, storing it in large databases or data warehouses as atomic data. Atomic data is the smallest unit into which data can be broken down.
2. Processing: Extracted data is processed by the database, aggregation software, or middleware. In this step, the data is “cleaned”—errors are corrected, formatting rules are applied, and garbage data is discarded.
3. Presentation: The aggregate is then presented in a readable form, such as charts or statistics, to be presented to non-technical users.

Benefits Of Data Aggregation

Data aggregation can benefit businesses in multiple ways. Here are a few of them:

Provides Insights Through Extracted Data

Data collection and data analysis are separate processes, and data does not always need analysis. Sometimes aggregate data can reveal information that can lead a business to make a change.

Enables Statistical Analysis

Aggregation facilitates the statistical analysis of large amounts of data which can lead to the discovery of patterns and trends.

Improves Data Quality

Data aggregators automatically collect, clean, and summarize data that can be shared for collaboration. Data aggregators can reduce labor, communication overhead, save time, energy, and money.

For more tools for data quality: Best Data Quality Tools & Software

Leads to Better Decision-Making

Data aggregation collects and summarizes information across the entire company, allowing a business to see the big picture.

Who Uses Data Aggregation?

Data is a vital source of information for all industries and at all levels. Businesses rely on data analysis for everything from price-setting and product development to planning, hiring, and marketing. Here are some of the ways data aggregation can be used across different industries.

• Marketing teams can aggregate data from campaigns and the different channels they use to reach customers. They might aggregate data from a specific campaign to see how it performed over time, and then aggregate the results of that campaign with others to see how it compares. Or they might aggregate data from each specific campaign and compare them to each other to see how they are being received across channels, populations, and cohorts.
• Websites, particularly content-driven sites, can aggregate visitors by location, time of visit, time spent, and which content was popular and which was not to understand the behavior of site visitors over time and improve engagement and retention.
• E-commerce sites can aggregate data from a time of day to understand peak and low visits, visitor demographics, the number of transactions, and whether customers made purchases based on recommendations. They can also aggregate data about competitors—like product offerings, promotions, and prices—to learn what they’re up against.
• Because the news can drive stock market activity, financial and investment firms are becoming more dependent on news-related data. Aggregating headlines and article copy can provide data for predictive analytics.
• Despite the tight burdens of such regulatory compliance as HIPAA, even the healthcare industry can benefit from aggregation—especially case analysis. By aggregating the data of numerous similar cases, medical experts can come up with more effective treatment methods to accelerate overall healthcare treatment.

Manual Vs. Automated Data Aggregation

Manual aggregation is, as the name implies, the process of gathering and extracting data and aggregating it manually—in a spreadsheet, for example. This can be a time-consuming process with a high risk of errors, which means it is better-suited to small amounts of data. It also has the potential to overlook some data.

Automated data aggregation is the process of using tools to extract and aggregate data from sources automatically. This can be done on a recurring basis at regular intervals.

Given the enormous amounts of data that businesses are capable of gathering, manual aggregation is increasingly less feasible than automation through data aggregators.

Top Data Aggregation Tools

Here are some of the most widely used data aggregation tools:

1. Microsoft Excel

Excel is a hidden gem of analytics with remarkable power and capability to load from data stores, clean and process data, and generate reports. It is often the entry-level tool for people new to analytics, and there are plenty of web resources on the subject.

2. Cloudera Distribution for Hadoop (CDH)

CDH is aimed at enterprise-class businesses with an emphasis on big data. The open source tool has a free platform distribution that encompasses Apache Hadoop, Apache Spark, Apache Impala, and more technologies, all for collecting and processing huge amounts of data. There is a commercial version as well.

3. MongoDB

MongoDB is a NoSQL, document-oriented database that comes in three “flavors”: free-to-use, small-to-medium business, and enterprise. The open source tool supports multiple operating systems, and its main features include aggregation, ad hoc queries, indexing, replication, and sharding—a means of splitting large datasets into manageable chunks.

4. Sisense

Sisense is a popular software package for preparing, analyzing, visualizing, and organizing data for a business, and is designed to address inquiries directly through a single channel as well as gather and present data as relevant insights through interactive dashboards.

5. Zoho Analytics

Zoho Analytics is a popular BI, data analytics, and online reporting tool for creating data visualizations and generating actionable business insights. Zoho Analytics is designed to be used by anyone regardless of their technical skills.

6. DbVisualizer

DbVisualizer is a feature-rich database management tool for consolidating and visualizing information from across multiple database applications. Developers and analysts can manage multiple databases and configure tables with the software’s drag-and-drop interface, and it also comes with an advanced SQL editor to write SQL queries.

7. Google Looker

Looker, which was acquired by Google, is a cloud-based data-discovery platform that provides companies with real-time access to relevant data to make better business decisions. Primarily a BI platform, it allows users to explore and transform data, but also to create reports and make them accessible to everyone.

8. Stata

Stata is a data analysis and statistical software solution designed and developed specifically for researchers from different disciplines, ranging from epidemiology to political science. It offers a point-and-click graphical user interface, comprehensive statistical tools, command-line features, complete data management capabilities, and publication-quality graphs.

9. Alteryx

Alteryx is focused on what it calls analytic process automation (APA), which unifies analytics, data science and machine learning (ML), and business process automation into one end-to-end platform to accelerate digital transformation, and is designed to be used by non-technical staff.

10. IBM Cloud Pak for Data

IBM Cloud Pak for Data is a fully integrated data and AI platform that modernizes how businesses collect, organize, and analyze data, forming the foundation to apply AI across their organizations. Built on Red Hat OpenShift, and available on any cloud, it is designed to help companies accelerate and manage the end-to-end AI lifecycle.

11. GoSpotCheck

GoSpotCheck is one of the top data collection tools for businesses that depend on gathering field data. It collects data in real-time and analyzes it instantly to help users complete tasks right then and there. It is mobile-based with built-in content collection and distribution that makes sharing information with the rest of the team easy.

12. Repsly Mobile CRM

Repsly Mobile CRM is a premier all-in-one field management CRM tool. It is a cloud-based field service software for mobile sales teams and fits SMB needs and covers a range of different industries. It provides a detailed customer history, data collection with fully customizable mobile forms, and real-time visibility into how a company’s brand and their competitors are presented at retail.

For data analysis software: Top Data Analytics Tools

Bottom Line: Data Aggregation

Data aggregation is a means of gathering raw data from multiple sources and simplifying it for statistical analysis and reporting. Aggregated data can be analyzed and reported on to provide insights about particular resources or resource groups, making it a vital source of information for all industries and at all levels.

For more information on big data: Big Data Trends and The Future of Big Data

To drive growth and maintain a competitive edge, organizations are continuously adopting new technologies to survive in today’s fast-paced digital ecosystems. Automation has emerged as a powerful tool for achieving these ends, enabling firms to streamline processes, enhance efficiency, and promote innovation. By leveraging automation, they can optimize their operations, reduce human error, and unlock new possibilities for growth.

This article explores automation—its benefits, implementation strategies, and its transformative potential across various industries.

What is Automation?

Automation refers to the process of automating repetitive tasks, workflows, and processes within an organization. It involves using software tools and technologies to replace manual, time-consuming activities with automated processes that require minimal human intervention. Automation can encompass a wide range of activities across different departments. For example, IT automation deals with automating server provisioning, software deployment, network configuration, system monitoring, and data backups, whereas customer service automation may involve using chatbots and other automation technologies to manage large caseloads.

Regardless of the department or organizational function involved, the primary goal of automation is to improve operational efficiency, reduce costs, enhance productivity, and increase service quality levels. By automating routine tasks, organizations can free up their staff’s time to focus on more strategic initiatives, such as innovation, problem-solving, and improving customer experiences.

The Role of AI in Automation

Artificial Intelligence (AI) is at the forefront of prominent innovations in automation, and the presence of AI-powered automation is ubiquitous across domains and industries. However, organizations in practice have been using automation for years, long before AI and machine learning (ML). For example, enterprises in heavy industries and manufacturing have been using automation technologies like assembly lines and mechanical robots to streamline their operations for decades. Infrastructure automation in IT involves the use of specialized software to deploy cloud-based servers and networks automatically, also without the use of AI.

That said, AI is a force multiplier for these organizations’ automation efforts. In the case of manufacturing, enterprises these days are using AI/ML-based diagnostic tools and digital twins to anticipate and remediate issues before they turn into business disruptions and failures. AI-based solutions also allow manufacturers to glean critical insights for optimizing processes and workflows. Similarly, the advent of AI has allowed IT departments to integrate infrastructure automation tools with intelligent agents to build automated fault tolerance into their environments and applications.

Automation Benefits By Category

Automation provides myriad benefits across a wide range of industries, but for most enterprises, the biggest draw is cost savings—specifically, reducing costs associated with manual labor, human errors, and system downtime. By minimizing the need for human intervention, businesses can lower labor expenses and redirect resources to higher-value activities. Additionally, automation can improve resource utilization, optimize system performance, and reduce the risk of costly errors or outages.

Operational Excellence Through Automation

Automation can help enhance efficiency and productivity by eliminating the need for manual intervention in repetitive tasks, allowing organizations to achieve greater efficiency and productivity. By automating processes, businesses can accelerate workflows, minimize errors, and ensure consistent results. This in turn results in faster response times, smoother operations, and increased overall productivity. However, in order to maintain sustainable growth through innovation, firms must achieve operational excellence to remain competitive.

IBM defines operational excellence as “an approach to business management that emphasizes continuous improvement across all aspects of the business and within all business processes by creating a culture where management and employees are invested in business outcomes and empowered to implement change.”

In the pursuit of operational excellence, automation also promotes a culture of continuous improvement and learning. As teams automate repetitive tasks, they gain insights into process inefficiencies, bottlenecks, and potential optimization opportunities. This knowledge can drive process reengineering efforts, leading to further efficiency gains and innovation.

Automating Reporting

Across all industries, teams rely on accurate and timely reporting to maintain continuous situational awareness regarding their respective projects and initiatives. To this end, reporting can be automated by pulling data from multiple systems, consolidating it, and presenting it in a predefined format. For example, sales and marketing departments can leverage robotic process automation (RPA) to assemble data from various systems—including Customer Relationship Management systems, marketing automation platforms, and social sentiment and analytics platforms, to name a few—merge the results into a consolidated report, and automate the report’s delivery to relevant stakeholders on a regular basis.

Security and Compliance Automation

Automation plays a crucial role in strengthening security and ensuring compliance with industry regulations. By automating security measures and functions like intrusion detection systems, vulnerability scanning, and log analysis, organizations can identify and respond to threats quicker and more effectively. Furthermore, automation helps organizations maintain consistent security controls and enforce compliance policies across their IT infrastructure.

Automating Integrations

In order to keep up with shifting business demands and requirements, organizations must remain agile and flexible with their tools and processes. However, in many cases these tools and processes are unable to scale accordingly or lack newly-required features. Automation can facilitate seamless integration between different systems and applications within an organization’s infrastructure as well as provide mechanisms to easily integrate with other systems and tools. This interoperability promotes data sharing, collaboration, and the development of interconnected ecosystems that drive digital transformation.

Robot Process Automation (RPA)

Robotic process automation describes technology that enables organizations to automate repetitive and rule-based tasks through the use of software robots. These robots mimic human interactions using software algorithms, allowing them to perform tasks such as data entry, data extraction, and process automation. RPA is designed to work across various applications and systems, interacting with user interfaces, performing calculations, and making decisions based on predefined rules.

Use Cases For Automation

Rendering Financial Decisions

As the underlying mechanics of AI are mathematical, it’s not surprising that banks and financial institutions use machine learning and AI algorithms to analyze large volumes of financial data to detect patterns, predict market trends, and automate trading processes. On the consumer and B2B front, banks are incorporating automation into their lending practices to provide advanced financial analytics and credit assessments. AI allows firms to service consumers and businesses with solutions better suited to their unique situation and requirements.

Improving Customer Service and Support

Customer service organizations are using automation to streamline support processes by automating repetitive tasks such as responding to frequently asked questions, generating standard email responses, or updating customer records. By enhancing the efficiency and effectiveness of customer support operations with AI, firms stand to improve response times and enhance customer satisfaction.

Automating Insurance Processes

The insurance industry was one of the early adopters of automation and has pioneered many applications of AI across the enterprise. For example, AI is crucial for helping risk and insurance professionals develop more comprehensive policies for better claims and underwriting outcomes. On the customer service side, many insurance carriers have automated the claims management process, from initial reporting to settlement.

Automating Retail Operations

According to McKinsey, almost a third of all sales and sales operations tasks can be automated with today’s technologies. And judging by the current AI adoption levels  among sales organizations, this percentage is certainly on the rise. AI automation is used in retail for inventory management, demand forecasting, and personalized marketing. Recommendation systems leverage AI algorithms to analyze customer preferences and browsing history, enabling personalized product recommendations. And in physical storefronts, robots and automated checkout systems are now being employed to streamline in-store operations.

Optimizing Shipping and Logistics

Automation plays a crucial role in shipping and logistics by optimizing route planning, managing fleet operations, and improving supply chain efficiency. Firms can use AI algorithms to analyze data from various sources like weather conditions, traffic patterns, and shipment details to make real-time decisions and improve delivery schedules.

Bolstering Accounting Data Integrity

For accounting departments, automation eliminates the risk of manual mistakes, ensuring accuracy and reliability in routine tasks. By enforcing standardized processes, organizations can achieve consistent results and reduce the likelihood of data integration errors that can lead to delays and costly fixes. For example, accounting and finance professionals are now using RPA to automatically extract data from invoices and input it into the organization’s accounting software, eliminating the need for manual data entry and reducing errors.

Streamlining IT Operations

IT teams these days more often use automation tools for  automatically rolling out IT infrastructure components. This modern approach is preferred over setting up and configuring servers and IT infrastructure manually, especially when it comes to cloud-native environments.

Popular IT automation tools like Ansible, Puppet, and Chef provide a mechanism for managing and scaling complex infrastructures effectively. They bring consistency, repeatability, and control to configuration management, resulting in improved efficiency, reduced errors, enhanced security, and streamlined operations.

Forecasting Energy and Utilities Usage

According to recent statistics, the global AI energy market is anticipated to top $7.7 billion by 2024. The energy sector is applying AI-powered automation technologies to myriad different industry processes, leading to improved worker safety levels and streamlined emergency response efforts. On the inventory side, firms operating in the energy sector are using automation for demand forecasting and energy optimization. Energy providers are using AI algorithms to analyze historical data to predict energy consumption patterns, optimize energy distribution, and identify potential equipment failures, leading to cost savings and improved reliability.

Bottom Line: Automation

Automation is a powerful enabler for businesses seeking to optimize their operations, reduce costs, and drive innovation. By eliminating repetitive tasks and streamlining workflows, organizations can achieve enhanced efficiency, productivity, and accuracy. The benefits of automation span cost reduction, improved reliability, scalability, security, and compliance. As technology continues to advance, the scope and impact of automation will no doubt follow suit. Organizations that embrace automation will position themselves for success in an increasingly competitive and dynamic business environment, where efficiency, agility, and innovation are key to thriving, not just surviving, in today’s business landscape.

The marketplace of data science tools approach tasks in remarkably different ways and use different methods to aggregate and process data and generate actionable reports, graphics or simulations.

Here’s a look at 15 of the most popular tools and what sets them apart.

Data Science Tools Comparison Chart

15 Data Science Tools for 2023

Data Cleaning and Preprocessing Tools

Trifacta

Trifacta is a cloud-based, self-service data platform for data scientists looking to clean, transform and enrich raw data and turn it into structured, analysis-ready datasets.

Pros:

• Intuitive and user-friendly
• Machine Learning-based
• Integrates with data storage and analysis platforms

Cons:

• Costly for smaller projects
• Limited support for programming languages

Pricing
There isn’t a free option of Trifacta. However, there’s a Starter option at $80 per user, per month for basic functionality. The Professional option costs $4,950 per user, per year for added functionality, but requires a minimum of three licenses. There’s also the option for a desktop-based or a cloud-based free trial.

OpenRefine

OpenRefine is a desktop-based, open-source data cleaning tool that helps make data more structured and easier to work with. It offers a broad range of functions, data transformation, normalizations and deduplication.

Pros:

• Open-source and free to use
• Supports multiple data formats: CVS, XML and TSV
• Supports complex data transformation

Cons:

• No built-in ML or automation features
• Limited integration with data storage and visualization tools
• Steep learning curve

Pricing
100 percent free to use.

DataWrangler

DataWrangler is a web-based data cleaning and transforming tool developed by the Stanford Visualization Group, now available on Amazon SageMaker. It allows users to explore data sets, apply transformations and prepare data for downstream analysis.

Pros:

• Web-based with no need for installation
• Built-in data manipulation operations
• Automatic suggestions for  appropriate data-cleaning actions

Cons:

• Limited integration with data storage and visualization tools
• Limited support of large datasets
• Limited updates and customer support

Pricing
The use of DataWrangler on the Amazon SageMaker cloud is charged by the hour, starting at $0.922 per hour at 64 GiB of memory for standard instances, and at $1.21 at 124 GiB of memory for optimized memory.

AI/ML-Based Frameworks

Scikit-learn

Scikit-learn is a Python-based and open-source library that encompasses a wide range of tools for data classification and clustering using AI/ML.

Pros:

• Comprehensive documentation
• Reliable and consistent API
• Wide range of algorithms

Cons:

• Limited support for neural networks and deep learning frameworks
• Not optimized for GPU-usage

Pricing
100 percent free to use.

TensorFlow

Developed by Google, TensorFlow is an open-source machine learning and deep learning library. It enables users to deploy various models across several platforms, supporting both CPU and GPU computation.

Pros:

• Scalable and suitable for large-scale projects
• Allows for on-device machine learning
• Includes an ecosystem of visualizations and management tools
• Open-source and free to use

Cons:

• Steep learning curve
• Dynamic data modeling can be challenging

Pricing
The library is 100 percent free to use, but when deployed on the AWS cloud, the typical price starts at $0.071 per hour.

PyTorch

PyTorch is an open-source ML library developed by Meta’s AI research team and based on the Torch library. It’s known for its dynamic computation graphs, computer vision and natural language processing.

Pros:

• Simplifies the implementation of neural networks
• Easy integration with Python
• Open-source and free to use
• Strong community support and documentation

Cons:

• Few built-in tools and components
• Limited support for mobile and embedded devices

Pricing
The library is 100 percent free to use, but when deployed on the AWS cloud, the typical price starts at $0.253 per hour.

Deep Learning Libraries

Keras

Keras is a high-level neural network library and Application Programming Interface (API) written in Python. It’s capable of running on top of numerous frameworks, such as TensorFlow, Theano and PlaidML. It allows users to simplify the process of building, training and deploying data-based deep learning models.

Pros:

• User-friendly and easy to use
• Extensive documentations
• Pre-made layers and components

Cons:

• Limited compatibility with low-level frameworks
• Complex models may suffer from performance issues

Pricing
100 percent free to use.

Fast.ai

Fast.ai is an open-source deep-learning library built on top of Meta’s PyTorch and designed to simplify the training of neural networks using minimal code.

Pros:

• User-friendly interface
• Built-in optimization for deep learning tasks
• Extensive documentation and educational resources

Cons:

• Limited customization options
• Smaller active community

Pricing
100 percent free to use.

Hugging Face Transformers

Hugging Face Transformers is an open-source, deep-learning library that focuses on natural languages processing models, such as GPT, BERT and RoBERTa. It offers pre-trained models along with the tools needed to fine-tune them.

Pros:

• Large repository of ready-use models
• Supports Python and TensorFlow
• Active online community

Cons:

• Limited open natural language processing tasks
• Steep learning curve

Pricing
The library is 100 percent free to use, but when combined with AWS Cloud and AWS Inferentia2, pricing starts at $0.76 per hour.

Big Data Processing Tools

Apache Spark

Apache Spark is a distributed and open-source computing system designed to simplify and speed up data processing. It supports a wide range of tasks including data transformers, ML and graph processing.

Pros:

• In-memory data processing for higher performance
• Built-in ML and graph processing libraries
• Integrates seamlessly with Hadoop ecosystems and various data sources

Cons:

• Processing is resource-intensive
• Requires pre-existing programming knowledge

Pricing
The system is 100 percent free to use, but when deployed on the AWS cloud, typical pricing starts at $0.117 per hour.

Apache Hadoop

Apache Hadoop is an open-source, distributed computing framework that processes large volumes of data across clusters of servers and databases. It consists of Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.

Pros:

• Highly-scalable and fault-tolerant
• Supports a wide variety of tools such as Apache Hive and HBase for data processing
• Cost-effective

Cons:

• Disk-based storage leads to slower processing
• Limited support for real-time data processing
• MapReduce as a steep learning curve

Pricing
The framework is 100 percent free to use, but when deployed on the AWS cloud, typical pricing starts at $0.076 per hour.

Dask

Dask is a flexible, parallel computing library for Python that enables users to scale numerous well-known workflows using APIs such as Scikit-learn and NumPy. It’s designed specifically for multi-core processing and distributed computing.

Pros:

• Interface similar to Python
• Support for dynamic, real-time computation
• Lightweight and compatible with Python workflows

Cons:

• Limited support for languages other than Python
• Not ideal for processing large datasets

Pricing
100 percent free to use.

Cloud-based Data Science Platforms

Google Colab

Google Colab is a cloud-based Jupyter Notebook environment in which users are able to write and execute Python code directly in their web browsers. It’s a collaborative platform for both data science and machine learning tasks with accelerated computations.

Pros:

• No setup or installation required
• Online access to GPUs and TPUs
• Supports real-time collaboration and data sharing

Cons:

• Limited computing resources available
• Lack of built-in support for third-party integration

Pricing
With a free version available, Google Colab pricing plans start at $9.99 per month for the Colab Pro plan and $49.99 per month for the Colab Pro+ plan; a pay-as-you-go option starts at $9.99 per 100 compute units, or $49.99 per 500 compute units.

Databricks

Databricks is a unified data analytics platform that combines ML with big data processing and collaborative workspaces, all in a managed cloud environment. It’s a comprehensive solution for data engineers, scientists and ML experts.

Pros:

• Seamless integration with Apache Spark
• Supports high-performance data processing and analysis
• Built-in tools for version control, data visualization and model deployment

Cons:

• Cost ineffective for smaller projects
• Steep learning curve
• Vendor lock-in

Pricing
With a 14-day free trial available, Databricks can be deployed on the user’s choice of Azure, AWS or Google Cloud. There’s a price calculator, enabling customization of subscriptions.

Amazon SageMaker

Amazon SageMaker is a fully managed, ML platform that runs on Amazon Web Services. It allows data scientists and developers to build, train and deploy machine learning models in the cloud, providing end-to-end solutions for data processing, model training, tuning and deployment.

Pros:

• Integrates seamlessly with the AWS ecosystem and tools
• Built-in algorithms for popular machine learning frameworks, such as MX Net, PyTorch and TensorFlow
• Wide range of tools for model optimization, monitoring, and versioning

Cons:

• Steep learning curve
• High-end pricing
• Vendor lock-in

Pricing
With a free tier available, Amazon SageMaker is available in an on-demand pricing model that allows customization of services and cloud capacity.

Factors to Consider When Choosing Data Science Tools

As the importance of data continues to grow and transform industries, selecting the right tools for your organization is more critical than ever. However, with the vast array of available options, both free and proprietary, it can be challenging to identify the ideal fit for specific needs.

There are a number of factors to consider when choosing data science tools, whether it’s data processing frameworks or ML libraries.

Scalability

Scalability is a crucial factor to consider early on in the decision-making process. That’s because data science projects often involve large volumes of data and computationally-intensive algorithms. Tools like Apache Spark, TensorFlow and Hadoop are designed with big data in mind, enabling users to scale operations across multiple machines.

It’s essential to ensure that a tool can efficiently manage the data size and processing demands of the project it is chosen for, both currently and in the future as needs evolve.

Integration With Existing Infrastructure

Seamless integration with an organization’s existing infrastructure and legacy software is vital for efficient data processing and analysis. This is where caution can prevent being locked into a specific vendor.

Many online tools and platforms, such as Amazon SageMaker and Databricks, are compatible with a number of legacy systems and data storage solutions. This enables them to complement an organization’s existing technology stack and greatly simplify the implementation process, allowing users to focus on deriving insights from data.

Community Support and Documentation

A strong online community and comprehensive documentation are particularly important when choosing data science tools to be used by smaller teams. After all, active user communities are able to provide troubleshooting assistance, share best practices, and even contribute to the ongoing development of the tools.

Tools like Keras and Scikit-learn boast extensive documentation in addition to a widespread and active online community. This makes them accessible to beginners and experts alike. When it comes to documentation, it’s crucial that the available documents include up-to-date information and are regularly updated with the latest advancements.

Customizability

The ability to flexibly customize tools is essential to accommodate unique project requirements, but to also optimize performance based on available resources. Tools like PyTorch and Dask offer some of the most useful customizability options compared to their counterparts. They allow users to tailor their data processing workflows and algorithms to their specific needs.

Determining the level of customization offered by a tool and how it aligns with a project is important to guarantee the desired level of control.

Learning Curve

While all tools have a learning curve, it’s important to find data science tools with complexity levels that match the expertise of the data science and analytics teams that will be using them.

Tools such as Google Colab and Fast.ai are known for their user-friendly and intuitive interface, but other programming-based tools, like Apache Spark and TensorFlow, may be harder to master without prior experience.

The Future of Data Science Tools

The rapid development and innovation in the fields of AI and ML are also driving the development of new algorithms, frameworks and platforms used for data science and analytics. In some instances, those advancements occur too fast, and staying informed about the latest trends ensures the ability to remain competitive in an economy reliant on deriving insights from raw data.

Automation is increasingly playing a prominent role in how data is gathered, prepared and processed. Using AI and ML, tools like AutoML and H2O.ai can be used to streamline data parsing by automating some of the numerous steps that go into the process. In fact, the growing role of automation in data science is likely to shape the industry’s landscape going forward, determining which tools and skill-set are more viable and in demand.

The same is likely to apply to quantum computing, as it holds great potential to revolutionize countless data processing and optimization problems, thanks to its ability to tackle complex and large-scale tasks. Its impact could potentially lead to new algorithms, frameworks and tools specifically designed for data processing in quantum environments.

Bottom Line: Data Science Tools

Choosing the right data science tools for an organization requires a careful evaluation of factors such as scalability, integration with existing infrastructure, community support, customizability and ease of use. As the data science landscape continues to evolve, staying informed about the latest trends and developments, including ongoing innovations in AI and ML, the role of automation and the impact of quantum computing will be essential for success in the data-driven economy.

With organizations awash in data, there’s a growing need to make it digestible, understandable and actionable for humans and not just computers. Data visualization software takes data and turns it into images that can communicate concepts and ideas and in a way that words and numbers alone cannot.

What Is a Data Visualization Tool?

Data visualization tools let users find key insights in data and display them in visual form. The practice involves pulling data from a database and creating dashboards and graphics like pie charts, bar charts, scatter plots, polar area diagrams, heat maps, timelines, ring charts, matrix charts and word clouds, to name a few.

By representing myriad data points graphically it’s possible to peer deeper into important numbers, trends, metrics and Key Performance Indicators (KPIs).

Not surprisingly, data visualization tools have moved from the domain of data scientists and IT departments and into the hands of business users. Organizations are now using visualization software to better understand such varied scenarios as customer sentiment and behavior, real-time sales, healthcare trends, departmental goals and market research. In addition, advertisers and media organizations use it to generate eye-catching graphics and infographics and display complex information in simple visuals.

Of course, different data visualization tools approach the task differently. Some lean toward more conventional Business Intelligence (BI) functions while others plug in live data from social media and various applications across an organization. Some also incorporate Artificial Intelligence and Machine Learning (AI/ML) to deliver more advanced functionality and insights. Most data visualization packages include templates and connectors for building robust models, graphics and dashboards.

If you’re in the market for the best data visualization software, take the time to understand what various vendors and applications offer, how they work and whether they’re able to accommodate your organization’s data visualization needs and budget.

How To Select The Best Data Visualization Software

When selecting a data visualization tool, it’s important to focus on several factors to narrow down the options.

• What type of visualizations do you require? Different tools provide different ways to aggregate and view data. Make sure you can easily connect to and input the data you require. Most of these packages come with a robust set of Application Programming Interfaces (APIs) for ingesting data.
• What type of platform does the software run on and what devices does it support? Some solutions are cloud-based, while others reside on desktop or mobile devices. Some vendors that support an on-premises model have applications that run only on Windows, which can present problems if you have teams using Macs. Make sure the software will work for you.
• Does the package adequately support your organization’s performance requirements? Some applications encounter difficulties with extremely large files, for example, while others don’t perform well in different situations. If the rendering engine can’t support the speed required for web pages and real-time dashboards that meet your own needs, you may have a problem.
• Does the application integrate with your workflows? Flexibility and scalability are often crucial. You may need to change templates, inputs or criteria from time to time—including other programs and platforms connected through APIs. Make sure the data visualization tool can support these changes.
• What does vendor support look like? An application may produce stunning visualizations, but building them can be difficult. If you’ll need help, will your vendor provide it? Make sure a vendor offers solid documentation and support, including videos and tutorials, and check on whether the vendor offers 24×7 phone support if you get bogged down.
• What does the package cost? Some solutions are free—Google Data Studio, for example—but may not deliver the features you need. Others may lock you into a specific cloud provider. Most vendors offer tiered pricing, including an enterprise option; review the choices carefully to find the ones that best align with your budget.
• What security protections does the solution offer? Cybersecurity is critical part of almost every aspect of computing these days. Make sure that any platform you’re considering provides adequate protections for accessing, securing and sharing data.

The Best Data Visualization Tools: Comparison Table

10 Top Data Visualization Tools and Software

Jump to:

• Databox
• Google Data Studio
• iDashboards
• Infogram
• Qlik Sense
• Sisense
• Tableau
• Visme
• Whatagraph
• Zoho Analytics

See more: What is Data Visualization?

Databox

The cloud-based business analytics platform databox generates data visualizations in real-time by pulling data from a wide variety of sources, including Google Analytics, Salesforce, HubSpot, Facebook, Mixpanel and Shopify. Databox offers more than 200 built-in dashboard templates, a robust set of APIs, metrics calculators, and mobile apps for viewing data visualizations. The vendor offers a tiered pricing model.

Pros

• Innovative features including looped data boards, scheduled snapshots and annotations
• More than 70 one-click integrations with data services
• More than 200 pre-built reports
• Intuitive interface and highly flexible visualizations

Cons

• Subpar integrations lead to inaccurate data and visualizations, according to some users
• Limited customization for reports
• Frequent bugs and crashes, according to users

Google Data Studio

Cloud-based Google Data Studio incorporates interactive dashboards and automated reporting and imports data from multiple sources, including Google Analytics, Google Ads and spreadsheets. It also integrates with more than 150 other cloud, SQL, e‑commerce and digital advertising platforms. Google Data Studio supports a wide array of data visualizations, including time series, bar charts, pie charts, tables, heat maps, geo maps, scorecards, scatter charts, bullet charts and area charts.

Pros

• Free
• Drag-and-drop interface doesn’t require coding skills or heavy technical knowledge
• Strong collaboration features
• Shareable dashboards
• Built in tool for calculating metrics and formulas
• Highly customizable

Cons

• Can be difficult to integrate with non-Google platforms
• Confusing functions, difficult to use
• Frequent bugs and crashes and subpar customer support, according to users

iDashboards

iDashboards “strives for real-time operational intelligence through rich visualization capabilities,” and combines data from upwards of 160 sources, offers hundreds of chart and design options, and builds dashboards that work on nearly any device. It also can use real-time data feeds to embed graphics and dashboard visualizations, which makes it possible to build dashboards for different organizational roles while supporting websites and mobile apps.

Pros

• Straightforward and easy-to-use drag-and-drop interface
• Pulls data from almost any source; comes with nearly 300 connectors, including all major cloud and application platforms
• Generates extremely rich data visualizations
• Highly flexible and customizable
• Pricing is attractive, particularly for SMBs

Cons

• Can be difficult to set up and configure
• Number of design options can be daunting to new users
• Some users have problems connecting to or importing very large source files
• Some premium features require additional licensing and costs

Infogram

Infogram is a cloud-based marketing and media tool that supports more than 35 types of interactive data visualization formats, including infographics, reports, dashboards, maps and charts, as well as social media assets for such sites as Facebook, LinkedIn and Pinterest. It provides a drag-and-drop interface, real-time collaboration and the ability to publish online. There’s a basic free version as well as four other tiers for creatives, SMBs and large enterprises.

Pros

• Large and varied collection of designer templates, including interactive charts, maps and animations
• Intuitive and easy to use interface
• Integrates well with Google Drive, OneDrive and Dropbox
• Powerful and elegant collaboration features for teams

Cons

• Free plan doesn’t allow customizations and file downloads to systems and devices
• More advanced features and plans can be pricey
• Some users report bugs and crashes
• No ability to work on projects offline

Qlik Sense

Qlik Sense is a self-service data analytics platform designed for a broad array of users, including executives, decision-makers and analysts. Available as both on-premise or cloud software, it provides drag-and-drop functionality and connects to numerous data sources, including Snowflake and other leading products. Qlik Sense generates a varied array of data visualizations through interactive dashboards, and the application includes an open API and toolsets.

Pros

• Powerful features and tools for building complex data visualizations from nearly any data source or set
• AI-based Smart Search feature helps users uncover data relationships
• Uses AI/ML for enhanced insights
• Real-time analytics and data visualization
• Excellent mobile device functionality

Cons

• Learning curve can be steep
• Requires some technical knowledge to use the software effectively
• Customizations can challenge some users
• Can be expensive, especially with add-ons

Sisense

The AI-powered Sisense analytics platform uses a robust set of APIs to generate data visualizations and actionable analytics. Available both in the cloud or on-premises, Sisense is highly customizable and includes data connectors for most major services including Snowflake, Salesforce, Adobe Analytics, Amazon S3, Dropbox, Facebook and numerous Microsoft applications. It’s suitable for use by non-data scientists and line-of-business users.

Pros

• Powerful features and fast and rich visualizations
• Intuitive user interface
• Customizable and flexible
• Generates reports and visualizations using natural language and other AI
• Highly rated customer support

Cons

• Slow performance with heavy data loads, according to some users
• May require knowledge of coding, including JavaScript and CSS, to format visualizations
• Documentation is lacking, particularly surrounding widgets, and can be difficult to understand according to some users

Tableau

Popular business intelligence platform Tableau works with a broad array of data sources and services from spreadsheets and conventional databases to Hadoop and cloud data repositories. It features smart dashboards and a highly interactive interface that lets users drag and drop elements, manipulate and combine data and views, and display data in numerous formats. Tableau includes robust sharing features.

Pros

• Fast and powerful
• Well-designed interface
• Consistently ranked as a leader by Gartner and others
• Supports all major platforms and works on almost any device
• Connects to hundreds of data sources and supports all major data formats

Cons

• Expensive
• Mixed reviews about customer support
• May require training to use the full set of features and capabilities on the platform
• Difficult to customize
• Lacks some important security controls

Visme

Visme is focused on creating visual brand experiences and other content, including flyers, emails, reports, e-books, embedded videos, animations and social media graphics. It incorporates a drag-and-drop interface and pulls data from numerous sources to generate illustrations, infographics, presentations and more. Visme offers a basic free service and tiered plans.

Pros

• Thousands of templates for infographics, presentations, charts, maps, documents and more
• Integrations with Slack, YouTube, Vimeo, Dropbox, Google Drive, SurveyMonkey, Mailchimp, Google Maps and many other products and services
• Strong collaboration features
• Excellent tutorials and other learning materials
• Highly rated customer support

Cons

• Some user complaints about graphics being frequently reused by different companies and websites
• Can be challenging to learn
• Interface can be slow and confusing
• Some complaints about frequent bugs
• Only more expensive plans have key privacy settings

Whatagraph 

Whatagraph is designed to handle performance monitoring and reporting, and marketing professionals use it to visualize data and build cross-channel reports. The application offers a variety of pre-designed templates and widgets and offers APIs for connecting numerous data sources, including Google Analytics, Facebook, LinkedIn, YouTube, HubSpot, Amazon Advertising and more.

Pros

• Excellent features and support for social media and marketing
• Built in integrations for more than 30 data sources
• Powerful cross-channel data integration and monitoring
• Automated features for sending reports

Cons

• Not highly customizable
• Cross-channel integrations can be complex and require considerable time to set up
• Some user complaints about the speed of the application
• Some complaints about subpar customer support

Zoho Analytics

The self-service BI and data analytics software is designed to ingest large volumes of raw data and transform it into actionable visuals and reports via dashboards. It is available in both on-premises and cloud versions. The platform can pull data from numerous sources, including Google Analytics, Mailchimp, YouTube, Salesforce and Twitter. It offers a tiered pricing model.

Pros

• More than 500 data connectors
• Strong collaborative features with security protections
• AI-based augmented analytics that let users create data visualizations using natural language

Cons

• Interface is not user-friendly or as intuitive as some users would like
• Can be slow to generate data visualization with very large data sets
• Features and support for mobile platforms and devices sometimes lacking
• Lacks flexibility, particularly in regard to changing reports, according to reviews

See more: Best Data Quality Tools & Software 2021

Bottom Line: Data Visualization Tools

As data visualization tools become increasingly available to business users in all fields, they open up possibilities for organizations to share complex data and communicate difficult ideas in clear and interesting graphical representations. The best data visualization tool is the one that best meets the needs of the user while also working with their existing data systems and applications and fitting into their budget. Most packages include templates and connectors for building robust models, graphics and dashboards, but the choice will also depend upon ease of use and the user’s technical ability.

As businesses discover new use cases and develop the infrastructure to support more IoT applications, the entire Internet of Things continues to evolve. Let’s look at some of the current trends in that evolution.

Table Of Contents

• Top 5 IoT Trends
  1. IoT Cybersecurity Concerns Grow
  2. IoT Advancements In Health Care
  3. 5G Enables More IoT Opportunities
  4. Demand For Specialized IoT Data Management
  5. Bundled IoT For The Enterprise Buyer
• The Future Of IoT
• Bottom Line: IoT Trends

Top 5 IoT Trends

IoT devices can help companies use their data in many ways, including generating, sharing and collecting data throughout their infrastructure. While some companies are leaping into IoT technology, others are more cautious, observing from the sidelines to learn from the experiences of those pioneering IoT.

When looking through these five key trends, keep in mind how IoT devices affect and interact with company infrastructure to solve problems.

1. IoT Cybersecurity Concerns Grow

As new IoT solutions develop quickly, are users being protected from cyber threats and their connected devices? Gabriel Aguiar Noury, robotics product manager at Canonical, which publishes the Ubuntu operating system, believes that as more people gain access to IoT devices and the attack surface grows, IoT companies themselves will need to take responsibility for cybersecurity efforts upfront.

“The IoT market is in a defining stage,” Noury said. “People have adopted more and more IoT devices and connected them to the internet.” At the same time they’re downloading mobile apps to control them while providing passwords and sensitive data without a clear understanding of where they will be stored and how they will be protected—and, in many cases, without even reading the terms and conditions.

“And even more importantly, they’re using devices without checking if they are getting security updates…,” Noury said. “People are not thinking enough about security risks, so it is up to the IoT companies themselves to take control of the situation.”

Ben Goodman, SVP of global business and corporate development at ForgeRock, an access management and identity cloud provider, thinks it’s important that we start thinking of IoT devices as citizens and hold them accountable for the same security and authorization requirements as humans.

“The evolution of IoT security is an increasingly important area to watch,” Goodman said. “Security can no longer be an afterthought prioritized somewhere after connectivity and analytics in the Internet of Things. Organizations need to start treating the ‘things’ in the Internet of Things as first-class citizens.”

Goodman said such a measure would mean that non-human entities are required to register and authenticate and have access granted and revoked, just like humans, helping to ensure oversight and control.

“Doing this for a thing is a unique challenge, because it can’t enter a username or password, answer timely questions, or think for itself,” he said. “However, it represents an incredible opportunity to build a secure network of non-human entities working together securely.”

For more information on IoT and security: Internet of Things (IoT) Security Trends

2. IoT Advancements In Healthcare

The healthcare industry has benefited directly from IoT advancements. Whether it’s support for at-home patient care, medical transportation, or pharmaceutical access, IoT solutions are assisting healthcare professionals with more direct care in situations where they cannot provide affordable or safe hands-on care.

Leon Godwin, principal cloud evangelist for EMEA at Sungard AS, a digital transformation and recovery company, explained that IoT not only makes healthcare more affordable—it also makes care and treatment more accessible and patient-oriented.

“IoT in healthcare will become more prevalent as healthcare providers look to reduce costs and drive better customer experience and engagement,” Godwin said. “This might include advanced sensors that can use light to measure blood pressure, which could be incorporated in watches, smartphones, or standalone devices or apps that can measure caloric intake from smartphone cameras.”

Godwin said that AI is also being used to analyze patient data, genetic information, and blood samples to create new drugs, and after the first experiment using drones to deliver organ transplants across cities happened successfully, rollout is expected more widely.

Jahangir Mohammed, founder and CEO of Twin Health, a digital twin company, thinks that one of the most significant breakthroughs for healthcare and IoT is the ability to constantly monitor health metrics outside of appointments and traditional medical tests.

“Recent innovations in IoT technology are enabling revolutionary advancements in healthcare,” Mohammed said. “Until now, individual health data has been mostly captured at points in time, such as during occasional physician visits or blood labs. As an industry, we lacked the ability to track continuous health data at the individual level at scale.

“Advancements in IoT are shifting this paradigm. Innovations in sensors now make it possible for valuable health information to be continuously collected from individuals.

Mohammed said advancements in AI and Machine Learning, such as digital twin technology and recurrent neural networks, make it possible to conduct real-time analysis and see cause-and-effect relationships within incredibly complex systems.

Neal Shah, CEO of CareYaya, an elder care tech startup, cited a more specific use case for IoT as it relates to supporting elders living at home—a group that suffered from isolation and lack of support during the pandemic.

“I see a lot of trends emerging in IoT innovation for the elderly to live longer at home and avoid institutionalization into a nursing home or assisted living facility,” Shah said. Through research partnerships with university biomedical engineering programs, CareYaya is field testing IoT sensors and devices that help with everything from fall prevention to medication reminders, biometric monitoring of heart rate and blood pressure—even mental health and depression early warning systems through observing trends in wake-up times.

Shah said such IoT innovations will improve safety and monitoring and make it possible for more of the vulnerable elderly population to remain in their own homes instead of moving into assisted living.

For more information on health care in IoT: The Internet of Things (IoT) in Health Care

3. 5G Enables More IoT Opportunities

5G connectivity will make more widespread IoT access possible. Currently, cellular companies and other enterprises are working to make 5G technology available in more areas to support further IoT development.

Bjorn Andersson, senior director of global IoT marketing at Hitachi Vantara, a top-performing IoT and  IT service management company, explained why the next wave of wider 5G access will make all the difference for new IoT use cases and efficiencies.

“With commercial 5G networks already live worldwide, the next wave of 5G expansion will allow organizations to digitize with more mobility, flexibility, reliability, and security,” Andersson said. “Manufacturing plants today must often hardwire all their machines, as Wi-Fi lacks the necessary reliability, bandwidth, or security.”

But 5G delivers the best of two worlds, he said—the flexibility of wireless with the reliability, performance, and security of wired networks. 5G provides enough bandwidth and low latency to have a more flexible impact than a wired network, enabling a whole new set of use cases.

Andersson said 5G will increase the feasibility of distributing massive numbers of small devices that in the aggregate provide enormous value with each bit of data.

“This capacity to rapidly support new apps is happening so early in the deployment cycle that new technologies and infrastructure deployment can happen almost immediately, rather than after decades of soaking it in,” he said. “With its widespread applicability, it will be feasible to deliver 5G even to rural areas and remote facilities far more quickly than with previous Gs.”

For more: Internet of Things (IoT) Software Trends

4. Demand For Specialized IoT Data Management

With its real-time collection of thousands of data points, the IoT solutions strategy focuses heavily on managing metadata about products and services. But the overwhelming amount of data involved means not all IoT developers and users have begun to fully optimize the data they can now access.

Sam Dillard, senior product manager of IoT and edge at InfluxData, a data platform provider for IoT and in-depth analytics use cases, believes that as connected IoT devices expand globally, tech companies will need to find smarter ways to store, manage and analyze the data produced by the Internet of Things.

“All IoT devices generate time-stamped (or time series) data,” Dillard said. “The explosion of this type of data, fueled by the need for more analytics, has accelerated the demand for specialized IoT platforms.”

By 2025, around 60 billion connected devices are projected to be deployed worldwide—the vast majority of which will be connected to IoT platforms, he said. Organizations will have to figure out ways to store the data and make it all sync together seamlessly as IoT deployments continue to scale at a rapid pace.

5. Bundled IoT For The Enterprise Buyer

While the average enterprise buyer might be interested in investing in IoT technology, the initial learning curve can be challenging as IoT developers work to perfect new use cases for users.

Andrew De La Torre, group VP of technology for Oracle Communications at cloud and data management company Oracle, believes that the next big wave of IoT adoption will be in bundled IoT or off-the-shelf IoT solutions that offer user-friendly operational functions and embedded analytics.

Results of a survey of 800 respondents revealed an evolution of priorities in IoT adoption across industries, De La Torre said—most notably, that enterprises are investing in off-the-shelf IoT solutions with a strong desire for connectivity and analytics capabilities built-in.

Because of specific capabilities, commercial off-the-shelf products can extend IoT into other industries thanks to its availability in public marketplaces. When off-the-shelf IoT aligns with industrial needs, it can replace certain components and systems used for general-use practices.

While off-the-shelf IoT is helpful to many companies, there are still risks as it develops—security risks include solution integration, remote accessibility and widespread deployments and usage. Companies using off-the-shelf products should improve security by ensuring that systems are properly integrated, running security assessments, and implementing policies and procedures for acquisitions.

The Future Of IoT

Customer demand changes constantly. IoT services need to develop at the same pace.

Here’s what experts expect the future of Iot development to look like:

Sustainability and IoT

Companies must embrace IoT and its insights so they can pivot to more sustainable practices, using resources responsibly and organizing processes to reduce waste.

There are multiple ways a company can contribute to sustainability in IoT:

• Smart energy management: Using granular IoT sensor data to allow equipment control can eliminate office HVAC system waste and benefit companies financially and with better sustainability practices.
• Extent use style: Using predictive maintenance with IoT can extend the lifespan of a company’s model of manufacturing. IoT will track what needs to be adjusted instead of creating a new model.
• Reusing company assets: Improved IoT information will help a company determine whether it needs a new product by looking at the condition of the assets and use history.

IoT and AI

The combination of Artificial Intelligence (AI) and IoT can cause industries, businesses and economies to function in different ways than either IoT or AI function on their own. The combination of AI and IoT creates machines that have smart behaviors and supports strong decision-making processes.

While IoT deals with devices interacting through the internet, AI works with Machine Learning (ML) to help devices learn from their data.

AI IoT succeeds in the following implementations:

• Managing, analyzing, and obtaining helpful insights from customer data
• Offering quick and accurate analysis
• Adding personalization with data privacy
• Providing assistance to use security against cyber attacks

More Use of IoT in Industries

Healthcare is cited as one of the top IoT industries, but many others are discovering how IoT can benefit their companies.

Agriculture

IoT can be used by farmers to help make informed decisions using agriculture drones to map, image, and survey their farms along with greenhouse automation, monitoring of climate conditions, and cattle monitoring.

IoT enables agriculture companies to have more control over their internal processes while lowering production risks and costs. This will reduce food waste and improve product distribution.

Energy

IoT in the energy sector can improve business performance and customer satisfaction. There are many IoT benefits for energy industry, especially in the following areas:

• Remote monitoring and managing
• Process optimization
• Workload forecasting
• Grid balancing
• Better decision-making

Finance

Banks and customers have become familiar with managing transactions through many connected devices. Because the amount of data transferred and collected is extensive, financial businesses now have the ability to measure risk accurately using IoT.

Banks will start using sensors and data analytics to collect information about customers and offer personalized services based on their activity patterns. Banks will then better understand how their customers handle their money.

Manufacturing

Manufacturing organizations gather data at most stages of the manufacturing process, from product and process assistance through planning, assembly and maintenance.

The IoT applications in the manufacturing industry include:

• Production monitoring: With IoT services’ ability to monitor data patterns, IoT monitoring provides optimization, waste reduction and less mundane work in process inventory.
• Remote equipment management: Remote work has grown in popularity, and IoT services allow tracking and maintaining of equipment’s performance.
• Maintenance notifications: IoT services help optimize machine availability by receiving maintenance notifications when necessary.
• Supply chains: IoT solutions can help manufacturing companies track vehicles and assets, improving manufacturing and supply chain efficiency.

For more industries using IoT: IoT in Smart Cities

Bottom Line: IoT Trends

IoT technology reflects current trends and reaches many areas including AI, security, healthcare, and other industries to improve their processes.

Acknowledging IoT in a business can help a company improve a company structure, and IoT will benefit a company’s infrastructure and applications.

For IoT devices: 85 Top IoT Devices