Top 15 Best Data Analytics Tools & Software Comparison 2020

Thursday, October 8, 2020
Michael Zunenshine
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Introduction

Humans are always working. We network, buy and build. Beneath all this activity lies an ocean of data. If you’re not collecting that data you’re missing out. 

Today you have tons of options for good data analytics tools. But you want the best data analysis platform that’s right for your business.

Don’t worry about the overabundance of choice. This article rounds up the best 15 data analytics tools so you can skip the comparison shopping.

 

 

Data analytics tools comparison chart (top 10 highest rated)

Product

Best for

Pricing (starting at)

URL

RapidMiner

Best data analytics platform overall

$7,500 /user/year

Visit

Tableau 

Best data visualization tool

$70 /user/month

Visit

KNIME

Good free open source data analytics tool

$29,000 /5 users/year 

Visit

Looker

Best data analytics software for BI

Contact vendor

Visit

Qlik

Good cheap alternative to RapidMiner

$30 /user/month

Visit

Sisense

Good data analytics software for enterprise

Contact vendor

Visit

Talend

Best big data analytics tool

$1,170 /user/month

Visit

Microsoft Power BI

Good data analytics tool for team collaboration

$9.99 /user/month

Visit

Domo

Good data analytics tool for building custom apps

Contact vendor

Visit

ThoughtSpot

Best data analytics platform for ease of use

Contact vendor

Visit

*Prices listed do not include free versions.

 

What are data analytics tools?

Data analysis tools help you collect large data sets from various sources and combine them into databases. Data analytics tools can be a specialty software solution meant for data scientists. But many data platforms are easy enough for anyone to use. 

Data platforms analyze data to tell you things about your business process. The results from data analysis help you shape future business decisions.

Business analytics and intelligence

Data analysis is part of business analysis and overall business intelligence (BI). You can divide up BA and BI data features into 4 categories:

  1. Descriptive analytics: Can the data tell us what something is.

  2. Diagnostic analytics: Can the data show us why something happened.

  3. Predictive analytics: Can the data tell us about what will happen.

  4. Prescriptive analytics: Can the data tell us what we should do.

The many tools of data analysis platforms

There are many core features of a data management platform. These are:

  • Data preparation: Taking raw data and getting it ready for analysis

  • Data mining: Applying algorithms to raw data to uncover new insights

  • Data modeling: Creating database categories to put raw data into

  • Data discovery: Collecting data and putting into categories

  • Data warehousing: Gathering data from multiple sources and putting it all together 

  • Data processing: Turning unstructured data into data ready for analysis

  • Data integration: Combining different kinds of data into a unified system

  • Data transformation: Converting data of one kind into data of another kind

A note on AI

Artificial intelligence plays a big role in data analysis. This is done in many ways.

Machine learning (ML) in data analysis allows for workflow automation. You can train your software to perform tasks on your data at regular intervals. These machine learning algorithms can teach themselves to further refine their analysis. This saves human time from repeating mundane tasks. It's also a great way to gain deeper insights the more data you collect.

Natural language processing (NLP) is another subset of AI in data analysis. This lets everyday users "talk to the data." Coders don’t have to create algorithms to analyze the data. With AI, non-tech users can speak or ask questions in regular conversational language. Using NLP, the data software can then understand your question and answer it. 

The visual aspect of data

One of the most important features of data analytics solutions is data visualization. Everyday users cannot look at reams of code and understand what's going on. Data visualization transforms data into a variety of charts, graphs, and other graphic solutions.

Data visualization should be easy to set up and use. With drag and dropping, or point and clicking, users can decide which data sets and factors to use to create visuals. You can quickly adjust the details to see the changes. 

Data visualization can also take in new data and update in real-time. This is great when it is displayed for teams to see like a=on an office wall monitor. They can watch the live visuals reflect the underlying data.

  

What are the best data analytics tools? Here's our top 15 list:

We've gone over lots of the best data analytics platforms. This is our ultimate list of the top 15 data analytics tools. They are described by niche, industry and features. Where possible, we include prices. Some good data platforms are even free. 

 

RapidMiner (best data analytics tool overall)

The research behind RapidMiner began at the Artificial Intelligence Unit at the Technical University of Dortmund in 2001. It became the data management tool RapidMiner in 2013.

RapidMiner aims to give in-depth business analytics for trained data scientists. But it also can be simple for everyday users with an intuitive user interface. For the latter there are templates, tutorials, and automation options. RapidMiner gets you seamless data preparation called Turbo Prep. It lets you retrieve data without complex SQL codes. RapdiMiner also gets you visual workflows which use machine learning. You can also automate RapidMiner to create instant predictive analytics models.  

RapidMiner Studio has a free version. The Professional version costs $7,500 per user per year. The Enterprise edition goes for $15,000 per user per year.

RapidMiner is best for:

  • SMBs

  • Large businesses

  • Enterprise

  • Machine learning

  • Data scientists

  • Predictive analytics

Website: RapidMiner

 

Tableau (best data visualization tool)

Tableau was founded in 20103 in California and today has its headquarters in Seattle. In 2019 Tableau was bought by the business SaaS giant Salesforce.

Today Tableaus is a leading data analytics platform. Its specialty is data visualization tools. Tableau puts ease of use first. Everything works with simple drag and drop. The dashboards are interactive and help you create data visualizations instantly. It does data preparation and integration no matter what your data sources. Data sets can come from big data, SQL, or even Excel spreadsheets. Tableau also does predictive data analysis, which helps you spot trends in real-time. Prescriptive data analysis even offers new insights on how to improve business efficiency.

There are many Tableau packages. Tableau Public is free. Other Tableau packagers begin at around $70 per user per month billed annually. Some are on-premise while others are hosted online.

Tableau is best for:

  • Free users

  • Startups

  • SMBs

  • Data visualization

  • BI

Website: Tableau

 

KNIME (good free open source data analytics tool)

KNIME is a Zurich-based data analytics platform. It was first used as a data processing tool for the pharmaceutical industry. Today its uses are universal.

KNIME is an open-source data analytics solution. KNIME wants everyone in the data science process to use its software. So it's very user-friendly. There is also a KNIME server which is a data management platform for collaboration. This is better for the enterprise level. KNIME handles the basics of data science and more. It does data preparation and wrangling. There is a suite of data visualization tools. What's more, you get business analytics features to help plan future moves, and other basic business intelligence tools. 

KNIME Analytics platform is free. KNIME Server, which can be hosted on AWS or Microsoft Azure, starts at $29,000 per year for 5 users.

KNIME is best for:

  • Free users

  • Startups

  • SMBs

  • Business analytics

Website: KNIME

 

Looker (best data analytics software for BI)

Looker Data Sciences was created by some of the software designers from Netscape in 2012. Today Looker is owned by the Google Cloud Platform, but still operates as its own data analytics platform.

Looker is an overall great BI tool. Using advanced analytics, it aims to help companies make business decisions. It can do data integration from any SQL source. You can create dynamic dashboards and share them with teams. These dashboards are intuitive and great looking. There are tools to automate data workflows. Looker also has unique data modeling layers which make data available to anyone in real-time. There is integration with Slack for better team collaboration. Google Marketing platform and Google Ads also work alongside Looker.

For prices, you can get a quote through Looker's website.

Looker is best for:

  • SMBs

  • Large business

  • Enterprise

  • BI

  • Automated workflows

Website: Looker

 

Qlik (good cheap alternative to RapidMiner)

Qlik is an old company dating back to the early 90s. In 2018 it was named Best Business Intelligence and Analytics Software by Gartner.

Qlik is a total end to end business intelligence solution. It has several suites of tools. These are for areas like data analytics, data integration and data warehouse automation. It also uses augmented intelligence which combines AI with human intuition. It uses natural language processing to help with business decision making. Qlik offers tools for IoT data analysis. You can even combine IoT data with other data streams like CRMs. All of Qlik's dashboards are interactive and simple. It also has mobile access for users.

Qlik Data Analytics has two prices. It's $30 and $70 per user per month billed annually. You can also get add-ons.   

Qlik is best for:

  • One-person business

  • Startups

  • SMBs

  • Data integration

  • Augmented intelligence

  • IoT data management

Website: Qlik

 

Sisense (good data analytics software for enterprise)

Sisense hails from Tel Aviv, Israel in 2004. Today it's headquartered in NYC with more offices across the States, Europe and Asia.

Sisense is a big player among business intelligence platforms. This is a data analytics tool used by everyone from data scientists to non-technical business users. It handles the back end of analyzing lots of unstructured data. It also does the front end of creating great data visualizations. The platform is split into three. There is Sisense for cloud data teams, business analysis and product teams. You can simplify bulk data with Sisense data warehousing and data preparation features. Live data dashboards are easy to build and share across teams. Sisense uses machine-learning algorithms to compare data sets or spot anomalies.  

To get a price quote from Sisense contact them through their website.

Sisense is best for:

  • Large businesses

  • Enterprise

  • Government

  • Data analysts

  • BI

  • Machine learning

Website: Sisense

 

Talend (best big data analytics tool)

Talend was founded by two buddy-entrepreneurs in 2005. They wanted to make the best on-cloud data integration tool.

Today Talend offers a range of products for cloud-based data integration and data mining. It connects data from over 900 data sources including Azure, AWS, Salesforce and Marketo. Setting up data warehouses and creating data lakes is very fast. Talend handles complex relational databases. With machine learning, you can do IoT analytics and big data processing. Talend also offers many free tools as add-ons. Some of these streamline ETL processes. There's also a speedy data loader.

The Talend Cloud Data Integration platform costs $1,170 per user per month or $12,000 per user per year.

Talend is best for:

  • Large businesses

  • Enterprise

  • Big data analytics

  • Data pipelines

Website: Talend

 

Microsoft Power BI (good data analytics tool for team collaboration)

Microsoft first launched its business intelligence suite of services in 2011. It is a real step up from basic Excel data management.

Power BI can connect large amounts of data from a variety of sources and does data preparation. It does data mining which lets you apply algorithms to data sets to uncover new insights. Microsoft BI also has incredible data visualization tools. It leverages AI to do diagnostic data analysis, finding answers to problems in the business process. You can even ask Power BI questions using natural language. Combined with Microsoft Azure, you get smooth data pipelines to warehousing and other cloud services. Microsoft Power BI is also a good tool for sharing data reports and other collaboration features..

Microsoft has two prices for Power BI. The Power BI Pro version goes for $9.99 per user per month. Power BI Premium for larger enterprises costs $4,995 per year.

Microsoft Power BI is best for:

  • Startups

  • SMBs

  • Large business

  • Enterprise

  • Team collaboration

  • Big data analysis

  • Data pipelines

Website: Microsoft Power BI

 

Domo (good data analytics tool for building custom apps)

Domo came out of some of the brains who worked at Adobe in 2010. First called Shacho, the name later changed to Domo. As a company Domo is known for being a leader in hiring female IT staff.

Domo is all cloud-based. It's a BI platform aimed at letting average non-tech users leverage data insights. Its data integration is robust and collects data from over 1,000 sources. The automated data pipelines can be monitored in real-time. For business analytics Domo has auto-machine learning features to uncover trends and insights. All Domo dashboards can be embedded in other software and accessed with desktop and mobile. There's a Domo app store to add more tools to the platform. You can also create low-code apps to add custom features.

Domo does custom pricing based on a variety of factors like data storage. You can get a quote through the Domo website.

Domo is best for:

  • SMBs

  • Large business

  • Enterprise

  • Business analysis

  • Data visualizations

  • BI

Website: Domo

 

ThoughtSpot (best data analytics platform for ease of use)

ThoughtSpot is a privately-help BI company that came out in 2012. By 2018 it had become a unicorn, meaning it's valued at over 1 billion.

ThoughtSpot could be used by data analysts but its specialty is for non-technical users. It's quite simple to use. The platform is hosted on the cloud. ThoughtSpot uses advanced AI and machine learning to help users understand their data. You can use natural language processing to get insights in real-time and share them with your team. It offers low-code templates to help tailor data analytics to specific business needs. ThoughtSpot has a module called SpotIQ. This helps uncover anomalies and identify data relationships. It isolates trends and does data segmentation. 

ThoughtSpot has two pricing plans. One is based on consumption and the other is based on capacity. Both are for unlimited users. For exact costs contact the vendor.

ThoughtSpot is best for:

  • SMBs

  • Large business

  • Ease of use

  • Natural language processing

  • Data diagnostics

  • Real-time insights

Website: ThoughtSpot

 

R Programming

The R Programming language is part of the R project. Its roots are in Bell Labs from the 70s.

The R Programming language is free software. It is part of the GNU general p[ublic license. R is used for statistical analysis, data mining and data analysis. It offers a data handling and storage facility. There are several tools you can use for various data analysis. You can also get data visualization features for on-screen or hard copies. The R programming language is a useful tool for polls, surveys, and database studies. This is not a platform for lay users. 

R is always free as part of GNU.  

R Programming is best for:

  • Free users

  • Data scientists

  • Data visualization

  • Polls and surveys

Website: R Programming

 

Xplenty

Xplenty is the brainchild of a team of engineers, data experts and DevOps. It's an American platform founded in 2012.

Xplenty is a cloud-based platform. It is a data pipeline solution. Plenty helps you with data integration, data processing and data preparation. The idea is Xplenty takes care of these steps before you move your data into the cloud for more in-depth data analysis. The interface is simple point-and-click. There's also lots of customization you can do through Xplenty's API. Xplenty integrates with many data sources. Some examples are social media like Facebook and CRM apps like Salesforce. 

Xplenty does not disclose its prices on its website. 

Xplenty Programming is best for:

  • Startups

  • SMBs

  • Large business

  • Custom data pipelines

Website: Xplenty

 

Oracle Data Cloud

The Oracle corporation needs no introduction. Their Oracle Data Cloud (ODC) is the Data as a Service (DaaS) suite of tools.

Oracle Data Cloud is ideal for marketers and other business analytics specialists. It uses contextual intelligence to help create targeted marketing campaigns. This makes sure the right ads meet the right audiences. The data management platform of ODC uses big data to further refine audience insights. It achieves this by giving you a more comprehensive data management solution.   

Oracle does not list prices on its website. 

Oracle Data Cloud is best for:

  • Large businesses

  • Enterprise

  • Marketing 

  • Business decisions

Website: Oracle Data Cloud

 

TIBCO Spotfire

TIBCO is a software company specializing in big data. It bought the data analytics platform Spotfire in 2007.

TIBCO Spotfire does data preparation for end to end data pipelines. It uses augmented intelligence to help find insights in data sets which can be used for making business decisions. It's easy to create custom data dashboards. It also has good predictive analytics which can be used for trend forecasting. There is real-time data streaming analytics which work with IoT devices. TIBCO Spotfire is a solid solution to transform data into neat visual dashboards.

TIBCO offers a free version of Spotfire. Paid plans are $25, $65 or $125 per month. 

TIBCO Spotfire is best for:

  • Free users

  • One-person business

  • Startups

  • SMBs

  • Augmented intelligence

  • Predictive analytics

  • IoT data analysis

Website: TIBCO

 

Apache Spark 

Spark came out of UC Berkeley's AMPLab back in 2009. The creators donated this big data analytics platform to the non-profit Apache Project in 2013.

This data modeling application is mainly used by data scientists and coders. Apache Spark is great for doing large amounts of data processing on clusters or batch data as well as for data streaming in real-time. It has an easy API which lets coders write their own apps for analyzing big data. They can do this with Python, Java, SQL and more. Spark can be run on its own cluster mode, or you can run it on other nodes like Hadoop YARN. There are also machine learning features that perform tasks like classification and recommendation. 

Apache Spark is a free and open-source data analytics engine.   

Apache Spark is best for:

  • Open-source data analysis

  • Free users

  • Startups

  • Hadoop 

  • Python

Website: Spark

 

Conclusion: What is the best data analytics software? 

So what is the final verdict? Well, no one data software is 100% superior to the whole data arena. 

RapidMiner is the best overall data analytics platform. But it's for those who are willing to buy a premium software. Tableau is the best data visualization tool. And if you are on a budget, remember that Tableau Public free. ThoughtSpot is solid for its simplicity. Finally, Microsoft Power BI is a good professional choice.

Don't let your data go to waste. Every bit of information might mean better business decisions and boosted revenues. 

 

FAQs:

What tools are used for data analytics?

There are many good tools for data analytics. Some of the best tools include RapidMiner, Tableau and Looker. There are also open-source tools like R Programming and KNIME.

Which is the best data analytics tool?

RapidMiner is the best data analytics tool overall. Tableau is the best data visualization platform. The best free data analytics tool is KNIME. 

What are data analysis tools in research?

Data analysis in research has four kinds of tools. The first two are descriptive and diagnostic analytics. The next two deal with the future. They are predictive and prescriptive analytics. 

 

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