Data analytics

15 Best Data Analytics Tools

What’s not measured can’t be improved. This business mantra has gained new importance in modern businesses. People and organizations produce huge amounts of data on a daily basis and the only way businesses can make use of it to improve their results is to use data analytics tools.

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Indrė Jankutė-Carmaciu

Jun 15 2020 8 min read

15 Best Data Analytics Tools

Table of Contents

  • Data Analysis – Why Is It Important for Businesses?
  • What Are the Benefits of Data Analytics Tools?
  • How to Choose a Data Analytics Tool?
  • List of the Best Data Analytics Tools
  • 1. Python
  • 2. Rebrandly
  • 3. Tableau
  • 4. Oracle
  • 5. Rapid Miner
  • 6. Sky Tree
  • 7. Talend
  • 8. Infogram
  • 9. SAS
  • 10. Apache Spark
  • 11. Sequentum Content Grabber
  • 12. Microsoft Power BI
  • 13. Looker
  • 14. Thoughtspot
  • 15. Qlik

Data-driven businesses have a lot more chances to acquire customers than less proactive businesses. Using a good data analytics tool is the only way they can exploit the massive amounts of data produced every day.

But, how to choose the best tool from the hundreds of options available today? Below is a list of some of the best data analytics platforms on the market, but first, let’s explain what data analysis is and why businesses need it in the first place.

Data Analysis – Why Is It Important for Businesses?

It’s the process of collecting, transforming, cleaning, and modeling data to find out important information that can help businesses make better-informed decisions and enhance their performance. The goal of this process is to extract valuable information from existing data and make decisions based on that data.

No matter the issue for which data exists, such as product research, market research, sentiment analysis, customer reviews, positioning, etc., analyzing data will produce meaningful insights that will help businesses make smart choices.

Today, data-driven decisions are the only way businesses can ensure growth and success.

Thanks to data analytics tools, users can sort through data to find trends, patterns, correlations, relationships, and anomalies that would otherwise be hard to locate.

What Are the Benefits of Data Analytics Tools?

Here are some of the most significant benefits of data analytics tools:

  • Personalization – today’s users want the highest level of personalization - a feature businesses can support only if they make informed decisions based on data;
  • Cost-reduction – data analysis can help businesses focus on relevant strategies instead of wasting their time and money on inefficient marketing activities and sales;
  • Trend anticipation – data analytics tools can help businesses predict the future needs of their customers and develop a new product or service before they even ask for it.

How to Choose a Data Analytics Tool?

To find the right data analysis tool for your business, you need to consider the users of the tool and your business requirements. Is the tool going to be used by non-technical people who would benefit from an intuitive interface, by professional data scientists and data analysts, or by both types of users?

Certain tools provide more on point-and-click analysis which is ideal for non-technical users, while others offer an interactive experience for iterating on code development, usually using SQL. You should also make sure the platform offers support for visualizations relevant to your business.

Another thing to consider when choosing the right data analytics platform for your business is its data modeling capabilities. Certain tools can perform data modeling themselves or support a semantic layer. Those who don’t want to use such tools should use a tool like dbt or SQL to model their data before analysis.

Last but not least, take the price and licensing into consideration. Some tools are free and others charge subscription or licensing fees. Choosing the most expensive tool doesn’t necessarily mean you have chosen the best for your business.

Therefore, check out the features of each free or paid tool before picking the right one for your business.

List of the Best Data Analytics Tools

Now that you know the importance of data analysis, the benefits of data analytics platforms, and the factors to look for in data analysis tools, take a look at some of the best and most popular options, in no particular order.

1. Python

Python is a free open source tool that supports both structured and functional programming methods. It’s generally used by data scientists and technical analysts, which is a high-level programming language. But, it’s similar to PHP, Ruby, and JavaScript so it’s easy to learn.

This tool can handle a wide range of analyses on its own as well as integrate with third-party packages for data visualization and machine learning.

Seaborn, Plotly, and Matplotlib are a few popular data visualization packages. Python can as well be used as a programming interface to other data analytics systems.

2. Rebrandly

Rebrandly is a marketing software that enables advertisers and affiliate partners to monitor, compare and optimize all their links and campaigns in one place.

With Rebrandly, marketers, affiliates and agencies can create "tracking links" and post them in forums, social networks, blogs, newsletters, pay-per-click campaigns, or any source they'd like to measure.

This way, they can focus their efforts on the ones that are growing their businesses.

Rebrandly collects important data such as which site the user came from, the approximate geographic location of the person, and much more. With this data, Rebrandly creates detailed, real-time marketing reports.

Moreover, Rebrandly monitors activities in real-time, to allow users to make decisions based on the most up-to-date information.

3. Tableau

This data analytics and visualization tool lets you create and share reports across mobile and desktop platforms, embedded in an application, or within a browser.

Tableau can run on-premises or on the cloud. A considerable part of the tool runs on top of VizQL, its core query language.

The drag-and-drop function of the tool allows users to customize, change, and rearrange data visualization elements as they want. Other features that make Tableau a great platform include advanced data calculations, comprehensive end-to-end analytics, a fully protected system that lowers security risks, and easy content discoveries.

This platform is available in several formats, including team-focused and individual platforms. However, it lacks support for advanced SQL queries.

4. Oracle

This data analysis tool is extremely popular among enterprise-level organizations. Oracle is a cloud-based tool and builds a completely self-driving and self-sufficient autonomous database.

The variety of features can make it a bit complicated for inexperienced users, but professional data analysts love it. The tool allows them to delve deeper into every aspect of their business.

Some of the best features of Oracle include great performance speed and incredibly fast data refreshes, clear and intuitive user interface, a variety of subjects and report customization options, different data visualization solutions, continuous software upgrades and improvements, data import capabilities, machine learning algorithms that obtain data insights, etc.

5. Rapid Miner

This open-source tool has plenty of in-depth data analytics capabilities and add-ons that users can apply to any type of business. It’s a true data analytics superpower that automates most of the analytical processes and makes operational processes considerably simpler, allowing the user to focus only on data interpretation.

RapidMiner has a clear dashboard with basic functions that are easy to learn. The platform can be extended using R and Python scripts and a wide range of third-party plugins are available through RapidMiners’ marketplace.

Some of the reasons why people love this tool include real-time data analyzing and tracking, more than 1,500 native algorithms, comprehensive reporting abilities, data cleansing and transformation, superior security features, scalability for use by teams of all sizes, support of many third-party integrations, etc.

6. Sky Tree

This data analytics platform is perfect for small and medium-sized businesses. It simplifies data science so that inexperienced users can understand it quickly.

Sky Tree is created to provide all-encompassing predictive machine learning mechanisms for smaller teams. This allows them to use the tool to create better products or services and design special offers for each customer separately.

The customer service team behind the platform is 24/7 available and ready to respond to users quickly. Other great features of Sky Tree include simple but attractive data visualization for comprehensible business decisions, data accumulation, and filtering, extensive data interpretation models, customizable sets of algorithms, etc.

7. Talend

Talend is designed to automate data integration, management, and filtering. The tool uses native code generations that allow users to run their data pipelines easily across all cloud providers and optimize performance across all platforms. It also checks data quality and allows master data management and big data integration.

This, in turn, ensures features like more accurate and simpler ETL and ELT processes, quicker completion of big data projects, an excellent combination of machine learning and natural language processing, and easier to use Spark.

8. Infogram

Infogram is great for those whose main concern is data visualization. The platform aims to help data analysts present their findings and ideas simply and attractively.

Infogram is the right tool for you if you’re looking for a tool that will allow you to create visually appealing reports that will impress potential clients.

Some of the things you can do with the tool are design attractive reports, infographics, dashboards, slides, and graphics, use over 500 maps and 40 charts, set permissions for team management, edit and restore documents, optimize reports for mobile experiences, and create individual tracking links for clients.

Such a straightforward platform can help even data scientists to ensure all stakeholders can easily understand operational processes.

9. SAS

SAS is the right tool for you if you want to find out what users say and think about your business, niche competitors, or industry trends. The tool relies on natural language processing, machine learning, and linguistics to comprehend the sentiment of online content and the meaning behind user reviews.

Thanks to the numerous research approaches, users can assess all kinds of unstructured texts.

Some of SAS’ best features include a more accurate analysis thanks to the identification of grammar mistakes and spelling errors, granular analysis of textual content, information extraction, attractive reports, a sentiment indicator display, identification of subjective information, etc.

10. Apache Spark

This open-source tool was designed for diligent data analysts. You can use it to understand massive datasets and design the appropriate reports. Apache Spark works with Java, SQL, Scala, Python, R, and the rest of the major programming languages.

The tool can accelerate internal operations up to a hundred times, being based on DAG scheduler, query optimizer, and other cutting-edge technologies. Another great thing about Apache Spark is that you can run it on all kinds of devices, operating systems, and apps.

The tool can accelerate disk-powered performance by up to ten times per project, improve Hadoop cluster performance, easily integrate with external apps and software solutions, and enable superior data processing.

11. Sequentum Content Grabber

This tool allows users to find and extract publicly available data from the Web quickly.

It also comes with many customization options which makes it a bit more complex.

Sequentum Content Grabber is a highly intuitive and user-friendly tool as it works with a simple click editor. The scalable system is great for both small-scale and large-scale data extraction projects.

This database tool allows effortless scheduling, handles operations holistically, and activates data management solutions.

12. Microsoft Power BI

This excellent business intelligence platform supports several data sources. It lets you create reports, dashboards, and visualizations, and share them. You can combine multiple reports and dashboards into a Power Bi app for easier distribution.

The tool integrates with Azure Machine Learning and lets you build automated machine learning models.

13. Looker

This cloud-based data analytics and business intelligence tool provides a unified data environment and centralized data governance with a focus on reusable components for professional data analysts and data scientists.

Looker uses an ELT approach to allow you to transform and model data as you need it.

The tool also features proprietary LookML language which utilizes SQL in a reusable and visual way. It integrates with popular workflow and collaboration tools easily, such as Segment, Slack, and Jira.

14. Thoughtspot

This platform features AI and searches engine-like interface that allows you to take a conversational approach to data analytics and exploration. In other words, it lets you explore data from numerous sources through natural language searchers and reports.

Thoughtspot’s AI system locates systems automatically to help you find patterns you didn’t know to look for. The tool also lets you automatically join tables from a range of data sources to break down data silos.

15. Qlik

This self-service business intelligence and data analytics platform supports both on-premises and cloud deployment. It supports data discovery and exploration by non-technical and technical users. The tool supports a variety of charts that you can customize with drag-and-drop modules and embedded SQL.

Published on Jun 15 2020

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Indrė Jankutė-Carmaciu

Indrė is a copywriter at Whatagraph with extensive experience in search engine optimization and public relations. She holds a degree in International Relations, while her professional background includes different marketing and advertising niches. She manages to merge marketing strategy and public speaking while educating readers on how to automate their businesses.