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Top 10 Data Analytics Tools | Tools Used for Data Analysis

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Why Data Analysis?

Companies that are not leveraging data analytic tools and techniques are falling apart. Since data analytics tools capture in products that automatically glean and analyze data, deliver information and predictions, you can improve prediction accuracy and refine the models. Here we discuss the top 10 Data Analytics Tools for success.

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Goals of Performing Data Analysis

You can analyze data. Extract actionable and commercially relevant information to boost performance. Several extraordinary analytical tools are available, that is the free and open source so that you can leverage it to enhance your business and develop skills.

Top Data Analytics Tools

Here is the list of top Analytics tools for data analysis that are available for free (for personal use), easy to use (no coding required), well-documented (you can Google your way through if you get stuck), and have powerful capabilities (more than excel). These data analysis tools will help you manage and interpret data in a better and more effective way. Here, we have explored the top 10 Data Analytics tools in Big Data.

#1 Tableau Public

What is Tableau Public

Tableau, one of the top 10 Data Analytics tools, is a simple and intuitive and tool which offers intriguing insights through data visualization. Tableau Public’s million row limit, which is easy to use fares better than most of the other players in the data analytics market.

With Tableau’s visuals, you can investigate a hypothesis, explore the data, and cross-check your insights.

Uses of Tableau Public

  • You can publish interactive data visualizations to the web for free.
  • No programming skills required.
  • Visualizations published to Tableau Public can be embedded into blogs and web pages and be shared through email or social media. The shared content can be made available s for downloads.

Limitations of Tableau Public

  • All data is public and offers very little scope for restricted access
  • Data size limitation
  • Cannot be connected to
  • The only way to read is via OData sources, is Excel or txt.

#2 OpenRefine

What is OpenRefine

Formerly known as GoogleRefine, the data cleaning software that helps you clean up data for analysis. It operates on a row of data which have cells under columns,  quite similar to relational database tables.

Uses of OpenRefine

  • Cleaning messy data
  • Transformation of data
  • Parsing data from websites
  • Adding data to the dataset by fetching it from web services. For instance, OpenRefine could be used for geocoding addresses to geographic coordinates.

Limitations of OpenRefine

  • Open Refine is unsuitable for large datasets.
  • Refine does not work very well with big data.

#3 KNIME

What is KNIME?

KNIME, ranked among the top Data Analytics tools helps you to manipulate, analyze, and model data through visual programming. It is used to integrate various components for data mining and machine learning via its modular data pipelining concept.

Uses of KNIME

  • Rather than writing blocks of code, you just have to drop and drag connection points between activities.
  • This data analysis tool supports programming languages.
  • In fact, analysis tools like these can be extended to run chemistry data, text mining, python, and R.

Limitation of KNIME

Poor data visualization

#4 RapidMiner

Same as KNIME. Poor data visualization.

What is RapidMiner?

RapidMiner provides machine learning procedures and data mining including data visualization, processing, statistical modeling, deployment, evaluation, and predictive analytics.

RapidMiner, counted among the top 10 Data Analytics tools, is written in the Java and fast gaining acceptance.

Uses of RapidMiner

It provides an integrated environment for business analytics, predictive analysis, text mining, data mining, and machine learning.

Along with commercial and business applications, RapidMiner is also used for application development, rapid prototyping, training, education, and research.

Limitations of RapidMiner

  • RapidMiner has size constraints with respect to the number of rows.
  • For RapidMiner, you need more hardware resources than ODM and SAS.

#5 Google Fusion Tables

What is Google Fusion Tables?

When talking about Data Analytics tools for free, here comes a much cooler, larger, and nerdier version of Google Spreadsheets. An incredible tool for data analysis, mapping, and large dataset visualization, Google Fusion Tables can be added to business analytics tools list. Ranked among the top 10 Data Analytics tools, Google Fusion Tables is fast gaining popularity.

Uses of Google Fusion Tables

  • Visualize bigger table data online:
  • Filter and summarize across hundreds of thousands of rows.
  • Combine tables with other data on the web:

You can merge two or three tables to generate a single visualization that includes sets of data. With Google Fusion Tables, you can combine public data with your own for a better visualization.

You can create a map in minutes!

Limitations of Google Fusion Tables

  • Only the first 100,000 rows of data in a table are included in query results or mapped.
  • The total size of the data sent in one API call cannot be more than 1MB.

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#6 NodeXL

What is NodeXL?

NodeXL is a free and open-source network analysis and visualization software. Ranked among the top 10 Data Analytics tools, it is one of the best statistical tools for data analysis which includes advanced network metrics, access to social media network data importers, and automation.

Uses of NodeXL

This is one of the best data analysis tools in Excel that helps in:

  1. Data Import
  2. Graph Visualization
  3. Graph Analysis
  4. Data Representation

NodeXL integrates into Microsoft Excel 2007, 2010, 2013, and 2016. It opens as a workbook with a variety of worksheets containing the elements of a graph structure like nodes and edges. It can import various graph formats like adjacency matrices, Pajek .net, UCINet .dl, GraphML, and edge lists.

Limitations of NodeXL

  • Multiple seeding terms are required for a particular problem.
  • Need to run the data extractions at slightly different times.

#7 Wolfram Alpha

What is Wolfram Alpha?

Wolfram Alpha, one of the top 10 Data Analytics tools is a computational knowledge engine or answering engine founded by Stephen Wolfram. With Wolfram Alpha, you get answers to factual queries directly by computing the answer from externally sourced ‘curated data’ instead of providing a list of documents or web pages.

Uses of Wolfram Alpha

  • Is an add-on for Apple’s Siri
  • Provides detailed responses to technical searches and solves calculus problems.
  • Helps business users with information charts and graphs, and helps in creating topic overviews, commodity information, and high-level pricing history.

Limitations of Wolfram Alpha

  • Wolfram Alpha can only deal with the publicly known number and facts, not with viewpoints.
  • It limits the computation time for each query.

#8 Google Search Operators

What is Google Search Operators?

It is a powerful resource which helps you filter Google results instantly to get most relevant and useful information.

Uses of Google Search Operators

  • Fast filtering of Google results.
  • Google’s powerful data analysis tool can help discover new information or market research.

#9 Solver

What is Excel Solver?

The Solver Add-in is a Microsoft Office Excel add-in program that is available when you install Microsoft Excel or Office. Ranked among the best-known Data Analytic tools is a linear programming and optimization tool in excel. This allows you to set constraints. It is an advanced optimization tool that helps in quick problem-solving.

Uses of Solver

The final values found by Solver are a solution to interrelation and decision. It uses a variety of methods, from nonlinear optimization and linear programming to evolutionary and genetic algorithms, to find solutions. It is one of the top 10 Data Analytic tools in use.

Limitations of Solver

  • Poor scaling is one of the areas where Excel Solver lacks.
  • It can affect solution time and quality.
  • Solver affects the intrinsic solvability of your model.

#10 Dataiku DSS

What is Dataiku DSS?

Ranked among the top 10 Data Analytic tools, Dataiku is a collaborative data science software platform that helps the team build, prototype, explore, and deliver their own data products more efficiently.

Uses of Dataiku DSS

It provides an interactive visual interface where they can build, click, and point or use languages like SQL. This data analytics tool lets you draft data preparation and modulization in seconds. Helps you coordinate development and operations by handling workflow automation, creating predictive web services, model health on a daily basis, and monitoring data.

Limitation of Dataiku DSS

  • Limited visualization capabilities
  • UI hurdles: Reloading of code/datasets
  • Inability to easily compile entire code into a single document/notebook
  • Still, need to integrate with SPARK

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Top 5 Data Analytics Tools and Techniques 

Here are some of the top Data Analytic tools and techniques that can be used for better performance:

  1. Visual Analytics

There are different ways to analyze the data. One of the simplest ways to do is to create a graph or visual and look at it to spot patterns. This is an integrated method that combines data analysis with human interaction and data visualization.

  1. Business Experiments

Experimental design, AB testing, and business experiments are all techniques for testing the validity of something. It is trying out something in one part of the organization and comparing it with another.

  1. Regression Analysis

It is a statistical tool for investigating the relationship between variables. For instance, the cause and effect relationship between product demand and price.

  1. Correlation Analysis

A statistical technique that allows you to determine whether there is a relationship between two separate variables and how strong that relationship may be. It is best to use when you know or suspect that there is a relationship between two variables and wish to test the assumption.

  1. Time Series Analysis

It is the data that is collected at uniformly spaced time intervals. You can use it when you want to assess changes over time or predict future events on the basis of what happened in the past.

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