10 Best Data Analytics Tools for Your Business

By | May 16, 2020
Data Analytics Tools

Data analytics is a process to manage and analyze the big set of data correctly to make it explainable, presentable for finding the conclusions. Therefore it is essential to have appropriate decisions through a sophisticated collection of data. There are various data analytics tools available for data analytics, and many organizations are using these data analytics tools to make the best decisions for them.  There are many processes and techniques of data analytics that are automated into algorithms and mechanical processes that work covering raw data for human consumption.

Types of Data Analytics

  1. Descriptive analytics: It helps to describe the entire situation at a specific time. For example, complete data of registered attendance in a month can be analyzed and described through Descriptive analytics.  
  2. Diagnostic analytics: This analytics process majorly focuses on the reason behind the situation. It involves more distinct data inputs and also little hypothesizing.
  3. Predictive analytics: This analytics is entirely based on the prediction, which means it works on future actions.
  4. Prescriptive analytics: This analytics is based on the suggestion regarding future actions. It focuses on the further process of the result provided by predictive analytics.

Best Data Analytics Tools

There are various tools available that can help you in the data analytics, but it is essential to have best tools for the best results, so let’s discuss the 10 best data analytics, and they are:

Vamware

1. R Programming

R Programming LanguageIt is one of the most prime data analytics tools because it is essential for a data scientist for data modeling and statistics. It can merely mold the data for the best presentable way possible. R runs and compiles on a vast range of platforms like Windows, macOS, and viz-UNIX. It consists of 11,556 packages that allow a person to browse the package by various categories. R also provides convenience to automatically install the new packages as per the requirement that can also well combine with big data.

Features of R programming:

  • Open-source
  • Great graphical capabilities
  • It can simply perform the complex statistical calculation
  • Comprehensive Environment

Pros

  • Open Source
  • A huge amount of packages
  • Highly Compatible
  • Quality Graphing and Plotting
  • Platform Independent

Cons

  • Weak Origin
  • Data Handling
  • Basic Security
  • Complicated Language
  • Lesser Speed

2. Tableau Public

TableauIt is free software that combines any data source with corporate Data Warehouse or Microsoft Excel or web-based data for creating the maps, data visualization, and dashboard by presenting it on the web in real-time. A data scientist can also share it with the client through social media because this tool allows downloading the file in various formats. If you want to achieve the best results from Tableau, then you need a good knowledge of data.

Features of Tableau public

  • Sharing and collaboration is amazing
  • Great dashboard
  • Tableau’s data sources
  • It has advanced visualization charts
  • Robust security system

Pros

  • Easy to use
  • Amazing visualization capabilities
  • Great performance
  • Multiple data connection sources
  • Mobile-friendliness

Cons

  • High cost
  • Inflexible pricing
  • Security issues
  • Poor versioning and BI capabilities.

3. Python

PythonIt is an object-oriented scripting language that is an open-source tool easy to maintain and write. It was developed in the 1980s by Guido van Rossum, and it supports both structural and functional programming methods. This is easy to understand because it is similar to JavaScript, PHP, and Ruby. Apart from it, python has amazing machine learning libraries like viz. Scikit Learn, Tensorflow, Keras, and Theano. Python can also be assembled on a type of platform like a MongoDB database, JSON, and SQL server.

Features of Python

  • It is easy to use and learn
  • Free and open source
  • wide and standard libraries
  • Integrated

Pros

  • Versatile and Simple to Use
  • Open Source and Vibrant Community
  • A huge amount of libraries
  • Great for Prototypes

Cons

  • Speed Limitations
  • Problems with Threading
  • Not significant to Mobile Environment

4. SAS

SASSAS is a programming language and environment for data manipulation, so SAS was developed in 1966 and modified in the 1980s and 90s. SAS is simple, manageable, and accessible, and it can analyze data from any source. In 2011, SAS introduced a massive amount of products for numerous SAS modules and customer intelligence. These products are introduced for social media, marketing analytics and web which is used for prospects and profiling customers

Features of SAS

  • Strong Data Analysis Abilities
  • Amazing management
  • Report Output Format
  • It supports data of various format

Pros

  • Easy to learn
  • Easy to debug
  • Tested algorithms
  • Data Security
  • Nice Output

Cons

  • High cost
  • It is not open source
  • Difficult Text Mining
  • Difficult than R

5. Apache Spark

Apache SparkApache was developed in 2009 by the University of California, Berkeley’s AMPLab. It is a fast large-scale data processing engine that is used to execute applications in Hadoop clusters 100 times faster in memory and ten times faster on disk. Spark is built on data science, and its concept makes data science smooth.

Features of Apache Spark

  • Swift Processing
  • Dynamic in Nature
  • Reusability
  • Real-Time Stream Processing

Pros

  • Amazing Speed
  • Ease of Use
  • Advanced Analytics
  • Apache Spark is powerful

Cons

  • It doesn’t have an automatic optimization process
  • Fewer Algorithms
  • Small Files Issue
  • Window Criteria

6. Excel

Microsoft ExcelIt is an essential and widely used analytical tool used in most industries. It does not matter if you are a SAS, Tableau, or R expert, and you still need the excel tool because it is essential for the client’s internal data analytics. This tool can analyze the complex tasks to summarize and provide a preview in the tabular format. This tool has a fantastic feature for business analytics that helps the modeling capabilities that have prebuilt options.

Features of Excel

  • Conditional Formatting
  • PivotTables
  • Paste Special
  • Add Multiple Rows

Pros

  • Well organized data
  • Features of Formula and Calculation
  • Third-Party Support
  • Office Integration

Cons

  • Calculation Errors
  • Time Consuming
  • High Cost

7. RapidMiner

RapidMinerThis tool is one of the most robust integrated data analytics platforms developed by a company that also provides predictive analysis and advanced analytics (data mining, machine learning, text analytics, visual analytics, etc.) with any programming. This tool can easily be incorporated through any data source types, including Access, Tera data, Excel, Oracle, Microsoft SQL, Ingres, IBM SPSS, IBM DB2, MySQL, etc. RapidMiner is powerful in that it can create analytics based on the settings of real-life data.

Features of RapidMiner

  • Application & Interface
  • Code Control
  • Data Exploration
  • Amazing Modeling options

Pros

  • It has flow-based programming that allows visualization of pipelines
  • It contains modules for machine learning, statistical analysis, etc.
  • No coding required
  • Easy to set up

Cons

  • Higher cost
  • ‘No coding’ creates challenges for the users sometimes.

8. KNIME

KNIMEThis tool was developed in January of 2004 by the software engineers’ team at the University of Konstanz. It is an open-source, integrated analytics and reporting tool. It allows the user to analyze the data model through visual programming, and it also integrates various components for machine learning and data mining via its concept of modular data-pipelining.

Features of KNIME

  • Data blending
  • Local automation
  • Data mining
  • Data manipulation

Pros

  • Handles large quantities of data
  • Easily integrates with cloud and data environments.

Cons

  • Some features are not easy to use.
  • It can drain memory.

9. QlikView

QlikViewThis tool consists of various unique features that have in-memory data processing and patented technology. Data association in this tool is automatically maintained, and it can be compressed to almost 10% of its real size. QlikView can provide data relationships, and it can be visualized through specific color for clarification between datasets.

Features of QlikView

  • Dynamic BI ecosystem
  • API availability
  • Application integration
  • Collaborative workspace

Pros

  • Dynamic BI Ecosystem
  • Data Sharing
  • Self-Service Tool
  • Low Maintenance

Cons

  • Less RAM Limit
  • Embedding
  • Require more purchases for various features.

10. Splunk

SplunkThis tool is used to analyze and search the data which is generated by the machine. It pulls all of the text-based log data to provide a more natural way for searching through the dataset. This search helps a user to perform all types of statistical analyses on the data and present it in various formats.

Features of Splunk

  • Distributed Search
  • Disaster Recovery
  • High Availability
  • Access Control
  • Performance Acceleration

Pros

  • It supports ad-hoc query
  • A powerful ecosystem

Cons

  • Higher pricing
  • This tool performs very slowly in some cases.

Conclusion

Data analytics is a highly used technique to find relevant results and make the best decision for the organization/business/company. There are a bunch of tools available that can be used for data analytics, but you always need the best tools and techniques to solve significant data issues. Therefore we have discussed top data analytics in this article that can help you in the various processes regarding data.

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