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Data Analytics For Enhanced Productivity And Business Growth

Krishnakumar Madhavan, Head IT, KLA

Krishnakumar is a result oriented, pioneering technologist with more than 25 years of experience in IT, network, and telecommunications architecture/infrastructure design. He currently serves for KLA as the Head of IT department.

As an enormous amount of data gets generated, the need to extract useful insights is a must for a business enterprise. Data Analytics has a key role in improving business. Here are 4 main factors which signify the needs for Data Analytics:

• Gather Hidden Insights – Hidden insights from data are gathered and then analyzed with respect to business requirements.

• Generate Reports – Reports are generated from the data and are passed on to the respective teams and individuals to deal with further actions for a high rise in business.

• Perform Market Analysis – Market Analysis can be performed to understand the strengths and the weaknesses of competitors.

• Improve Business Requirement – Analysis of Data allows improving Business to customer requirements and experience.

As the word suggests Data Analytics refers to the techniques to analyze data to enhance productivity and business gain. Data is extracted from various sources and is cleaned and categorized to analyze different behavioral patterns. The techniques and the tools used vary according to the organization or individual.

Business Administration have the capability to perform Exploratory Data Analysis, to gather the required information.

Top Tools in Data Analytics
With the increasing demand for Data Analytics in

the market, many tools have emerged with various functionalities for this purpose. Either open-source or user-friendly, the top tools in the data analytics market are as follows.

• R programming – This tool is the leading analytics tool used for statistics and data modeling. R compiles and runs on various platforms such as UNIX, Windows, and Mac OS. It also provides tools to automatically install all packages as per user-requirement.

With the increasing demand for data analytics in the market, many tools have emerged with various functionalities for this purpose

• Python – Python is an open source, object-oriented programming language which is easy to read, write and maintain. It provides various machine learning and visualization libraries such as Scikit-learn, TensorFlow, Matplotlib, Pandas, Keras etc. It can also be assembled on any platform like SQL server, a MongoDB database or JSON

• Tableau Public – This is a free software that connects to any data source such as Excel, corporate Data Warehouse etc. It then create visualizations, maps, dashboards etc with real-time updates on the web.

• QlikView – This tool offers in-memory data processing with the results delivered to the end-users quickly. It also offers data association and data visualization with data being compressed to almost 10% of its original size.

• SAS – A programming language and environment for data manipulation and analytics, this tool is easily accessible and can analyze data from different sources.

• Microsoft Excel – This tool is one of the most widely used tools for data analytics. Mostly used for clients’ internal data, this tool analyzes the tasks that summarize the data with a preview of pivot tables.

• RapidMiner – A powerful, integrated platform that can integrate with any data source types such as Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase etc. This tool is mostly used for predictive analytics, such as data mining, text analytics, and machine learning.

• KNIME – Konstanz Information Miner (KNIME) is an open source data analytics platform, which allows you to analyze and model data. With the benefit of visual programming, KNIME provides a platform for reporting and integration through its modular data pipeline concept.

• OpenRefine – Also known as Google Refine, this data cleaning software will help you clean up data for analysis. It is used for cleaning messy data, the transformation of data and parsing data from websites.

• Apache Spark – One of the large-scale data processing engine, this tool executes applications in Hadoop clusters 100 times faster in memory and 10 times faster on disk. This tool is also popular for data pipelines and machine learning model development.

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