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Data Analytics

Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is also known as data analysis. Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals - all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical record or new information that has been processed for real-time analytics uses.

Inside the data analytics process

The analytics process starts with data collection, in which data scientists identify the information they need for a particular analytics application and then work on their own or with data engineers and IT staffers to assemble it for use. Data from different source systems may need to be combined via data integration routines, transformed into a common format and loaded into an analytics system, such as a Hadoop cluster, NoSQL database or data warehouse. In other cases, the collection process may consist of pulling a relevant subset out of a stream of raw data that flows into, say, Hadoop and moving it to a separate partitioning the system so it can be analyzed without affecting the overall data set. Once the data that's needed is in place, the next step is to find and fix data quality problems that could affect the accuracy of analytics applications. That includes running data profiling and data cleansing jobs to make sure that the information in a data set is consistent and that errors and duplicate entries are eliminated. Additional data preparation work is then done to manipulate and organize the data for the planned analytics use, and data governance policies are applied to ensure that the data hews to corporate standards and is being used properly. At that point, the data analytics work begins in earnest. A data scientist builds an analytical model, using predictive modeling tools or other analytics software and programming languages such as Python, Scala, R, and SQL. The model is initially run against a partial data set to test its accuracy; typically, it's revised and tested again, a process known as "training" the model that continues until it functions as intended. Finally, the model is run in production mode against the full data set, something that can be done once to address a specific information need or on an ongoing basis as the data is updated. In some cases, analytics applications can be set to automatically trigger business actions, for example; stock trades by a financial services firm. Otherwise, the last step in the data analytics process is communicating the results generated by analytical models to business executives and other end users to aid in their decision-making. That usually is done with the help of data visualization techniques, which analytics teams use to create charts and other infographics designed to make their findings easier to understand. Data visualizations often are incorporated into BI dashboard applications that display data on a single screen and can be updated in real time as new information becomes available.

Which data analytics tools are used by Data Analysts?

  • Tableau Public
  • OpenRefine
  • KNIME
  • RapidMiner
  • Google Fusion Tables
  • NodeXL
  • RapidMiner

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