Why Analytics ?

The term "analytics" has been used by many business intelligence (BI) software vendors as a buzzword to describe different functions. Data analytics is used to describe everything from online analytical processing (OLAP) to CRM analytics in call centers. Banks and credit cards companies, for instance, analyze withdrawal and spending patterns to prevent fraud or identity theft. Ecommerce companies examine Web traffic or navigation patterns to determine which customers are more or less likely to buy a product or service based upon prior purchases or viewing trends. Modern data analytics often use information dashboards supported by real-time data streams. So-called real-time analytics involves dynamic analysis and reporting, based on data entered into a system less than one minute before the actual time of use. [Margaret Rouse, Editorial Director, WhatIs.com].

Data Analytics has come into existence because huge volume of data generated in every activity of our lives and with unprecedented expansion of business and economies. Big data is data base that is too large and complex for conventional data tools to capture, store, model or analyze. When put to good use, analysis of Big Data allows analysts to spot trends extract insights and make predictions.

Data analytics and data of voluminous dimensions or big data have evolved with development of information science and information technology revolution. Since last 5-10 years, benefits of data analytics in business is realized and discussed in corporate boardrooms with aplomb. The last 10 years of information technology and mobile technology revolution has enabled generation of about 2.5 quintillion bytes of data every day. That scale of data is more than the entire information generated two years ago in digital world. Also, the capability of advanced Business intelligence and Business Analytics tools featured in software like SPSS, SAS, R, QLIKVIEW, COGNOS, SQL to name a few, are being utilized to gain knowledge and draw valuable insights from the information hidden in big data within reasonable series of timeframe, which is gradually evolving fast into real time terms.

Analytics of big data is a recent phenomenon simply because we are generating data in astronomical dimensions today. In 2000, the ratios of information were shared between digital and non-digital storage was 1:3. Today, it is estimated that nearly 1300 exabytes of digital information exists globally out of which just 2% is non-digital and rest 90% is digital but unstructured!

Google processes data every day in peta bytes and so does Facebook which is less than a 10-years old in peta bytes every 24-hour. Hours of video in hundreds is uploaded on YouTube every minute, about 3 billion ‘like’ buttons are pushed every day on Facebook, and there are more than 400 million tweets per day. About 2.5 Exabyte of data gets generated every day and that data every month doubles! All this is leading to explosion of data.

Just one flight from New York to London generates about 10 terabytes of data! Billions of shares algorithmic traded on the US stock markets daily and customer transactions collected by Walmart accumulate peta bytes every hour worth analysis of data. Credit card transactions count into 1000s in seconds. All this is creating a need to manage this data differently.

All this big data generated, if collected and analyzed can boost organizations to drive their growth. Businesses have realized that analyzing this huge volume of data can give valuable insights into major business operations - production and planning, supply chain and logistics, marketing, sales, prospecting, pre and post sales customer relations management including customer segmentation, customer profiling and churn trending/churn prediction, quality control, maintenance, vendor development, research and development, product innovation, invite venture capital investment, talent/human resources management forecasting employee attrition/ training effectiveness/ calculating cost of human capital to drive revenues and sustain through economic uncertainties. Modeling business scenarios assists in capacity building in risk taking through risk analysis, and predictive analysis can enable business to perform better by getting closer to the holistic picture of the market.

In organizations, analytics skills enable professionals to convert extensive data by applying statistical and quantitative analysis into valuable insights that can drive efficient, smarter and faster decisions. Therefore, analytics in organizations can make decisions and strategies based on data driven rather than only on gut feelings. This data which can be analyzed helps organizations keep tabs on real time customer trends. So can effectiveness of a strategy be determined in real time.

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