Anticipating the Changes in Big Data Analytics

Big data and analytics have immense potential in many industries from banking to sports and from medicine to retail. The effective use of data for creating business value is important, even if not exactly new. Businesses that wish to grow have always wanted to derive insights from information in order to make better, fact-based, smarter, real time decisions: it is this demand for depth of knowledge that has fueled the growth of big data tools and platforms.

Those enterprises that wish to lead the change must include big data from both within and outside the enterprise, including structured and unstructured data, machine data, and online and mobile data to supplement their organizational data and provide the basis for historical and forward-looking (statistical and predictive) views.

While adoption and use of big data and analytics is not too vast, there is a growing confidence and familiarity with the technology. Currently the technologies that are using big data analytics widely are software and web based applications, smart phones and tablets, social media and enterprise solutions to a small extent. According to EY’s Global Forensic Data Analytics Survey 2014:

  • 72% of respondents believe that emerging big data technologies can play a key role in fraud prevention and detection
  • Yet only 7% of respondents were aware of any specific big data technologies
  • And only 2% were actually using them Anticipating changes in big data analytics begins with anticipating the varied applications of the technology

This begins with an understanding of areas where the potential use of big data is ‘around the corner’ or ‘on the horizon’. Supply chain management, cloud service brokerage, bring your own cloud and enterprise/business solutions are areas where use of big data analytics is around the corner. While digital money, internet of things, in-memory computing and cyber havens are areas where application of big data analytics is on the horizon.

Analytics helps to optimize key processes, functions and roles. The goal is to use analytics to improve the efficiency and effectiveness of every decision and/or action. The value chain for analytics begins with:

  • Leveraging state of art tools and techniques, that are now readily available at economical costs, to manage and extract relevant data from big data sources
  • Analytics applications ranging from historical reporting, through to real-time decision support for organisations based on future predictions
  • Use of insight generated by analytics to drive change that benefits organisations
  • The decisions that are arrived at are fed into a continuous feedback loop, which gets recorded as transactions or behavior history for future reference

The changes that can possibly occur and be anticipated in Big Data Analytics may take place in the step where actual analytics is performed to gain useful insights that aid companies. Advanced analytics can be Prescriptive, Predictive or Descriptive based on a foundation of mathematical complexity consisting of rules or algorithms that give rise to Business Intelligence:

  • Prescriptive analytics: Used to determine which decision and/or action will produce the most effective result against a specific set of objectives and constraints
  • Predictive analytics: Leverage past data to understand why something happened or to predict what will happen in the future across various scenarios
  • Descriptive analytics: Mine past data to report, visualize and understand what has already happened – after the fact or in real-time

Sophisticated analytics can allow companies to discover root causes, analyse micro-segments of their markets, transform processes and make fairly accurate predictions about future events or customers’ propensity to buy or engage. Yet, big data, the emerging availability of storage technology platforms and the latest analytical algorithms are enablers to business success — not a guarantee of it.

Author: Richa Kapoor
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