Whether the business is small/medium/large, analytics has become an integral part with the BAU. With incredibly rapid increase in big data with the onset of IoT and web logs; data analytics has become a core element and cannot be considered optional at any level. Data creation is likely to increase ten times to 163 zettabytes (zb) by 2025, as per a study done by IDC, Data Age 2025 (Priyanka Sangani ET Bureau Apr 04, 2017).
Over the years, there is a pattern that has emerged which follows a logical chronology when it comes to the implementation of analytics. The journey begins with information and goes towards optimization. The implementation stages are based upon requirements which are ubiquitous with managing any and every project/business no matter the size or duration. It starts with a historical analysis; it can be micro (logs/ accumulated data) or macro (industry) level analysis. The need here is to understand the standing viz a viz the systematic and unsystematic variables of the current business environment. Historical analysis is the stepping stone for the decision makers to set objectives, goals and vision for the project. Performing historical analysis coupled with descriptive analysis (monitoring progress based on real-time data) sounds feasible and next steps can be effectively planned.
The next stage arrives with accumulation of adequate data to be able to interpret what went right and not so right over the recent past of a project/business. This is where we perform diagnostic analytics to understand why did it happen? “It” being the insights we inferred from historical analysis. Based on what and why that we analysed in the above stages we try to understand what might happen next. This is where predictive analytics comes into the picture. Here we do forecast studies, create algorithms taking into factor the whats and whys and hence being able to ameliorate objectives & goals. This is the stage where machine learning can play a game changer role if incorporated correctly. Next is the prescriptive analysis where AI plays a major role. We want to make the best possible decision considering ROI, CVP analysis, risk mitigation etc. as this is where optimization happens, and this takes the most time and effort.
“Data is the new Oil” Clive Humby, UK Mathematician and architect of Tesco’s Clubcard, 2006.
The expression above holds much more truth than it did when it was coined in 2006. Each concern is unique and will present a unique set of challenges but the overall requirements remain the same. The ones paying the due resources at each stage are bound to be ahead of the curve and have no issues with positive and long-term sustainability.
*Must refer Gartner’s analytics maturity model