Analysis has been in existence since the inception of mankind. In some form or another we as human beings have always analysed people, processes or things around us. In the past this was based only on gut-feel, beliefs, judgements, pre-conceived notions and on what we had seen happening previously.
Analysis today is done more scientifically and is based on hard data, to support, validate or negate the gut-feel or presumption. Analytics as the term now coined is defined as the application of computer technology, operational research &statistics and domain knowledge to solve problems in business, industry and social life, to aid efficient and effective decision making. The backbone for this problem solving is the underlying data on which analytics is based.
In recent times, analytics has gained importance due to four important factors:
1) availability of large amounts of data, in various forms both structured and unstructured
2) easier, cheaper storage and retrieval of this data
3) a need to take decisions at a faster pace to stay competitive in the market
4) a need to get the most of out of every dollar spent
Analytics can be fragmented into four basic types, based on the business intelligence it provides.
How analytics evolved
Earlier we used standard static reports and charts to know what happened;
Then we added dynamism (ad-hoc) to these reports to know how much, how frequently or where this happened.
Being curious in nature we further wanted to know where exactly the problem was and what actions are needed to rectify the same. Hence we used OLAP (on-line analytical processing) tools to slice and dice the information in any which way we wanted and used alerts to inform us when something went wrong, so that corrective action could be taken or necessary escalations made.
Facing the same problems over and over again, forced us to statistically analyse the data, to know the root cause of the problem, in order to prevent it from occurring again.
We also saw seasonal, repetitive happenings sometimes, provoking us to think about trends in the information provided. This led to the use of forecasting techniques to study the trends in data.
Going along this evolution journey, fear of the unknown has always intrigued us to know what is in store for us in future. Earlier we used fortune tellers to know our fate, today we use predictive models for the same. These models are based on statistical algorithms using probabilities for the happening of an event or otherwise.
It is not enough to know one’s fate, especially when the outcome is not with which we are satisfied. Hence the quest goes on to explore the best that can happen to us, under which constraints and along with that which liberties we can take. This quest led us to the use of operations research (OR) technique of optimization, to get the best we possibly can, with the firewall of constraints around us.
From gut-feel to optimization, we have come a long way indeed. As we go up the value chain, we need to train ourselves and the organization to which we belong, in the statistical techniques, analytical tools, and assimilation of the business scenario and interpretation of the same.
Let us start the process of analytics training, step by step, up the value chain at www.edvancer.inAbout the author:Ms. Savita Kirpalani is part of Edvancer’s Advisory Board. She has over 30 years of experience in the analytics industry. After working with Unilever for 12 years she became the GM & Global Head- Strategic Solutions with Syntel Ltd. She was also the VP & Global Head of LEAN & Training at Syntel. Lately she is a strategic advisor in analytics & business intelligence. She has international experience spread across America, Europe, Asia & Africa and specializes in the FMCG industry. She is a professional member of the International Institute of Analytics.
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