Analytics is to drive, measure as well as improve decisions.
Analytics is permeating into business’s daily life, It’s no surprise to see more organizations intend to adopt analytics in guiding decision making. Should analytics drive your decisions or should it be used to measure you decision's success?
- Properly done analytics should drive decisions. Properly done analytics will give you information that you wouldn't have otherwise. Analytics are useful in guiding decision making, but always make sure that the recommendations suggested pass the "does this make sense" test. It is important to make sure that you are using the right inputs and a model that adequately fits the problem to carry out the analysis. Otherwise, the recommendations may not be optimal, the classical case of "Garbage in, Garbage out".
- Analytics are of paramount importance when planning and deriving results following implementation. If one uses the approach of defining a hypothesis for the problem space and the possible solution options, and then using statistical analysis to test and characterize the resultant set to the hypothesis, then analytics is required for both. Because once an approach is decided upon and implemented, a complete solution will include measures of the target results that validate success in achieving the desired business outcomes.
- Analytics is to drive, measure as well as improve decisions. When focused on operationalizing analytics, meaning implementing predicting analytics results in operational systems in real or near real-time, analytics can 1) drive (automate) decision making, and by monitoring those decision results analytically, then 2) measure-Analytics can be very useful to measure "success" or improvement between different scenarios, it is important to be sure that the right things are measured to come to correct conclusions about the performance.3) improve the decision and, therefore, the overall system and business performance over time.
- Decision making is both science and art; it takes both data analytics and intuition, in order to make the effective decisions. Well designed experiments can yield great insights that never would have been uncovered if one goes with experience and "gut feeling.". But at the end of the day, it is also critical to see if the outcome bears any resemblance to the predictions. Sometimes the differences are due to poor (or great) implementation. It is important to understand WHY things happened so those learning can be applied to the next iteration or project That all being said, analytics is important, but do not forget common sense.