Saturday, November 8, 2014

Analytics vs. Wisdom

Analytics answers the questions, but wisdom frames the right questions.

The purpose of human decision-making is to form the right questions and validate the assumptions. Analytics can give you the best answer to the right question. But it takes wisdom to frame the right questions.

There is nothing in Analytics that prevents it from being used on forward-looking projections, scenarios, and uncertainties. Generally speaking, decision-making is forward-looking, traditional Business Intelligence is backward looking. While information gleaned from historical data can be factored into any decision, the "wisdom" in the decision process incorporates the decision maker's tolerance for risk. The advanced analytics, such as predictive analytics (predict what will happen) and prescription analytics (how shall you respond to it). Prescriptive analytics (optimization models) are quite close to insight and do guide in decision-making, even in strategic decisions.

Bayesian probability is one interpretation of the concept of probability. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, the proposition whose truth of falsity is uncertain. Bayesian Probability is highly analytical, yet forward-looking. The key is to orient the Analysis around decision variables tied to the forecasted outcome with uncertainty. However, this advantage seems to fall apart when making long-term big-bet strategic decisions where precedents established by Bayesian views of data will only go so far to help the decision-maker. Past data won’t account for a radical shift in a market, nor will it support a belief that one of your product lines will become extinct in the next five years. The good distinction between strategic vs. operational decisions is defined by time horizons. Analytics and big data at this point are perhaps better suited to operational decisions which tend to be shorter-term and cyclical where high correlations between decisions and outcomes are more likely to be observed. 

It’s human’s wisdom which is essential to form the right question. Under uncertainty, it will still be up to the decision-maker and his/her wisdom to assign probabilities and his/her willingness to take action will be based on his/her (and his/her company's) risk tolerance. Analytics may help forecast the potential risks, but it can't tell a decision-maker how much risk to accept under uncertainty--you need a human to express that preference, and continue to questioning:
-Do you want to maximize the mean expected result? 
-Do you want to maximize the probability of making your goals? 
-Do you want to minimize regret? (worst case outcome.) 

Human decision making becomes important and essential when there are multiple criteria, as in multi-objective optimization, and the decision changes according to which criteria have priority. There is fuzziness in the decision because there is fuzziness in conflicting criteria. Optimization requires objective viewpoint. Most strategic decisions involve many people with different objectives. Analytics and Optimization are great tools to find the right Answer. Optimization models can help but they don't quite get up to wisdom. That takes leadership, experience, and courage. Most likely you want to do all three simultaneously, which means finding acceptable tradeoffs between these competing objectives. This at the very least is a sensitivity analysis of the optimization parameters. The visualization approach to explore the trade-offs between different objectives.

Analytics is making an evolutionary journey from capturing hindsight to insight and foresight, it is becoming a significant tool in decision-making via answering questions, however, it can not replace human wisdom which is essential to frame the right questions, the Big WHYs are often more important than what and how.



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