In business, as in life, there are a number of unknowns that we constantly have to notice, interpret, and react to. A superior strategy includes prediction or some sort of range of expectation(s) for key actions (e.g. product releases, announcements), the accuracy of which are the result of factors like good intuition, observation, and pattern recognition. As such, the strategic minded individual should be collecting information from multiple sources in order to make the transition from expectation to outcome as seamless as possible as often as possible. There will be colossal misses, due to misinterpretation of given data, failure to collect appropriate data, or a high degree of chance in the actual outcome. Example: The cult film Donny Darko was released to major theaters around the time of the September 11 attacks. Part of its failure as a major release has been attributed, post-hoc, to the particularly sensitive aspect of one of the movie’s main events – an airplane engine falling from the sky and destroying Donny’s house. Estimating the success of this movie when it was being prepared for its release would never have included a “terrorist airplane attack” factor. That said, reasonable ranges of expectations can be provided most of the time.
A huge advantage to narrowing the range of possibilities of a particular forecast or outcome while also maintaining a decent accuracy rate is to engage in lateral thinking and converging on answers through the use of techniques like proxy variables. One place that I have seen analysts and non-analysts falter when trying to predict a business outcome is their failure to engage in creative thinking around ways to estimate unknowns. Rarely is an estimating technique as simple as plugging in a few values to a known formula – especially when tacking innovative solutions. Frankly, if it was this easy, then analysts and strategic thinkers would have a very short shelf-life as they would come in, set up the magic eight ball, and be done forever. An analyst’s job is to explore, research, and create (art + science) answers to the right questions. Note that I didn’t say all questions. An additional part of the analyst’s job is to act as a noise filter by taking and refining the key pieces of business requests and squeezing them down to basic elements of what needs to be known.
In the following posts I plan to tackle a number of specific topics revolving around approaches to making good predictions and providing superior answers, not from a cookbook style point of view but from a higher level. Let’s get meta and call it a strategy around creating strategy. The approach I take is that of the analyst as a curator, a gardener, and a scientist. The analyst is proactive, inquisitive, provides unique insight and through knowledge of the data, surfaces questions that have never been asked.
Every business is different, but often the core remains the same: a good or service is being offered to a consumer. Working from that core, business questions around directions to take an offering, how it will fare in the open market, how to improve it, minimum viable product requirements and other more mundane day to day curiosities will arise. The analyst should be able to tackle these as a matter of course, and recognize the larger questions implied by the smaller ones and vice versa. Rarely is there a single question, and rarely is there a single answer to any given question. In the following posts I plan to explore the world of the analyst from my own personal lens, providing an overarching description and then digging into specific topics. The following is my off-the-cuff laundry list of expected posts. Hope you enjoy them.
- The role of the strategist and analyst
- Answers 101: Defining the right questions
- Using proxy variables to improve estimates and answers
- Information sources and intelligent approaches to information
- Core data needs: Quality, Breadth, and Volume
- Skunkwork Analytics: your often undefined job