In 1963, Bernard Forscher wrote a fascinating letter in Science Journal magazine -- in which he likened scientists to builders tasked with making edifices (explanations or laws) out of bricks (facts). These ‘builders’ became so obsessed with making bricks that over time there were more bricks than edifices. Bernard concludes his story with the assessment that builders were no longer making a distinction between a pile of bricks and edifices.

The parallels between Forscher’s story and today’s rapidly evolving analytics world are stark. With the amount of data that’s produced in the world today, analytics teams in organizations run the risk of spending too much time assimilating data and performing analytics (bricks) -- but not enough effort in translating this to actionable recommendations (edifices).

The key is to actively think and design the problem solution based on how it’s ultimately going to be used by the business. This bias towards consumption of analytics during its creation can help organizations capture real results from the investments in decision sciences and analytics. In this article, I propose three battle–tested strategies to drive this consumption.

These include, over-invest upfront to find the right question, design the right “creation-consumption distance” and appreciate connections between problems. Collectively, these strategies make up the “consumption bias” mindset.

1) Over-invest upfront to find the right question 

Peter Drucker is quoted as saying that there’s nothing more dangerous than the right answer to a wrong question. And, most questions that are initially posed do not incorporate the consumption perspective; therefore these will not lead to a transformative impact.

The starting point must ideally be a search for the right question. Create hypotheses and frame questions around them to nip the risk of making unfounded assumptions. Sometimes the right question can be as simple as asking ‘how will this analysis be used?’ Consider the problem of finding lifetime value of your customers (CLTV). It starts with the innocuous question, “What is the life time value of our customers?” The approach to solving this would be very different if the intended use was for an ROI-based marketing budget allocation vs. customer segmentation. Based on the intended consumption, the question can be improved. In this case, a good question could be “How to design CLTV to aid customer segmentation?”

At other times, identifying the right question may be about reframing the question itself so a more consumable solution can be found. For instance, a common question that a distribution company asks itself is ‘Which items being purchased from our suppliers will experience price raise in the next 6 months?’ Distributors often ask this question to decide which items to buy in advance. Deeper analysis reveals that there are two forces at play here – industry factors that affect all suppliers and supplier-specific behavior. There is value in understanding each separately. So, instead of trying to predict where the price will change, consider breaking it down into two more insightful questions:

  • ‘Which items should experience a price increase?’ – looking at industry factors like supply demand, regulations, etc.
  • ‘Which items will experience a price increase?’ - exploring supplier-specific factors such as, whether the supplier is a leader/follower and how their business is faring.

This kind of reframing helps improve consumption since the first question can hardly be influenced but the second supplier-specific question can help create some supplier-specific strategies.
2) Design the optimal “creation-consumption distance”

The time and effort it takes from the point of creation of analytical insights to the consumption of analytics via actionable steps indicates the “distance” between creation and consumption. Therefore, more stakeholders means greater the distance between them. Business leaders should carefully consider the trade-offs that affect the distance between creation and consumption. Shorter distance is not always the right answer.

  • Which group should produce the analytics? Let’s take campaign measurement as an analytical problem. Having the marketing team that created the campaigns to measure the results would reduce the distance. However, business leaders must consider if a different team should produce the analytics to create a more unbiased and holistic view. Perhaps, a centralized analytics team that has a broad view of the business or the finance team that will can consume the analysis and validate the numbers could do well.
  • When to involve different stakeholders? Involving multiple stakeholders typically increases the time taken to complete the analysis. However, the upside of including these stakeholders early is that they feel invested in the solution. This could increase the speed of consumption. Quick iterations via a hypothesis driven approach will ensure stakeholders are in the loop at every stage by being able to provide “mini” insights. This creates trust, which can further accelerate consumption. This will also afford teams the time to go back to the drawing board multiple times and come up with more creative ways to solve a problem. The analytics team should take a broader “creation-consumption distance” perspective – being cognizant of the fact that if they fail to do so in an effort to minimize time to creation of analytics, they might inadvertently increase the consumption period.
  • Are the end-users ready to receive the analysis? An analytical team should keep the end users in mind and be aware of the time required to spend to truly leverage the solution. This could be in the form of a checklist of tools and techniques they need to be trained on to understand and implement the end results. For example, the end user must be aware that if the objective of an analytical exercise is just to forecast revenue more accurately, the same model might not apply in cases like scenario-based budget planning. Factors like complexity of the problem, business sensitivities and scale of consumption should also be considered as decisions are made. For example, counter intuitive or negative results should be relayed more objectively with the help of strong and robust data analysis as evidence. Spending time with end users during the creation of analytics can save time during consumption, thereby improving the “creation-consumption distance.”  

3) Appreciate connections between problems

In today’s inter-connected problem space, very rarely does a solution to a single problem drive impact and rarely get implemented in isolation. Solutions to connected problems have a greater chance of driving impact and being consumed.

Therefore, to be greedy about consumption one has to map out the inter-connected problem network upfront during the problem solving process. Often, this mapping helps discover connections previously unnoticed, leading to a more holistic solution. For example, the insights from an analysis on setting the price of a new online product, based on feature usage, may also be used to determine what features are effective and the gaps that need to be addressed.  

These connections can also serve as powerful prioritization tools for more lasting impact. The connected problems can be further mapped into a simple matrix across effort and impact. This mapping, in turn, can help identify problems that would lead to the maximum return on effort spent. Connected insights like these play a huge role in developing a coherent consumption strategy. Each of these insights plays a part in successful implementation. Organizations that spend time and effort connecting problems will have a higher probability of breakthrough innovation and long-term solutions.

Final thoughts – Power of the Consumption Bias Mindset

Driving a consumption bias in organization is eventually a mind-set change. It requires regular execution and demonstration of the three outlined strategies.

First, spend time upfront towards arriving at the right question, keeping in mind the manner in which the results will be consumed. This often involves reframing the question itself. Second, carefully evaluate various trade-offs to determine the optimal distance between the creation and consumption of analytics. Shorter distance may be faster but may not always be the best. We discussed one example illustrating its effect on objectivity above. Lastly, taking time to study the connections between problems could lead to disproportional benefits – both in terms of increased consumption through rigorous prioritization as well creating ‘A-ha’ moments by identifying new patterns.

Therefore, organizations must plan ahead and design a system to ensure the spotlight is kept on consumption at all points of analysis.  Creation without consumption is like playing ‘air guitar’ – you may be playing all the right notes, but no one will be able to hear it.

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