Less Data but Better Business Decisions: A DIET Approach

During the research for their book Stop Spending, Start Managing: Strategies to Transform Wasteful Habits, professors Tanya Menon and Leigh Thompson asked 83 executives how much their companies waste on analytics per day. They estimated their daily expenses at $7,731. In many cases, this investment does not pay off because companies are not able to turn data into effective business solutions. They end up with so-called garbage can decision making, a situation where a lot of problems and possible solutions are thrown into a discussion until a random decision is made. The discussion is chaotic: it lacks a clear structure and priorities. Menon and Thompson describe the final outcome as “applying arbitrary data toward new problems, reaching a subpar solution”. To overcome garbage can decision making, they propose a 4-step DIET approach to data, which explains how to turn them into information of great business value.  

Step 1: Define

Asked to brainstorm ideas about a problem, people tend to suggest solutions – often those that have worked once before. Too much focus on problem-solving, however, can hinder the perception of the extent of the problem, note Menon and Thompson. In their opinion, brainstorming sessions should not be based on clear-cut definitions but a flexible approach to the problem at hand. This will bring different perspectives to the table, expose hidden assumptions and raise further questions. To get a better understanding of the complexity of the problem, team members should explain each other how they understand it, present its causes as well as share their assumptions. Another option is to briefly write about the problem from a few perspectives (e.g. the customer, supplier and competitor) and later contrast them to get some new insights.

The next move is “a disciplined data search”: avoid postponing your decisions by screening your data requests with if-then statements. “Ask yourself a simple question: If I collect the data, then how would my decision change? If the data won’t change your decision, you don’t need to track down the additional information,” explain Menon and Thompson.

Step 2: Integrate

Once you have clearly defined the problem and the scope of the data you need, you should analyze how well they fit together. The integration will help break down hidden assumptions as well as organize and utilize the information effectively. Menon and Thompson suggest creating a KJ diagram (named after its author, Kawakita Jiro) to determine the causal relations between facts. You should write the facts on notecards and group them by relations such as an increased number of clients after an initiative or a drop in sales because of a project delay. Having organized the information, you will be able to notice some patterns and find their connections in the data more easily.    

Step 3: Explore

Now is the time to explore some ideas or solutions suggested by your KJ diagram. Menon and Thompson recommend trying the passing game to facilitate the process of collaborative exploration. First, you should assign each team member a distinct idea to work on for 5 minutes. They can either draw or write about it. Then ask them to pass their creation to another person to further develop the idea, while they take over someone else’s work. You can have as many passes as you want. At the end, discuss the results of the teamwork. Menon and Thompson note that this kind of collaboration makes team members confront directions they would never consider otherwise. They advise discussing the most useful ones in more detail.

Step 4: Test

Finally, you should run some tests to critically evaluate the feasibility of the solution. All the members of your team should be actively involved in the testing process.

Menon and Thompson observe that people tend to over-collect data which support their prior beliefs and under-collect data which dismiss them. In their opinion, even a single test which helps overcome confirmation bias gives a broader perspective on a solution as well as highlights its defects.

In their final comment, Menon and Thompson stress that the DIET solution to garbage-can decisions does not promote the idea of “cutting out data entirely”. Instead, it explains how to better utilize the data you have during a 4-step screening process to make well-informed business decisions.

Reference:  Tanya Menon and Leigh Thompson, Harvard Business Review