manufacturingtechnologyinsights

The New Era of Analytical Requirements: Three Focus Areas

By Kathy Chiang, VP, Business Insights, Aspirent And Wunderman Data Management, Andrew Wells, CEO, Aspirent

Kathy Chiang, VP, Business Insights, Aspirent

As we move up the value chain in analytics from descriptive to actionable analytics, we need to go beyond simply gathering requirements based on asking questions of the users of the solution. In the old era, it was about the ‘question.’ We asked users of the analytical solutions questions centered on what they wanted to know about a particular subject matter and created dimensions and facts to develop answers.

"In the world of analytics, businesses that are able to make the journey up the analytical maturity curve will win in the market place"

The problem with this approach is that it rarely leads to action, rather we are simply enabling users to answer questions on the current state of the business. If we wanted to enable the user to take the best course of action, we would be stumped. Few of our traditional tools and methods help us craft a list of choices the user can make to solve a problem or take advantage of an opportunity.

Moving up the analytical maturity curve requires a new approach to gathering requirements. As we move from describing the problem to diagnosing root causes, we look to solve for ‘why’ something happened rather than only ‘what’ happened. As we travel further up the analytical maturity curve to the predictive phase, we look to forecast the future. And finally in the prescriptive phase we look to solve for what we want to occur.

As you progress further through each of the phases, you get closer to helping the user make a decision rather than answer a question. This leads to a fundamental change in the approach and output of the current requirements gathering methods for analytics, that is centering the requirements process around the decision, not the question. To achieve this end, we recommend three focus areas for your analytical requirements:

1. Decision Architecture–In this new era of action focused analytics, we need to solve for what decisions the analytical users want to make, leading to a new approach in gathering requirements centered on the decisions and actions the user takes to solve a particular problem. We recommend putting together a decision architecture centered on particular business problems or domains enabling the architects and developers to see how end users make decisions and the information needed to support them.

Wunderman Data Management, Andrew Wells, CEO, Aspirent

2. Actionable Analytics–One item missing from most analytics today is actionability. In the new era of analytics, we ask the question, ‘What are the actions that come from the decision?’ If our analytical solutions are not actionable, then the value derived from them is extremely limited. From our observations, analytics that simply describe what happened in the business have short shelf lives. To achieve actionability, you need to gather not only the decisions the user makes, but also the corresponding actions. Gathering requirements with this lens enables you to upgrade your analytics. Furthermore, through understanding the decisions and actions you willgain insight into data you need to gather to enable the analytics. This data may be found within the company walls purchased or in some cases unattainable. 

3. Decision Theory–In the new era of analytics, we need to not only enable the analytical user with the ability to make decisions and actions, but also help them navigate to the right choice. We recommend leveraging Decision Theory to construct the approach for making a decision. Decision Theory helps you structure the decision process to guide a person to the correct outcome. Decision Theory, along with Behavioral Economics, is focused on understanding the components of the decision process to explain why we make the choices we do. There are several practical techniques you can consider when building analytical solutions, including:  Decision Matrix, Probability, Prospect Theory, Choice Architecture, and Cognitive Bias.

In the world of analytics, businesses that are able to make the journey up the analytical maturity curve will win in the market place. In order to do this, you will need to change your approach for capturing requirements based on the old era of descriptive analytics centered on questions, to the new era centered on decisions and actions. As you make this journey, enriching your analytics with Decision Theory empowers your analytical user to make the optimal choices and take the right actions that drive value.

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