Data Analytics: How to Know the Right Business Questions to Ask

My blog posts this year have a common theme of using data and analytics to answer business questions. One of my earlier blog posts explained why I am excited about Teradata’s focus on giving organizations a way to invest in answers. My last post focused on using analytics to answer the toughest questions. Let’s move on to discuss how to know the right business questions to ask when leveraging data analytics.

Everyday examples of how analytics are being applied within a given industry or within a specific business functions are written about and shared. Many Teradata customer examples are available as well. These are all great for inspiring ideas as to what is possible. But in order to translate ideas into action, one must identify what is relevant for each particular situation and prioritize the ideas generated to identify the right question to ask next.

To identify what is right for your organization, focus on the business outcomes that align to key business strategies for your organization. Categories of business outcomes include:

• Asset Optimization
• Customer Experience
• Finance Transformation
• Operational Excellence
• Product Innovation
• Risk Mitigation

As the interested stakeholders for each of the business outcomes will vary, the next step is to work with each of the groups to identify the key business questions that analytics can help to answer. The brainstormed list of business questions can be grouped and refined into specific Analytic Use Cases that capture:

• Objective / Problem Statement
• Expected Outcome
• Business Benefit

Below is an example analytic use case related to the Customer Experience business outcome:

Once the analytic use cases are identified they can be mapped to a 2x2 quadrant that compares the business view of business value with feasibility. As seen in the diagram below, the use cases grouped into the top right quadrant are the candidates to consider further.

For this step, I prefer to use a workshop approach as I find the discussion between the different stakeholders (or business areas) of where to place a specific use case quite informative.

Up to this point, the focus has been on the business view and if the business area has all that is needed to go and develop the required analytics. Those use cases can then be prioritized within the appropriate team (in Agile terms, add the use case as a user story to the backlog so that it can be prioritized).

For the other use cases, it is now time to validate the feasibility with other stakeholders including in technical areas. In this process, key dependencies (such as making additional data available) can be noted. Once the top right quadrant is confirmed, then those analytic use cases can serve as the high-level requirements and be put into the pipeline for development.

This analytic use case approach can also be used to document your current capabilities and serve as vehicle for knowledge sharing. Imagine you are a new analyst or data scientist with a library of analytic use cases -- you no longer risk re-inventing the wheel, rather you can build upon and extend what has already been built. Imagine the power in being able to share analytic capabilities and inspire adoption across the entire company!

Whether you draw inspiration from inside or outside your organization, identifying and focusing on the priority analytic use cases will ensure you are asking the right business questions.

But you cannot stop there! It is important to note that priorities will change. Make sure that you have not created a static set, but rather a dynamic process that evolves to respond to changes to either business strategies or technology (including access to new data sources). In this way, you will be able to ask the right questions for today… and for the future.