Research from Gartner and Mckinsey has shown that over 80% of the analytics projects fail. So, what is the most effective strategy to derive value from data and analytics? The key is to have strong use cases. The use case is a proven communication tool as it provides the business a standard and an effective method for sharing the needs of the business with the data analytics team. 

What exactly is a data analytics use case? Fundamentally, a data analytics use case is the manner in which the business user leverages data and the analytics system to derive insights to answer tangible business questions for decision making.  

There are four keys to creating meaningful use cases for data analytics. I call these the four O’s: 

1. Objectives. Ensure that the use cases formulated are clearly tied to the strategic business objectives of the organization. Don’t work on a use case because you have the data, technology, and report developer available in your company; avoid availability bias. Instead pick a use case that is relevant for the company even if it is foundational, evolutionary, or revolutionary.
   

2. Ownership.  Identify the one person who is accountable for the use case; the individual who will own the use case and drive results from the insights derived. As the saying goes, “Everybody’s job is nobody’s job.” 

3. Outcomes. It’s hard to get senior management to buy into a use case based on intangible benefits, such as quality, productivity, and engagements. Instead, focus on measuring tangible outcomes, such as ROI (Return on Investment), Customer Churn, and Net Working Capital. These KPIs should be tied to the three universal business objectives: increased revenue, lower expenditures, and reduced risk.
   

4. Optimal Library. Even today many business leaders believe that more is always better. Unfortunately, there is a limit on the amount of time and business resources companies have at their disposal. Hence create a library of about 12 use cases for better focus and management. This approach will also help identify repeatable patterns so that scalable analytics solutions can be designed and deployed.

Once the use cases have been formalized, the next phase is the prioritization of the use cases based on rewards and risk (including cost ,complexity, duration, and risk of implementation.) This prioritization will form the basis for an analytics roadmap that drives business value from data and analytics. 

Figure 1. Risk/Reward Matrix

Plot your use cases on a Risk/Reward matrix.

At the same time, it’s important to know what not to do. Although data has the potential to become a business asset, if not managed well, it can quickly become a huge liability for the company. As Benjamin Franklin said, “Never confuse motion with action”. 

Building the use cases is by no means the end; it is in fact one of the first and fundamental steps in building a data analytics strategy.  Once the use cases are developed, it’s important to understand what people, process, and technology capabilities are required to support the use cases. Then you take those capabilities and build them into a data strategy designed to support the development of your top priority use cases. Along the way, you will be building out an enterprise data infrastructure designed to support any use case and data source in the future.