Most organizations are still trying to wrap their heads around how they can use data and what kind of business results analytics can drive. Today is Opposite Day. In honor of this special day, I would like to provide you with a few tips on how organizations should approach data and analytics to find answers that help drive business outcomes. Many organizations have been approaching analytics wrong, and it has hindered the potential of what it can do for an organization.
I have worked with customers who have made the following statements when requesting analytical support:
- “I don’t know what I don’t know…"
- “Show me EVERYTHING! Then we’ll see what to do…”
- And my favorite: “Run everything by everything!”
Houston, we have a problem. When I hear these statements it makes me cringe a little. Running everything—otherwise referred to as data mining—is not a focused method of improving process performance with analytics.
I urge organizations to do the opposite of what they have done in the past in terms of testing everything. Business process analytics should be more actionable. In other words, I recommend that organizations use value pathing to help focus analytics on actions. Running everything only means that there will be more data to run and go through that might not necessarily answer your fundamental questions.
The first step is for organizations to understand how business process analytics can be used. Common applications include:
- evaluating the business impact of process improvement efforts;
- building the business case for strategic initiatives/programs; and
- identifying key enablers/drivers of process performance
The second step requires obtaining and incorporating stakeholder input, working through the process of defining a business problem or challenge, and then scoping what type of data is available and how it can be used. This allows research questions to be discussed and creates a clear vision of what outcome the organization hopes to achieve, as opposed to having an analyst or statistician hunt for relationships that would require additional meetings with subject matter experts to determine the relevance to the goal. The outcome or goal has to be established first.
As I mentioned above in the second step, once the business problem has been defined, an organization has to measure and assess current performance. If you haven’t been collecting the necessary data, then establishing a baseline on certain measures is important. Top-performing organizations are already collecting measures that address cost, cycle time, efficiency, and productivity.
The third step requires an organization to identify drivers/enablers of higher performance on business outcomes. For example, an organization may look at the factors driving higher customer satisfaction, lower cost, or lower cycle time.
Organizations today use the “value path” to link process performance, business outputs, and organizational goals in support of value creation. The “value path” is a tool that helps identify measures that align with organizational goals. The value path allows an organization to apply analytics to business process improvement. When you align the measures correctly, a meaningful story starts to emerge. Organizations have to start with measureable outcomes and then work backwards to create measures and indicators of activity focused on that outcome.
- An organization’s business goal is to: Improve the “managing customer sales” process (in order to increase revenue)
- The analytics objective is to find out: Does professional development training for salespeople improve the “managing customer sales” business process?
- Data needed: Data collected on the professional development training, sales process, and revenue
- Analysis Results: As the use of professional development training increased, the sales process improved, which in turn increased revenue
In this example, the organization believes that professional development (driver) will improve manage customer sales (process). It is expected that professional development would improve sales cycle time, process efficiency, and sales staff productivity. With this hypothesis, the organization then looks at how the process impacts the outcome. So the organization hypothesizes that managing customer sales (process) impacts revenue (outcome) by increasing volume of sales and the amount per sale.
Organizations that successfully improve process performance with analytics do so by identifying outcomes in a strategic way. Having relevant analytics results requires understanding the link (i.e., cause and effect) between business inputs and business outcomes. Ensuring business value requires you to make the desired business outcomes explicit.
- Data and Analytics Glossary
- Change Management Practices for Establishing a Data-driven Culture (Best Practices Report)
- Analytics Case-in-point: Moving Beyond Benchmarks for Improved Performance
- Analytics Case-in-point: Improving Banking Performance with Business Process Analytics
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