Business intelligence is the collection and reporting of Key Performance Indicators. Its primary goal is to answer the question: What happened? A secondary goal is to use historical view to infer what will happen in the future.
For instance, a seasonal change to revenue occurs for a great many organizations. We can predict that a historical pattern of cyclic ebb and flow for revenue will continue.
Business intelligence typically focuses on data like revenue, customer support metrics, costs, margins, and other KPIs. It can be complex; data can be sorted by geography, business unit and time period, and filtered in any number of ways. It can be aggregated from a variety of sources; it can be stored in huge central repositories or at its origin point. In all these cases, business intelligence reports results: How did the business perform in comparison to a target or to historical data?
Data analytics, on the other hand, looks at the statistical relationship between two or more KPIs. Data analytics addresses the question: Why did it happen? Or, given a set of potential circumstances (data points): What will happen? Data analytics is the logical “next step” in understanding the data.
The distinction between business intelligence and data analytics can seem minor on the surface. Suppose plant manager Susan is looking at a chart that shows data regarding overall plant efficiency. The data show that her plant is making less product per standard hour this year than it did last year. She is looking at the chart, trying to figure out what caused the change. Is Susan “doing” business intelligence or data analytics? Business intelligence is the activity through which the data was gathered, organized, and reported. Without business intelligence, Susan wouldn’t have a chart that shows her what happened. Data analytics is what Susan is doing as she studies the chart to figure out why it happened.
Each requires a specific skill set; Business intelligence requires skill with database systems to aggregate, store, retrieve and filter data as well as skill with reporting tools capable of displaying the results in a way that helps users understand the data. Data analytics, on the other hand, requires skills in statistics with its seemingly arbitrary formulas and naming conventions.
Both business intelligence and data analytics have value in and of themselves but, together, their value increases exponentially—providing crucial information with which a leader can effectively make decisions.
But even the most effective business intelligence and data analytics won’t lead to better decisions if the organization hasn’t also built a good decision-making culture. A leader’s job, then, isn’t just to make good decisions with these tools—it’s to create an organization where good decisions get made.
Key Elements of a Good Decision-Making Culture
1. Curiosity. A good decision-making culture is built on a spirit of curiosity. Such a culture is made up of people who are continually asking themselves and each other, “I wonder what would happen if….” “I wonder how A and B are related.” “I wonder why X happens the way it does.”
2. Expectations. Leadership promotes a good decision-making culture by holding the clear expectation that everyone will generate “I wonder…” statements. People with different positions will have different points of view, different experiences, and different hypotheses; e.g., they’ll be creating their own “I wonder…” statements. Simply put, the more folks who are wondering, the more innovation, creativity and good decision-making there will be.
3. Transparency. Leaders assure that relevant business intelligence is widely distributed throughout the organization. If only the “experts” have access to the data needed to satisfy the “I wonder…” statements, others will understand that they aren’t allowed to be curious. They won’t be able to pursue new avenues of inquiry and, eventually, they’ll stop trying.
4. Training. Access to data is of limited value if no one knows how to organize and analyze that data. Leaders should invest in training, including hiring data analytics experts—employees trained in the use of fundamental as well as advanced statistical analysis tools as well as supporting infrastructure (Minitab, etc.). These “in-house experts” could teach others as well as support the data organization and analysis efforts of individuals and teams.
5. Discipline. Organization members must be expected to display discipline with respect to continual business intelligence and data analytics. Susan won’t benefit much from a “one-off” data analytics push to solve one problem. True strategic improvement will come when she and her colleagues create a flywheel of momentum with respect to the “Plan/Do/Check”Act” wheel; i.e., gather and organize business intelligence, look for patterns among the data (data analytics), make decisions and create actions based on those patterns (execution), monitor and assess the effectiveness of the decisions and actions (back to business intelligence).
Managers, then, can’t simply import the tools and expect good results. Just as is the case in any effective change effort, attention to creating a culture that supports the change is vital. Whether the initiative focuses on operations improvement, an implementation of new technologies, or better decision making, it will be successful to the extent that managers include steps to build and sustain an appropriate decision-making culture.
Rick Bohan, principal, Chagrin River Consulting LLC, has more than 25 years of experience in designing and implementing performance improvement initiatives in a variety of industrial and service sectors.
