In making business decisions, we start with a business problem or opportunity; we collect relevant data, identify alternatives and make a decision. We use BI to help us in every step of the way from data collection to data analysis and sometimes even in executing the decision. Our goal is to make an effective decision but more importantly, we want to make sure that a standard process is established. We then try to automate this process by establishing a model. (We want to depend on standard processes rather than counting on human judgment every time the same business decision is encountered). We want to use the model and the process so we are not affected by human biases and we take full advantage of the power of technology. We standardize the process and expect reliable and predictable results.
Figure: 1 Sample Decision-Making Path with Metrics
When implemented correctly, this approach works well. It works better when the decision is less complex. For instance, a lender may use several metrics, such as credit scores, debt-to-income ratios, etc., to assess credit worthiness of their customers. Based on the guidance established by these metrics, they make a decision to extend a line of credit or not. They have the process automated so every time they have to make a decision, they refer to the system. In more complex decisions, such as identifying appropriate price and inventory levels, these systems are not enough, and organizations usually create competency centers to deal with these decisions.
Today, we see that businesses are becoming more connected and more complex. Business decisions that bring competitive advantage are complex decisions. Using the same path to make a decision with the same set of metrics no longer brings competitive advantage. We realized in a very painful way that every penny does count. And if in our business decisions, we can find a way to save a penny or justify a way to charge a penny more, we have beaten our competition. For instance, when all lenders are using similar techniques to evaluate customer credit worthiness, one lender decides to incorporate some new metrics in their decision-making process. Of course, they cant be picking metrics randomly, so what they do is take a look at their historical data and try to determine segmentation that can be used in establishing the new metrics. They might find that in their existing customer base, default rate of customers isnt necessarily higher in the last two years for customers with shorter credit history. This immediately tells them the customer behavior had changed and the initial assumptions arent valid anymore. Whats more, they can now offer more attractive terms without the fear of default to customers who dont have a long credit history.
In order to get to that conclusion, they would have to use the full power of BI tools and some statistical analysis. In the end, they find themselves with increased revenues and profits, which would have never happened had they not changed the metrics.
Figure 2: Decision-Making Framework with Evolving Metrics
In such a connected and complex business world, we see that its necessary to have a decision-making process where key components are standard but there is an extra step that reviews relevant metrics, identifies thresholds and introduces new metrics when necessary. In doing this, the following actions are necessary at the least:
- Review existing metrics and thresholds.
- Introduce new and more relevant metrics.
- Remove old and obsolete metrics.
- Keep monitoring performance of the process by measuring impact on revenue, costs and profits.
This process of evaluation of metrics and key performance indicators doesnt have to take place every day. The intervals of this process depend on how dynamic the specific business is for that organization. Still, the idea is to try to obtain competitive edge by examining the automated decision-making processes, fine-tuning the existing ones and introducing new ones. This initiative naturally gets more complicated and expensive as the business decision being tackled gets more complex, but there is certainly a big impact on the overall strategy and the success of the organization. The world evolves, businesses evolve and customer behavior evolves continually. In order to keep up with the evolution, BI processes must evolve, too. Whats more, the evolution of BI and metrics must be at a pace faster than the evolution of the business environment; otherwise, we go back to the cause-and-effect approach that is no longer good enough to maintain competitive advantage.
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