Collecting data and computing the results provide a ton of useful information. Specifically, logically linked results offer intelligent data that explain the state of business. Here are ten ways to leverage the information.
- Establish a reference for status quo
The tracking provides data on current performance level, reflecting the capabilities of the resources allocated to do the work. The snapshot provides a baseline for future reference. For example, the monthly sales results for a full year capture the cyclical variation. They offer a reference for comparison when market conditions change.
- Determine gap between performance achieved and target
The comparison shows how close the actual performance matches expectations. A wide gap reflects problems with execution or doubts about the reasonableness of the set target. For example, a 20% revenue shortfall for the month indicates that there are problems.
- Identify problems that require fixing
Measurements that show persistent deviations indicate the source of the problem. Exploring the source uncovers the particular areas that need fixing. It is easier to hone in when there are supporting data. For example, low sales can be attributable to low store traffic.
- Reveal drivers that accelerate result
Upstream activities that propel work swiftly through the process optimize execution. These drivers are focal points for ensuring that little would stand in the way. For example, short cycle time for the review of a client’s profile expedites the finalization of investment choices.
- Plan viable workflows
Elapse time in between work activities extends the cycle time for the end-to-end process. This poses a challenging to efficiency. For example, the waiting for invoice approval delays the processing of payment which affects the overall workflow.
- Validate assumptions for change
Change encompasses unknowns that the business needs to accommodate by making assumptions. Evaluating the results allows the business to reflect on whether the assumptions are legitimate. For example, testing an employee on using a new tool provides feedback on whether concurrent training is a good idea despite the benefit of shortening the training time.
- Test correlations
A causality relationship or a lack of it is a powerful indicator of what to manage closely. The results demonstrate the sensitivity and the extent of impacts from different variables of execution. For example, the inverse relationship between customer satisfaction and cost of shipment provides information on delivery timeliness that an online store needs to be aware of.
- Understand relative values
Results have implied value associated with what they meant to represent. The information provides knowledge on the product or service consumed. For example, the nutrition label on a jar of pasta sauce informs the consumer the nutrients to be consumed. The information can be used to compare with the recommended daily sodium intake.
- Filter choices for decision-making
Results provide facts on performance, the supporting rationale for decision-making. There is a high probability of attaining the same outcomes under the same conditions. For example, the high error rate associated with a manual workaround led to a system enhancement to automate the specific task.
- Refine solution
Measuring the specific aspects of a trial offers indication on undesirable outcomes. Adjustments can be made to improve the product or service. For example, the time required for data entry hasn’t improved with the new software because the screen configuration is clunky. Changes are needed to regroup the data fields in a more logical manner.
Results provide useful data when the appropriate parameters are monitored. It is about quality, not quantity.