Managing with KPIs: tools, processes and techniques for KPI data gathering and analysis

Data gathering, visualization and analysis is one of the major challenges of managing KPI framework. One of the key factors for success in this direction is understanding how to correctly develop and deploy processes, techniques and tools for KPI data management.  As a matter of fact, data gathering and KPI activation is the most important steps towards setting right KPIs for your organizations and making most of them.

 Data Gathering:
There are many techniques and methods you can use to gather right data. For instance, you can meet KPI data custodians in person in addition with involving them in a community of practice regarding KPIs. Similarly, there are many tools that can help you gather and utilize data in best manner possible. One such tool is data gathering template which you can easily find from various resources.

A structured approach in activating KPIs sets the bases for an efficient data gathering process. KPI activation should be coordinated in order to ensure that all data custodians have the same understanding in regards to their roles and tasks.

In this regard, there are many challenges you may face while gather data. For instance, 20-30% of the KPIs have never been measured and their data is not available. Most often, data custodians delay the reporting due to conflicting schedules. Similarly, data inaccuracies occur due to human or software errors.

Data Quality Dimensions:
You also have to ensure that data you have collected fulfills all the quality criteria you have set. For instance, you have to ascertain if all the necessary date is present or not. Is the data available when you actually need it or are the relationships between entities and attributes as well as systems consistent? Does data reflect the real world objects or a verifiable source? Are all data values within the value domains specified by the business?

Data Analysis:
There are many approaches to data analysis and all of them are really helpful. The first and the most important of these techniques is data analysis process. It reveals interdependencies between KPIs or other variables that influence business performance and helps identify the real cause of problems. Similarly, you can use various techniques such as Root Cause Analyses to better analyze the data.

Problems are best solved by eliminating causes, not by eliminating only the symptoms. Root Cause Analysis is a continuous improvement technique and prevents problems over the time. There is another data analysis technique which asks the question Why 5 times to help you measure data. Toyota has used this system to maximum effect. This technique aims at completing the analysis of causal relationship, by addressing the question “Why?” for 5 times. This technique is very easy to learn and implement and identifies the causes of the problems.


Monitoring and Improving KPIs:
The KPI Institute has presented two very effective strategies to monitor and improve KPIs.

Single Loop Learning is used when the current goals, values and strategies are sound, not questionable. The emphasis is on techniques and their effectiveness. In this type of learning the decision-making process and the organizational structure are accepted, the emphasis being on fixing unwanted variances. Moreover, errors that can be corrected are detected and acted upon, while maintaining the basic structure.

Double Loop Learning is used when the strategy is reviewed. It is focused on learning and reviewing previous situations. Assumptions, premises and speculations are questioned in order to raise the decision-making process efficacy. After identifying errors; the organizational rules, policies and objectives are re-examined and modified.

Finally, key performance indicators can greatly assist you in effectively measure the performance of your organization and whether you are on track to achieve set goals.

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