Measuring the performance of data processes correctly
In the sixth edition of The Latest Think! we already presented an alternative to the conventional ROI with the Return on Operational Technologies (ROOT), a measurement method from The Group of Analysts for the evaluation of software in companies. In addition to the technology perspective, a special focus is placed on the data processes – i.e. the operational element in which the actual value creation takes place.
Successfully implemented data processes have a multidimensional positive effect on the organization, while poorly implemented processes can lead to the value creation potential being lost. But how exactly do you measure data value performance? In the latest issue of The Latest Think! we not only describe the methodology in detail, but also provide clear examples of how to carry out and visualize data value performance measurement.
No measurement without key performance indicators
Key performance indicators (KPIs) are central and common tools for analyzing business practices, processes or campaign successes. However, defining suitable KPIs is anything but easy. Many project managers tend to adapt common metrics without questioning their actual meaningfulness for their own company. When designing KPIs for data processes, it is important to consider the individual starting position, requirements and target situation and to compile measurement factors that comprehensively reflect these KPIs.
The seven KPIs of the Data Value Chain are:
- Process transparency
It measures the extent to which a data process is known within the organization. For example, centrally stored information on responsibilities and rules for the data process indicate a high level of process transparency. - Efficiency benefits
This involves analysing the extent to which a data process has a positive effect on efficiency gains within an organization. For example, the degree of automation can be used as a basis for evaluation. - Effects on quality
Data quality is a key issue for companies and as such depends on the sum of all data processes. This KPI measures the extent to which the individual data stream contributes to achieving the company’s quality objectives. - Competitive advantage
Competitive advantages can be realized from a wide variety of perspectives. Data processes can contribute to competitiveness, for example by shortening time-to-market or optimizing product development. - Corporate transparency
This KPI measures the extent to which the data process contributes to the company achieving its transparency goals. This can be relevant for sustainability reporting, for example. - Data excellence
This KPI can be used to measure the selective quality of the data processed in the individual process – this is an essential measured value, especially for central data processes such as onboarding. - Cost savings
Here, the cost savings realized by the individual data process are documented and measured in concrete terms. This can be, for example, the reduction in manual effort thanks to automation.
Not all of these KPIs are equally important for every data process and every company. It is therefore important to emphasize once again that project managers reflect on the requirements, goals and challenges of each data process from these seven perspectives and formulate the individual KPIs accordingly and place them in a value range that makes sense for their purpose.
When is it measured?
Data Value Performance measures the defined KPIs at three different points in time. The actual situation is used as the starting point. In the case of a system change, this is the original data process in the old system. This measured value is also called Present Value Performance (PVP). A target value is defined as a reference value – the target state or Expected Value Performance (EVP). It shows the ideal value of the KPI.
The progress of Data Value Performance is measured at regular intervals between these two extreme values – these measurements are also called Measured Value Performance (MVP). Across all KPIs, this value provides a fairly accurate assessment of the gradual achievement of the project’s objectives with regard to the data processes.
For whom is Data Value Performance Measurement worthwhile?
But when does it even make sense to go to the trouble of designing and measuring data value performance? Due to the central importance and high degree of networking in the area of product content management, it makes sense to regularly analyze all data processes in the context of product communication using the data value performance methodology and to check where there is a need for action. Especially within the areas of product information management (PIM) and digital asset management (DAM) as well as in the context of neighboring areas such as channel management and syndication, data value performance offers great potential for companies in any industry.
If you also want to put the performance of your data processes to the test, download the issue of The Latest Think! or contact us directly and without obligation!
