How to improve data quality in BPM?

In order to ensure data quality in a business it is usually necessary to divide data into more or less important areas. Certain data is not particularly important for the daily operations of the business while other data is outright critical for the business.

In our work with ensuring data quality we have often heard leaders complain about the quality of for instance client master data. When we then ask them what the level is, or what an acceptable level would be, we rarely receive a clear answer. In order to work in a systematic and structured manner with improving a company’s data quality it is necessary that the management reach a decision on what good data quality is. For example, can the company live with 5% of the order forms not being correctly filled in? Or should this limit be 10% or 2%? When the issue is safety, regarding for instance handling of poisonous materials, the company typically needs to know the location and condition with 100% accuracy without exceptions.

The result of this work is several parameters that define the level of good, medium and poor data quality for selected business-critical data. These levels can be broken down further, if needed.

This part of DQM is about authority, procedures for reporting and escalation and delegating responsibilities and duties in the business as related to data quality.

This system is meant to ensure that the company can rely on work being done regarding securing, measuring, reporting and perhaps improving data quality in areas such as critical clients, financial ratios, production data, logistics data, etc.

This might result in creating a CDO position as well as dismantling – or centralising – the company’s data ownership. The task here is to ensure that there is a clear understanding of who is responsible for the selected business-critical data. Often, the task will include breaking down processes in detailed data flow charts.

The challenge for companies that grow through mergers or acquisitions is often that several different ERP systems are used simultaneously. It might well be very costly to integrate and ensure the use of a joint IT system and it might also be necessary to be able to work with quality assurance across systems. This makes it crucial for the company to create joint quality assurance processes and quality management system across the systems.

Often, an IT integration project will be very costly with a time frame rarely less than six months. A well-equipped DQM project will be able to deliver visible results within approximately three months, and the costs will typically be five to ten times lower than the realised profit.

This step is meant to result in a well-proportioned and structured data quality organisation across the business, where duties and responsibilities are well defined according to the selected business-critical data. At this point, the company must be in control of reporting, data ownership, escalation procedures and data quality criteria.

During this phase, the selected business-critical data is measured which often results in quite a few mini-analyses within each data area. For this task it might be necessary to employ both qualitative and quantitative methods and at times it will not be possible to measure the area directly, in which case indicative data will be measured instead.

The most essential thing, however, is that when the first data quality measurements for a business-critical area are carried out, a forward-pointing method for measuring quality within that same area is identified and established. The practically oriented measurement must be described in order to become a natural part of the data quality management system.

Working on introducing data quality audits might help to ensure momentum, steady improvement and management focus. A data quality audit is somewhat similar to a financial audit but focuses instead on the consistency and quality of data and normalising data generating and data modifying processes. Additional focus is placed on whether the data quality system is robust, and whether measurements, procedures, preventive measures and continuous adaptations are carried out. Such a review provides management and investors with a rapport, where data quality is treated separately and which describes the control and management of the business-critical data of the company.