When a product is produced, packaged, distributed and then consumed, it goes through a supply chain. Supply chain management is about management (through strategies/standards), improvement and ongoing optimization of this process. Similarly, data goes through more or less the same process within organizations. It is not hard to imagine the ever-increasing amount of data that companies are facing.
As data grows exponentially, it is important to think about how to manage that growth: data management.
The foundation of data management is data governance. Data governance refers to all the agreements and ground rules that define how an organization handles its data. These frameworks help organizations achieve efficient, structured and standardized data management processes. Roles and responsibilities are also part of this. Whether we’re looking at supply chain management or data management, one thing is certain: defining roles and responsibilities is a crucial part of successful data governance. This step ensures that standards and policies around data management can be secured. In other words, data governance and data management cannot function separately.
Within the domain of data management, there are a number of sub-topics, including:
1. Master Data Management
2. Data Quality
3. Business Intelligence
5. AI and Machine Learning
The importance of good data quality
In this blog post, we will take a closer look at data quality. Data quality is an indication of how accurate, complete and relevant a data object is, and whether it is consistent with the required specifications/business rules. A data object could be an item, customer or supplier in the system or ERP software.
Problems related to data or information arise from poor data quality, which can be prevented by data governance and data management. As part of data governance, business rules (or agreements) should be established to prevent poor data from being stored. An example of a business rule is: “a purchased item should always contain an (alternative) unit of quantity, where the weight always contains a value above 0”. This can be supplemented by a preselected group of units and the roles/responsible employees that are associated with them.
Measuring data quality
Once all business rules are captured and validated (including other details such as technical data, roles and responsibilities, the processes to which a data object belongs, etc.), it is possible to measure data quality. The business rules can be translated into operational reports and management reports. It is important to make sure that business rules are complete, clear and validated. Operational reports may include one or more statements that reference data inconsistencies based on the validated business rules.
Generating relevant reports is not easy; it requires insight and expertise. These reports are then easily distributed, since roles and responsibilities are also defined within data governance. Management reports are linked to operational reports, showing numbers and totals (often shown in percentages). This makes it easier to detect structural problems and improve and/or resolve the underlying issues, ultimately resulting in better data quality.
Applicable during data migrations
An additional advantage is that once the data quality reports have been set up, these reports can be used during data migrations. During an acquisition, for instance, the ERP-related data of the legacy system must be transferred. Before the data is actually migrated, this data can first be loaded into the data quality report to have the new data validated based on the business rules that are already in it, and the data quality scores can also be viewed immediately. This is just simply a matter of loading data and therefore hugely quick to deploy. So you can be sure that the new data is instant/validated and first time right in the system.
Data governance ensures systematic improvement in data quality
Without data governance, data quality problems will be solved on an ad-hoc basis rather than finding systematic solutions. In the longer term, an ad-hoc approach takes unnecessary time and money. And it leads to unnecessary risks that processes will get stuck. In summary, data governance helps to systematically improve data quality by:
1. defining standards and business rules and having them validated by the business (responsible employees/roles);
2. monitoring data quality using operational and management reports;
3. addressing or identifying structural problems caused by poor data quality.
This works both ways. Being actively engaged in data governance makes it possible to verify that business rules are still valid. But it also helps you identify which business rules still need to be defined. Once those are added, you’ll have even more insight into the available data by using the overviews and management reports we discussed above. The same applies to roles and responsibilities. In practice, this often leads to synergy between the business and IT.
Tentive Data Management Consultants
The data management consultants at Tentive are experienced in improving data quality, helping to set up roles and responsibilities (data governance), drafting and validating business rules, and using them to generate operational overviews and KPIs, for example with PowerBI.
Is your organization eager to dive into data governance, but lacking the manpower or data management expertise? Feel free to contact us; we would be happy to discuss options with you.