Top 5 Redenen voor Data Governance

Before diving deep into PIM (product information management), it is sensible to check your data governance processes and tools. How do you manage your (product) data? Where?

In this article, we dive deep into some of the pitfalls of Data Governance and why you need it.

Experian conducts a large-scale Data Management (DM) survey annually among over 1,000 employees engaged in DM-related activities across various functions, such as IT, finance, customer services, marketing, and risk management. The most recent version of this research, conducted in 2018, yielded several key findings:

• 55% of the respondents believe that unreliable data has a substantial disruptive effect on the organization;
• 73% of respondents agree that it is often difficult to predict when and where the next data challenge will take place;
• 68% of respondents believe that the increasing amount of data makes it increasingly difficult to comply with legal requirements;
• 69% of respondents believe incorrect data will undermine their ability to deliver an excellent customer experience.

The above figures make it clear that if organizations fail to establish an effective Data Governance structure, this can have an irrevocable impact on, among other things, competitiveness and the ability to perform activities by applicable laws and regulations (Compliance).

Data Governance, How was it again?

Data Governance is a term gaining popularity, but its definition varies among individuals. It serves as a framework for organizations to efficiently manage their data to meet their specific needs. It involves establishing policies for data management and identifying roles and responsibilities within the organization.

But Why does an organization need Data Governance?

Current Data

Good quality data is a precious asset that, if properly managed, can provide a lasting competitive advantage. It is, therefore, surprising that organizations often define the business requirements for their data but then invest little or no energy in setting up processes to maintain and monitor the quality of the data. In this case, there is data neglect, as a result of which the value of the data will decrease over time. In addition, many organizations do not have the right tools to (permanently) check and improve the quality of their data. If an end-user is not confident in the data being used, this will lead to the search for workarounds to clean and enrich data. This adds time and costs to the process and results in a proliferation of IT solutions for end-users (and thus additional management costs for organizations).

Laws and Regulations

Managing and mitigating Compliance risks becomes more difficult and expensive as the size and variety of data increase. Currently, many organizations choose to meet only the minimum data management requirements set by the legislator. There are quite a few comments about this ‘checklist’ approach. In this manner, organizations can effectively monitor and manage specific subsets of data, but it does not contribute significantly to achieving a culture change. To foster a proactive approach towards data quality, organizations need to actively embrace it. In addition, it has become apparent over the years that legislators have improved their understanding of Data Governance and data quality, making their requirements on these topics increasingly detailed and broader with each new regulation. The more proactive organizations have now recognized that embracing Data Governance can give them a competitive advantage.

Cost Reduction

Practice shows that when an organization holistically approaches the Data Governance issue, considerably less time and money is spent on ad hoc solutions for optimizing data needed to support business processes.

Customer Experience

Given the amount of money organizations spend on Customer Relationship Management (CRM) systems, you would expect that they now have the ultimate golden record of their customers.

Unfortunately, the opposite remains true; most organizations do not have accurate customer information. Many organizations operate in silos, with each department managing its data in its systems that are often not in sync with the systems outside the department. Without proper data governance measures, marketing automation and customer support databases can easily be contaminated with duplicated, incomplete, inaccurate, or outdated information about your customers. And we all know from our own experience that nothing is more frustrating than being addressed with the wrong name, finding multiple versions of the same catalogue on the doormat, or receiving the same promotional email ten times in the mailbox.

Changing Datasets

You have undoubtedly heard of the term Big Data. But does this also apply to the terms Open Data and Linked Data?

Big Data refers to handling one or multiple datasets that exceed the capabilities of regular database management systems. Open Data denotes freely accessible information, with licenses and terms of use outlining the availability conditions. The objective of open data is to minimize restrictions on re-use. Linked Data is a digital approach to publishing structured data, making it easily accessible and usable online.

These new forms of data open up a whole new world of application possibilities. But they also present new challenges in Data Governance and data quality. It is, therefore, important that an organization quickly designs a solid basis for a Data Governance framework for the existing data and implements this framework before breaking the head on dealing with these new forms of data from a Data Governance point of view. After all, you cannot expect to successfully combine large amounts of different data with existing data if you are unable to understand, let alone manage, your data.

Finally, the amount of data stored continues to grow exponentially and is subject to continuous change. This should increase the urgency for organizations to implement a data governance framework. Because let’s be honest: if you don’t already understand and manage your data, how do you expect to be able to do that at the current speed at which changes are taking place?