Before you dive deep into the world of 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.
Based on the information provided by Experian which conducts a large-scale Data Management (DM) survey every year among more than 1,000 employees who perform DM-related activities in various types of functions (including IT, finance, customer services, marketing, and risk management), the main findings from the most 2018 version of this Global Data Management Research were:
- 55% of the respondents believe that unreliable data has a strong 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 that 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 in accordance with applicable laws and regulations (Compliance).
Data Governance, How was it again?
Data Governance is a term that is becoming increasingly popular but is defined differently by many. In fact, it is a framework through which an organization can effectively manage its data so that it meets the needs of the organization concerned. Data Governance includes policies that determine how an organization manages its data and which roles and responsibilities have been identified with regard to data management.
But Why does an organization need Data Governance?
Good quality data is an extremely valuable 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 with the aim of maintaining and monitoring 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 that is being used, this will lead to the search for workarounds to clean and/or enrich data. This not only adds time and costs to the process but also 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 increases. Currently, many organizations choose to meet only the minimum data management requirements set by the legislator. There are quite a few comments to make about this ‘checklist’ approach. Perhaps in this way, certain subsets of data are properly monitored and managed, but it hardly helps to achieve a culture change that organizations need to adopt a proactive attitude towards their own data (quality). 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.
Practice shows that when an organization approaches the Data Governance issue in a holistic way, considerably less time and money is spent on ad hoc solutions for optimizing data needed to support business processes.
Given the amount of money that organizations spend on Customer Relationship Management (CRM) systems, you would expect that they now have the ultimate golden record of their customers.
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Unfortunately, the opposite remains true; most organizations do not have accurate customer information. Many organizations operate in silos, with each department managing its own data in its own systems that are often not in sync with the systems outside the department. Without proper data governance measures, databases used for marketing automation and customer support 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 catalog on the doormat, or receiving the same promotional email 10 times in the mailbox.
You have undoubtedly heard of the term Big Data. But does this also apply to the terms Open Data and Linked Data?
These new forms of data open up a whole new world of application possibilities. But they themselves also present new challenges in the field of 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 how to deal 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 own data.
Finally, the amount of data stored continues to grow exponentially and is subject to continuous change. This should result in an increasing 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?
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