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When discussing with customers, stakeholders and colleagues, we often notice that the terms “Data Governance” and “Data Management” are used interchangeably - creating a lot of confusion, especially when nobody dares to ask what the difference actually is. High time to demystify these concepts once and for all and explain why your organization needs both to reach its strategic ambitions.
The modern data context
“Data is the new oil” and “Managing your data as a real asset” – you have probably already heard it before. The promised land of data that might quickly become a mirage in a desert of challenges. Organizations around the globe are pressed to “do more” with their data and extract additional value from it - usually with varying degrees of success.
Today, organizations operate in what we can call “the modern data context” – a world where data is created at an increasing speed, where volumes of produced data are exploding, where types of available and exploitable data have never been more diverse, where sources of and connectors to data are everywhere and where organization’s technical data landscapes often have difficulties to adapt to this new reality. And to top it all off, these organizations are facing a growing number of strict data & privacy regulations they need to comply with.
In this modern data context, many organizations struggle to exploit tangible value from their data and no one is immune to it. Data Analysts might wonder if Turkey should be taken up in European sales figures, while Data Scientists might encounter data discrepancy issues and spend a significant amount of their time finding and preparing the appropriate data before being able to exploit it. Digital transformation projects might be delayed (or worse - fail) by unforeseen impacts on opaque data flows. Business stakeholders might lose trust in erroneous sales figures and prefer to exploit themselves the data directly by using under-the-radar tooling (that may or may not be appropriate) which floods your technical landscape. In parallel, marketing operatives have trouble to create the needed “client 360°” view to enhance their cross-sell activities due contradictory customer data in several systems. All of this in a context where employees may lack (data) maturity and have trouble understanding how data can support the realization of an organization’s strategic goals.
If this sounds familiar, then your organization will probably benefit from a clarified Data Governance framework and enhanced Data Management approach.
Data Governance complements Data Management activities, and vice-versa
When moving from small & slow data to big & fast data, a quick reflex is to start tackling technical transformations to accelerate the ingestion and exploitation of these increasing volumes of data. However, at least as important will be the vision on how to organize around your data. Previously informal (often local) data responsibilities and organically grown processes will quickly limit your organization’s abilities to make the most out of your data. In this new data reality and if your organization sees its data as a real asset, it is becoming critical to clarify who has authority over what type of data, who has what kind of responsibility over it, and how to collaborate internally to keep this data fit for purpose. As an organization, you will therefore need to clarify/formalize who is in charge of what data-wise and define a minimal set of processes to be able to operate around data throughout your organization. This is what we call “Data Governance”. Make no mistake – Data Governance is not about an additional administrative layer or an increased headcount of your data organization. It is about having the right people at the right place with the right data responsibilities, it is about formalizing and guiding behaviour around the definition, production and usage of your data assets.
Governing your organization’s data is necessary, but not sufficient. Next to this, your data records will need to be actively managed. We imply here all types of data, from operational/transactional data to data for analytical purposes. Simply ingesting (even transformed) data into your organization’s systems will not be enough. Throughout your data’s lifetime, you will need to ensure/maintain its quality, make the data transparent and understandable to its users, deal with data redundancy, data discrepancy, data security, data access etc. This is what we call “Data Management”, and it encompasses several types of activities such as metadata management, master data management, data quality management, data modelling and architecture…
As you will probably have understood by now, Data Management without Data Governance makes no sense. Wanting to enhance your organization’s customer data quality without a clear view on who should be responsible for this and who will define and enforce data quality rules just won’t work. In parallel, having a clear Data Governance framework without any Data Management activities in place will add little to no value. Knowing who has ownership of the product data domain when this data is not actively managed won’t solve your data challenges.
What’s in it for your organization
When presenting our case for Data Governance and Data Management to organizations, we often get the question “what’s in it for us”? Though the business case will be different for every organization specifically, research from leading institutes (e.g. Gartner, Forrester, IDC) have tried to quantify benefits in more generic terms, proving a positive ROI on investments in Data Governance and Data Management activities. While this is nice in theory, we still prefer to experience this ourselves by putting it into practice. Based on our projects implemented at our customers, we have seen how a clear Data Governance framework and an enhanced Data Management approach create value on 4 main axes:
Increasing the productivity of Data Users – e.g. by enabling self-service capabilities, providing a better view on what data is available where, by streamlining analytics processes…
Increasing the efficiency of the organization – e.g. by standardizing data processes, formalizing roles and related data responsibilities, by organizing knowledge & experience sessions…
Enabling the digital and sales capabilities – e.g. by enabling a data-driven customer experience, by making data impact analysis possible (understand data flows and make them visible), by improving the quality of your customer targeting…
Optimizing the risk management activities – e.g. by the identification and pro-active management of critical data elements, by an enhanced data access management, by responding quicker to evolving legislation…
An important part of any Data Governance & Data Management approach is to track, quantify and report on the generated value and its impact on your organization’s bottom-line. This will help your organization to further prioritize its Data Management activities accordingly, while supporting its journey to a higher data maturity.
An essential piece of your data strategy
Data Governance and Data Management activities are clearly complementary and should be considered together as an integral part of your data strategy. Successfully implemented, these activities will help your organization to reach its strategic business objectives, while adding real value along the way.
Any questions? Contact firstname.lastname@example.org
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