On Data Governance & Data Management
Data Management without Data Governance will not be effective; Data Governance without Data Management will not be efficient.
The Context
Data has never been more important in any organization or for any individual as it is in this period in time.
Every organization wants to stay relevant in their industry and to their customers. They have goals to grow their business and at the same time comply with data privacy regulations such as GDPR.
In this environment, an enterprise-wide (or organization-wide) Data Governance and Data Management strategy needs to be in place.
Unfortunately, some organizations will define some form of Data Governance and assign people to “own” certain data domains but they do not have a platform or technology to scale or automate the execution of data governance/data management tasks. Still, other organizations will focus on Data Management by investing on a technology that enables them to manage their data in a scalable and efficient manner but then they realize, they need the business policies or rules.
They miss the fact that both Data Governance and Data Management need to be in place; and these need to be well-integrated to add value to the organization in a sustainable manner.
Again… Data Management without Data Governance will not be effective; Data Governance without Data Management will not be efficient.
Data Governance and Data Management
Data Governance and Data Management comes hand in hand. They should be properly planned, executed and maintained together as part of an organization’s data strategy.
Data Governance will ensure that there is the right authority in place for defining data ownership, data policies & standards and data stewardship while Data Management will put in place the right process and technology that enables correct, complete and timely data for the organization.
For example, the data policies & standards on master data defined by data owners and executed & enforced by data stewards can be best implemented across the organization using the right data management platform (e.g. Master Data Management or MDM for master data).
Every request for creation & update of a master data can be controlled and checked against a set of standards, rules and policies using user-friendly workflows defined within the MDM platform.
All data quality checks & steps (e.g. profiling, correction, standardization, matching, merging, consolidation, enrichment, etc.) based on standards and policies approved by the Data Governance Council (DGC) can be defined in the MDM platform and executed in a scalable and efficient manner by the data stewards.
On the other hand, if the MDM platform was implemented without aligning it to the right business and data standards, rules and policies, the MDM platform will not add value to the business and to the organization and then business users will lose trust on the data within it and eventually stop using it.
That is why both Data Governance and Data Management should be in place as part of an organization’s Data Strategy.
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