Build Change Management Around Your Data Governance Adoption For Sustainability.
Activating Data Governance without Change Management in place is a disservice to any Data Governance adoption effort in your organization.
Facts:
· The sustainability of your data governance adoption and overall data maturity depends hugely on the proactiveness of instituting a Change Management as part of your execution strategy.
· Change Management must never be an afterthought for your data governance activation.
You may have checked off all the needful milestones for effective governance, But your takeoff on the ground and in your environment is simply lifeless without an active change management in place for ongoing stewardship around your key pillars of governance(Ownership, Metadata Management, Lineage Management, Data Quality Management and Issues Management).
Governance around your data health is a continuous transformational journey that must be well planned out and strategically sustained.
The recognition of Change Management process as part of your governance execution framework is as critical for your adoption success as the actual activation of governance and stewardship around your data.
Why Is Change Management Important As Part Of Data Governance Adoption?
Data is fluid, and change is continuous. Hence the governance and due diligence around the health of your data is a continuous exercise that requires the needful cultural transformation to continue evolving as your data environment changes.
As the dynamic of your data needs and demand changes, every guardrail and standard of care put in place around your data governance must be reviewed and adjusted for relevance and currency. A standardized process must be put in place to handle ongoing changes around your key pillars of governance.
Your governance adoption's sustainability over time will keep you winning and maturing your overall data management.
How Do You Build Change Management Around Your Data Governance Adoption?
Change management must be embedded and built into each pillar of your governance activation. i.e.
Ownership Change Control – Ownership establishes accountability and responsibility for managing data across its lineage from creation to consumption. Hence, Roles and Responsibilities established as part of your governance adoption must have a well-defined onboarding and offboarding process with a good handshake between actors in each data role as people transition in and out of roles or in and out of the organization. This must be well built into the formalization of your critical data roles and responsibilities.
Metadata Change Control- Ongoing changes to your metadata and glossaries for definition and use of data might change over time due to several internal and external mandates. A change management process must be put in place for your data glossaries and other metadata to reflect any ongoing changes to the purpose and use of your data.
Data Lineage Change Control – Introduction of new data sources and retirement of sources as your data environment matures and adjusts to ongoing data needs and changes. An effective Change Management process must be in place from day one of your governance activation to manage the front-door process of introduction and Management of these sources and the impact of the same on existing downstream and consumption layers.
Establishing change control over data lineage involves a partnership between the Data governance office, IT, and Business to develop a solid communication, documentation, and approval process on ongoing changes. Changes to either the sources or the transformation logic of elements must be closely monitored and managed as they may impact existing and downstream consumption points.
Changes to data architecture (table, technical field name, etc.) must be carefully reviewed, approved, and communicated to data consumers where data sourcing methodologies may need updates.
Data Quality Change Control – Establishing consistent data quality rules requires implementing effective change control to ensure exception reporting is built according to data governance and data owners' expectations. The foundational fact that quality is fluid and target metrics can change over time necessitate continuous monitoring and enforcement of adequate controls with new rules and updates to the existing.
To this effect, you must establish a change management process to ensure that only approved standards and business rules are implemented. You must identify and assign accountability and responsibility for DQ rule standards with those who own or create data; certify that new rules and changes to existing ones are published and transparent to data consumers.
Issues Remediation Change Control - Data Quality and Issues Remediation are tightly linked, and both efforts must be connected for optimal data quality to be realized.
A compelling issues management process is driven by lifecycle phases where there is an agreed accountability between the Business, IT, and Data Governance office with defined roles and tasks to drive remediation and closure. A solid change management process must be put in place to facilitate needful accountability for each data role and issues owner to be held accountable, track delays and breakdowns in issues resolution, and facilitate issues closure.
In a nutshell, having a solid and effective change management process in place as part of your data governance adoption is one of the most critical success factors of an effective, governed, trusted, business-ready data environment. It must be well thought and planned into your data governance framework of execution of each standard of care and governance activity.
Need practical help on how to establish Change Management as part of your Data Governance adoption? Book a Free Call with me to discuss your challenges, and we can explore simple strategies to actualize your governance success.
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