Master Data Management can support a total view of common data (customer, product, location, etc.) Agile data governance can support the development and implementation of effective master data management
Multiple regulatory compliance initiatives such as Anti-Money Laundering (AML) and other counter-terrorism acts have shown the need for a 360-degree view of common data, for customers and products.
Master Data Management (MDM) allows an organization to know who its customers are and where they have been, not only at the point of engagement but throughout the relationship with them. It is essential to become intimately familiar with their transactional behavior to serve each customer, and to identify potential risks around suspicious activities that could expose the organization to legal and regulatory challenges. Master data management incorporates several other components of enterprise data management, including data governance and metadata management to achieve an organizational view of commonly used data.
As a direct response to the demand for a single customer view, coupled with internal pressures for market growth, MDM has emerged at the forefront of many organizations’ strategic initiatives.
But with so much poor data quality and un-trusted data in systems, maintaining duplicate and redundant data over the years has cost organizations dearly and acted as a brake on potential business growth.
Not having a 360-degree customer view has made it difficult for organizations to answer simple basic questions such as:
- How many customers do we have?
- What is the correct definition of a customer?
- Who are our most profitable customers?
- What is the lifetime value of a typical customer?
The embarrassing truth is that most organizations cannot answer these seemingly simple questions, at least without serious effort. In addition, many organizations have been reporting erroneous customer figures as different silos and lines of business fail to work cohesively to manage their master data assets.
The annual cost and impact of data quality issues that are rooted in ungoverned data with little or no formal accountabilities around critical enterprise data have propelled the need for many organizations to fix their MDM problem.
It’s evident that the need for ‘trusted data’ continues to appear in nearly all data initiatives. However, most organizations are still struggling with their MDM rollout simply because it’s addressed from a one lens angle. It’s one thing to fix the problem by mastering the formerly bad data; it’s another thing to make the solution sustainable by treating the root problem of disparate common data.
The value of a ‘Stewardship culture‘ around data assets cannot be overemphasized.
For MDM to be sustainable and rightfully implemented, it must be positioned in a governed environment where stewardship around the mastered data, and the associated culture of data governance are implemented.
Questions to consider…
- Why does the organization find it hard to sustain an MDM rollout?
- Is the organization incorporating Data Governance and Data Stewardship into the MDM deployment strategy?
- Is the organization committed to changing the bad behavior around data that created the same problems?
- Is the organization building communities of data stewards and cumulative responsibilities around enterprise data through your Data Governance activities?
- Has the MDM initiative begun without a culture of data governance and data stewardship, including due diligence for mastered data?
These questions are essential to examine before diving into an MDM strategy.
As organizations continue to position MDM as one of the most important initiatives for advocating trusted and business-ready data, it is essential to address the reality that creating a single source of truth through de-duping duplicates and removing redundancies through a sophisticated MDM match and merge can solve only half the problem.
Embarking on MDM as a singular initiative does not address the root problem of bad data creation and poor accountability in an ungoverned data environment.
For the Return on Investment (ROI) of any MDM efforts to be fully realized, it is crucial to incorporate Data Stewardship and Data Governance into the rollout strategy. Data Stewardship and Data Governance naturally optimizes the sustainability of an MDM rollout. MDM that incorporates Data Stewardship and Data Governance offers many benefits because it points the organization in the right direction for creating a data-driven culture.
Steps to MDM Success
- Incorporate Data Governance and Data Stewardship into the MDM Strategy – it will accelerate both MDM delivery and Data Governance adoption
- Go Agile with a Data Governance approach – to show faster value which fosters greater trust and commitment to involvement
- Choose the Most Viable Product (MVP) for the first governed data set.
Agile Data Governance
Agile data governance is an option when implementing data governance as part of any master data management effort (broad or narrow).
Agile data governance prevents “boiling the ocean”, allows the organization to be strategic with its delivery roadmap, avoids over-promising on the project plan, and supports realistic expectations (sprint by sprint). The mantra for agile data governance is “start small and show value quickly and consistently”.
Agile Data Governance Options
‘Thin Slice’ Agile Data Governance for MDM:
This approach offers an option to build the data governance roadmap by iterative increments of data sets starting with the MVP data.
Depending on the organization’s Data Maturity level at the start of MDM, this option offers flexibility to engage and build communities of stewardship focused on Ownership, Metadata Management, Data Lineage Management, Data Quality Rules, and Issues Remediation at a minimal level on a selected critical data set to “hit the ground running” and increase maturity level and range of MDM and data governance scope over time.
Taking this approach enables the organization to build a stronger case for funding and executive commitment since it provides some tangible successes to showcase.
‘Deep Dive’ Agile data Governance for MDM:
In this approach the organization adopts a more mature level of governance for the MVP data iteratively by deepening the maturity level of the same set of data over time before expanding the scope of MDM data to be governed.
With the ‘Deep Dive’ approach, the data set to govern is relatively small and scope is focused on the MVP (Most Viable Product) deemed to be most critical to the enterprise’s strategic goals.
This approach is recommended where the optimal maturity of the MVP data set must be achieved at an accelerated rate.
Tips to Agile Data Governance Success
Following are some of the high-level tips and conclusions recommended to support starting agile data governance:
- Review the organization’s business goals and data demand, and align the agile delivery to them
- Build a prioritization framework around these business goals and data demand
- Identify and review the MDM data sets and critical data elements (CDE)
- Rank and prioritize governance of the MDM data based on competing demands in the prioritization framework
- Prioritize remaining data sets into groups of P1, P2, P3 based on the defined framework
- Choose the agile approach based on organization’s data maturity level and requirements
- Execute and measure value for each delivery accordingly.
Conclusion
An agile data governance approach can provide an unparalleled opportunity to leverage an organization’s investment in MDM, and to accelerate a Data Governance program. Using agile data governance for MDM helps showcase the value of anchoring the MDM initiative to the Data Governance program, enabling the organization to realize enterprise benefits for mastering trusted data.
