In recent years, there has been a growing awareness among organizations with regards to their data and the role it plays in the success or failure of their most critical business functions. Some of this recognition is driven by regulation, others by IT advances such as cloud technologies and big data, while others still by the desire to have high quality trusted data.
This shift in mindset is evidenced by the change in technology budgets from a concentration on hardware and infrastructure purchases, towards leveraging and making the best use of corporate data assets. In line with this has been the rise in popularity of Master Data Management (MDM) systems.
Used in the management of critical shared data entities such as security, product, or client master, MDM, when properly implemented, can form the cornerstone of an organization’s Enterprise Data Management (EDM) framework.
MDM – Not the Silver Bullet
The goals of MDM are to identify, validate, and resolve data issues as close to source as possible, whilst creating a “Gold Copy” master dataset for downstream consumption. MDM provides many benefits, and when implemented correctly can ensure consistency, completeness, and accuracy of core shared data sets. But MDM is not the silver bullet of providing enterprise data quality. At its core, MDM manages just a single (though important) area of an organization’s data universe – namely, business entities. Looking a little deeper into an organization’s data use, we find that many business and technology functions rely on a mixture of operational data, reference data, metadata, and audit information, in addition to the master data, with the quality of each being equally important. MDM does a commendable job of ensuring the shared master data is managed correctly and is fit for purpose; however, MDM does not represent a full Data Governance or EDM program. Quality is only part of the data equation, whereas organizations need a broader view and transparency into the data they plan on using for critical decisions, and this is something that MDM systems are simply not adept at.
Organizations seeking a comprehensive data management strategy need to address some additional capabilities:
- Definition: What does a specific business term mean? Are there any other names (synonyms), phrases, or abbreviations that this term could also be known by? Is the term part of or the result of a calculation?
- Data Classification and Retention Policies: Data may be classified many ways based on both internal and external policies. In recent years, with far reaching regulation such as BCBS 239, GDPR or MIFID II, the need to classify critical or personal data, understand policy, usage, access, and distribution rights have come more to the fore, with increased fines, financial, and reputational risks for lack of compliance.
- Original Source: Where did the data come from? Are there multiple sources? Are there any sourcing / priority rules when creating the “Gold Record?” What is the authoritative source for a particular set of data? Can it be overwritten?
- Search: What data is available and from where? Is it shared? Which business functions are using which set(s) of data? How do I find the right data set, right source, and system to perform the business function?
- Collaboration: How can people become more knowledgeable about and around organizational data? How can they contribute their expertise? When does content become knowledge?
- Enterprise Data Quality: How trustworthy are the various types of data in use? Is there a pattern or trend to the various domains of data?
- Operating Models: It’s fair to say that the data won’t manage itself; there needs to be policies, procedures, and resources applied to ensure the operations and drive quality, security, access rights, sourcing, and the proper use of data throughout the organization.
- Responsibilities: Probably the most contentious area and the one most companies struggle with is where accountability lies. Assigning people to roles is one component, but what are the expectations? How do we establish models of interaction and measure effort of staff navigating through the cultural, political, and personality land mines to ensure the optimal use of an organization’s resources and their data?
MDM’s myopic focus makes it impossible to address these areas across the organization’s broader spectrum of data, and highlights the key differentiators and importance of Data Governance to the organization.
“MDM Without governance…is just data integration!” – Aaron Zornes [1]
Proper governance sits on top of MDM, data movement, or data warehouse initiatives, and ensures active participation of the business in the definitional, sourcing, quality, and accountability perspectives. Before embarking on large scale data driven initiatives, especially ones that bring large cultural and operational changes, it’s imperative that data governance is established early and incorporated into every phase of the project. Data projects that neglect data governance run the risk of delivering a technical masterpiece that is both impractical and too complex for the business to understand or utilize. Integrated Data Governance can also ensure the businesses backing and active participation in initiatives that are often perceived by the business as a technology exercise owned and operated by IT.
“Through 2016, only 33 percent of organizations that initiate an MDM program will succeed in demonstrating the value of information governance.” – Gartner [2]
This astonishing statistic from Gartner solidifies the fact that business participation in many MDM implementations is lacking, and that businesses fail to understand, embrace, or value these multi-million dollar investments. Review any statistics on failed or underachieving MDM projects and it will most likely point to a lack of data governance incorporation to manage the people, processes, and most importantly, the data needed to succeed.
“The data governance, prioritization, people and process aspects of implementing an MDM solution will likely derail the project before the technology fails.” – Wang and Karel [3]
The Value of a Business Glossary
The establishment of a data governance framework, operating, and reporting models are a great first step for organizations to manage their data. In much the same manner as organizations inventory their other corporate assets with HR and finance systems, the data assets need to be properly defined, inventoried, managed, and ultimately opened to collaboration. Organizations typically start this process with internal solutions leveraging spreadsheets, SharePoint, or some other homegrown solution. The challenge with these solutions arise as more and varied types of data assets need to be collected, along with the ability to track lineage, workflow, impact analysis, or collaborative capabilities for the various data governance roles. In the end, the glossary becomes the glue that ties the data governance capability into the MDM project, ensuring business participation, accepted business term definition, and assigned and documented accountabilities for the governance of the mastered domains.
Knowing up front MDM’s capabilities and especially its limitations can help an organization to incorporate solutions that provide a full 360° view, with understanding and transparency into their corporate data assets.
References
[1] – Zones, A MDM software vendors struggling with data governance, Tech Target, July 2012
[2] – Gartner Says Master Data Management Is Critical to Achieving Effective Information Governance. http://www.gartner.com/newsroom/id/1898914
[3] – Thomas Wailgum, Master Data Management: Companies Struggle to Find the Truth in Massive Data Flows, CIO, 2008
