by Lio Grealou, senior advisor to QR_ and founder of Xlifecycle Ltd
What drives good data management practices? What does good data governance look like? How is effective data governance contributing to value creation and ongoing business transformation? In turn, how business transformation contributes to enabling better data governance practices?
Every business is as good as the data quality and customer relationships it creates, as good as the people who author and consume such data; based on how effectively data fosters enterprise collaboration and creative thinking towards product and service innovation. (image credit: PEXEL)
In a previous article, I discussed fundamentals of data management, focusing on data governance as a key driver to architect and capitalize on value realization from new product introduction and business operations.
Referring to data governance initially brings the light on data management policies, procedures, and practices of an organization. There is clearly a lot more to it: from the top-floor, considering how business leaders and process owners embrace the value of data, to data stewards, and leveraging knowledge from key users and enterprise architects. The latter are day-to-day custodians of operational data and platforms.
In this second article of the series, I elaborate on what it takes to implement effective data governance, developing it at the right pace accordingly tailoring it to the organization context.
Success criteria for effective data governance
Every business relies on data management as part of their value creation chain. Effective governance and business change refer to top-level commitment and leadership, from vision definition to ongoing cross-functional alignment leadership. Effective data governance is no exception: it requires strategic accountability at the top, combined with aligned and integrated operating standards, processes and tools.
Beyond executive accountability, there are other critical success factors to build a culture of effective data governance and management (adapted and extended from Marinos, 2004):
- Breaking functional and data silos by focusing on integration (a.k.a. the so-called Digital Thread when mentioning the current marketing jargon).
- Data analytics at all operational level: with the ability to compare, cascade, roll up, and drill down into data in a timely, accurate, and transparent manner.
- Data continuity across the extended enterprise and beyond to the supply chain and open innovation partner networks.
- Continuous compliance tracking, to ensure process adherence and data quality are maintained throughout.
- Data complexity management (which is different from simplification which is not always possible).
- Strategic control and change management process to ensure visibility and transparency without hindering delivery effectiveness.
- Data, process, and platform alignment to avoid “management blind spots” when it comes to working with a single version of truth (Grealou, 2016).
- Ongoing awareness and education as data governance and enterprise architecture are not static disciplines; this is to support the sustainability of data management effectiveness.
- Enabling business and associated digital transformation, by capturing, communicating, defining, verifying change requirements.
- Delivering business benefits from business transformation, by driving change adoption, analytics, continuous improvement, and ongoing support.
Building the data governance team accountability
Data governance is not only about IT systems and applications, neither is it only related to the role of data and integration architects; it is very much about business ownership and leadership: from data identification and planning to data monitoring, adherence policing, and compliance assessment, including data quality verification and continuous improvement.
There are 3 key roles or stakeholder groups contributing to successful data governance:
Data owners: they drive leadership team ownership, business goals, benefit realisation opportunities, support the decision-making process; they focus on how data creates value for the organisation (strategy for how data is used to enable better decisions and deliver better products and services).
Data stewards: they drive data quality (content fit-for-purpose), business rules and dependencies, data semantics, compliance with policy, business process adherence; they focus on how data is authored and consumed (in business terms = end-user perspective) and how value is derived from data.
Data custodians (a.k.a. data managers): they drive the implementation and ongoing administration of data sources (records), access control / security, interfaces (data bridges), linkages (data reference), data archive / migration; they focus on how data is managed / maintained (technically = IT perspective).
In addition, the role of chief data officer (CDO) is on the rise to “bear responsibility for the firm's enterprise wide data and information strategy, governance, control, policy development, and effective exploitation”; it can “play a valuable role in helping the organization value its data across the enterprise” (Gartner, 2020). Interestingly, Tom McCall reported from a Gartner survey that 33% of organizations measure the benefits that each type of information asset generates, and 24% manage these assets as if they were on the balance sheet. Hence the importance for granular configuration item definition and interdependency management; this is also to ensure proper configuration status accounting and change management.
Driving towards data governance excellence
Led by the CDO, the data governance team is to define business capabilities and transformation priorities, build the required engagement models with the relevant parties (internally with end-users, IT, finance, learning and development functions, as well as externally with vendors, implementation partners, infrastructure, and support suppliers, etc.).
Gradually developing and tailoring the data governance team to the organization context is critical to its success. For example, Devenport and Bean (2020) warned about the risk of having “too many roles for one CDO”, in addition to the often-controversial IT relationship and contribution of the CIO for technical implementation (…).
“Since there are many different information-related jobs within firms today—chief information officer, chief data officer, chief digital officer, etc. —clarity on who is supposed to do what is a necessity. We expect that the number of CDO jobs will continue to grow in organizations, but CDOs will only succeed if their roles are clearly specified.”
Data governance and, broadly speaking, data management improvements follow iterative steps to ensure smooth user adoption, process change, and adequate learning, while minimizing business disruption and other potential disbenefits occurring along the way. Excellence comes from transparent end-to-end practices that remain effective and efficient as organizations mature and grow their operations. Data governance helps stakeholders at all levels understand better how their organization works as value creation ecosystem, supported by structures and empowerment that enable people to continuously mitigate and fix problems.
What are your thoughts?
References:
Petzold B, Roggendorf M, Rowshankish K, Sporleder C (2020); Data Governance That Delivers Value ; McKinsey.
Davenport TH, Bean R (2020); Are You Asking Too Much of Your Chief Data Officer? HBR.
Grealou L (2016); Single Source of Truth vs Single Version of Truth ; virtual+digital.
McCall T (2015); Understanding the Chief Data Officer Role ; Gartner.
Marinos G (2004); Data Management: An Executive Briefing: We're Not Doing What? ; DM Review Magazine (September 2004 Issue).