What drives good data management practice? What does good data governance actually look like? How does effective data governance contribute to value creation and ongoing business transformation? And in turn, how does business transformation contribute to enabling better data governance practices?
Every business is as good as the data quality and customer relationships it creates — and as good as the people who author and consume that data. The test is how effectively data fosters enterprise collaboration and creative thinking toward product and service innovation.
In a previous article, I discussed the fundamentals of data management, focusing on data governance as a key driver to architect and capitalise on value from new product introduction and business operations. Referring to data governance initially brings to mind an organisation's data management policies, procedures, and practices. There's a lot more to it: from the top floor, considering how business leaders and process owners embrace the value of data, down through data stewards, and leveraging knowledge from key users and enterprise architects — the latter being the day-to-day custodians of operational data and platforms.
In this second article of the series, I cover what it takes to implement effective data governance — developing it at the right pace and tailoring it to context.
Success criteria for effective data governance
Every business relies on data management as part of its value-creation chain. Effective governance and business change refer to top-level commitment and leadership — from vision definition through to ongoing cross-functional alignment. 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, several critical success factors contribute to building a culture of effective data governance and management (adapted and extended from Marinos, 2004):
- Breaking functional and data silos by focusing on integration (the so-called "Digital Thread" in current marketing jargon).
- Data analytics at every operational level: the ability to compare, cascade, roll up, and drill down into data in a timely, accurate, and transparent manner.
- Data accountability across business functions — recognising that data governance is not purely an IT discipline.
Building the data governance team accountability
Data governance isn't only about IT systems and applications, nor is it only about the role of data and integration architects. It's very much about business ownership and leadership: from data identification and planning through to data monitoring, adherence policing, compliance assessment, and continuous improvement — including data quality verification.
Three key roles or stakeholder groups contribute to successful data governance:
- Data owners — Drive leadership-team ownership, business goals, benefit realisation opportunities, and decision-making support. They focus on how data creates value for the organisation (strategy) and how data is used to enable better decisions and deliver better products and services.
- Data stewards — Drive data quality (content fit-for-purpose), business rules and dependencies, data semantics, compliance with policy, and business-process adherence. They focus on how data is authored and consumed — the end-user perspective — and how value is derived from data.
- Data custodians (or data managers) — Drive the implementation and ongoing administration of data sources (records), security controls and interfaces (data bridges), linkages (data references), and data archive and migration. They focus on how data is managed and maintained — the IT perspective.
In addition, the role of chief data officer (CDO) is on the rise — bearing "responsibility for the firm's enterprise-wide data and information strategy, governance, control, policy development, and effective exploitation" and playing "a valuable role in helping the organisation value its data across the enterprise" (Gartner, 2020). Tom McCall reported from a Gartner survey that 33% of organisations measure the benefits each type of information asset generates, and 24% manage those assets as if they were on the balance sheet — underlining the importance of granular configuration-item definition and interdependency management. That's also necessary to ensure proper configuration-status accounting and change management.
Driving toward data governance excellence
Led by the CDO, the data governance team defines business capabilities and transformation priorities — building the engagement models with the relevant parties, internally (end users, IT, finance, learning and development) and externally (vendors, implementation partners, infrastructure, and support suppliers).
Gradually developing and tailoring the data governance framework to the organisation context is critical. Davenport and Bean (2020), for example, warned about the risk of having "too many roles for one CDO" — and the often-controversial IT relationship with the CIO for technical implementation.
Since there are many different data governance-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 organisations, but CDOs will only succeed if their roles are clearly specified.Davenport & Bean, 2020
Data governance — and more broadly, data management — improvements follow iterative steps to ensure smooth user adoption, process change, and adequate learning, while minimising business disruption and other potential downsides. Excellence comes from transparent end-to-end practices that remain effective and efficient as organisations mature and grow. Data governance helps stakeholders at every level understand how their organisation works as a 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; DM Review Magazine, September 2004 issue.
