What is data governance—and what role should finance play?
Why the finance team is critical to establishing a data management foundation that delivers consistent, high-quality analytics.
The C-suite is demanding faster and better forecasting and insights. Your finance team is ready to deliver. But is your data?
It’s a critical question that often gets lost amid the headlines and leap-forward attention on whizzy new tools like artificial intelligence (AI), data visualization and real-time reporting. But getting the data right in the first place—the critical inputs that enable all those fancy new tools to actually work—sounds a bit … well, less interesting. And, besides, data management isn’t the finance team’s job, right?
Think again. Finance is often the new leader in driving business value, and it is uniquely positioned to act as a catalyst for an analytics-driven enterprise. Given its regular interaction with essential data companywide, the finance team is increasingly responsible for turning that information into actionable, forward-looking insights that go beyond its traditional reporting responsibilities.
To do that, finance will need to partner with IT and other teams to help the company establishes a consistent, business-driven approach to data governance and management—and can actually deliver on the potential of all of those fancy new analytics tools.
But What Is It?
Data governance and management is a core component of a successful enterprise data and analytics (D&A) platform, along with overall strategy, required technology systems and organizational alignment.
Specifically, data governance and management defines a consistent foundation of high-quality data, and then ensures the ongoing day-to-day management and maintenance of that foundation, covering essential operational tasks like adding, modifying and sharing data, as well as establishing user access and security protocols.
Just as important is the stipulation that data governance and management is no longer simply “IT’s job.” Our KPMG experts are consistently seeing that the companies having the most success with overall D&A initiatives are those that approach the ongoing data management component as a business-led, technology-enabled partnership.
With finance as that business lead, working closely with IT and other teams, the company will be able to drill down on the quality data that drives results—and avoid the “garbage in, garbage out” biases of disparate or even competing data from disconnected teams that is left to IT to govern and manage.
Building the Framework
KPMG helps clients establish data governance and management best practices through its Enterprise Data Management (EDM) Framework. The EDM framework is designed to help define and enable core data standards and management that enable effective business operations, financial planning and management, and minimize customer impact.
Our delivery approach is typically underpinned by this methodology and accelerated with the application of tested data standards design and globally tested templates. This EDM framework establishes a holistic, fully integrated view of data management and governance across six key areas:
Data architecture and metadata
Enterprise data models and the related definitions of the business and technical language to ensure a common understanding and reduce the risk of incorrect usage.
Ongoing stewardship, analysis, monitoring and remediation to deliver truly “trusted information” that drives meaningful insights across the business—and minimizes compliance and operational risk from poor-quality data.
Master and reference data
The consistent reuse and common definition of master data and reference data to optimize business processes and maximize a standardized view of information for reporting and analytics.
This pulls in unstructured data like documents, e-mails and web pages and keeps it accessible, accurate and current to support efficient business operations.
Privacy and security
Policies for privacy and compliance, identifying attributes of sensitive information, and ensuring that controls are in place to protect personally identifiable information and comply with regulatory and legislative requirements.
Data and content lifecycle
Guidelines, processes and controls that cover data rules from initial capture to final archiving or deletion, fulfilling requirements of information retention to ensure regulatory and legislative compliance.
To achieve this business-led, technology-enabled data governance and management, finance will need to play a leading role—and set realistic expectations. Partnering, road-mapping and piloting will be essential disciplines, since next-generation D&A platforms aren’t developed overnight.
Our KPMG teams have identified some important steps to productive data governance and management initiatives, rooted in our learnings from recent projects.
First, start with the end in mind and have a clear vision of where you want to go. You will need to establish an overall strategy from the outset, of course, and it will be essential to have alignment on that strategy across the enterprise.
Next, as you start to define and establish the governance and management processes, think about future-proofing things as you go. Does a specific data policy or definition tie all the way back to an overall company strategy? And think through any potential unknowns. For example, can you define the core data model with internal teams, or do you need outside help?
Last, set a target. This means tangible goals and an initial timeline that maps the effort into manageable phases—for example, a few small pilot initiatives to start. Without clear targets and deadlines, you’ll soon be back to data management as usual, with that outdated refrain: “Data governance—isn’t that IT’s job?”