It was not that long ago that pundits were ringing in a new era of big data in which all of a company’s information, together with the abundance of data available in the world, would come together in a glorious engine of growth for companies everywhere. To their credit, many organizations sat up and took notice. Today, it’s accepted wisdom that data and analytics provide an essential tool in competitive differentiation.
Still, too many companies embrace data and leave it at just that. To be sure, leading companies in every industry—including retail, telecommunications, pharmaceuticals, and banking and insurance—have adopted advanced analytical methods and high-performance data-handling capabilities to improve cost performance and increase revenues.
Indeed, many have achieved small, early wins with pockets of analytics applications, but scaling those wins requires internal development of a full set of data capabilities.
In the coming years, the companies with the best data capabilities—and best data quality—will dominate. On the basis of our client work, BCG has built a structured framework that defines the requisite capabilities for the transformation of a company’s operating model and for achieving success in today’s data race. (See Exhibit 1.) At the heart of the matter, data governance comprises four building blocks: data structures, data policies, data tools, and the organization’s participants and target operating model (TOM).
Data governance may include organizational and technological elements that facilitate the sustainable improvement of a company’s data quality. Our relatively broad definition of data quality includes data completeness, accuracy, consistency, accessibility, and the qualities that are important to the particular business and are ultimately determined by that individual company.
Good data matters—not least for compliance and operational excellence. Some industries, such as banking and pharmaceuticals, are subject to regulations that compel them to improve their data in a sustainable way. But in addition to complying with regulatory obligations, clean data allows an organization to optimize efficiency, offer modern and streamlined customer journeys, anticipate continually evolving customer needs and desires through effective advanced analytics and artificial intelligence (AI), and even create new businesses.
Good data also offers innumerable opportunities for unlocking value. A major European bank freed up several billion dollars of capital after improved exposure data decreased its regulatory capital buffers. A leading consumer goods company generated hundreds of millions of dollars annually after implementing a hyperpersonalized approach that leveraged customer data by integrating it with external data in AI models. A global steel manufacturer reduced costs by hundreds of millions of dollars by streamlining its supply chain data, integrating internal and external data flows, and using the data to optimize its operations. Properly implemented, data governance that is focused on data quality can help companies reap significant quantifiable benefits.
We hear too many complaints about the quality, consistency, and accuracy of company data, as well as the difficulty of accessing it. In the same breath, many blame problematic data for their inability to leverage advanced analytics and AI. In a recent benchmarking study that covered more than 600 companies, BCG found that more than 60% of those companies assessed their data governance capabilities at various levels of underdevelopment.
What’s keeping companies from developing and embedding data governance that can improve the quality of their data? There are three primary culprits:
The failure to implement data governance has very real and long-lasting ramifications. We’ve seen companies embark on multiyear projects, struggle with regulations, institute governance rules that aren’t needed, and spend millions of dollars on tools without moving the needle much on improving data quality or creating value.
Best practices create the optimal environment for successful data projects. (See Exhibit 2.)
Data Structures. This is the starting point. Data structures help create an inventory and shared language around data. They include descriptions of the company’s data, defined and classified in glossaries, domains, families, models, dictionaries, and flows. This isn’t exactly new: companies have been using such tools for decades. More often than not, however, they are outdated, incomplete, and not fit for purpose.
The most important of these structures are data glossaries and data domains, which help define, organize, and assign the management of company data.
A data glossary serves as an important exercise in semantics. The glossary is a list of the terms by which the company’s data is categorized, so the selection of the terms is crucial to the way the business is conducted and aligns the organization on their meaning and use. For example, a leading retail bank spent several months moving from “individual” customers to “household” customers and deciding which data should be associated with them. A global automaker needed a similar effort to define and categorize “spare parts.” A leading luxury goods company had trouble with “points of sale.” Fully owned boutiques are, of course, points of sale, but what about multiple stands in a department store? Does each serve as one or as one of several points of sale? Should they be described by the same data? And how are “sales” defined? Nominal revenues? Nominal revenues minus discounts? Minus commissions? These are some of the concerns that data glossaries address.
Data domains focus on where data resides and specify ownership of the data defined in the glossary. This high-level, unapologetic classification allows for no gaps and no overlaps. Achieving this is easier said than done. Some critical data is used in varying use cases and with different understandings of its meaning and purpose. It thus seems owned by many people or corporate entities. For example, who owns the customer data? The sales department? Marketing? Finance?
Once the critical data has been classified, data owners are assigned to each domain and given ultimate responsibility for all data quality decisions, taking into account the specific needs for the data quality of their business area. It’s worth noting how important it is to untie the overlapping and conflicting uses of data and assign all data to well-defined domains. When data is spread across domains, responsibility is diluted and decisions and actions necessary for the domain’s quality are easily overlooked.
Data domains also play useful roles in identifying areas for improving data quality. Matching domains—and their subdivisions and families—to data used by various ongoing and near-future projects and use cases is an easy and very effective way to prioritize areas in which data governance actions should be focused. (See Exhibit 3.) This approach has, therefore, a broad impact on the efficiency of an organization.
A data dictionary (not to be confused with a data glossary) focuses on data about the data—metadata. Metadata can be descriptive and related to data models, security restrictions, quality indicators, and data governance. A data dictionary is either passive (maintained separately from the dataset it describes) or active (updated automatically when the dataset structure changes).
Data flows help stakeholders understand the paths data takes in an organization. They provide visibility on data location and status, simplifying error tracing back to the error source. Data flows have become increasingly important for the data governance of companies subject to the European Union’s General Data Protection Regulation.
Data Policies. Once a company has organized and clarified its data, it should develop rules related to, for example, its processes, actions, roles, and budget allocation principles. We have seen several companies take the wrong approach to this, launching multiyear efforts to develop policies about everything and anything. This invariably leads to a bureaucratic and inefficient “Ministry of Data” effort. Because such initiatives generate no value, top management generally discontinues them.
Our position on this point is clear: all companies need clear and unambiguous definitions of data quality, measurement guidelines, and the key roles and responsibilities that govern improvements.
At least two dozen sets of criteria are available to describe data quality. They range from the very basic that are easy to meas-ure with simple key quality indicators (KQIs), such as “completeness,” to the very complex that can be measured only by applying sophisticated business rules, such as “accuracy.” Which and how many should a company use? Opt for simplicity. On the basis of the needs and uses of data, companies should select a small number of criteria and enshrine them as the data quality definition.
A company should next specify the ways these criteria will be applied and measured and establish a baseline for the current situation. It’s important to note that criteria are not equally important across domains. For example, the quality of customer email addresses is likely less important than the quality of sales data. While the criteria remain the same, the KQI objectives may be different, reflecting the various levels of quality needed for each type of data. It is the role of all data owners to define the quality objectives of their specific domains.
The data quality policy must also lay out the processes by which KQIs are defined and measured, the roles and responsibilities of the people participating in their improvement, the budget allocation process for these activities, and, more generally, all organizational and technical elements necessary for continuously monitoring and sustainably improving the quality of the data.
Other policies may or may not be called for. Should a company have problems with its reference data (a resource for other data), it probably needs a master data management (MDM) policy; if it has no referential problem, it does not need an MDM. Companies that are dealing with data accessibility problems should create a user access rights policy.
As a general rule, data policies must correspond with and address the company’s specific data issues and their root causes. They are a means to an end and play important roles in the smooth functioning, consolidating, and streamlining of data efforts. No more than that. Forgetting this can lead to unnecessary effort, the waste of resources, and excessive bureaucracy. We have seen companies developing numerous data policies, but it is quite rare that more than four or five policies are needed for effective data governance.
Data Tools. Just as data policies require the simplicity approach, the approach to data governance has no need of myriad tool sets. Still, it is important to consider two types: basic data hygiene and advanced tools.
It’s possible to build a list of data domains or a data dictionary using a spreadsheet or to represent a data flow using any kind of graphic software. However, sharing and updating such elements across a community of several hundred users in a few or many locations can be cumbersome and overly complex, if not outright impossible.
Enter data hygiene tools. These relatively basic, built-for-purpose tools help companies build and maintain their data structures, data glossaries, dictionaries, and flows efficiently.
Advanced tools perform sophisticated tasks. Some tools, for example, those focused on a specific area such as MDM or data lineage, serve a single purpose, while others that are multipurpose cover the full spectrum of data functions, such as calculating KQIs and workflow management.
Implementation of data tools is not a panacea and can consume resources that could be better employed in other data-related tasks. Data tools should be adapted to each company’s needs and should help enforce its data policies.
The Data Organization’s Participants and Target Operating Model. Successful data governance requires multilayer management that is focused on business but spans both business and IT. It is typically built around chief data officers and their teams—the only people specifically dedicated to data governance—plus a data governance council (DGC), data owners, data stewards, and data custodians.
Data Governance Council. The DGC is the overarching data-related decision-making body that delegates authority to the chief data officer (CDO) and appoints data owners. It defines the company’s data strategy and sets priorities for data governance objectives, standards, and policies and resolves issues escalated from other levels of the data organization.
The DGC typically includes all senior data stakeholders: key data owners (typically, heads of business units or corporate functions), the chief information officer, the data protection officer, the chief information security officer, and the CDO. It is usually chaired by the chief operating officer or the equivalent. The CDO is responsible for setting the agenda and executing strategies and decisions made in the council.
Data Stewards. Data stewards, part of each data owner organization, report to the data owner, working to achieve the data quality business objectives defined by the data owner. Data stewards are responsible for applying data policies and standards in their domain and for providing guidance to IT.
Depending on company size and the domain for which data stewards are responsible, several of them may support a data owner, dedicating a significant amount of their time (roughly 20% to 30%) to data-related tasks.
And what should data governance’s TOM be? We see four main organizational archetypes:
It is not uncommon for companies to begin by using a federated model with a doer CDO and, as the organization matures and builds its capabilities, to evolve to a CDO-as-facilitator model.
Over the past few years, data has been established as a fundamental source of business value. Companies compete in an environment characterized by enormous—and growing—data sets, stringent data regulations, and frequent data-powered disruptions. In this context, data governance—and the resulting improvement of data quality—provides a way to achieve not just short-term results but also to embed data in the organization and succeed in its data and analytics journey.
It just requires some attention and dedication to a data transformation. Any company can get it right.
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