How to explain data governance to beginners

Data management and data governance cover a range of complex data capabilities—data monetization, data compliance, data quality, data remediation, data architecture and more. While most people recognize the importance of data, these concepts are difficult to explain to those who aren’t data experts. It’s hard to define these core concepts quickly and definitively when advising colleagues and clients.

The analogy that has worked best to convey these capabilities is to compare data management to real estate management. Both require skills to effectively organize, maintain and use valuable assets. Thinking of data management in this way not only sheds light on its underlying capabilities but also helps others visualize how these capabilities operate together.

FIGURE: The comparison between data management and real estate management

Let’s build our comparison of data governance to real estate management, one data capability at a time:

• Data asset: At the heart of the analogy lies the data asset, which corresponds with the building or property in real estate management. A data product or a data set is another way to perceive the data asset. Adequately governed and nurtured data and real estate management revolve around managing assets that generate value. When mismanaged, however, these lead to risks and losses.
• Data (product) ownership: A critical concept in data management is ownership—responsibilities may be delegated to others, but at the end of the day, one person or team should own the data. In the building analogy, this would be the property owner or landlord.
• Data stewardship: Data stewardship involves assigning responsibility for managing data assets to specific individuals or teams, for example, to ensure that data is sufficient in quality. In real estate management, data stewardship can be compared to the role of property managers who are responsible for the upkeep and maintenance of a property.
• Data consumers or users: Various individuals and business processes may consume the data, either internally or externally from the organization. This is similar to the tenants who use the building for their own purposes, whether it’s as their residence or for commercial use.
• Data monetization: Data monetization involves leveraging data assets to generate revenue—for example, by selling data to other organizations. In real estate management, this would be equivalent to finding ways to generate income from a property, such as renting space out to tenants or for special events, selling advertising space on the building, allowing telecoms to install cell towers on it or selling the property altogether.
• Data contract: A data contract is a formal agreement between a data producer and data consumer, confirming what data to exchange and the corresponding formatting and quality requirements. This is similar to a lease agreement, which describes what is expected of the landlord and the conditions of the property. It also outlines what the property can be used for (and specifically, what can’t be done to or with it). A data contract can serve similar purposes.
• Value quantification: In both cases, it’s a worthwhile exercise to estimate the value of the asset. Just as the value of a property depends on its location, size and condition, the value of data depends on its relevance, accuracy and accessibility.
• Data security and access controls: Data security refers to the protection of data assets from unauthorized access, use or disclosure. In real estate management, data security can be compared to the use of locks, alarms and security systems to protect a property from theft or vandalism.
• Data architecture: This is similar to the blueprint of a property, which defines the layout, design and construction of the building. Similarly, data architecture involves the design and structure of data storage and retrieval systems. Architecture standards can provide guidelines and best practices for how buildings are constructed, and data architecture standards serve the same purpose for data assets.
• Data domains: Just as a city is divided into neighborhoods, data can be divided into domains based on its subject matter. Any property belongs to a single neighborhood, and together, all neighborhoods include all properties—the same holds for data assets and domains. Each neighborhood has its own characteristics, such as demographics and property values, and similarly, each data domain has its own attributes and requirements. An organization like a homeowners association (equivalent to data domain owners or stewards) can be set up to oversee that these requirements are implemented.
• Data policies and standards and regulatory compliance: These are comparable to the different regulations that govern the use and development of properties, such as zoning laws, environmental regulations, building codes and fire codes. Similarly, data policies and standards define the rules for managing data in an organization. These rules are derived from applicable regulations in various geographies where the data is used, including laws related to data privacy and data protection.
• Metadata management: Metadata is data about the data. It can describe the data asset in terms of the data attributes it contains, who owns it, who has access, who did access it and when, its location, how many records there are and the size of the total asset. It can be compared to detailed information about a property and its features—for example, the total square and cubic footage, the owner, the number of rooms, its location and who has keys to the building.
• Data quality: Data quality refers to the fitness-for-purpose of data as measured along dimensions like accuracy, completeness and consistency. In real estate management, data quality is similar to the condition and upkeep of a property, for example, whether it has any defects, code violations or safety hazards.
• Data remediation: Data remediation refers to the process of identifying and correcting data quality issues. In real estate management, data remediation is like the process of identifying and correcting property defects, such as a leaky roof or a faulty foundation, to maintain the property’s value and safety.
• Data usage: This is similar to the measurement of the usage of properties, which helps determine their potential value. This may include occupancy rates—and perhaps even more detailed logs of who entered the building, when and for how long. Similarly, data usage measurement involves tracking and measuring how and by whom data is used in an organization and to what extent data assets are adopted.
• Interoperability: This is analogous to the compatibility of a property with other properties and upstream or downstream systems, as well as its ability to share common infrastructure or resources. For example, a building is connected to the electrical grid, water network and sewage system. Each of these connections comes with precisely defined standards in terms of voltage, water pressure, pipeline sizes and sewage standards. In a similar sense, data interoperability refers to the ability of the asset to exchange data and work together seamlessly with various other systems and applications, subject to common standards.
• Data storage: Data storage is like the physical size and foundational structure of a property. A property might have to be of a certain minimum size, for example to accommodate industrial machines or to house families of a certain size. Similarly, data storage refers to the physical or virtual storage capacity in databases, data warehouses or data lakes.
• Data life cycle: This can be compared to the life cycle of a property, which involves stages such as construction, maintenance, renovation and demolition. Similarly, data life cycle management involves managing data through stages such as creation, storage, usage, archiving and disposal.
• Data integration: Different properties and neighborhoods are connected by roads and transportation systems. A particular building may provide easy access to public transport and a nearby highway. Data integration involves connecting data from different domains and sources, which can involve tasks such as data cleansing, data mapping and data transformation to ensure that data from different systems can be used together. Without integration, you can’t access or use the data, the same way you would not be able to enter or make use of a building that’s not integrated.
  
  When looking to explain the concepts of data management and data governance, this real estate analogy offers a helpful way to understand how various data capabilities work together to support the organization’s overall data strategy.