Chapter 3
DAMA’s Data Management Principles

Managing data presents unique challenges connected to the nature of data itself. Even with its unique characteristics, data management still shares characteristics with other forms of management. It involves knowing what data an organization has and what might be accomplished with it, then determining how best to use data assets to reach organizational goals. Like other management processes, it must balance strategic and operational needs. It must also account for the unique properties of data reviewed in Chapter 2.

To help organizations strike this balance, DAMA has developed a set of principles that recognize the challenges of data management and help guide data management practice.

At the highest level, these principles boil down to four assertions (see Figure 5) which we will review in this chapter:

• Data is valuable
• Data management requirements are business requirements
• Data management depends on diverse skills
• Data management is lifecycle management

DAMA’s data management principles provide a lens through which to understand how your organization manages its data. After reviewing their implications, we will look at them in the context of data management maturity. A maturity model defines a progression of increasing control over a set of processes. When an organization gains an understanding of process characteristics, it can put in place a plan to improve its capabilities. It can also measure improvement and compare itself to competitors or partners, guided by the levels of the model. Data management maturity models describe details of data management processes that can be used for this type of evaluation. We will return to the concept of data management maturity in Chapter 12 when we discuss how to assess the current state of your organization.

Data is valuable

• Data is an asset with unique properties: Data is an asset, but it differs from other assets in important ways that impact how it is managed. The most obvious of these properties is that data is not consumed when it is used, as are financial and physical assets.
• The value of data can and should be expressed in economic terms: Calling data an asset implies that it has value. While there are techniques for measuring data’s qualitative and quantitative value, there are not yet standards for doing so. Organizations that want to make better decisions about their data should develop consistent ways to quantify that value. They should also measure both the costs of low quality data and the benefits of high-quality data.
• Effective data management requires leadership commitment: Data management involves a complex set of processes that, to be effective, require coordination, collaboration, and commitment. Getting there requires not only management skills, but also the vision and purpose that comes from committed leadership.

Data management requirements are business requirements

• Managing data means managing the quality of data: Ensuring that data is fit for purpose is a primary goal of data management. To manage quality, organizations must ensure they understand stakeholders’ requirements for quality and measure data against these requirements.
• It takes Metadata to manage data: Managing any asset requires having data about that asset (number of employees, accounting codes, etc.). The data used to manage and use data is called Metadata. Because data cannot be held or touched, to understand what it is and how to use it requires definition and knowledge in the form of Metadata. Metadata originates from a range of processes related to data creation, processing, and use, including architecture, modeling, stewardship, governance, data quality management, systems development, IT and business operations, and analytics.
• It takes planning to manage data: Even small organizations can have complex technical and business process landscapes. Data is created in many places and is moved between places for use. To coordinate work and keep the end results aligned requires planning from an architectural and process perspective.
• Data management requirements must drive Information Technology decisions: Data and data management are deeply intertwined with information technology and information technology management. Managing data requires an approach that ensures technology serves, rather than drives, an organization’s strategic data needs.

Data management is lifecycle management

• Data management is lifecycle management: Data has a lifecycle and managing data requires managing its lifecycle. Because data begets more data, the data lifecycle itself can be very complex. Data management practices need to account for the evolving lifecycle of data.
• Different types of data have different lifecycle characteristics: And for this reason, they have different management requirements. Data management practices have to recognize these differences and be flexible enough to meet different kinds of data lifecycle requirements.
• Managing data includes managing the risks associated with data: In addition to being an asset, data also represents risk to an organization. Data can be lost, stolen, or misused. Organizations must consider the ethical implications of their uses of data. Data-related risks must be managed as part of the data lifecycle.

Data management depends on diverse skills

• Data management is cross-functional: A single team cannot manage all of an organization’s data. Doing so requires a range of skills and expertise. Data management requires both technical and non-technical skills and the ability to collaborate.
• Data management requires an enterprise perspective: Data management has local applications, but it must be applied across the enterprise to be as effective as possible. This is one reason why data management and data governance are intertwined.
• Data management must account for a range of perspectives: Data is fluid and changing. Data management must constantly evolve to keep up with the ways data is created and used and the data consumers who use it.

Data management principles and data management maturity

You now understand the importance of data management, the challenges of data management, and the principles of data management. Your organization is undoubtedly applying some of these principles, as it is undoubtedly following some of the practices that will be described in the upcoming chapters. But unless the organization raises its awareness through a level of self-assessment, it is unlikely to be able to improve its practices.

A capability maturity assessment is a very good means to this end. Capability Maturity Assessment is an approach to process improvement based on a framework – a Capability Maturity Model – that describes how characteristics of a process evolve from ad hoc to optimal.11

With each new level, process execution becomes more consistent, predictable, and reliable. Processes improve as they take on characteristics of the levels. Progression happens in a set order. No level can be skipped. Levels commonly include:

• Level 0 Absence of capability
• Level 1 Initial or Ad Hoc: Success depends on the competence of individuals
• Level 2 Repeatable: Minimum process discipline is in place
• Level 3 Defined: Standards are set and used
• Level 4 Managed: Processes are quantified and controlled
• Level 5 Optimized: Process improvement goals are quantified

Within each level, criteria are described across process features. For example, a maturity model may include criteria related to how processes are executed, including the level of automation of those processes. It may focus on policies and controls, as well as process details. Such an assessment helps identify what is working well, what is not working well, and where an organization has gaps.

The maturation of the use of data management principles could progress as illustrated in Figure 6, where an organization moves from limited knowledge of data management principles to a state where the principles become drivers of organizational improvement.

A Data Management Maturity Assessment (DMMA) can be used to evaluate data management overall, or it can be used to focus on a single functional or knowledge area, or even a single process or idea (such as the degree to which an organization follows data management principles).

Whatever the focus, a DMMA can help bridge the gap between business and IT perspectives on the health and effectiveness of data management practices. A DMMA provides a common language for depicting what progress looks like across data management functions and offers a stage-based path to improvement which can be tailored to an organization’s strategic priorities. Thus, it can be used both to set and to measure organizational goals, as well as to compare one’s organization against other organizations or industry benchmarks.

What you need to know

• DAMA’s data management principles were developed in response to the challenges presented by managing data.
• The principles enable an organization to take a more strategic approach to managing data.
• The principles can be used to formulate policy, define procedures, and enable strategic decisions.
• Staff involved with any aspect of data management should be familiar with these principles and be able to apply them to the work that they are accountable for.
• DAMA’s data management principles can also be used in conjunction with a Data Management Maturity Assessment to understand the organization’s current state and define a roadmap for improvement.