Chapter 1
The Importance of Managing Data

Even before the rise of information technology, information and knowledge have been keys to competitive advantage. Organizations that have reliable, high-quality information about their customers, products, services, and operations can make better decisions than those without data (or with unreliable data). But producing high-quality data and managing it in ways that enable it to be used effectively is not a simple process.

This chapter will review the following concepts, which are important to any organization that wants to improve its data management capabilities:

• Data’s ubiquity – the fact that almost every organizational process creates or consumes data or both
• Data’s value as an asset
• Why it is important to understand data management separately from technology management
• The range of activities and functions involved with managing data

Data is everywhere

Organizations have always needed to manage their data, but advances in technology have expanded the scope of this management need. Data is pervasive across organizations. Almost every business process – from setting up customers, to transacting purchases, to contacting customers for feedback and services – uses data as input and produces data as output. Most of this data is in electronic form, which means that it is malleable: it can be stored in large quantities, manipulated, integrated, and aggregated for different uses, including business intelligence and predictive analytics. It also provides evidence of an organization’s compliance (or lack of compliance) with laws and regulations.

Technical changes have enabled organizations to use data in new ways to create products, share information, create knowledge, and improve organizational success. But the rapid growth of technology and with it human capacity to produce, capture, and mine data for meaning has intensified the need to manage data effectively.

Data as an asset

An asset is an economic resource, that can be owned or controlled, and that holds or produces value. Assets are often thought of as property, but with the strong implication that they can be converted to money. Data is widely recognized as an enterprise asset, although many organizations still struggle to manage data as an asset. For example, data is not yet accounted for in most organizations’ balance sheets.

If asked, many senior executives would say that their organization’s data is a valuable asset. It is not only necessary to business operations, but it can also provide insight into customers, products, and services. Nevertheless, research shows that very few organizations treat their data as an asset.1 For many it can even be a liability. Failure to manage data is similar to failure to manage capital. It results in waste and lost opportunity. Poorly-managed data presents ethical as well as security risks.

Even executives who recognize data as an asset may not be able to describe exactly what that means, since data differs from other assets in important ways. Nevertheless, the primary driver for data management is to enable organizations to get value from their data, just as effective management of financial and physical assets enables organizations to get value from those assets. Deriving value from data does not happen in a vacuum or by accident. It requires organizational commitment and leadership, as well as management.

Data management vs. technology management

Data management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets, throughout their lifecycle.

You may think, “Isn’t that what our information technology department already does?” Unfortunately, no. IT usually does not focus on data. IT focuses technology, technological processes, the people who build applications, and the tools they use to do so. Historically, IT has not focused on the data that is created by or stored in the applications it builds. If anything, IT has tended to be dismissive of the data itself (because IT professes to have no control over data) – despite the fact that many data management functions are part of IT.

Though data management is highly dependent on technology and intersects with technology management, it involves separate activities that are independent from specific technical tools and processes.

Given this definition, what does data management actually involve? What does it mean to manage data effectively? Like all forms of management, data management involves planning and coordinating resources and activities in order to meet organizational objectives. The activities themselves range from the highly technical, like ensuring that large databases are accessible, performant, and secure, to the highly strategic, like determining how to expand market share through innovative uses of data. These management activities must strive to make high-quality, reliable data available to the organization, while ensuring this data is accessible to authorized users and protected from misuse.

Data management activities

Data management activities can be understood in groups: some focus on governance to ensure the organization makes sound, consistent decisions about data; others are foundational and focus on enabling the management, maintenance, and use of data over time; and some focus on managing the data lifecycle, from obtaining data through disposing of it (see Figure 2).

• Governance activities help control data development and reduce risks associated with data use, while at the same time, enabling an organization to leverage data strategically. These activities establish a system of decision rights and responsibilities for data, so that an organization can make consistent decisions across business verticals.2 Governance activities include things like:
  • Defining data strategy
  • Setting policy
  • Stewarding data
  • Defining the value of data to the organization
  • Preparing the organization to get more value from its data by
    • Maturing its data management practices
    • Evolving the organization’s mindset around data though culture change
• Lifecycle activities focus on planning and designing for data, enabling its use, ensuring it is effectively maintained, and actually using it. Use of data often results in enhancements and innovations, which have their own lifecycle requirements. Lifecycle activities include:
  • Data Architecture
  • Data Modeling
  • Building and managing data warehouses and marts
  • Integrating data for use by business intelligence analysts and data scientists
  • Managing the lifecycle of highly critical shared data, like Reference Data and Master Data
• Foundational activities are required for consistent management of data over time. Integral to the entire data lifecycle, these activities include:
  • Ensuring data is protected
  • Managing Metadata, the knowledge required to understand and use data
  • Managing the quality of data

Foundational activities must be accounted for as part of planning and design, and they must be carried out operationally. These activities are also supported by and integral to the success of governance structures.

Data management knowledge areas

The work of data management is carried out by people working in data management functions or knowledge areas, which require different skills and expertise (see Figure 3).

DAMA International has defined eleven knowledge areas:

• Data Governance provides direction and oversight for data management activities and functions by establishing a system of decision rights and responsibilities for data. These rights and responsibilities should account for the needs of the enterprise as a whole.
• Data Architecture defines the blueprint for managing data assets by aligning with organizational strategy and establishing designs to meet strategic data requirements.
• Data Modeling and Design is the process of discovering, analyzing, representing, and communicating data requirements in a precise form called the data model.
• Data Storage and Operations includes the design, implementation, and support of stored data to maximize its value. Operations provide support throughout the data lifecycle from planning for to disposal of data.
• Data Security ensures that data privacy and confidentiality are maintained, that data is not breached, and that data is accessed appropriately.
• Data Integration & Interoperability includes processes related to the movement and consolidation of data within and between data stores, applications, and organizations.
• Document and Content Management includes planning, implementation, and control activities to manage the lifecycle of data and information found in a range of unstructured media, especially documents needed to support legal and regulatory compliance requirements.
• Reference and Master Data Management includes ongoing reconciliation and maintenance of core critical shared data to enable consistent use across systems of the most accurate, timely, and relevant version of truth about essential business entities.
• Data Warehousing and Business Intelligence includes the planning, implementation, and control processes to manage decision support data and to enable knowledge workers to get value from data via analysis and reporting.
• Metadata Management includes planning, implementation, and control activities to enable access to high-quality, integrated Metadata, including definitions, models, data flows, and other information critical to understanding data and the systems through which it is created, maintained, and accessed.
• Data Quality Management includes the planning and implementation of quality management techniques to measure, assess, and improve the fitness of data for use within an organization.

These knowledge areas represent activities at the core of data management. Any organization trying to get value from its data must engage in them. But they are also evolving. Changes in our capacity to create and use data mean that other concepts could also be considered data management “knowledge areas” (such as data ethics, data science, Big Data management, and emergent technologies).

Data management professionals working in these knowledge areas help an organization:

• Understand and support the information needs of the enterprise and its stakeholders, including customers, employees, and business partners
• Capture, store, and ensure the integrity and quality of data to enable its use by the enterprise
• Ensure the security, privacy, and confidentiality of data by preventing inappropriate access, manipulation, or use

What you need to know

• The goal of data management is to enable an organization to get more value from its data.
• In a world dependent on data, reliable data management practices are becoming more critical.
• Data management includes governance, foundational, and lifecycle activities.
• Data management involves a range of skills – from strategic to highly technical.
• Data management practices are rapidly evolving as business needs and technological capacity evolve.