Understanding Data Management

Introduction

• Data is a vital enterprise asset for organizations.
• Data and information provide insight into customers, products, and services.
• Data assists in innovation and strategic goal achievement.
• Few organizations actively manage data as an asset for ongoing value.
• Deriving value from data requires intention, planning, coordination, commitment, management, and leadership.
• Data Management involves the development, execution, and supervision of plans, policies, programs, and practices.
• These deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.
• A Data Management Professional works in any facet of data management: from technical management throughout its lifecycle to ensuring data utilization.
• Data management professionals fill roles from technical (e.g., database administrators) to network administrators.

Business Drivers

• Information and knowledge are key to competitive advantage.
• Organizations with reliable, high-quality data make better decisions.
• Failure to manage data is similar to failure to manage capital, resulting in waste and lost opportunity.
• The primary driver for data management is to enable organizations to get value from their data assets, like effective management of financial and physical assets

Goals

• Data management goals include:
  • Understanding and supporting the information needs of the enterprise and its stakeholders.
  • Capturing, storing, protecting, and ensuring the integrity of data assets.
  • Ensuring the quality of data and information.
  • Ensuring the privacy and confidentiality of stakeholder data.
  • Preventing unauthorized or inappropriate access, manipulation, or use of data and information.
  • Ensuring data can be used effectively to add value to the enterprise.

Data

• Long-standing definitions of data emphasize its role in representing facts about the world.
• In information technology, data is understood as information stored in digital form.
• Data is not limited to digitized information; data management principles apply to data captured on paper and in databases.
• Today, many things are called 'data' that would not have been in earlier times, such as names, addresses, and birthdates.
• Facts about people can be aggregated, analyzed, and used to make a profit, improve health, or influence public policy.
• Even within a single organization, there are often multiple ways of representing the same idea.
• There is a need for Data Architecture, modeling, governance, and stewardship, and Metadata and Data Quality management, all of which help people understand and use data.
• Organizations have always needed to manage their data, but changes in technology have expanded the scope of this management need as they have changed people's understanding of what data is.
• Rapid growth of technology and human capacity has intensified the need to manage data effectively.

Data and Information

• Recognizing data and information need preparation for different purposes drives home a central tenet of data management: Both data and information need to be managed.
• Both will be of higher quality if managed with uses and customer requirements in mind.
• The terms are used interchangeably in the DMBOK.

Data as an Organizational Asset

• An asset is an economic resource that can be owned or controlled, and that holds or produces value.
• Assets can be converted to money.
• Data is widely recognized as an enterprise asset.
• Understanding of what it means to manage data as an asset is still evolving.
• Today's organizations rely on their data assets to make more effective decisions and to operate more efficiently.
• Businesses use data to understand their customers, create new products and services.
• Organizations aim to stay competitive by stopping decisions based on gut feelings or instincts
• Instead use event triggers and apply analytics to gain actionable insight
• Being data-driven includes the recognition that data must be managed efficiently and with professional discipline, through a partnership of business leadership and technical expertise.
• Business today must co-create information solutions with technical data professionals working alongside line-of-business counterparts.

Data Management Principles

• Data management involves knowing what data an organization has.
• Data management involves a complex set of processes that, to be effective, require coordination, collaboration, and commitment.

Data Management Challenges

• Data management presents unique challenges due to the properties of data.
• Physical assets can be pointed to, touched, and moved around, unlike data.
• Physical assets can be in only one place at a time
• Financial assets must be accounted for on a balance sheet
• Data is durable and easy to copy and transport.
• Data is not easy to reproduce if it is lost or destroyed
• Data can be stolen without being gone
• Data can be used for multiple purposes, even by multiple people at the same time

Data Management Strategy

• A strategy is a set of choices and decisions that together chart a high-level course of action to achieve high-level goals.
• A data strategy should include business plans to use information to competitive advantage and support enterprise goals.
• Data strategy must come from an understanding of the data needs inherent in the business strategy: what data the organization needs, how it will get the data, how it will manage it and ensure its reliability over time, and how it will utilize it.
• A data strategy requires a supporting Data Management program strategy: a plan for maintaining and improving the quality of data, data integrity, access, and security while mitigating known and implied risks.
• In many organizations, the data management strategy is owned and maintained by the CDO and enacted through a data governance team
• Deliverables from strategic planning for data management include:
  • A Data Management Charter: Overall vision, business case, goals, guiding principles, measures of success, critical success factors, recognized risks, operating model, etc.
  • A Data Management Scope Statement: Goals and objectives for some planning horizon (usually 3 years) and the roles, organizations, and individual leaders accountable for achieving these objectives.
  • A Data Management Implementation Roadmap: Identifying specific programs, projects, task assignments, and delivery milestones
• The data management strategy should address all DAMA Data Management Framework Knowledge Areas relevant to the organization.

Data Management Frameworks

• Some lenses to see data management and apply concepts presented in the DMBOK.
• The first two, the Strategic Alignment Model and the Amsterdam Information Model show high-level relationships that influence how an organization manages data.
• The DAMA DMBOK Framework describes Data Management Knowledge Areas, as defined by DAMA, and explains how their visual representation within the DMBOK.
• The final two take the DAMA Wheel as a starting point and rearrange the pieces to better understand and describe the relationships between them.

Strategic Alignment Model

• The Strategic Alignment Model abstracts the fundamental drivers for any approach to data management.
• At its center is the relationship between data and information.
• Information is most often associated with business strategy and the operational use of data.
• Data is associated with information technology and processes which support physical management of systems that make data accessible for use.

Amsterdam Information Model

• The Amsterdam Information Model, like the Strategic Alignment Model, takes a strategic perspective on business and IT alignment.
• Known as the 9-cell, it recognizes a middle layer that focuses on structure and tactics, including planning and architecture.
• It recognizes the necessity of information communication as the information governance and data quality pillar.

DAMA-DMBOK Framework

• While the DAMA Wheel presents the set of Knowledge Areas at a high level, the Hexagon recognizes components of the structure of Knowledge Areas, and the Context Diagrams present the detail within each Knowledge Area.
• None of the pieces of the existing DAMA Data Management framework describe the relationship between the different Knowledge Areas.

DMBOK Pyramid (Aiken)

• Aiken's framework uses the DMBOK functional areas to describe the situation in which many areas may be put in place in an arbitrary order.

DAMA Data Management Framework Evolved

• Aiken's pyramid describes how organizations evolve toward better data management practices.
• Business Intelligence and Analytic functions have dependencies on all other data management functions.
• They depend directly on Master Data and data warehouse solutions.
• Both, in turn, are dependent on feeding systems and applications.
• Reliable Data Quality, data design, and data interoperability practices are at the foundation of reliable systems and applications.
• Data governance, which within this model includes Metadata Management, data security, Data Architecture and Reference Data Management, provides a foundation on which all other functions are dependent.
• Data governance activities provide oversight and containment, through strategy, principles, policy, and stewardship, enabling consistency through data classification and data valuation.

DAMA and the DMBOK

• The intention in presenting different visual depictions of the DAMA Data Management Framework is to provide additional perspective and to open discussion about how to apply the concepts presented in DMBOK.
• As the importance of data management grows, Frameworks become communications tools within the data management community and between the data management community and our stakeholders.
• Since at least the 1980s, organizations have recognized that managing data is central to their success.
• As the ability and desire to create and exploit data has increased, so has the need for reliable data management practices.
• How a particular organization manages its data depends on its goals, size, resources, and complexity, as well as its perception of how data supports its overall strategy.
• Understanding the wider context of data management will enable organizations to make better decisions about where to focus as they work to improve practices within and across these related functions.