CC6 - Chapter 1 Flashcards
part 1 & part 2
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 lifecycles.
Data Management
is any person who works in any aspect of data management (from technical management of data throughout its lifecycle to ensuring that data is properly utilized and leveraged) to meet strategic organizational goals. Data management professionals fill numerous roles, from the highly technical (e.g., database administrators, network administrators, programmers) to strategic business (e.g., Data Stewards, Data Strategists, Chief Data Officers).
- fill numerous roles, from highly technical (e.g., database administrators, network administrators, programmers) to strategic business roles (e.g., Data Stewards, Data Strategists, Chief Data Officers).
Data Management Professional
are wide-ranging. They include everything from the ability to make consistent decisions about how to get strategic value from data to the technical deployment and performance of databases. Thus data management requires both technical and non-technical (i.e., ‘business’) skills. Responsibility for managing data must be shared between business and information technology roles, and people in both areas must be able to collaborate to ensure an organization has high quality data that meets its strategic needs.
Data management activities
are not just assets in the sense that organizations invest in them in order to derive future value. They are also vital to the day-to-day operations of most organizations. They have been called the ‘currency’, the 'life blood’, and even the ‘new oil’ of the information economy. Whether or not an organization gets value from its analytics, it cannot even transact business without data.
Data and information
Information and knowledge hold the key to competitive advantage. Organizations that have reliable, high quality data about their customers, products, services, and operations can make better decisions than those without data or with unreliable data. Failure to manage data is similar to failure to manage capital. It results in waste and lost opportunity.
The primary driver for data management is to enable organizations to get value from their data assets, just as effective management of financial and physical assets enables organizations to get value from those assets.
Data management – Business drivers
- In relation to information technology, it is also understood as
information that has been stored in digital form(though data is not limited to information that has been digitized and data management principles apply to data captured on paper as well as in databases). Still, because today we can capture so much information electronically, we call many things ______ that would not have been called ______ in earlier times– things likenames,addresses,birthdates, what one ate for dinner on Saturday, the most recent book one purchased.
1. Data
- the “
raw material of information” - “
data in context”
2. Data and Information
- An asset is an
economic resource, that can be owned or controlled, and that holds or produces value. Assetscan be converted to money. Data is widely recognized as an enterprise asset, though understanding of what it means to manage data as an asset is still evolving.
3. Data as an Organizational Asset
- Data management shares characteristics with other forms of asset 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. This balance can best be struck by following a set of principles that recognize salient features of data management and guide data management practice.
4. Data Management Principles
Because data management has distinct characteristics derived from the properties of data itself, it also presents challenges in following these principles.
5. Data Management Challenges.
Physical assets can be pointed to, touched, and moved around. They can be in only one place at a time.
Financial assets must be accounted for on a balance sheet. However, data is different.
Data is not tangible. Yet it is durable; it does not wear out, though the value of data often changes as it ages. Data is easy to copy and transport. But it is not easy to reproduce if itis lost or destroyed.
5.1. Data Differs from Other Assets
Value is the difference between the cost of a thing and the benefit derived from that thing. For some assets, like stock, calculating value is easy. It is the difference between what the stock cost when it was purchased and what it was sold for. But for data, these calculations are more complicated, because neither the costs nor the benefits of data are standardized.
5.2. Data Valuation
Data is not tangible. Yet it is durable; it does not wear out, though the value of data often changes as it ages. Data is easy to copy and transport. But it is not easy to reproduce if itis lost or destroyed.
5.3. Data Quality
Deriving value from data does not happen by accident. It requires planning in many forms. It starts with the recognition that organizations can control how they obtain and create data. If they view data as a product that they create, they will make better decisions about it throughout its lifecycle.
5.4. Planning for Better Data
Management Metadata describes what data an organization has, what it represents, how it is classified, where it came from, how it moves within the organization, how it evolves through use, who can and cannot use it, and whether it is of high quality. Data is abstract. Definitions and other descriptions of context enable it to be understood. They make data, the data lifecycle, and the complex systems that contain data comprehensible
5.5. Metadata and Data
Data management is a complex process. Data is managed in different places within an organization by teams that have responsibility for different phases of the data lifecycle. Data management requires design skills to plan for systems, highly technical skills to administer hardware and build software, data analysis skills to understand issues and problems, analytic skills to interpret data, language skills to bring consensus to definitions and models, as well as strategic thinking to see opportunities to serve customers and meet goals.
5.6. Data Management is Cross-functional
Managing data requires understanding the scope and range of data within an organization. Data is one of the ‘horizontals’ of an organization. It moves across verticals, such as sales, marketing, and operations.
5.7. Establishing an Enterprise Perspective
Today’s organizations use data that they create internally, as well as data that they acquire from external sources. They have to account for different legal and compliance requirements across national and industry lines.
5.8. Accounting for Other Perspectives
Like other assets, data has a lifecycle. To effectively manage data assets, organizations need to understand and plan for the data lifecycle. Well-managed data is managed strategically, with a vision of how the organization will use its data.
5.9. The Data Lifecycle
Managing data is made more complicated by the fact that there are different types of data that have different lifecycle management requirements. Any management system needs to classify the objects that are managed.
5.10. Different Types of Data
Data not only represents value, it also represents risk. Low quality data (inaccurate, incomplete, or out-of-date) obviously represents risk because its information is not right. But data is also risky because it can be misunderstood and misused.
5.11. Data and Risk
Data management activities are wide-ranging and require both technical and business skills. Because almost all of today’s data is stored electronically, data management tactics are strongly influenced by technology. From its inception, the concept of data management has been deeply intertwined with management of technology.
5.12. Data Management and Technology
The Leader’s Data Manifesto (2017) recognized that an “organization’s best opportunities for organic growth lie in data.” Although most organizations recognize their data as an asset, they are far from being data-driven.
5.13. Effective Data Management Requires Leadership and Commitment
a set of choices and decisions that together chart a high-level course of action to achieve high-level goals. In the game of chess, a strategy is a sequenced set of moves to win by checkmate or to survive by stalemate. A strategic plan is a high-level course of action to achieve high-level goals. Typically, 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. The strategy must also address known challenges related to data management.
6. Data Management Strategy
is a formal document that outlines an organization's principles, guidelines, and framework for managing its data, defining roles, responsibilities, and processes to ensure data quality, security, compliance, and accessibility across the entire data lifecycle, aligning with the organization’s overall strategy and goals.
A Data Management Charter
- is a
document that clearly defines the boundaries and parameters of a data management project, outlining what data will be included, the processes to be implemented, the expected deliverables, and any limitations or exclusions, ensuring all stakeholders have a shared understanding of what is and is not included within the project scope.
data management scope statement
-
outlines a structured plan for an organization to effectively manage its data, including key phases like data assessment, governance establishment, data quality improvement, integration, storage, and security measures, with defined timelines and responsible parties to achieve optimal data utilization for informed decision-making.
Data Management Implementation Roadmap
- Data management involves a set of
interdependent functions, each with its own goals, activities, and responsibilities. - Data management professionals must balance
strategic and operational goals, business and technical requirements, risk and compliance, and various interpretations of data quality. - Different frameworks provide
different perspectivesto approach data management, clarifyingstrategy, roadmaps, team organization, and function alignment.
DATA MANAGEMENT FRAMEWORKS
- Developed by
Henderson and Venkatraman (1999). - Focuses on the
relationship between data and informationwithin an organization. - Information is associated with
business strategy and operational use of data. - Data is linked to
IT processesthat supportphysical data management and accessibility.
1. Strategic Alignment Model
- Developed by
Abcouwer, Maes, and Truijens (1997). - Also called the
9-cell model. - Recognizes a
middle layerbetween business and IT that focuses onplanning and architecture. - Helps align
data management strategieswith an organization’stactical and operational needs.
2. The Amsterdam Information Model
expands on data management by defining Knowledge Areas that make up the scope of data management.
The DAMA-DMBOK Framework
Three key visual representations describe DAMA-DMBOK Framework:
a. The DAMA Wheel – Places Data Governance at the center, surrounded by other Knowledge Areas (Data Architecture, Data Modeling, Data Quality, etc.).
b. The Environmental Factors Hexagon – Shows how people, processes, and technology interact.
c. The Knowledge Area Context Diagram – Details data management activities and their relationships using the SIPOC (Suppliers, Inputs, Processes, Outputs, Consumers) approach.
- Describes how organizations evolve in data management.
-
Outlines four phases for improving data maturity:
Phase 1: The organization implements basic database capabilities through applications.
Phase 2: They address data quality challenges, focusing on Metadata and Data Architecture.
Phase 3: Establish Data Governance to structure and support data management.
Phase 4: Organizations leverage well-managed data for analytics and business intelligence.
4. The DMBOK Pyramid (Aiken)
- Another variation of the
DAMA frameworkdeveloped bySue Geuens. - Highlights the
dependencies between data management functions. - Shows that
Business Intelligence and Analyticsrely on allother Knowledge Areas(Data Architecture, Data Quality, Data Integration, etc.). - Positions
Data Governanceas essential for ensuring organizationsextract value from their data
5. DAMA Data Management Framework Evolved
DAMA AND THE DMBOK
- DAMA (Data Management Association International) was founded to address data management challenges.
- The DMBOK (Data Management Body of Knowledge) serves as an authoritative reference for data management professionals.
Purpose of the DMBOK: - Provides a functional framework for enterprise data management practices.
- Establishes a common vocabulary for data management concepts.
- Serves as the fundamental reference for the CDMP (Certified Data Management Professional) exam.
- describes the central role that data ethics plays in making informed, socially responsible decisions about data and its uses. Awareness of the ethics of data collection, analysis, and use should guide all data management professionals.
Data Handling Ethics
- describes the technologies and business processes that emerge as our ability to collect and analyze large and diverse data sets increases.
Big Data and Data Science
- outlines an approach to evaluating and improving an organization’s data management capabilities.
Data Management Maturity Assessment
- provide best practices and considerations for organizing data management teams and enabling successful data management practices.
Data Management Organization and Role Expectations
- describes how to plan for and successfully move through the cultural changes that are necessary to embed effective data management practices within an organization.
Data Management and Organizational Change Management
