DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition)

Organizations that focus only on direct lifecycle functions will not get as much value from their data as those that support the data lifecycle through foundational and oversight activities. Foundational activities, like data risk management, Metadata, and Data Quality management, span the data lifecycle. They enable better design decisions and make data easier to use.

The Strategic Alignment Model (Henderson and Venkatraman, 1999) 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. Surrounding this concept are the four fundamental domains of strategic choice: business strategy, information technology strategy, organizational infrastructure and processes, and information technology infrastructure and processes.

Data Management program objectives that are SMART (specific, measurable, actionable, realistic, time-bound)

many organizations, the data management strategy is owned and maintained by the CDO and enacted through a data governance team, supported by a Data Governance Council. Often, the CDO will draft an initial data strategy and data management strategy even before a Data Governance Council is formed, in order to gain senior management’s commitment to establishing data stewardship and governance.

successful data management must be business-driven, rather than IT-driven.

Successful data management requires sound decisions about technology, but managing technology is not the same as managing data. Organizations need to understand the impact of technology on data, in order to prevent technological temptation from driving their decisions about data. Instead, data requirements aligned with business strategy should drive decisions about technology.

is clear that, while we are still waiting for Accounting to put Information on the balance sheet as an asset, the regulatory environment increasingly expects to see it on the risk register, with appropriate mitigations and controls being applied.

for many important decisions, we have information gaps – the difference between what we know and what we need to know to make an effective decision. Information gaps represent enterprise liabilities with potentially profound impacts on operational effectiveness and profitability.

It costs money to produce data. Data is valuable only when it is consumed or applied.

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.