This 600-page reference book occupies a prime position by my desk. I find myself turning to it time and time again for trusted answers to the questions I’m posed related to Data Strategy.
The DMBOK covers 14 topic areas, plus 3 additional chapters on how to implement the organizational changes necessary to improve Data Management. Where the text really shines is in providing insight on organizational principles around Data Governance and Change Management. Discussion of technical areas such as Data Storage are useful from a beginner’s standpoint. The DMBOK is also the reference guide for the open book CDMP exam, which confers excellence in Data Strategy.
☝️ Note that these hyperlinks to the DMBOK are affiliate links. Doing your Amazon shopping via one of these links helps support my writing and management of the CDMP Study Group. My aim is to help Data Scientists, Analysts, Engineers, and Software Developers gain the CDMP credential to advance their goals – whether that’s attaining a leadership position, proving an understanding of Data Management to clients, or getting a new job in this dynamic and essential field. Thanks in advance for your support!
Here are the 14 topic areas covered in the DMBOK:
Data Management Process — end-to-end management of data, including collection, control, protection, delivery, and enhancement. Read more.
Data Ethics — code of conduct encompassing data handling, algorithms, and other practices to ensure that data is used appropriately in a moral context. Read more.
Data Governance — practices and processes to ensure formal management of data assets. Read more.
Data Quality — assuring data is fit for consumption based on its accuracy, completeness, consistency, integrity, reasonability, timeliness, uniqueness/deduplication, validity, and accessibility. Read more.
Data Architecture — specifications to describe existing state, define data requirements, guide data integration, and control data assets, according to the organization’s data strategy. Read more.
Data Modelling and Design — translation of business needs into technical specifications. Read more.
Metadata Management — information about data collected. Read more.
Master and Reference Data Management — reference data is information used to categorize other data found in a database, or information that is solely for relating data in a database to information beyond the boundaries of the organization. Master reference data refers to information that is shared across a number of systems within the organization. Read more.
Document and Content Management — technologies, methods, and tools used to organize and store an organization’s documents. Read more.
Data Storage and Operations — characterization of hardware or software that holds, deletes, backs up, organizes, and secures an organization’s information. Read more.
Data Security — implementation of policies and procedures to ensure people and things take the right actions with data and information assets, even in the presence of malicious inputs. Read more.
Data Integration and Interoperability — use of technical and business processes to merge data from different sources, with the goal of readily and efficiently providing access to valuable information. Read more.
Data Warehousing and Business Intelligence — a data warehouse stores information from operational systems (as well as other data resources, potentially) in a way that is optimized to support decision-making processes. Business intelligence refers to the use of technology to gather and analyze data, then translate it into useful information. Read more.
Big Data — extremely large datasets, often composed of various structured, unstructured, and semi-structured data types. Read more.