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Should I buy the Revised Edition of the DMBoK?

Neil

What you need to know about the Revised Edition of the DMBoK

On March 19th this year, DAMA released a revised edition of the DMBoK, and if this is the first time you’re buying the DMBoK, it’s the edition you’re going to end up with. It is described as the most up to date and authoritative source of information for data management professionals. So this video is for those of you who have a copy of the old edition and you’re looking to answer a couple of questions.

So the first question people want to know, should I buy the revised edition if I’ve already got a copy of the DMBoK? And the second question is can I pass the CDMP Fundamentals Exam if I only have a copy of the previous edition? Let’s tackle that second question first.

Should I buy the revised edition if I've already got a copy of the DMBoK?

If you’ve spent your own money on the DMBoK and you’re preparing for the CDMP, you probably want to know, can I pass the exam if my source text is the previous version? So let’s get this out of the way first. The main concepts in the DMBoK are stable. This is best practice, and it doesn’t change just because they’ve revised the book. There are updated definitions in many of the knowledge area context diagrams. However, these are mainly improving readability rather than changing the underlying principles. And if you’re taking the CDMP Fundamentals Exam, you should get to a point where you do not need to look things up in the book during the exam, it’s going to hold you back. Either the previous or the revised edition will get you where you need to be, and we continue to see students pass with flying colors that are using an older copy.

Should I spend another $79 to get the DMBoK Revised Edition?

If you’re yet to buy the DMBoK, you’re going to end up with a new edition. However, those who’ve already got a copy of the previous edition need to know whether to spend another $79 to get this revised edition. If you’ve got an existing copy and plan to use it for the exam, you’re absolutely fine to stick with the old version. If you’re using it as a reference text, you’re absolutely fine to stick with the old version. I would only buy the new edition if money is no object, and $79 is a trivial sum to you, or if your company is going to fund the purchase and you’re not spending your own money. The revised edition is a better book than the original edition, but if you’re struggling financially, it’s not necessary to buy it, and you should save your money for something else.

What are the Major Changes in the Revised Edition of the DMBoK?

So what do you need to know if you’re making the decision to buy a copy of the revised edition? Here’s the three main improvements:

The data quality chapter has had a major upgrade.
- The order of the concepts has been streamlined, the wording and definitions has been simplified and clarified, and the tables introducing data quality dimensions have been improved, with clear examples of these to help students understand how to identify real world quality issues and connect them to the underlying dimensions.
- They’ve also expanded section six of this chapter to connect data quality with data modelling, master and reference data management, metadata management, data integration and interoperability, and data governance.
- So the new version is worth buying for these updates alone.
A lot of work has clearly gone into revising the definitions for most of the context diagrams, which improves the clarity and makes each knowledge area easier to understand for a beginner.
- DAMA has also updated the goals and tweaked some of the inputs and deliverables, techniques, tools and metrics.
- The biggest improvements in the is in the activity section of each context diagram, where DAMA has ordered the activities chronologically so they make more sense as a process flow.
Throughout the book, “data governance program” has been replaced with “data governance function”. This might seem like a minor detail, but it’s one of many improvements to make the terminology clearer, easier to understand, and more consistently applied throughout the book.
- On its own, this is a cosmetic upgrade, but when you have a book the size of the DMBoK written by so many different authors, achieving this degree of consistency is hard.
- The clarifications and standardisation ought to make it easier to follow the concepts throughout the book, for a beginner to the field.

Would you recommend the Revised Edition of the DMBoK?

Simply put, the revised edition is a better book. Of course it is. The DAMA team have clearly listened to feedback and invested a lot of time in creating this revised edition. At the same time, the CDMP Fundamentals Exam tests exactly that – the fundamentals of data management – and these have been stable for years. None of the underlying concepts have changed. The book has simply improved the language used to introduce them. If you’re midway through your CDMP preparation journey and have an older version with notes in Keep It, we’ve had students clear the CDMP with scores in the high 80s this month, and they did not bother to buy the latest version.

Specific Details of the DAMA DMBoK Revised Edition Improvements

I have painstakingly gone through both copies and compared them, so you don’t have to. Here are the main additions/changes so you can make an informed choice to spend the $79:

Chapter 1 – Data Management

The content has not changed substantially, most updates are minor typos or consistency changes (e.g. data governance becomes Data Governance, punctuation changes for readability/grammar)
There are minor adjustments to the content – e.g. a “Data Governance Program” has been swapped with “Data Governance Function” to clarify that the function is what enables the organisation to become data-driven, rather than the program
The four phases of the Activities in a Context diagram have been re-ordered from “Plan (P), Develop (D), Operate (O), and Control (C).” to “Plan (P), Control (C), Develop (D), and Operate (O)” – this may also cascade through the context diagrams for subsequent sections (I have not yet checked) so the Activities section makes more sense to people

The main change seems to be the font is slightly easier (for me) to read, some diagrams appear slightly smaller, and the content is on different pages to the original edition.

Chapter 2 – Data Ethics

The Goals in the Context diagram have changed, I think for the better – more focused on preventing risk and adjusting the culture around data use – focused on the impact to people and organisations rather than more generic “community responsibility”
DAMA publishes the Context Diagrams in the public domain – https://www.dama.org/cpages/dmbok-2-image-download – note that as of the time of writing the old edition images are showing, so please lobby DAMA to update these

Chapter 3 - Data Governance

The “Data Governance Program” is now always referred to as “Data Governance Function” – which is clearer
The Activities in the Context Diagram have been re-ordered, with “define the strategy” as the first step and “define the organisation” as a subsequent step – which makes sense. The order needs to reflect the sequence of events you’d actually run (see our Data Governance course/methodology for details)
Deliverables now include a satisfaction survey, data value and a data management maturity assessment (only the first is a meaningful change, the other two are cosmetic language improvements)
The Data Governance Council gets a bit of an update – it has Executives from the organisation (in centralised or federated models, or from business units in replicated models) – this is more a clarification than a change
It’s injected the fact that “owner” can also be a synonym for steward, alongside custodian (again, check out the roles and responsibilities section in our Data Governance course to learn more)
The Data Owner becomes a business person accountable for data in the organisation (again see our course as this is what we have pushed there for a long while) – and further down in the Goals section, Business owners are now referred to as Data Owners
The Escalation Path for Figure 20 has been clarified – with issues being resolved by escalating according to the labels in the diagram (Stewardship > escalation > Business Unit Data Governance > Data Governance Council > Data Governance Steering Committee – it remains to be seen whether they have adjusted the exam questions to align with this escalation path or whether they still refer to the first edition of the DMBoK
Clarifying that the Business Glossary is typically the responsibility of Business Data Stewards, and that its contents include security and privacy classification
The coordination with Data Architecture is interesting. Historically the DMBoK and exam has had Data Governance and Architecture collaborating to guide all other Data Management functions. The latest version seems to downgrade Data Governance – rather than having the Data Governance Council sponsoring and approving data architecture artefacts, it appears to now be only “a stakeholder the might sponsor or approve a business focused enterprise data model. I suspect there has been pushback as Enterprise Architecture groups are stronger in many organisations and have been in place longer than Data Governance.
Rather than sponsoring Data Valuation we’re now expecting Data Governance to define the data valuation method – there does not seem to be any more information on HOW to do that, so if you need pointers then check out our Data Valuation course
The Organisation and Culture section promotes the need to include Change Management as part of the Strategy in order to drive sustainability – again this is nothing new, check out our Data Governance course to get details on the importance of this (or read the Change Management chapter in the DMBoK)

And that’s it.

Chapter 4 - Data Architecture

Again, minor tweaks in the Context Diagram – “Manage Enterprise Requirements within Projects” takes on a more prominent role than it did – probably just correcting the indentation in the activities section

The main change in this chapter is the shrinking of diagrams which reduces the number of pages without reducing the content you have to read. Our existing Data Architecture content will still be fine to help you wrestle through these concepts.

Chapter 5 - Data Modeling and Design

Context Diagram: More explicit that we expect conceptual, logical and physical models
Context Diagram: Goals have been clarified and put into bullets – focusing on how data fits together and the fact this helps us reduce the cost of support, developing new applications and ensures we re-use data wherever possible
Context Diagram: the inputs and deliverables have been tweaked with more clarity on what comes in and the fact outputs include definitions, issues/questions and lineage as well as just the data models
Context Diagram: Participants also include Data Analysts and Data Consumers
Context Diagram: Techniques now include forward/reverse engineering and approach determination, Data Profiling tools have been added to the toolkit and the metrics now focus on the scorecard
Rewording on the Composite and Compound key definitions in section – I’m not sure they’ve improved the wording here
Change of “EmployeeGenderCode” to EmployeePreferredPronoun” as an example in the section on Domains – the concept is the same, it’s updated to reflect changes in culture around gender and pronouns
The section on FCO-IM or Fully Communication Oriented Modeling has been updated with a new Figure 43, check out the new version – or you can just read about it on Wikipedia
Tweaks on the language used to describe Data Vault

Aside from updates to the explanation (which may or may not add clarity, I don’t find the updates particularly easier to read than the older text) there’s not a massive change in this chapter.

Chapter 6 - Data Storage and Operations

The usual improvements removing typos, unnecessary language, or clearing up spelling/grammar/consistency issues

Context Diagram – addition to the deliverables to make it clearer that we are adding a disaster recovery plan for data WITHIN the business continuity plan, plus added “Database Technology Specialists” and “Data Modeller” to the Participants, and” Executive and Managers” to the consumers
Figure 57 – Coupling – has had the legend for “Tightly Coupled” and “Loosely Coupled” switched to match the text (it’s safe to say this has NOT been tested on the exam otherwise they’d have issued an update sooner)
Blockchain databases have been updated to reflect they can securely manage “non-fungible” transactions (vs just Financial transactions, which might have been the more prevalent use case when the original was released)
Section 2.2 is now “Manage Database Operations” rather than “Manage Databases” – but the content is the same, so it is a minor terminology update

Chapter 7- Data Security

Context Diagram: Definition adjustment – we’re now delivering this “within cultural and regulatory considerations”. Techniques now include “Efficient Search for Encrypted Data” in the context diagram, and the Tools has “anti-virus and security software” plus “webpage security” and “firewalls” explicitly mentioned.
Section 3.2 has been renamed “Webpage Security” rather than “HTTPS” – no changes to the content
The Metrics section has been moved out of Section 4 (Techniques) and into section 6 (Data Security Governance) in order to align it with the structure of other chapters (we measure any Knowledge Area activity for the purpose of tracking adherence to our Data Governance rules and adjusting/improving work to ensure it’s achieving our objectives – hence it belongs here)

Chapter 8- Data Integration and Interoperability

Context Diagram: They have improved the definition to clarify that Data Integration is about moving data within and between data stores, applications and organisations, and that Data Interoperability is about ensuring multiple systems are able to communicate. I think this is clearer, and one of the main improvements so far.
Context Diagram: Activities have been updated – in the plan phase we’re to “Collect Business Rules” vs “Ensure Business Rule Compliance”, whereas in the Design DII Solutions phase they have spelled out that we need to “Design DII Architecture” and “Model Data Hubs, Interfaces, Messages and Data Services”, plus a clarification that we’re going to “Map {Data} Sources to Targets”. The Develop phase makes minor adjustments to the language – including “Develop a Publication Approach”, and moves “Maintain DII Metadata” to the appropriate phase (Implement and Monitor)
Context Diagram: The suppliers now include “Architecture Board” and the Participants changes “ETL Service Interface Developers” into “DII Developers” to reflect the fact there are many DII approaches. Consumers now includes “Customers and Partners” as well as “Data Scientists”.
Context Diagram: Techniques has had a big overhaul – reducing redundancy and specificity to include “Loosely coupled applications, Minimise the number of interfaces, Create Standard Canonical Interfaces and Service Orchestration” as the most important elements, with “Business Rules Engine” added to Tools, and tweaks to the wording of the Metrics section
Oddly enough, the rest of the chapter has cosmetic changes, with a slight re-order to ensure the activities flow in chronological order (but no change to the description of these activities)

Chapter 9 - Document and Content Management

Context Diagram: Another major update to the definition – it’s now “Controlling the capture, storage, access and use of data and information stored predominantly outside relational databases” rather than “Planning, implementation and control activities for lifecycle management of data and information found in any form or medium”. This is a useful update as the original definition was a bit “all things to all people”. It’s probably worth memorising the new definition as you can get questions on definitions in the exam – or annotate your existing DMBoK to reflect the change as they publish the updated diagrams publicly for free
Context Diagram: the Activities section has been tidied up, moving Planning activities together, expanding “Manage the Lifecycle” to include activities like “Manage Versioning and Control”, “Backup and Recovery”, “Manage Retention and Disposal” and “Audit Documents and Records”. The “Publish and Deliver Content” section has also been expanded to include “Provide Access, Search and Retrieval” and “Deliver through accessible channels” – and the Deliverables now includes “E-Discovery approaches” accordingly
Terminology update to align with the rest of the DMBoK Revised Edition – the “Information governance program” is now always referred to as the “Information governance function” – however they don’t seem to capitalise “Governance” for this term, unlike references to “Data Governance” elsewhere in this new edition
In section 1.3.2.9 Ontology, it clarifies that the Data Model calls out the entity to which an attribute belongs and the valid VALUES for that attribute, when used as part of a Taxonomy.

Chapter 10 - Reference and Master Data

Context Diagram: Definition change, and this time I think I prefer the old version “Managing shared data to meet organizational goals, reduce risks associated with data redundancy, ensure higher quality, and reduce the costs of data integration.” vs the new version “Managing reconciled and integrated data through stewardship and semantic consistency in support of enterprise-wide needs to share its data assets.” – what do you think? Do you prefer the old or new definition?
Context Diagram: The Goals have also been edited, and this time I prefer the new wording: “1.) Ensuring the organization has complete, consistent, current, authoritative Master and Reference Data across organizational processes, 2.) Enabling Master and Reference Data to be shared across enterprise functions and applications. 3.) Lowering the cost and reducing the complexity of data usage and integration through standards, common data models, and integration patterns.“
Context Diagram: Inputs have changed – “Business Drivers” and “Data Glossary” are out, “Candidate Data Stores and Values” and “Metadata” are in. The Activities have minor wording tweaks to better align with the phases they’re in, and the Deliverables now include “Data Exception Reports”
Context Diagram: Consumers have been updated to include “Subject Matter Experts” and remove “Master Data Analysts” and “Data Archtiects”
Context Diagram: the Tools section has had a refresh – “Data Profiling and Quality” plus “Data Sharing/Integration Architecture” tools are out, and “Data Remediation”, “Operational Data Stores” and “Data Sharing Hubs” are in.

That’s it – the rest of the changes are cosmetic/grammar improvements. It might be worth studying the tools that have been explicitly called out in the new Context Diagram but these edits are not substantial and don’t change the understanding shared in this knowledge area

Chapter 11 - Data Warehousing and Business Intelligence

Context Diagram – definition adjustment to “Planning, implementation, and managing an integrated data system to support knowledge workers engaged in reporting, query, and analysis.” – this makes it clearer that we’re integrating data into a system to support knowledge workers, so I like this adjustment, as previously it did not mention the fact the integrated data system was the objective of our work.
Similarly, the goals have been adjusted slightly: “1. To build and maintain the data system with the technical and business requirements needed to deliver integrated data that supports operational functions, compliance, and business intelligence. 2. To create insights to support and enable effective business analysis and decision making.” again, the focus being about the data system, and the second point being enhanced to show this is about creating insights vs simply supporting and enabling business analysis and decision making.
The deliverables have had minor tweaks – the main being the need for a Reporting Strategy (I’m less keen on this – if we have a “strategy” for everything it starts to become over-used and meaningless. This would just be a subcomponent of a Data Strategy, which would be there to support the business strategy anyway – hopefully that’s clear by now.
The Consumers now have “Partners” and “Subject Matter Experts” – there is some change to the names of the Participants but it’s cosmetic
The Tools now have “Business Intelligence Tools” listed rather than “Analytic Applications”
Section 4.3 has been renamed “Audit Data that can be Queried” has been changed to “Queryable Data”, the text in the section remains the same

The rest of the edits are cosmetic/readability improvements

Chapter 12 - Metadata Management

Context Diagram definition change – we’re now performing “activities that contributes to the ability to process, maintain, integrate, secure, audit and govern other data” – which is an improvement over performing “activities to enable access to high quality, integrated metadata” – as this calls out the benefit of metadata management (helping us manage and use data) vs doing the job for the sake of it.
Goals – now updated to include Technical Lineage provision – which is a good clarification as many students miss the link to Data Architecture and Modeling. They’ve also updated goal #3 so it’s not simply proving access to metadata, it’s to “enable known level of trust in data exchange” – i.e. we can drive up trust in data using the metadata
Inputs include Metadata Policy and Standards, which makes sense as these guide the work, and the Activity “Query, Report and Analyze Metadata” has been tagged with its phase (O – Operations).
Data Curators have been added to the list of Consumers of these Knowledge Area deliverables
Integration Tools have been added to the list of tools, and “Metadata Repository Contribution and Metadata Usage Reports” have been replaced in the Metrics with “Metadata Repository Activities” – hardly a massive update
Figure 87 has been updated to ensure it reflects what it depicts – “Bi-Directional Metadata Architecture” NOT “Hybrid Metadata Architecture”

Chapter 13 - Data Quality

This is the big one, with some major changes:

The Introduction has a much better explanation of what Data Quality is, why we do it, and how to identify it, before launching into the “Effective data management…” spiel about the impact of Data Quality on Data Management. None of this should be new if you’re using this course – data is high quality when it meets the needs of the business and is “fit for purpose” (i.e. fit for the purpose it is going to be used for)
The introduction further elaborates that high quality data is context driven – Context Creates Value – i.e. a customer dataset with no email addresses could be high quality (in the context of serving a customer in-store with details of prior purchases) AND it could be low quality (in the context of sending out an email marketing campaign). Data Quality can only be assessed if you know how someone intends to use the data and what outcome they need it to help them achieve
It highlights the fact that Data Quality Management is a Program – much like Data Governance – i.e. it is not a one-time effort to clean data, it is an ongoing process to detect data that doesn’t meet business needs and perform actions to ensure the data is fit-for-purpose
Context Diagram – Definition – this has been tweaked to remove reference to “applying quality management techniques” (which are probably not well understood) to apply “techniques for collecting and handling data ensuring it addresses the needs of the enterprise and local consumer and is fit for use.” – a minor but useful improvement
Context Diagram – Inputs – has been updated to include Operational Metadata and Data Sharing Agreements as source documents to identify data quality requirements.
Context Diagram – Activities – “Define Data Quality Framework (P)” has been added as a first Planning step – sensible, given we need this to perform any further activities. It carries on remove the DQ Strategy step, adds in details about defining dimensions and business rules, and then removes some details of subsequent steps to make it more high-level than the old context diagram, except for the step “Develop and Deploy Data Quality Operations (D,O,C)” which now includes sub-steps: “1. Manage Data Quality Rules, 2. Measure and Monitor Data Quality, 3. Develop Procedures for Managing Data Issues, 4. Establish Data Quality Service Level Agreements, 5. Data Quality Response”.
Context Diagram – Deliverables – the main inclusion here is the output of “Remediated Data” and “Certified Data” – i.e. data that has gone through an improvement process and is now both fit-for-purpose, and certified to be used for that purpose by the DQ/DG team
Context Diagram – the Suppliers now include Data Analysts and Data Vendors, we have lost the Compliance team from the Participants and replaced by the Subject Matter Experts that can tell us when data is fit for purpose, and now the Consumers are just “Knowledge Workers” rather than calling out people being in a business role.
Context Diagram – Techniques – now lists these “Data Quality Metrics, Profiling, Preventive and, Corrective Actions, Root Cause Analysis, Corrections” – the inclusion of Profiling makes sense, but I’m not sure why we need “Corrections” and “Profiling, Preventive and Corrective Actions” as it seems redundant
Context Diagram – Tools – has big changes too: “Data Profiling Tools, Business Rule Engine, Data Parsing and Formatting, Data Transformation and Standardization, Data Enrichment, Incident Management” – this is a lot more comprehensive than the older diagram which had much less
Context Diagram – Metrics – much improved: “Return on Investment, Levels of Quality, Data Quality Scorecard, Data Issues Reports, Service Level Conformance, Data Quality Plan Progress” – this continues the focus of the revised edition to make it more about business impact – what is the ROI for improving data, for example? There’s no point improving data quality unless it is worth more to do so than it costs to do.
Business Drivers: additions to highlight the work is to improve the trust in data, to enhance the experience of stakeholders that use data, to make the organisation more effective, and it flips the lens from “costs of poor data” toward “benefits of good data” – which is an interesting change of perspective (it seems DAMA has realised the stick didn’t motivate and hopes carrots have more impact…)
Goals – similarly, this paints a picture of the future for a Data User where they are able to wander into the office free from the worry that their data will require a whole day’s work just to get it fit-for-purpose. I like the framing. Interestingly, they’ve dropped the link to Lifecycle Management (students struggle to work out what this means) and to Governance (the link it still important, but the governance reference in this chapter was never really a goal of DQ)
Section 1.3.1 “Data Quality” has been axed in the new version, probably because the introduction explains what Data Quality is up-front, rather than waiting a bunch of pages to introduce the main character – Critical Data moves into this slot, and you’ll see in our question references that the pages/numbering for this chapter are the most significantly different
Critical Data – expanded here to show how to identify it, by linking back to the Business Drivers, customer experience, effectiveness, and efficiency. It also gives a scale of criticality to help you prioritise things – e.g. Regulatory/Compliance data being critical by default, as well as other knowledge areas like Master Data etc.
Data Quality Dimensions – this section has had a major facelift. The aim here is to help readers link the dimension(s) to the standards we have set for our data, our ability to measure Data Quality, and to real-world business impact of improving data along one or more dimension. Lots of the older content has been moved to section 8.2 “Further Reading on DQ Dimensions” – moving some of the more ambiguous text into its own section to improve clarity of reading and comprehension.
Table 29 – DQ Dimensions – this has been improved by only including dimensions where “there is general agreement and describes approaches to measuring them”. The additional examples make each dimension easier to understand and easier to apply (both in the real-world AND in situations during the exam where you’re tested on real-world examples and asked to identify the right dimension)
Figure 92 has been swapped out. Rather than the confusing mapping relationship between DQ Dimensions, this is now “The Shewhart Chart with the role of a Data Quality Team indicated” – Plan, Do, Check, Act but colour coded to show what the DQ team executes (Plan, Do) vs what the Business Operations Team are responsible for (Check, Act)
Data Quality Business Rules has been moved under the section on Dimensions (it’s in 1.3.4 not 1.3.7), and it has been re-written to make it easier to understand and to connect-the-dots between the DQ Dimensions and the Rules you might write to determine whether data complies or not
Data Quality ISO Standard is relegated into section 8.1 – the Appendix (I’m not sure anyone will miss it)
The Data Quality Improvement cycle has the Figure 92 mentioned above, in exchange for the old Figure 93 (the only addition is calling out which team is responsible for what action). The text is improved to explain this in more detail, and Table 30 “Data Quality Activities” has been changed to describe the tasks you’re expected to perform during the Plan, Do, Check and Act stages. Table 30 used to be “DQ Metric Examples” and these are now in Table 32 (Metrics section) “Data Quality Metric Examples” – which I’ll describe later
Common Causes of DQ Issues has replaced Figure 95 “Barriers to Managing Information as a Business Asset” with Figure 93 “Sources of Data Quality Issues” – it has also simplified the language and aligned it with the subsequent subsection titles (e.g. “1.3.8.1 Issues Caused by Lack of Leadership” is changed to match Figure 93’s text “1.3.5.1 Issues Caused by Lack of Oversight”
Issues Caused by Lack of Oversight has been expanded to explain what this looks like in practice – with an example company where a Customer Support call is measuring the employee ONLY on time to complete the call without linking this to Data Quality metrics for the data captured during that call. They have also called out that Data Quality issues from this lack of oversight can come because Data Quality is “not a priority”
Issues Caused by Data Entry Processes – this section has also been re-written and improved with additional insight into poor engagement and poor usability – those that have been tripped up by our questions about causes of DQ issues might want to read this version
Issues Caused by Data Processing Functions has been improved by changing the perspective – rather than focusing on “Incorrect assumptions about data sources” and describing issues in a passive voice, the Revised Edition looks at this as “No regard for “downstream” processes”, also about Data Lineage but from the perspective of the process or user that creates the data so you know who (or what) you should focus your attention on fixing. It also looks at the changing nature of business rules, processes and data structures as possible challenges to your DQ program
Issues Caused by System Design – improved by rewording the language to make it easier to read. Rather than “Temporal data mismatches” (which sounds like something from Back to the Future) they talk about “Timing errors” instead. The section about Master data is expanded to include Reference and Master data management and reinforced to highlight that your DQ program will be impacted poorly by poor practices in Reference and Master data management
Issues Caused by Fixing Issues has had a few sections added – looking at methods for data recovery functions (so users don’t have to manually patch data) and a warning to ensure Reference data is changed INFREQUENTLY – check out the content we have on Reference Data and DQ to learn more about why this is important
Data Profiling, Data Quality and Data Processing, Data Parsing and Formatting and Data Transformation and Standardisation sections have been dropped from the Essential Concepts section and dropped into the Tools and Techniques area (section 3/4) where they belong
Section 2.1 is now “Define a Data Quality Framework” – reflecting the change in the Activities section of the Context Diagram – it just moves section 2.2 (Define a Data Quality Strategy) up
Section 2.2 becomes “Define High Quality Data” – and continues the theme of repeating that we MUST focus on the data that has the greatest value to our business and prioritised (see Critical Data for more info) – which has been moved up from the old section “Identify Critical Data and Business Rules” and replaced by “Identify Dimensions and Supporting Business Rules”, which has only cosmetic changes
Measure and Monitor Data Quality has been updated, removing the complex equations on page 479 and replacing them with “Figure 94 Illustrations of data defects trends over time” plus an explanation of what these trends might mean. They have also removed Table 30 (see above) and amended “Table 31 Data Quality Monitoring Techniques” to link the Granularity to the DQ Dimension and a potential Treatment
Establish Data Quality Service Level Agreements has additional content to show you’ll need these at key boundaries where data is shared – e.g. inside/outside the organisation, as well as across silos or business units inside your organisation.
Develop Data Quality Reporting becomes “Data Quality Response” in 2.7.5 – not much else has changed
Section 3 – Tools – has had a shakeup, with some of the content from Essential Concepts dropping in here, the most notable omission being “Metadata repositories” and “Modeling and ETL Tools” with “Business Rule Templates and Engines” coming in. Much of this is just re-ordered content, but it does belong in this section more appropriately than elsewhere in the chapter.
Section 4 – Metrics – also the recipient of moved content rather than a major re-write, ordered to follow a logical sequence from metrics to measure data quality through methods to find, fix and prevent errors.
Organization and Cultural Change – has been expanded to include ways to help people accept data quality can be improved, managing the politics of those trying to derail your program, and ensuring those that are fighting DQ issues today are brought into the program rather than feeling they’ll be replaceable (because they’re battling the DQ issues and are therefore important to business success today).
Section 6 has changed from being purely about “Data Quality and Data Governance” to the broader church of “Data Quality and the other Knowledge Areas” – this is a big improvement to connect the knowledge areas together. I often see students struggle with e.g. Master and Reference Data or Data Modelling as well as Data Quality, and this update goes some way to “connect the dots” between these Knowledge areas as well as Metadata Management, Data Integration and Interoperability AND Data Governance
As noted above, this is the only Knowledge Area where they’ve also include a section 8 – Appendix – seemingly to house the parts of the chapter they didn’t want to delete, but couldn’t shoehorn naturally into the flow and sequence of the rest of this chapter

Overall this is a major improvement for the Data Quality chapter and if I was studying for the exam I would prefer to use this version rather than the original. If $79 is a small sum of money to you, or if you can persuade your company to cover the cost, then I would absolutely recommend getting the Revised Edition just for the more comprehensive, well structured and considered version of this Chapter, given how important Data Quality is to the overall role of Data Management and Governance.

Chapter 14 - Big Data

Context Diagram definition – now “The handling of large amounts of data (Big Data) and paradigm and statistical analytics (Data Science) of many different types of data to find answers and insights.” – this doesn’t really say a lot more than the original definition.
Context Diagram Goals – #2 “Support the iterative integration of data source(s) into the enterprise” has gone – we don’t want to integrate new data just for the sake of it, #4 has also gone “Publish data using visualization techniques in an appropriate, trusted, and ethical manner” which has been replaced with “Package communications of model outputs for stakeholders and decision-makers”, and “Integrate existing organizational practices with best practices in data management, big data, and data science.” – presumably so we know the Data Management practices for other types of data still apply here.
Inputs have had an update – now we have “Business Strategy, Business Case, Information Requirements, Information Sources & Metadata, IT Standards, Analytical Models, Data Models” which are actually more useful inputs than the older version inputs like “Build/Buy/Rent Decision Tree”
Activities now includes “Establish Big Data Environments (D)” as step 2 – clearly we need an environment in which to perform this work. We also have Step 8 “Communicate Output to Stakeholders (D)” before we are going to the “Deploy and Monitor (O,C)” final step
Similarly the Deliverables now include “Big Data Landscape” as well as “Operations Plan”, “Model Performance” and “Model Enhancement Plan” – small changes but it is more comprehensive
Participants now include Data Curators (otherwise your Data Lake becomes a Swamp), and Data Quality and Governance Specialists rather than the more generic/ambiguous “DM Managers” from the original
Consumers of this Knowledge Area have been increased to include “Intermediaries, Stakeholders and Customers” – I’m not sure this adds much but it’s not a bad enhancement
There’s a few more Techniques added – Analytic Modelling, Big Data Modelling, Columnar Compression (moved from tools). Tools now have “MPP Shared-Nothing Technologies”, as well as “Big Data Cloud Solutions” and “Statistical Computing and Graphical Languages”. Metrics splits out “Data Usage Metrics” into “Technical Usage Metrics” and “Loading and Scanning Metrics”
Section 1.3.4 Big Data Architecture Components removes a section about the difference between ETL and ELT – which is probably a hindrance to those taking the exam as there are questions that test this and it’s worth knowing (it isn’t a particularly tricky concept)

Not a massive change, but watch out for the ELT/ETL concept as it could be worth a point or two on the exam.

Chapter 15 – Data Management Maturity Assessment

Context Diagram definition – minor tweak to show it’s about assessing the current state of data management AND opportunities for improvement
The Goals have been tightened up and are much clearer – (paraphrased: “Evaluate current state so we know what we can improve, align with the organisation’s strategic direction, put a vision together to match this strategic direction and enable an integrated plan for improvement.” – much better than goals like “To educate stakeholder about concepts, principles, and practices of data management, as well as to identify their roles and responsibilities in a broader context as the creators and managers of data” which are not real goals for performing an assessment
The Activities section has been expanded and is simpler – e.g. under “Plan the Assessment Activities” you have: “1. Define Objectives, 2. Choose a Framework, 3. Define Organizational Scope, 4. Define Interaction Approach, and 5. Plan Communications” – breaking it out step-by-step (to be honest, all of this is stuff you could probably generate using ChatGPT or similar)
The main change in the Deliverables is adding “Executive Briefings” – although I presume anyone that has gone through the effort to perform a Maturity Assessment has done so with a view of presenting it, so this might be redundant information
Suppliers now include “Benchmark Provider” – you might need to hire in an external consultant to do this work, just for the purpose of “proving” that this is “industry best practice”. However if your business trusts your judgement, save the money – message me directly if you want help as I might put a short course out on this

Chapter 16 – Data Management Organization and Role Expectations

Cosmetic improvements/grammar changes

Chapter 17 – Data Management and Organizational Change Management

Cosmetic improvements/grammar changes

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