Data Vault 2.0 - Beyond the Model

In this article we will explore what Data Vault 2.0 really is, and what is beyond the Modeling aspects themselves. These are thoughts around the architecture, the methodology, and the implementation strategies from a business level.

(C) Copyright Dan Linstedt, 2018 all rights reserved, REPRINTS NOT ALLOWED WITHOUT EXPRESS WRITTEN CONSENT

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Data Models are a necessary part of business intelligence. It doesn't matter if it's data warehousing, operational systems design, big data solutions, data science, or data lakes. To understand the data, it must be modeled. In other words: concept models, logical models, physical models (for performance, access, storage), all play a role.

Ask any data scientist if they can establish any deep learning or neural net system without ANY model, and their answer should be no. If it's not "no" then they need to go back to school, because neural nets themselves are models including both process and data (function and form).

What's the big deal about the Data Vault Model?

The Data Vault Model is no different. The Data Vault model is a conceptual model, and yes, I defined the original model specifications (as mentioned here) to stand the test of time. Yes, the original DV modeling paradigm consists of concepts which tie together business function to form (storage), and yes the model is representative of a neural network or a graph network. It consists (for those who are interested) of business concepts called Hubs (unique lists of business keys), Links (associations across business keys), and Satellites (descriptive data over time / contextual data).

The DV Model is built based on learning systems designs. Those that "break the standards" or "break the patterns" literally are saying that they know a better design than the human brain. Which I find hard to believe. If they have a design that is better than the human brain and they can prove it, then they should patent it - they could be rich!

Those that "break the standards" and opt for creating their own standards, are making the statements that band-aids are capable of standing up to a monsoon rain storm. Have you ever taken a bunch of band-aids, rolled them in to a ball, then soaked them in water? What happens to them? Do they stay in a ball? or deform and separate?

Returning to the authorized standards that I create and teach, I patterned the Hubs and Satellites after memory neurons, and the Links after dendrites and synapses. To put it plainly - vectors (as a graph database would treat them). If you return to my original definition in Super Charge Your Data Warehouse, you will find a small section written on "grading" the links - applying strength and confidence ratings to the associations.

This is one of the hidden secrets or powers of the Data Vault model structure. However, you cannot achieve its full potential with just the model alone - you need to incorporate the right methodology, and the right architecture in order to gain the most benefit.

That said, a model is just a model. Once you've gotten your data modeled, and organized, you can access it, load it, store it - however, to make it efficient, you need guidelines and standardized patterns for the functions that utilize the data sets. This, dives in to the methodology and implementation components.

Why is the methodology important?

Wouldn't it be grand if all our systems adhered to the following simple principles? I've been doing this for a long long time (30+ years), and I have learned, that all systems, both natural world and anything we humans build - all follow the same paradigms of evolution - that is, unless management derail the efforts, or we learn incorrectly from non-authorized / non-authoritative sources to do the wrong thing.

With a non-authorized source only teaching up to level 2, we frequently fall short of our goals overall. This is where Data Vault 1.0 projects fail.

The 5 levels of CMMI - distilled to usable form

1. You can't understand what you don't research
2. You can't define what you don't understand (standards, context, concepts)
3. You can't identify what you don't define (KPA's and structure)
4. You can't measure what you don't identify (KPA's and KPI's)
5. You can't optimize what you can't measure (KPI's and retrospective adaptation)

These five levels: understand -> define -> identify -> measure -> optimize repeat themselves over and over again, not only in the modeling aspects of data, but well in to the methodology, architecture and implementation. How fast you can execute these processes in business becomes paramount to your delivery speed. THIS is what the DV2 Methodology brings to the table, and enables you and your teams to do at an enterprise level.

Speaking of which, have you ever participated in a sprint review? More-over have you ever optimized / fixed what the sprint review found? If yes, then you've experienced CMMI Level 5 process at the methodology level from an agile standpoint.

As you can see, data modeling lives in level 2 - defining what you have, it may provide level 3 (when Data Vault modeling is done properly) by identifying proper Hubs and business keys. However, it can't begin to touch levels 4 or 5 for maturity without methodology, architecture, and implementation.

What about these automation tools?

Glad you asked. Automation tools are very very helpful, IF they follow the methodology and implementation standards for you. That said, these tools also need to follow the standards that I propose, rigorously and to the letter. Anything less, and they will be recreating (in a faster format) the road to failure for you.

This is why I take the time to authorize tool vendors for Data Vault 2.0, so that when they make claims about supporting DV2, you can know and trust their statements. Vendors may do good things, and may produce some or part of the DV2 standards, however any deviation at all, and it can lead to problems.

Back to the methodology…

The methodology brings with it not only the peace of mind that you and your teams are executing properly, but also tried and tested methods for success. As I said in my earlier article: 10+ years of research and design, with 30,000 test cases – followed by another 15 years of implementation, and evolution for big data, IOT and unstructured data platforms.  There is a lot there, and to ignore this part of it would simply be suicide.

To only pay attention to the data model (by itself) will give you benefits, yes, but only 10% of the overall benefit that you are expecting from your enterprise BI solution. Is this really what you want to sign up for?? Do you really want to sign up to be certified in JUST Data Vault Modeling only to have your implementations fail / fall flat because they can’t deliver on time? I don’t think so. You have too much at risk to take this approach.

The Data Vault methodology brings your team in at CMMI level 3 (maturity level 3), and gets them started with pre-defined templates, process designs, loading patterns, query patterns, performance and tuning patterns. But more than that, the methodology also brings in Disciplined Agile Delivery specifically tuned for Data Warehousing / Enterprise Analytics / Enterprise Business Intelligence. 

The methodology teaches your team how to apply the proper functions, methods, and ways of working that can drive your team forward in an agile and rapid fashion. I’ve spent the better part of my professional life working with global distributed teams, multi-cultural teams, large and small teams – and I can tell you that having a solid methodology / approach to the ways of working, interacting and executing on a vision is the only way to succeed. Not only that, but the teams can easily work in parallel swim lanes and not get off on to their own customized way of executing.

Why the hype? Why Bother?

One massive reason for all of this: Elimination of re-engineering.  I’m sure, if you’re a CEO, CIO, CSO, CTO, or director, that you’ve experienced this pain in your BI solution. I know I have, over and over again before creating the DV2 methodology. Re-Engineering of a solution usually goes like this:

• We had to re-engineer because our data got “too big”
• We had to re-engineer because our queries didn’t perform anymore
• We had to re-engineer because our data load times got to long
• We had to re-engineer (our ways of working) because our teams are no longer responsive to business requirements
• We had to re-engineer because each team built a “silo solution”
• We had to re-engineer because each team followed their own standards, and now none of their solutions fit together to meet the needs of the enterprise.
• We had to greenfield everything (stop and shut it all down and start again) because our environments are disparate, and are teams can’t communicate with one another
• We had to re-engineer because a new executive came on board and wants things their way.

Take your pick, I have a million and one of these, been through them all – seen them all. Re-Engineering is the single biggest failure of Business Intelligence / Analytics solutions the around the world. Because I have seen these things, lived through these things, I’ve incorporated the mitigation strategies IN to the DV2 methodology. SO YOUR TEAMS DON’T HAVE TO! This clears the path for your teams to do what they need to do: DELIVER on business promises in rapid succession.

Re-engineering is caused by faulty design. In the data model for the data warehouse, re-engineering is caused by introduction of “conditional design”. For example: “When this happens, we need these types of table structures.” Or “When these fields are NULL, then these fields are filled, and these numbers mean X not Y.”

In the methodology, re-engineering is caused by embedding data hierarchy rules up-stream of the structured storage base (data warehouse). Also, it may be caused by inefficient methods of team collaboration (everyone having a different idea as to how to approach parallel team and agile ways of working). It may be caused by designing standards that “fit the exception and not the rule”. It may be caused by poor instruction, or by breaking standards that have been tried and tested and proven to work. 

Re-engineering is caused when two parallel teams are merged – and they’ve each followed their “own standard” or their own “methodology” for building and integrating.  These are problems that need to be addressed at multiple levels. The DV2 methodology prescribes guidelines for all of this. Automation tooling (if I may) accelerates delivery done properly IF the teams are sticking to the authorized standards.

Implementation, is a part of the methodology. Without the proper methodology and team dynamics, implementation can go out the window. In truth, the implementation is really where the automation tools live. They can do best by “automating” according to standards, the workflow processes that lead to 100% standards based generation. They can keep the teams on-track, under budget, and truly save tons of headache.  Parallel teams around the world can and should leverage a shared metadata repository – which these tools offer.

Implementation is the crown jewel of the methodology (in a way), because without proper DV2 implementation standards, none of the implementations are built the same way twice.  Implementation of the proper standards leads to definitive and successful programs, that said, implementation itself is not a means to an end – it must be combined with the methodology, architecture, and data model to work in concert. The sum of the whole is much greater than the sum of each individual part.

What about Architecture? The final piece?

Architecture is necessary as a premise. Without architecture, we cannot see the bigger picture. Without architecture our parallel global teams descend in to chaos, and revert to building “their own picture”. Properly focused architecture provides the guidelines, rails, center pieces for all teams to follow. It is the program direction. 

The DV2 architecture is built to encompass hybrid solutions, the DV2 architecture is platform agnostic. The DV2 architecture brings definition and understanding across the board to all the teams, and all the resources so that the goals of the enterprise are not lost. That said, there are multiple levels of DV2 architecture:

• Systems Architecture
• Process Architecture
• People Architecture
• Data Architecture
• Information Architecture
• Enterprise Architecture
• Solutions Architecture (summary of some of the parts)

When you think of architecture, you probably only focus on “Systems Architecture”, while important, it’s not the only piece! You need to consider all the other components of architecture as well – and that’s what the DV2 architecture contains. No, we won’t arrive on site with your architecture done. We will arrive on site with DV2 Methodology guidelines and defined deliverables that tell us what to build for each of these architectures.

The resulting architecture is yours, what you do with it is up to you. Then again, this is what my Data Vault 2.0 authorized instructors learn to teach and help you with. The authorized instructors go way beyond just the data vault model and are geared to help you with all aspects of your build out.

In summary:

I hope you enjoyed this journey through Data Vault 2.0, and I hope now, that you begin to see and understand that Data Vault 2.0 is so much more than just the Data Vault Model. I have hundreds more pages I could write on this subject, and if you have a specific question or want to dive in to a specific topic, then please e-mail me or contact me. 

I’d be happy to answer your questions, get on a call with you and your management, and discuss the values and virtues of Data Vault 2.0.  (As would any of my authorized instructors).

I am starting a new (free to join) community called the Data Vault Alliance, I will have a business and a technical channel, and many articles from different authors appearing there. Articles like this one as well, will appear there. For now, simply sign up, and once we open the doors you will be automatically notified.