The Seven Deadly Sins of Data Modelling

Originally published 7 October 2009

Data modelling has been around since the mid 1970s; and for the last 30 years, I’ve been lucky (or not) to be involved in this field. When it first emerged it promised so much ...

“A single consistent definition of data”

“Master data records of reference”

“Reduced development time”

“Improved data quality”

“Impact analysis”

……and so on.

Who in their right mind would NOT want to have these benefits realised in their organisations? Surely they are no brainers?

Yet why is it, some 3 decades on, that in many organisations, the benefits of data modelling still need to be “sold” and in others the big benefits simply fail to be delivered? Is there something that WE the information architecture community are doing wrong? I’d like to suggest seven key areas where we are committing deadly sins!

1.    Lack of Focus on Benefits

Why is data modelling undertaken? It’s not for the benefit of the modellers or the information community themselves. If you have one take away from this section, please take this ...


We are all (or should be) ambassadors for our organisations and the wider information management community. We need to recognise that this is a business, not an academic exercise. So, cutting to the chase, what are the things we need to be aware of?

Project requirements vs. big picture

As much as we may like everybody to develop their models with the whole enterprise in mind, it’s critically important to recognise that most model consumers will be in a “project” environment. Introducing “enterprise wide” dependencies on them may simply be unpalatable. Thus, recognise that there may well be sub-optimal approaches that projects need to employ. However, you as an enterprise wide information architect must have your eyes on the wider picture.

Reward drives behaviour

Leading on from above, if project teams are incentivised to bring their projects in on time and budget, and you start introducing actions that “benefit the enterprise” but there’s no bonus or reward in it for them, then don’t be surprised when you’re turned down. I once worked in an organisation where project teams were rewarded for developing data components for re-use and for re-using existing data components. This company consistently had the best models I’ve ever seen.


Really the same as above, but I like the acronym. WIIFM = “What’s In It For Me?” Whenever you’re asking someone to do something for “the good of the enterprise” always put yourself in their shoes and apply the WIIFM test!


Collect metrics on productivity, performance and the like. You WILL be asked for this at some point.
“So just why are we doing this data modelling stuff, exactly what benefits have we realised?”
Space and time don’t permit me to fully expand on this within this article; however I will be writing another article about quantifying modelling benefits.

Evidence and quotes

In addition to the metrics, make sure you constantly collect evidence and where possible quotes from satisfied senior users in your organisation. Make this a standard part of your engagement with projects – don’t wait until 9 months after you’ve finished – maybe the enthusiastic manager has moved on by then!
“We reduced the number of bespoke messages and re-development in this project by 22% saving almost $1.5m.”

Sustained improvement

Look at applying something like the Information Management Maturity Model. Aim for continual improvement. Even consider professional certification for information professionals (more on this later).

2.    Forgetting the Purpose

Why are we producing the model – what’s its purpose?

Must we always follow a top down approach?

What about bottom up or even middle out?

Do we need to incorporate history or the “as-is” position?

These and a whole host of other questions need to be asked when you embark upon a modelling project. And remember that a data model is not JUST for DBMS development (please see my article, Data Modelling is Not Just For DBMSs, Part 1 and Part 2). Look at this survey we undertook at a large client with over 300 modellers, the main reasons for producing a data model make interesting reading…

Why produce a data model?

Top 10 reasons in a large multinational organisation (2007/8)

  1. Capturing Business Requirements

  2. Promotes Reuse, Consistency, Quality

  3. Bridge Between Business and Technology Personnel

  4. Assessing Fit of Package Solutions

  5. Identify and Manage Redundant Data

  6. Sets Context for Project within the Enterprise

  7. Interaction Analysis: Compliments Process Model

  8. Pictures Communicate Better than Words

  9. Avoid Late Discovery of Missed Requirements

  10. Critical in Managing Integration Between Systems

3.    Language and Intellectual Snobbery

Past baggage

Unfortunately, in many organisations the term “modelling” often has some historical baggage associated with it. Frequently this stems from earlier exercises where lots of workshops were conducted and no apparent benefit for the user realised. In addition to being great information architects, we must also be great communicators and ensure we give regular feedback on the project, especially to the people who have given us their time. In these days of intranets and SharePoint, there’s simply no excuse for failing to keep a project blog or SharePoint going.

Inappropriate language

I don’t mean Serena Williams’s outburst to the line judge (although on some projects, the “F” word is in too regular use). I’m referring here to discussing inappropriate levels or types of information to different audiences. Don’t show DDL, tables and physical models to a business user. Likewise, don’t show a high level conceptual model to a DBA. Please make your message and language relevant to the audience.

Banish bigotry

The most embarrassed I ever felt about being in the Information management profession was in a previous company when I witnessed a colleague telling a business user why he didn’t like the notation we’d adopted (Barker, as it happens) and how “in his opinion the UML class model notation was far superior.” It’s difficult enough in many cases to get business colleagues to the table to discuss this Information stuff. I’d rather we produced ANY type of data model than argue with the business over the type and notation. Methodology bigots and their dogma must be banished!

4.    Discipline

Dumbing down

It’s not just about drawing a picture! Remember there’s way more to data modelling, particularly the business engagement.

Don’t forget the metadata

Related to the point above, the metadata is key to making a model that is useful. Include descriptions, notes use cases and examples.

Training and the right personnel

I never fail to be surprised at how many staff (and scarily many are from large international consultancies) are placed onto projects as “data modellers” yet really don’t understand the basics of why models are required, the levels of models, yet alone how to effectively engage and develop them. There are many excellent training courses available. And now professional certification (e.g., DAMA Certified Data Management Professional) can help weed out the interlopers too.

Relevant standards and guidelines

Use them! If you don’t have any, develop some. There are plenty of good ideas and standards that you can borrow from.


The majority of problems in this field stem from poor communication. Give constant feedback. Speak to business users in their language. Don’t try and walk through a 150 entity model in one session – break it up into small chunks. These “soft skills” are (in my humble opinion) the biggest area where improvement is required in the data architects community.


Be honest with yourself and your business colleagues. Most of the time this modelling stuff isn’t easy. Ask them to help you understand their business.

5.    Inappropriate positioning

Please don’t do modelling for its own sake. Understand the purpose of the modelling effort you’re embarking on.

Is it for communication only?

Is it for development of a physical DBMS?

The purpose and level required of the model will affect the way you approach its development and the end result.

All too often we see data modelling performed in isolation, the so called information silos of DM, PM, DBA, etc. Also it’s often left until too late in the life cycle to be useful, or the methodology bigots take way too long in completing a “useful” model by giving too much focus on the final 20% to be “theoretically perfect.”

Regarding the “fight for survival,” it’s also sadly common to see data modelling considered “an overhead” or cross departmental charging for a data modelling infrastructure. These are sure signs that the organisation hasn’t truly bought-in to the concept.

Finally, regarding models themselves, you may see (or in this case not see actually) hidden or unpublished models. What’s the point of developing a model then NOT sharing it? Sadly, you’ll frequently encounter limited re-use of modelling concepts (just how many definitions of CUSTOMER or PRODUCT de we really need?). This is in many cases due to projects being left to their own devices – “Sorry, we can’t engage with data management on THIS project – the train has already departed.” It’s often a consequence of the data management function not being resourced appropriately thus models not being subject to peer or cross-domain review and / or the DM function not being seen to add value.

6.    Failing to Adapt

As outlined at the start of this article, modelling’s early days focused on the development of DBMSs. But now, the application landscape of most businesses is not made up simply of bespoke solutions. They use ERP packages, BI and DW, XML message based systems, SOA and more. Data modelling does and should have a place in these, but unfortunately, the majority of organisations (and practitioners) haven’t caught up. Also, modelling needs to utilised in the “non traditional” data areas including master data, transaction data, MI/BI, and unstructured data.

There are many useful data modeling tools on the market, however selecting the one that’s most appropriate for your organisation is rarely done. Example criteria for examining data modeling tools suitable for high-level models can be found in my book, Data Modeling for the Business: A Handbook for Aligning the Business with IT Using High-Level Data Models. Good usage is more important than choosing the academic “best.”

Additionally, I believe the industry has been done a disservice by many ERP package vendors. Just how many provide a logical data model with their packages? This makes the integration of the COTS package within the overall information architecture of your company a more difficult task. However, it should still be done.

7.    Square Pegs and Round Holes

Many information management practitioners have become walking TLA factories (TLA = three letter acronym). We talk of IAM, DM, MDM, EDM, EII, CDI, SOA and so on. But do we take the time to explain to our colleagues what these really mean and why they are of significance?

We need to have the right people in the right roles. In today’s business climate, simply being a good technical modeller isn’t enough. We have to be good communicators. We have to demonstrate genuine professional credibility. The certification that is coming at last can help with this.

But do remember what I said at the outset - nobody owes us a living.

It’s up to us to successfully engage with the business. We should communicate our successes, create communities of interest and let people know why this is undertaken and how it will benefit them.

Fundamentally, it’s up to us!

So what’s our part in fixing this? Well, that’s the topic of my next article.
  • Chris BradleyChris Bradley

    Christopher Bradley has spent almost 30 years in the data management field, working for several blue-chip organisations in data management strategy, master data management, metadata management, data warehouse and business intelligence implementations.  His career includes Volvo as lead database architect, Thorn EMI as Head of Data Management, Reader's Digest Inc as European CIO, and Coopers and Lybrand’s Management Consultancy where he established and ran the International Data Management specialist practice. During this time, he worked upon and led many major international assignments including data management strategies, data warehouse implementations and establishment of data governance structures and the largest data management strategy undertaken in Europe. 

    Currently, Chris heads the Business Consultancy practice at IPL, a UK based consultancy and has been working for several years with many clients including a British HQ’d super major energy company.  Within their Enterprise Architecture group, he has established data modelling as a service and has been developing a group-wide data management strategy to ensure that common business practices and use of master data and models are promoted throughout the group.  These have involved establishing a data management framework, evangelising the message to management worldwide, developing governance and new business processes for data management and developing and delivering training. He is also advising other commercial and public sector clients on information asset management.

    Chris is a member of the Meta Data Professionals Organisation (MPO) and DAMA, and has archived Certified Data Management Professional Master status (CDMP Master). He has recently co-authored a book Data Modelling For The Business –  A Handbook for Aligning the Business with IT Using High-Level Data Models. You can reach him at

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