10 things stopping you from becoming a data-driven organisation.

Man penning through an iPda looking at data (for an unknwon reason)
Data. Pleasure or Pain? Photo by Adeolu Eletu on Unsplash

Recently I was asked whether I had any advice or ‘best practice’ to organisations seeking to become ‘data driven’. This isn’t my long-term specialism, so I was flattered, but not really the right person to declare ‘best practice’ and responded so. But then I reflected a while on all the interesting work I had done for clients in this area (which turns out to be a fair bit) and I realised that some of the really difficult things I’ve tussled with, might be helpful lessons for others, so I pulled them together here.

These 10 things are not a manuscript of wisdom from a textbook, but they’re real-world lessons.

Lessons from the pain. Photo by Gemma Chua-Tran on Unsplash
  1. The problem is almost certainly behavioural and cultural, but the preference will be to think it’s the technology and poor data (or predecessors!).
  2. There’s no point having data and generating insight if there isn’t an operating model and decision culture that can consume it.
  3. Remember; “Without data, you’re just another person with an opinion”. Then remember that it’s 2020 and an opinion is often the only thing people care about. So plan for this reality and be sure you anticipate an emotional response.
  4. Don’t let people think they’re going to jump to ‘Data Driven’. There’s a bunch of stages that I think need to be acknowledge (Data informed, Data led…!) even if not transitioned through.
  5. The 4 levels of data analytics are fundamentally true in my experience. People will want predictive analytics without the basics (descriptive analytics) under control. That’s not to say that you cannot move onwards with what you have and provide some valuable analytics based on partial or sketchy data. The issues materialise though, because organisational focus now shifts to the new and exciting and forgets to fix the foundations of your data… and you know what they say about a house built on sand…
  6. Kill the endless, existential conversations defining the difference between data, information, knowledge etc. They’re broadly a waste of time (and assuming you use time to deliver value, then perhaps this is poor expenditure).
  7. Treat data as an asset, manage it so.
  8. Understand the underpinning meta-model that each person holds in their head. Whether or not anyone wants to talk it, it will exist, so you might as well manage that correctly.
  9. Don’t confuse the data with the system that stores it.
  10. Teach people to understand the basic difference between the ‘thing’ and the ‘fact about the thing’ (i.e. dimensions/entities/types/classes vs properties/attributes/fields). If you don’t pay attention to this, your data makes no sense.

For a bonus point, I’d also make the case for having a decent understanding of the motives. Typically I see an expectation that it will improve decision quality (it might) but that’s only a small fraction of what you should be thinking about. When moving towards a data-driven organisation, there can be improvements in Decision Efficiency, Decision Repeatability and (my favourite) Decision Velocity.

Creating quick and beautiful UML diagrams with UMLet

If you have the need to quickly pull together a simple view for a diagram, document or drawing, there’s no need to spin up a modelling tool or wrestle with Vision (or more likely wrestle with your IT department to get them to install Vision for you!) Look no further than UMLet (That’s https://www.umlet.com/ ) 

I’ve been using it for years but just thought it a good idea to mention it here.

It’s pretty simple to get the hang of, and using text based programming makes it really easy to copy and paste areas of your diagram. Take a look at a simple Sequence Diagram I pulled together in about 5 minutes:

Which have the underlying code of:

title: Combining Private and Public Primitives
SUBMITTER~id1|SERVICE (Private)~id2|SERVICE (Public)~id3|SIP (Private)~id4|SIP (Public)~id5|COMPOSITOR (Public)~id6|EVALUATOR (Public)~id7

iframe{:Registering a Private Service with a Private SIP (Out of Band)

id2->id4:id2,id4:Service Contract
id4->id4:id4,id4:Register and store
id2->id4:id2,id4:Service Contract (update TTL, Location)

iframe{:Registering a Public Service with a Public SIP (Out of Band)

id3->id5:id3,id5:Service Contract
id5->id5:id5,id5:Register and store
id3->id5:id3,id5:Service Contract (update TTL, Location)

iframe{:Finding available SERVICEs
id1->id4:Request for Service Contract
id4->id4:id4: Find the Service Contract
id4->id1:Service Contract

id1->id5:Request for Service Contract
id5->id5:id5: Find the Service Contract
id5->id1:Service Contract

iframe{:Using SERVICEs and evalutating risk
id1->id2:Input (to be kept private)
id2->id2:id2: Evaluate Input for risk
id2->id1:Service Result (a)

id1->id3:Input (to can be used in public)
id3->id3:id3: Evaluate Input for risk
id3->id1:Service Result (b)

id1->id6:Service result (a) + Existing Risk Profile
id6->id6:id6: Combined Service Result and Risk Profile
id6->id1:Risk Profile

id1->id6:Service result (a) + Existing Risk Profile
id6->id6:id6: Combined Service Result and Risk Profile
id6->id1:Risk Profile

id1->id7:Risk Profile
id7->id7:id7: Evaluate Risk Profile



Government Technology Codes of Practice

For many years I’ve worked for, or with, UK Government (and associated entities) delivering large scale systems based solutions. For the greater part, I’ve found a great deal of sense spoken about the strategy and direction that the Government wishes to take, particularly of late (without making any judgement on success in achieving this strategy).

One of the things I am a fan of, and we’ve seen a lot more recently, are open, pan-government strategies and approaches that can be re-used and referred to by the many (many!) arms of Government. The “Digital By Default” is something many of us will have heard about. The 26 points outlined there, will (to many) seem like common sense, but it’s easy to lose sight of them in the rushes and pressures of day to day Systems Engineering (and supplier management!)

Today I wanted to just highlight a new guideline that I hadn’t come across before, but in a similar vein to Digital by Default, provides a number of points to consider (or adhere to, depending on your situation). The Technology Code of Practice might look fairly similar at first glance, but it’s well worth a read, and for those of you working on Government systems as a day to day function, might like to check your current work against it.

Whatever your views on pan-Government initiatives, I hope you, like me, appreciate the open publication of such initiatives.

Measuring the unmeasurable

As systems engineers, we are often required to quantify and measure certain concepts that initially appear too abstract to get a handle on. This is often a problem at initial stages of the systems engineering lifecycle and particularly at project start up or engineering mobilisation phases of the project lifecycle. Customers (and comparative internal stakeholders with similar interests such as project control) will start making requests of the engineering team along the lines of “how secure is the solution”, or “how modifiable is it”? Whilst one would hope that any requirements team worth their salt has agreed a decent requirements set that are well parameterised, there will always be idealistic high level requirements that feel insufficiently defined and immeasurable.

Whilst the inexperienced engineer might make initial judgements based around convoluted methods of pseudo-assessment, there are a number of approaches that might be better suited and are worth examination. By pseudo-assessment, I refer to methods used to elicit approximations of quantification and measurement based on either subjective views from experienced Subject Matter Experts (SMEs) or reflective judgements based on measurements taken on related areas. “It is highly secure because we have built it in accordance with the RMADS” or “It is easily modifiable because it has a component based architecture” for example.

Described here is a formal method of quantifying abstract qualities such as information security, reliability or data quality and, where appropriate, applying metrics to those areas. The seasoned systems engineer will no doubt shrug off such methods as obvious, but not only do they deserve explicit mention (and thus this text) but perhaps clarification and where possible, references to real world area in which they can be used. This work is not my own, it is mainly based on a paper by Pontus Johnson, Lars Nordstrom and Robert Lagerstrom from the Royal Institute of Technology, Sweden. I came across it in the publication “Enterprise Interoperability – New Challenges and Approaches” , published by Springer which will set you back a little over a hundred pounds at current UK prices. For the “real” version (including the maths), see their paper titled “Formalizing Analysis of Enterprise Architecture”. My interpretation (or bastardisation!) is a personal account of some of the concepts and I do not claim to be the authority on this (disclaimer over!).

The description here is a considerably less formal than the paper from which it came, and no doubt will be criticised for this dumbed down description however, this serves only to re highlight its use and perhaps make it more accessible. If the reader enjoys getting involved in the maths, they are welcome to go and access the paper and produce their own interpretation. In fact this is encouraged.


Architecture Theory Diagrams

When looking to paramterise and measure an abstract property, a reasonable approach would be to examine what “goes into” that property to make it what it is. A simple format for this method would be as follows:

1)            Decompose the abstract property into sub properties

2)            Try to quantify and measure the sub properties

3)            Aggregate the answered properties according to a schema to answer the initial abstract property.

This method makes a number of important assumptions:

1)            You believe that the abstract property can be formally decomposed to suitable properties

2)            You trust that the composition of the sub properties fully describe the abstract property

It will be noted that the method does not rely on the ability for sub properties of the abstract property to be sufficiently parameterised and measurable because step 2 of formal decomposition method is (theoretically infinitely) recursive and thus such sub properties will be found.

The Architecture Theory Diagram (ATD) approach extends this approach in a number of useful ways.

First, the ATD method formalises the nomenclature of abstract property decomposition by providing us with the following terms:

An Operationalised Property is property for which it is believed to be practicably possible to get a credible measure. That is to say that for the abstract property Information Security, Operationalised Properties might be properties such as Link Encrypted or Firewall installed (clearly both Boolean enumerated attributes).

Intermediate Properties are neither abstract nor operationalised. These properties exist only to serve the purpose of providing useful decomposition steps between the abstract properties and the operationalised properties.

Definitional Relations merely illustrate that a property is defined by its sub properties. This is broadly equivalent to a Composition relation in UML. The interesting use of the definitional relationship here is that they are to carry weightings. That is to say that each operational property is not equal and can be weighted according to its influence on the parent property. (Before anyone comments that the notation uses a UML aggregation symbol… this is the notation given for ATDs!)


Operationalised Properties are then given property values, and this is where we receive even more flexibility. The values assigned, are derived from expert opinion, direct measurement or otherwise and values are enumerated according to a suitable schema. The “plausibility” factor is the belief that we have the property carries the value attributed (Dempster-Shafer Theory).

Hopefully you will quickly see that the following steps are to start aggregating the values back up the decomposition to enable a value to be calculated for the abstract property. The accumulation of value to the abstract property is according to the weighted definitional relations and from this the maths gets quite complicated. I shall make no attempt to explain it… partly because at points it is beyond me but, if this has started to give you a flavor of the “art of the possible” then I strongly encourage you to look for the paper (or get in touch) and use it for your own purposes.

The strength of this method is that it gains suitably credible values for abstract properties and can be backed up by some useful maths to do the computation for you. The weighted definitional relations and incorporation of Dempster-Shafer theory supply the useful format for compiling these values into a useful measure of the abstract property.

I would certainly encourage anyone that has a use to explore this method, or adapt it for their purposes and, as always, I would welcome comment, feedback or thoughts.

Antisocial Engineering

Definitions for Engineering vary wildly depending on who is delivering them, but for a minute, consider Engineering as a behaviour, in the same way as your accountant can “account” in a good way, or a bad way, Engineers can “engineer” in many different ways. My least favourite way? Unsociably. There’s no need for it, there’s no excuses for it and YOU shouldn’t do it.

Antisocial Engineering is all around us, but we let people get away with it, often because we’re too scared to confront others for fear of being “a jobsworth” or sometimes simply because we don’t recognise it. Think I’m being picky? Then you’re probably an Antisocial Engineer.

Antisocial Engineers are a tricky breed, indeed many of us show Antisocial Engineering behaviours on occasions and don’t even recognise it. Antisocial Engineering is the skipping of steps because it’s convenient. It’s the quick change from the specification because that will get it working (when you ultimately forget to later update the spec.). Antisocial Engineering is asking someone else to be non-compliant to the ICD so you don’t have to up issue the document. Get the idea? Antisocial Engineering is for your own convenience.

Why is antisocial Engineering so bad when it gets the job done? Well the problem is that so often it only gets YOUR job done, and then you have to spend your time (or watch others waste theirs) convincing others to overlook your small changes, or help them implement fixes so that their system/subsystem/component/artefact conforms to your new pseudo-interface. As soon as your team size rises above one, it no longer becomes effective to make undocumented changes (i.e hacks) to get things done. To be really harsh, if your team size is one and you’re making undocumented hacks to get the job done then you’re hacking, not engineering.

This view is often at odds with a number of other viewpoints. From the viewpoint of a “results driven business” it is picky and anal at the expense of the project. From the viewpoint of a PM looking to close out a project phase it is inhibitive and frustrating and from the viewpoint of an Antisocial Engineer it is unnecessary. Unfortunately, the merit of this rigidity lies beyond the scope of these individuals and lies in that of the greater good. It might not be in the phase of your project, it might not even be within your company or your phase of the lifecycle but the merit lies there and others will thank you.

So dot your “I”s and cross your “T”s, do what you have said you’re going to do, stick to the script and document your work. Be a “Social Engineer’ and slowly, together, we will make the world (of engineering!) a better place.

Finding good text – The Angry Architect

It’s often difficult to find good resources as an architect. Most come from a specific viewpoint (Zachman evangelist, TOGAF evangelist, Microsoft employee…. 😉 ) or are very specific in their implementation of architecture development (often heavily biased towards software, or hardware microprossor applications). The reviews on Amazon are often a good indication but even finding good texts can be a hassle.

The Angry Architect is something I stumbled upon with the good ‘ol GOOG. It provides quick and concise (often opinionated) reviews of texts that might be of use to the system, software or enterprise architect.

Whatever your domain, have a look at “The Angry Architect”. Thoughts and opinions welcome.

The Angry Architect

New methods for developing systems and enterprise architectures.

In this post I describe some of my thoughts about the parallels between systems engineering and software development. It’s a discussion piece, meant to help start and inform debate. Comments are very welcome.

New methods for developing systems and enterprise architectures.

Matthew Lindsay BEng Hons MIET AMIMechE



This discussion paper examines the parallels  between the systems engineering discipline of architecture development and similar design aids in other engineering disciplines. It suggests that architecture development is more closely related to software engineering practices than it is to the development of mechanical artefacts. The paper then proposes the use of the Source Code Control System (SCCS) paradigm as of benefit to architecture development for architecture configuration control as well as for capturing Design Rational (DR). The paper then proposes that the software engineering method of Test Driven Development (TDD) could be applied to architecture development to bring increased focus for architecting activities (and hence efficiency increases) as well as providing a better method of architecture maturity assessment. Finally the paper proposes the quantification of architectural entities in a similar manner to “Source Lines of Code” (SLOC) within the software engineering domain to help estimation of cost for future architectural work.

The use of architectures for capturing information about system design is nothing new and indeed has been common practice for many organisations since the last 1980s [1]. Whilst there have been a great many developments, particularly in the use of Architecture Frameworks (AFs) to formalise and structure architecture development, many big issues still remain [2]. The issue of maturity assessment of architecture artefacts, the difficulties surrounding configuration control of highly dynamic, linked, parametric entities and the ongoing debates surrounding standardisation and reuse of architecture information all remain to be conclusively solved. Many of these problems are unique to architecture development but some of these issues have been encountered elsewhere, in other specialisms of Engineering.

Architecting as a practice sits quite cleanly in the systems engineering domain, principally because it touches on so many other disciplines. Most often, the architect will find themselves interacting with software engineers, hardware engineers, mechanical engineers, Subject Matter Experts (SMEs) as well as the usual project personnel (project managers, finance etc). When viewed from the perspective of engineering as a whole, its relatively recent emergence as a discipline and its tangential association with other fields in engineering, it might be assumed that it would have inherited operating procedures from those disciplines that surround it. Indeed, by its very nature, architecture should aggregate collective knowledge and provide value above and beyond the component entities. Unfortunately this doesn’t seem to be the case and many of the more recent practices, now commonplace in other fields have been overlooked, often without a suitable alternative.

Initial inspection would not offer any immediate parallels between developing architecture or developing, for example, a mechanical object, or a piece of code yet there are clearly equivalent themes that run through both and techniques that can be read across. Often the dilemma is in finding an equivalent paradigm for this mapping.

Architecture development is a highly dynamic activity, yet, requires the rigorous traceability of a production design akin to that of a design drawing for an automotive component or aerospace artefact. This is particularly true in high integrity applications such as defence or finance in which it is often used. In the mechanical engineering domain, this is managed (in the digital world) through the configuration control of digital assemblies with lockable parametric associations and access control to artefact parts. In previous years however, this would have been controlled through the physical “Drawing Office” and signed off by the Chief Engineer. In this process it is often slow and laborious to effect changes; often having to raise a Change Request and carry out impact analysis on the associated artefacts and ensuing analysis. Software development typically works on tighter design cycles and the relationship between discrete entities of code are better understood and often specified through rigorous interface definition specifications. Software engineers do not have to account for a legacy of paper based systems and poorly aligned toolsets. Even new mechanical “integrated CAE design suites” often lack the advanced CFD, FEA, DFM/A, costing analysis or other specialist tools required meaning that a longer “round trip” is required in the waterfall Engineering design approach. This is less of an issue when isolated to the mechanical world because typically the design/build cost ratio is skewed largely towards build and thus long round trips in the design cycle are still comparatively small (and cheap) compared to manufacture. This is not true of the software lifecycle where “build” is often in the order of minutes or hours whereas “design” is orders of magnitude larger [3]. Given this perspective, where should the systems engineering approach to architecture best align itself? Is developing an architecture more akin to developing software, or to developing a physical artefact? One might argue neither, yet the proposition exists that the rapid development cycles in software engineering is well aligned to the tight iterations of architecture development and that so many architectures are never physically instantiated that the design to build ratio is skewed in a similar manner to software development. Given this, it would be prudent to examine software best practice for a given scenario in architecture development. Take for instance Source Code Control System (SCCS) such as CVS, SVN or GIT. In this paradigm, local versions of the design (code) are stored in a repository and “checked in” to the primary model. Indeed this primary model could be many levels deep but most often, code is checked into the “whole entity”. When code is check in, it is validated against the build, deconflicted against new changes, the comments (or potentially “Design Rationale” (DR)) [4] captured and traceability maintained. Most also offer the options to “Branch”, “Merge” and “Fork” trunk builds to experiment and adapt the design to suite specific purposes or examine potential options in much the same way that an architect might build “as is”, “to be” and design proposition models,

The SCCS paradigm could be well harnessed in the development of an architecture. A local version of the model and appropriate views would be edited and changed entities “checked back in”, whilst simultaneously capturing at source, the associated DR for the changes and deconflicting it against the construct that already exists. Indeed, the action of identifying the affected views and entities (and entity relationships) upon “check in” would immediately flag up the effect of the changes and launch the analysis and deconfliction process. This could be coupled with a powerful “locking” mechanism on entities, relationships and views the to ensure integrity is maintained.

The power of SCCS might mot be the only principle that could be harnessed from the software engineering discipline. The issues related to maturity assessment of architectures are often ignored and dismissed as irrelevant on the assumption that the Architecture shall only be used as a discussion and analysis tool. Whilst it is true that an architecture tends not to be “executed” or “instantiated” in the literal manner (as code can be “compiled” or a design can be “manufactured”),  its maturity is no less important, especially in architecture centric projects in which the information model contained might be the central location of the “Design”. Current approaches to maturity assessment tend to focus on either a qualitative approach such as the “Enterprise Scorecard” or “Architecture Tradeoff Analysis Method (ATAM) [5,6] or qualitative analysis of specific architecture products. Neither of these approaches are universally suitable for iterative architecture development and indeed the subjective nature of arbitrary architecture product assessment could be misleading when assessments are aggregated upwards rather than downwards (bottom up assessment of product maturity as opposed to top down assessment of the required products).

In recent years, one successful approach to software development has been that of “Test Driven Design” (TDD). In this methodology  unit tests are written before any solution is attempted and the developer writes just enough code to be able to pass the test. On initial impressions, this methodology is often resisted because it requires a lot of “up front” effort before any problem solving can be undertaken. Once correctly implemented however, the methodology provides considerable benefits for software integrity, programmer efficiency as well as maturity assessment. The test harness serves as both a specification and an assessment framework. This paradigm could be well translated into the architectural domain. If the architecture team wrote “unit tests” for the architecture (i.e. decompose the architecture purpose into granulated questions that must be answered by individual or related products) then the purpose of architecture work would be well bounded into meeting that specification rather than “documenting the world”. Running the test harness each time updates are implemented would also serve as a trigger to alert architects when newly committed changes either break the integrity of the model or invalidate previously passed tests.

The third proposition for “methodology pull-through” is to relate the common practice of relating “Source Lines of Code” (SLOC) within software engineering to the quantification of architectural entities, relationships and views for the purposes of estimation. Whilst it certainly wouldn’t provide a de facto solution for estimating the cost of future architectural work, similar metrics could provide tangible estimation methods to project managers estimating for architecture centric projects.

This paper has discussed the parallels between the systems engineering based practice of architecting and the established practices in the Software Engineering discipline. It has proposed the value of the use of a “Source Code Control System” (SCCS) for the architectures as a way to control architecture development in terms of integrity, configuration control, revision control and capture of design rationale. In addition, this paper has discussed the potential benefits of “Test Driven Design” (TDD) for architecture development in order to help architects maintain architecture integrity, focus architectural effort better measure architecture maturity. Finally the paper proposes the quantification of architectural products as an analogous proposition to the measurement of “Source Lines of Code” in order to offer a tangible metric for the estimation of future architectural effort.



[1] Zachman, J.A. “A Framework for Information Systems Architecture.” IBM Systems Journal, Volume 26, Number 3, 1987.

[2]  Sessions, R “A Comparison of the Top Four Enterprise-Architecture Methodologies”. ObjectWatch, Inc, http://msdn.microsoft.com/en-us/library/bb466232.aspx

[3] Reeves, J W “Code as Design: Three Essays by Jack Reeves”, 1992, 2005 Developer.* Magazine http://www.developerdotstar.com/mag/articles/reeves_design_main.html

[4] Ali Babar, M and Gorton, G “A tool for Managing Software Architecture Knowledge” IEEE Computer Science, Second workshop on sharing and reusing architectural knowledge, rationale and design intent. 2007, IEEE.

[5] Schekkerman J, “Enterprise Architecture Scorecard” version 2.1, Institute for Enterprise Architecture Development http://www.enterprise-architecture.info

[6] Bass, L and Kazman, R “Architecture-Based Development”, April 1999, Carnegie Mellon Software Engineering Institute, CMSU/SEI-99-TR-007, ESC-TR-99-007