# The Chief Marketing Officer and the CDO – A Modern Fable

This Fox has a longing for grapes:
He jumps, but the bunch still escapes.
So he goes away sour;
And, ’tis said, to this hour
Declares that he’s no taste for grapes.

— W.J.Linton (after Aesop)

 Note: Not all of the organisations I have worked with or for have had a C-level Executive accountable primarily for Marketing. Where they have, I have normally found the people holding these roles to be better informed about data matters than their peers. I have always found it easy and enjoyable to collaborate with such people. The same goes in general for Marketing Managers. This article is not about Marketing professionals, it is about poorly researched journalism.

Prelude…

I recently came across an article in Marketing Week with the clickbait-worthy headline of Why the rise of the chief data officer will be short-lived (their choice of capitalisation). The subhead continues in the same vein:

Chief data officers (ditto) are becoming increasingly common, but for a data strategy to work their appointments can only ever be a temporary fix.

Intrigued, I felt I had to avail myself of the wisdom and domain expertise contained in the article (the clickbait worked of course). The first few paragraphs reveal the actual motivation. The piece is a reaction [1] to the most senior Marketing person at easyJet being moved out of his role, which is being abolished, and – as part of the same reorganisation – a Chief Data Officer (CDO) being appointed. Now the first thing to say, based on the article’s introductory comments, is that easyJet did not have a Chief Marketing Officer. The role that was abolished was instead Chief Commercial Officer, so there was no one charged full-time with Marketing anyway. The Marketing responsibilities previously supported part-time by the CCO have now been spread among other executives.

The next part of the article covers the views of a Marketing Week columnist (pause for irony) before moving on to arrangements for the management of data matters in three UK-based organisations:

• Camelot – who run the UK National Lottery

• Mumsnet – which is a web-site for UK parents

• Flubit – a growing on-line marketplace aiming to compete with Amazon

The first two of these have CDOs (albeit with one doing the role alongside other responsibilities). Both of these people:

[…] come at data as people with backgrounds in its use in marketing

Flubit does not have a CDO, which is used as supporting evidence for the superfluous nature of the role [2].

Suffice it to say that a straw poll consisting of the handful of organisations that the journalist was able to get a comment from is not the most robust of approaches [3]. Most of the time, the article does nothing more than to reflect the continuing confusion about whether or not organisations need CDOs and – assuming that they do – what their remit should be and who they should report to [4].

But then, without it has to be said much supporting evidence, the piece goes on to add that:

Most [CDOs – they would probably style it “Cdos”] are brought in to instill a data strategy across the business; once that is done their role should no longer be needed.

Now as a Group Theoretician, I am a great fan of symmetry. Symmetry relates to properties that remain invariant when something else is changed. Archetypally, an equilateral triangle is still an equilateral triangle when rotated by 120° [5]. More concretely, the laws of motion work just fine if we wind the clock forward 10 seconds (which incidentally leads to the principle of conservation of energy [6]).

Let’s assume that the Marketing Week assertion is true. I claim therefore that it must be still be true under the symmetry of changing the C-level role. This would mean that the following also has to be true:

Most [Chief marketing officers] are brought in to instill a marketing strategy across the business; once that is done their role should no longer be needed.

Now maybe this statement is indeed true. However, I can’t really see the guys and gals at Marketing Week agreeing with this. So maybe it’s false instead. Then – employing reductio ad absurdum – the initial statement is also false [7].

If you don’t work in Marketing, then maybe a further transformation will convince you:

Most [Chief financial officers] are brought in to instill a finance strategy across the business; once that is done their role should no longer be needed.

I could go on, but this is already becoming as tedious to write as it was to read the original Marketing Week claim. The closing sentence of the article is probably its most revealing and informative:

[…] marketers must make sure they are leading [the data] agenda, or someone else will do it for them.

I will leave readers to draw their own conclusions on the merits of this piece and move on to other thoughts that reading it spurred in me.

…and Fugue

Sometimes buried in the strangest of places you can find something of value, even if the value is different to the intentions of the person who buried it. Around some of the CDO forums that I attend [8] there is occasionally talk about just the type of issue that Marketing Week raises. An historical role often comes up in these discussions is that of Chief Electrification Officer [9]. This supposedly was an Executive role in organisations as the 19th Century turned into the 20th and electricity grids began to be created. The person ostensibly filling this role would be responsible for shepherding the organisation’s transition from earlier forms of power (e.g. steam) to the new-fangled streams of electrons. Of course this role would be very important until the transition was completed, after that redundancy surely beckoned.

Well to my way of thinking, there are a couple of problems here. The first one of these is alluded to by my choice of the words “supposedly” and “ostensibly” above. I am not entirely sure, based on my initial research [10], that this role ever actually existed. All the references I can find to it are modern pieces comparing it to the CDO role, so perhaps it is apochryphal.

The second is somewhat related. Electrification was an engineering problem, indeed it the [US] National Academy of Engineering called it “the greatest engineering achievement of the 20th Century”. Surely the people tackling this would be engineers, potentially led by a Chief Engineer. Did the completion of electrification mean that there was no longer a need for engineers, or did they simply move on to the next engineering problem [11]?

Extending this analogy, I think that Chief Data Officers are more like Chief Engineers than Chief Electrification Officers, assuming that the latter even exists. Why the confusion? Well I think part of it is because, over the last decade and a bit, organisations have been conditioned to believe the one dimensional perspective that everything is a programme or a project [12]. I am less sure that this applies 100% to the CDO role.

It may well be that one thing that a CDO needs to get going is a data transformation programme. This may purely be focused on cultural aspects of how an organisation records, shares and otherwise uses data. It may be to build a new (or a first) Data Architecture. It may be to remediate issues with an existing Data Architecture. It may be to introduce or expand Data Governance. It may be to improve Data Quality. Or (and, in my experience, this is often the most likely) a combination of all these five, plus other work, such as rapid tactical or interim deliveries. However, there is also a large element of data-centric work which is not project-based and instead falls into the category often described as “business as usual” (I loathe this term – I think that Data Operations & Technology is preferable). A handful of examples are as follows (this is not meant to be an exhaustive list) [13]:

1. Addressing architectural debt that results from neglect of a Data Assets or the frequently deleterious impact of improperly governed change portfolios [14]. This is often a series of small to medium-sized changes, rather than a project with a discrete scope and start and end dates.

2. More positively, engaging proactively in the change process in an attempt to act as a steward of Data Assets.

3. Establishing a regular Data Audit.

4. Regular Data Management activities.

5. Providing tailored Analytics to help understand some unscheduled or unexpected event.

6. Establishment of a data “SWAT team” to respond to urgent architecture, quality or reporting needs.

7. Running a Data Governance committee and related activities.

8. Creating and managing a Data Science capability.

9. Providing help and advice to those struggling to use Data facilities.

10. Responding to new Data regulations.

11. Creating and maintaining a target operating model for Data and is use.

12. Supporting Data Services to aid systems integration.

13. Production of regular reports and refreshing self-serve Data Repositories.

14. Testing and re-testing of Data facilities subject to change or change in source Data.

15. Providing training in the use of Data facilities or the importance of getting Data right-first-time.

The above all point to the need for an ongoing Data Function to meet these needs (and to form the core resources of any data programme / project work). I describe such a function in my series about The Anatomy of a Data Function.

There are of course many other such examples, but instead of cataloguing each of them, let’s return to what Marketing Week describe as the central responsibility of a CDO, to formulate a Data Strategy. Surely this is a one-off activity, right?

Well is the Marketing strategy set once and then never changed? If there is some material shift in the overall Business strategy, might the Marketing strategy change as a result? What would be the impact on an existing Marketing strategy of insight showing that this was being less than effective; might this lead to the development of a new Marketing strategy? Would the Marketing strategy need to be revised to cater for new products and services, or new segments and territories? What would be the impact on the Marketing strategy of an acquisition or divestment?

As anyone who has spent significant time in the strategy arena will tell you, it is a fluid area. Things are never set in stone and strategies may need to be significantly revised or indeed abandoned and replaced with something entirely new as dictated by events. Strategy is not a fire and forget exercise, not if you want it to be relevant to your business today, as opposed to a year ago. Specifically with Data Strategy (as I explain in Building Momentum – How to begin becoming a Data-driven Organisation), I would recommend keeping it rather broad brush at the begining of its development, allowing it to be adpated based on feedback from initial interim work and thus ensuring it better meets business needs.

So expecting that a Data Strategy (or any other type of strategy) to be done and dusted, with the key strategist dispensed with, is probably rather naive.

Coda

It would be really nice to think that sorting out their Data problems and seizing their Data opportunities are things that organisations can do once and then forget about. With twenty years experience of helping organisations to become more Data-centric, often with technical matters firmly in the background, I have to disabuse people of this all too frequent misconception. To adapt the National Canine Defence League’s [15 long-lived slogan from 1978:

A Chief Data Officer is for life, not just for Christmas.

With that out of the way, I’m off to write a well-informed and insightful article about how Marketing Departments should go about their business. Wish me luck!

Notes

From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases

# Convergent Evolution

No this article has not escaped from my Maths & Science section, it is actually about data matters. But first of all, channeling Jennifer Aniston [1], “here comes the Science bit – concentrate”.

Shared Shapes

The Theory of Common Descent holds that any two organisms, extant or extinct, will have a common ancestor if you roll the clock back far enough. For example, each of fish, amphibians, reptiles and mammals had a common ancestor over 500 million years ago. As shown below, the current organism which is most like this common ancestor is the Lancelet [2].

To bring things closer to home, each of the Great Apes (Orangutans, Gorillas, Chimpanzees, Bonobos and Humans) had a common ancestor around 13 million years ago.

So far so simple. As one would expect, animals sharing a recent common ancestor would share many attributes with both it and each other.

Convergent Evolution refers to something else. It describes where two organisms independently evolve very similar attributes that were not features of their most recent common ancestor. Thus these features are not inherited, instead evolutionary pressure has led to the same attributes developing twice. An example is probably simpler to understand.

The image at the start of this article is of an Ichthyosaur (top) and Dolphin. It is striking how similar their body shapes are. They also share other characteristics such as live birth of young, tail first. The last Ichthyosaur died around 66 million years ago alongside many other archosaurs, notably the Dinosaurs [3]. Dolphins are happily still with us, but the first toothed whale (not a Dolphin, but probably an ancestor of them) appeared around 30 million years ago. The ancestors of the modern Bottlenose Dolphins appeared a mere 5 million years ago. Thus there is tremendous gap of time between the last Ichthyosaur and the proto-Dolphins. Ichthyosaurs are reptiles, they were covered in small scales [4]. Dolphins are mammals and covered in skin not massively different to our own. The most recent common ancestor of Ichthyosaurs and Dolphins probably lived around quarter of a billion years ago and looked like neither of them. So the shape and other attributes of Ichthyosaurs do not come from a common ancestor, they have developed independently (and millions of years apart) as adaptations to similar lifestyles as marine hunters. This is the essence of Convergent Evolution.

That was the Science, here comes the Technology…

A Brief Hydrology of Data Lakes

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following:

As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams. Even back then, these were used for activities such as Analytics, Dashboards, Statistical Modelling, Data Mining and Advanced Visualisation.

Overlapping with the above, from around 2012, I began to get involved in also designing and implementing Big Data Architectures; initially for narrow purposes and later Data Lakes spanning entire enterprises. Of course some architectures featured both paradigms as well.

One of the early promises of a Data Lake approach was that – once all relevant data had been ingested – this would be directly leveraged by Data Scientists to derive insight.

Over time, it became clear that it would be useful to also have some merged / conformed and cleansed data structures in the Data Lake. Once the output of Data Science began to be used to support business decisions, a need arose to consider how it could be audited and both data privacy and information security considerations also came to the fore.

Next, rather than just being the province of Data Scientists, there were moves to use Data Lakes to support general Data Discovery and even business Reporting and Analytics as well. This required additional investments in metadata.

The types of issues with Data Lake adoption that I highlighted in Draining the Swamp earlier this year also led to the advent of techniques such as Data Curation [6]. In parallel, concerns about expensive Data Science resource spending 80% of their time in Data Wrangling [7] led to the creation of a new role, that of Data Engineer. These people take on much of the heavy lifting of consolidating, fixing and enriching datasets, allowing the Data Scientists to focus on Statistical Analysis, Data Mining and Machine Learning.

All of which leads to a modified Big Data / Data Lake architecture, embodying people and processes as well as technology and looking something like the exhibit above.

This is where the observant reader will see the concept of Convergent Evolution playing out in the data arena as well as the Natural World.

In Closing

Lest it be thought that I am saying that Data Warehouses belong to a bygone era, it is probably worth noting that the archosaurs, Ichthyosaurs included, dominated the Earth for orders of magnitude longer that the mammals and were only dethroned by an asymmetric external shock, not any flaw their own finely honed characteristics.

Also, to be crystal clear, much as while there are similarities between Ichthyosaurs and Dolphins there are also clear differences, the same applies to Data Warehouse and Data Lake architectures. When you get into the details, differences between Data Lakes and Data Warehouses do emerge; there are capabilities that each has that are not features of the other. What is undoubtedly true however is that the same procedural and operational considerations that played a part in making some Warehouses seem unwieldy and unresponsive are also beginning to have the same impact on Data Lakes.

If you are in the business of turning raw data into actionable information, then there are inevitably considerations that will apply to any technological solution. The key lesson is that shape of your architecture is going to be pretty similar, regardless of the technical underpinnings.

Notes

 [1] The two of us are constantly mistaken for one another. [2] To be clear the common ancestor was not a Lancelet, rather Lancelets sit on the branch closest to this common ancestor. [3] Ichthyosaurs are not Dinosaurs, but a different branch of ancient reptiles. [4] This is actually a matter of debate in paleontological circles, but recent evidence suggests small scales. [5] See: [6] A term that is unaccountably missing from The Data & Analytics Dictionary – something to add to the next release. UPDATE: Now remedied here. [7] Ditto. UPDATE: Now remedied here

From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases

# Building Momentum – How to begin becoming a Data-driven Organisation

Introduction

It is hard to find an organisation that does not aspire to being data-driven these days. While there is undoubtedly an element of me-tooism about some of these statements (or a fear of competitors / new entrants who may use their data better, gaining a competitive advantage), often there is a clear case for the better leverage of data assets. This may be to do with the stand-alone benefits of such an approach (enhanced understanding of customers, competitors, products / services etc. [1]), or as a keystone supporting a broader digital transformation.

However, in my experience, many organisations have much less mature ideas about how to achieve their data goals than they do about setting them. Given the lack of executive experience in data matters [2], it is not atypical that one of the large strategy consultants is engaged to shape a data strategy; one of the large management consultants is engaged to turn this into something executable and maybe to select some suitable technologies; and one of the large systems integrators (or increasingly off-shore organisations migrating up the food chain) is engaged to do the work, which by this stage normally relates to building technology capabilities, implementing a new architecture or some other technology-focussed programme.

Even if each of these partners does a great job – which one would hope they do at their price points – a few things invariably get lost along the way. These include:

1. A data strategy that is closely coupled to the organisation’s actual needs rather than something more general.

While there are undoubtedly benefits in adopting best practice for an industry, there is also something to be said for a more tailored approach, tied to business imperatives and which may have the possibility to define the new best practice. In some areas of business, it makes sense to take the tried and tested approach, to be a part of the herd. In others – and data is in my opinion one of these – taking a more innovative and distinctive path is more likely to lead to success.

2. Connective tissue between strategy and execution.

The distinctions between the three types of organisations I cite above are becoming more blurry (not least as each seeks to develop new revenue streams). This can lead to the strategy consultants developing plans, which get ripped up by the management consultants; the management consultants revisiting the initial strategy; the systems integrators / off-shorers replanning, or opening up technical and architecture discussions again. Of course this means the client paying at least twice for this type of work. What also disappears is the type of accountability that comes when the same people are responsible for developing a strategy, turning this into a practical plan and then executing this [3].

3. Focus on the cultural aspects of becoming more data-driven.

This is both one of the most important factors that determines success or failure [4] and something that – frankly because it is not easy to do – often falls by the wayside. By the time that the third external firm has been on-boarded, the name of the game is generally building something (e.g. a Data Lake, or an analytics platform) rather than the more human questions of who will use this, in what way, to achieve which business objectives.

Of course a way to address the above is to allocate some experienced people (internal or external, ideally probably a blend) who stay the course from development of data strategy through fleshing this out to execution and who – importantly – can also take a lead role in driving the necessary cultural change. It also makes sense to think about engaging organisations who are small enough to tailor their approach to your needs and who will not force a “cookie cutter” approach. I have written extensively about how – with the benefit of such people on board – to run such a data transformation programme [5]. Here I am going to focus on just one phase of such a programme and often the most important one; getting going and building momentum.

A Third Way

There are a couple of schools of thought here:

1. Focus on laying solid data foundations and thus build data capabilities that are robust and will stand the test of time.

2. Focus on delivering something ASAP in the data arena, which will build the case for further investment.

There are points in favour of both approaches and criticisms that can be made of each as well. For example, while the first approach will be necessary at some point (and indeed at a relatively early one) in order to sustain a transformation to a data-driven organisation, it obviously takes time and effort. Exclusive focus on this area can use up money, political capital and try the patience of sponsors. Few business initiatives will be funded for years if they do not begin to have at least some return relatively soon. This remains the case even if the benefits down the line are potentially great.

Equally, the second approach can seem very productive at first, but will generally end up trying to make a silk purse out of a sow’s ear [6]. Inevitably, without improvements to the underlying data landscape, limitations in the type of useful analytics that be carried out will be reached; sometimes sooner that might be thought. While I don’t generally refer to religious topics on this blog [7], the Parable of the Sower is apposite here. Focussing on delivering analytics without attending to the broader data landscape is indeed like the seed that fell on stony ground. The practice yields results that spring up, only to wilt when the sun gets hot, given that they have no real roots [8].

So what to do? Well, there is a Third Way. This involves blending both approaches. I tend to think of this in the following way:

First of all, this is a cartoon, it is not intended to indicate actual percentages, just to illustrate a general trend. In real life, it is likely that you will cycle round multiple times and indeed have different parallel work-streams at different stages. The general points I am trying to convey with this diagram are:

1. At the beginning of a data transformation programme, there should probably be more emphasis on interim delivery and tactical changes. However, imoportantly, there is never zero strategic work. As things progress, the emphasis should swing more to strategic, long-term work. But again, even in a mature programme, there is never zero tactical work. There can also of course be several iterations of such shifts in approach.

2. Interim and tactical steps should relate to not just analytics, but also to making point fixes to the data landscape where possible. It is also important to kick off diagnostic work, which will establish how bad things are and also suggest areas which could be attacked sooner rather than later; this too can initially be done on a tactical basis and then made more robust later. In general, if you consider the span of strategic data work, it makes sense to kick off cut-down (and maybe drastically cut-down) versions of many activities early on.

3. Importantly, the tactical and strategic work-streams should not be hermetically sealed. What you actually want is healthy interplay. Building some early, “quick and dirty” analytics may highlight areas that should be covered by a data audit, or where there are obvious weaknesses in a data architecture. Any data assets that are built on a more strategic basis should also be leveraged by tactical work, improving its utility and probably increasing its lifespan.

Interconnected Activities

At the beginning of this article, I present a diagram (repeated below) which covers three types of initial data activities, the sort of work that – if executed competently – can begin to generate momentum for a data programme. The exhibit also references Data Strategy.

Let’s look at each of these four things in some more detail:

1. Analytic Point Solutions

Where data has historically been locked up in either hard-to-use repositories or in source systems themselves, liberating even a bit of it can be very helpful. This does not have to be with snazzy tools (unless you want to showcase the art of the possible). An anecdote might help to explain.

At one organisation, they had existing reporting that was actually not horrendous, but it was hard to access, hard to parameterise and hard to do follow-on analysis on. I took it upon myself to run 30 plus reports on a weekly and monthly basis, download the contents to Excel, front these with some basic graphs and make these all available on an intranet. This meant that people from Country A or Department B could go straight to their figures rather than having to run fiddly reports. It also meant that they had an immediate visual overview – including some comparisons to prior periods and trends over time (which were not available in the original reports). Importantly, they also got a basic pivot table, which they could use to further examine what was going on. These simple steps (if a bit laborious for me) had a massive impact. I later replaced the Excel with pages I wrote in a new web-reporting tool we built in house. Ultimately, my team moved these to our strategic Analytics platform.

This shows how point solutions can be very valuable and also morph into more strategic facilities over time.

2. Data Process Improvements

Data issues may be to do with a range of problems from poor validation in systems, to bad data integration, but immature data processes and insufficient education for data entry staff are often key conributors to overall problems. Identifying such issues and quantifying their impact should be the province of a Data Audit, which is something I would recommend considering early on in a data programme. Once more this can be basic at first, considering just superficial issues, and then expand over time.

While fixing some data process problems and making a stepped change in data quality will both probably take time an effort, it may be possible to identify and target some narrower areas in which progress can be made quite quickly. It may be that one key attribute necessary for analysis is poorly entered and validated. Some good communications around this problem can help, better guidance for people entering it is also useful and some “quick and dirty” reporting highlighting problems and – hopefully – tracking improvement can make a difference quicker than you might expect [9].

3. Data Architecture Enhancements

Improving a Data Architecture sounds like a multi-year task and indeed it can often be just that. However, it may be that there are some areas where judicious application of limited resource and funds can make a difference early on. A team engaged in a data programme should seek out such opportunities and expect to devote time and attention to them in parallel with other work. Architectural improvements would be best coordinated with data process improvements where feasible.

An example might be providing a web-based tool to look up valid codes for entry into a system. Of course it would be a lot better to embed this functionality in the system itself, but it may take many months to include this in a change schedule whereas the tool could be made available quickly. I have had some success with extending such a tool to allow users to build their own hierarchies, which can then be reflected in either point analytics solutions or more strategic offerings. It may be possible to later offer the tool’s functionality via web-services allowing it to be integrated into more than one system.

4. Data Strategy

I have written extensively about Data Strategy on this site [10]. What I wanted to cover here is the interplay between Data Strategy and some of the other areas I have just covered. It might be thought that Data Strategy is both carved on tablets of stone [11] and stands in splendid and theoretical isolation, but this should not ever be the case. The development of a Data Strategy should of course be informed by a situational analysis and a vision of “what good looks like” for an organisation. However, both of these things can be shaped by early tactical work. Taking cues from initial tactical work should lead to a more pragmatic strategy, more aligned to business realities.

Work in each of the three areas itemised above can play an important role in shaping a Data Strategy and – as the Data Strategy matures – it can obviously guide interim work as well. This should be an iterative process with lots of feedback.

Closing Thoughts

I have captured the essence of these thoughts in the diagram above. The important things to take away are that in order to generate momentum, you need to start to do some stuff; to extend the physical metaphor, you have to start pushing. However, momentum is a vector quantity (it has a direction as well as a magnitude [12]) and building momentum is not a lot of use unless it is in the general direction in which you want to move; so push with some care and judgement. It is also useful to realise that – so long as your broad direction is OK – you can make refinements to your direction as you pick up speed.

The above thoughts are based on my experience in a range of organisations and I am confident that they can be applied anywhere, making allowance for local cultures of course. Once momentum is established, it still needs to be maintained (or indeed increased), but I find that getting the ball moving in the first place often presents the greatest challenge. My hope is that the framework I present here can help data practitioners to get over this initial hurdle and begin to really make a difference in their organisations.

Notes

 [1] Way back in 2009, I wrote about the benefits of leveraging data to provide enhanced information. The article in question was tited Measuring the benefits of Business Intelligence. Everything I mention remains valid today in 2018. [2] See also: [3] If I many be allowed to blow my own trumpet for a moment, I have developed data / information strategies for eight organisations, turned seven of these into a costed / planned programme and executed at least the first few phases of six of these. I have always found being a consistent presence through these phases has been beneficial to the organisations I was helping, as well as helping to reduce duplication of work. [4] See my, now rather venerable, trilogy about cultural change in data / information programmes: together with the rather more recent: [5] See for example: [6] Dictionary.com offers a nice explanation of this phrase.. [7] I was raised a Catholic, but have been areligious for many years. [8] Much like $x^2+x+1=0$. For anyone interested, the two roots of this polynomial are clearly: $-\dfrac{1}{2}+\dfrac{\sqrt{3}}{2}\hspace{1mm}i\hspace{5mm}\text{and}\hspace{5mm}-\dfrac{1}{2}-\dfrac{\sqrt{3}}{2}\hspace{1mm}i$ neither of which is Real. [9] See my rather venerable article, Using BI to drive improvements in data quality, for a fuller treatment of this area. [10] For starters see: and also the Data Strategy segment of The Anatomy of a Data Function – Part I. [11] [12] See Glimpses of Symmetry, Chapter 15 – It’s Space Jim….

From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases

# Did GDPR highlight the robustness of your Data Architecture, the strength of your Data Governance and the fitness of your Data Strategy?

So GDPR Day is upon us – the sun still came up and the Earth is still spinning (these facts may be related of course). I hope that most GDPR teams and the Executives who have relied upon their work were able to go to bed last night secure in the knowledge that a good job had been done and that their organisations and customers were protected. Undoubtedly, in coming days, there will be some stories of breaches of the regulations, maybe some will be high-profile and the fines salutary, but it seems that most people have got over the line, albeit often by Herculean efforts and sometimes by the skins of their teeth.

Does it have to be like this?

A well-thought-out Data Architecture embodying a business-focussed Data Strategy and intertwined with the right Data Governance, should combine to make responding to things like GDPR relatively straightforward. Were they in your organisation?

If instead GDPR compliance was achieved in spite of your Data Architectures, Governance and Strategies, then I suspect you are in the majority. Indeed years of essentially narrow focus on GDPR will have consumed resources that might otherwise have gone towards embedding the control and leverage of data into the organisation’s DNA.

Maybe now is a time for reflection. Will your Data Strategy, Data Governance and Data Architecture help you to comply with the next set of data-related regulations (and it is inevitable that there will be more), or will they hinder you, as will have been the case for many with GDPR?

If you feel that the answer to this question is that there are significant problems with how your organisation approaches data, then maybe now is the time to grasp the nettle. Having helped many companies to both develop and execute successful Data Strategies, you could start by reading my trilogy on creating an Information / Data Strategy:

I’m also more than happy to discuss your data problems and opportunities either formally or informally, so feel free to get in touch.

From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases