The revised and expanded Data and Analytics Dictionary

The Data and Analytics Dictionary

Since its launch in August of this year, the peterjamesthomas.com Data and Analytics Dictionary has received a welcome amount of attention with various people on different social media platforms praising its usefulness, particularly as an introduction to the area. A number of people have made helpful suggestions for new entries or improvements to existing ones. I have also been rounding out the content with some more terms relating to each of Data Governance, Big Data and Data Warehousing. As a result, The Dictionary now has over 80 main entries (not including ones that simply refer the reader to another entry, such as Linear Regression, which redirects to Model).

The most recently added entries are as follows:

  1. Anomaly Detection
  2. Behavioural Analytics
  3. Complex Event Processing
  4. Data Discovery
  5. Data Ingestion
  6. Data Integration
  7. Data Migration
  8. Data Modelling
  9. Data Privacy
  10. Data Repository
  11. Data Virtualisation
  12. Deep Learning
  13. Flink
  14. Hive
  15. Information Security
  16. Metadata
  17. Multidimensional Approach
  18. Natural Language Processing (NLP)
  19. On-line Transaction Processing
  20. Operational Data Store (ODS)
  21. Pig
  22. Table
  23. Sentiment Analysis
  24. Text Analytics
  25. View

It is my intention to continue to revise this resource. Adding some more detail about Machine Learning and related areas is probably the next focus.

As ever, ideas for what to include next would be more than welcome (any suggestions used will also be acknowledged).
 


 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

 

A truth universally acknowledged…

£10 note

  “It is a truth universally acknowledged, that an organisation in possession of some data, must be in want of a Chief Data Officer”

— Growth and Governance, by Jane Austen (1813) [1]

 

I wrote about a theoretical job description for a Chief Data Officer back in November 2015 [2]. While I have been on “paternity leave” following the birth of our second daughter, a couple of genuine CDO job specs landed in my inbox. While unable to respond for the aforementioned reasons, I did leaf through the documents. Something immediately struck me; they were essentially wish-lists covering a number of data-related fields, rather than a description of what a CDO might actually do. Clearly I’m not going to cite the actual text here, but the following is representative of what appeared in both requirement lists:

CDO wishlist

Mandatory Requirements:

Highly Desirable Requirements:

  • PhD in Mathematics or a numerical science (with a strong record of highly-cited publications)
  • MBA from a top-tier Business School
  • TOGAF certification
  • PRINCE2 and Agile Practitioner
  • Invulnerability and X-ray vision [3]
  • Mastery of the lesser incantations and a cloak of invisibility [3]
  • High midi-chlorian reading [3]
  • Full, clean driving licence

Your common, all-garden CDO

The above list may have descended into farce towards the end, but I would argue that the problems started to occur much earlier. The above is not a description of what is required to be a successful CDO, it’s a description of a Swiss Army Knife. There is also the minor practical point that, out of a World population of around 7.5 billion, there may well be no one who ticks all the boxes [4].

Let’s make the fallacy of this type of job description clearer by considering what a simmilar approach would look like if applied to what is generally the most senior role in an organisation, the CEO. Whoever drafted the above list of requirements would probably characterise a CEO as follows:

  • The best salesperson in the organisation
  • The best accountant in the organisation
  • The best M&A person in the organisation
  • The best customer service operative in the organisation
  • The best facilities manager in the organisation
  • The best janitor in the organisation
  • The best purchasing clerk in the organisation
  • The best lawyer in the organisation
  • The best programmer in the organisation
  • The best marketer in the organisation
  • The best product developer in the organisation
  • The best HR person in the organisation, etc., etc., …

Of course a CEO needs to be none of the above, they need to be a superlative leader who is expert at running an organisation (even then, they may focus on plotting the way forward and leave the day to day running to others). For the avoidance of doubt, I am not saying that a CEO requires no domain knowledge and has no expertise, they would need both, however they don’t have to know every aspect of company operations better than the people who do it.

The same argument applies to CDOs. Domain knowledge probably should span most of what is in the job description (save for maybe the three items with footnotes), but knowledge is different to expertise. As CDOs don’t grow on trees, they will most likely be experts in one or a few of the areas cited, but not all of them. Successful CDOs will know enough to be able to talk to people in the areas where they are not experts. They will have to be competent at hiring experts in every area of a CDO’s purview. But they do not have to be able to do the job of every data-centric staff member better than the person could do themselves. Even if you could identify such a CDO, they would probably lose their best staff very quickly due to micromanagement.

Conducting the data orchestra

A CDO has to be a conductor of both the data function orchestra and of the use of data in the wider organisation. This is a talent in itself. An internationally renowned conductor may have previously been a violinist, but it is unlikely they were also a flautist and a percussionist. They do however need to be able to tell whether or not the second trumpeter is any good or not; this is not the same as being able to play the trumpet yourself of course. The conductor’s key skill is in managing the efforts of a large group of people to create a cohesive – and harmonious – whole.

The CDO is of course still a relatively new role in mainstream organisations [5]. Perhaps these job descriptions will become more realistic as the role becomes more familiar. It is to be hoped so, else many a search for a new CDO will end in disappointment.

Having twisted her text to my own purposes at the beginning of this article, I will leave the last words to Jane Austen:

  “A scheme of which every part promises delight, can never be successful; and general disappointment is only warded off by the defence of some little peculiar vexation.”

— Pride and Prejudice, by Jane Austen (1813)

 

 
Notes

 
[1]
 
Well if a production company can get away with Pride and Prejudice and Zombies, then I feel I am on reasonably solid ground here with this title.

I also seem to be riffing on JA rather a lot at present, I used Rationality and Reality as the title of one of the chapters in my [as yet unfinished] Mathematical book, Glimpses of Symmetry.

 
[2]
 
Wanted – Chief Data Officer.
 
[3]
 
Most readers will immediately spot the obvious mistake here. Of course all three of these requirements should be mandatory.
 
[4]
 
To take just one example, gaining a PhD in a numerical science, a track record of highly-cited papers and also obtaining an MBA would take most people at least a few weeks of effort. Is it likely that such a person would next focus on a PRINCE2 or TOGAF qualification?
 
[5]
 
I discuss some elements of the emerging consensus on what a CDO should do in: 5 Themes from a Chief Data Officer Forum and 5 More Themes from a Chief Data Officer Forum.

 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

 

The peterjamesthomas.com Data and Analytics Dictionary

The Data and Analytics Dictionary

I find myself frequently being asked questions around terminology in Data and Analytics and so thought that I would try to define some of the more commonly used phrases and words. My first attempt to do this can be viewed in a new page added to this site (this also appears in the site menu):

The Data and Analytics Dictionary

I plan to keep this up-to-date as the field continues to evolve.

I hope that my efforts to explain some concepts in my main area of specialism are both of interest and utility to readers. Any suggestions for new entries or comments on existing ones are more than welcome.
 

 

Do any technologies grow up or do they only come of age?

The 2016 Big Data Maturity Survey (by AtScale)

I must of course start by offering my apologies to that doyen of data experts, Stephen King, for mangling his words to suit the purposes of this article [1].

The AtScale Big Data Maturity Survey for 2016 came to my attention through a connection (see Disclosure below). The survey covers “responses from more than 2,550 Big Data professionals, across more than 1,400 companies and 77 countries” and builds on their 2015 survey.

I won’t use the word clickbait [2], but most of the time documents like this lead you straight to a form where you can add your contact details to the organisation’s marketing database. Indeed you, somewhat inevitably, have to pay the piper to read the full survey. However AtScale are to be commended for at least presenting some of the high-level findings before asking you for the full entry price.

These headlines appear in an article on their blog. I won’t cut and paste the entire text, but a few points that stood out for me included:

  1. Close to 70% [of respondents] have been using Big Data for more than a year (vs. 59% last year)
     
  2. More than 53% of respondents are using Cloud for their Big Data deployment today and 14% of respondents have all their Big Data in the Cloud
     
  3. Business Intelligence is [the] #1 workload for Big Data with 75% of respondents planning on using BI on Big Data
     
  4. Accessibility, Security and Governance have become the fastest growing areas of concern year-over-year, with Governance growing most at 21%
     
  5. Organizations who have deployed Spark [3] in production are 85% more likely to achieve value

Bullet 3 is perhaps notable as Big Data is often positioned – perhaps erroneously – as supporting analytics as opposed to “traditional BI” [4]. On the contrary, it appears that a lot of people are employing it in very “traditional” ways. On reflection this is hardly surprising as many organisations have as yet failed to get the best out of the last wave of information-related technology [5], let alone the current one.

However, perhaps the two most significant trends are the shift from on-premises Big Data to Cloud Big Data and the increased importance attached to Data Governance. The latter was perhaps more of a neglected area in the earlier and more free-wheeling era of Big Data. The rise in concerns about Big Data Governance is probably the single greatest pointer towards the increasing maturity of the area.

It will be interesting to see what the AtScale survey of 2017 has to say in 12 months.
 


 
Disclosure:

The contact in question is Bruno Aziza (@brunoaziza), AtScale’s Chief Marketing Officer. While I have no other connections with AtScale, Bruno and I did make the following video back in 2011 when both of us were at other companies.


 
Notes

 
[1]
 
Excerpted from The Gunslinger.
 
[2]
 
Oops!
 
[3]
 
Apache Hadoop – which has become almost synonymous with Big Data – has two elements, the Hadoop Distributed File Store (HDFS, the piece which deals with storage) and MapReduce (which does processing of data). Apache Spark was developed to improve upon the speed of the MapReduce approach where the same data is accessed many times, as can happen in some queries and algorithms. This is achieved in part by holding some or all of the data to be accessed in memory. Spark works with HDFS and also other distributed file systems, such as Apache Cassandra.
 
[4]
 
How phrases from the past come around again!
 
[5]
 
Some elements of the technology have changed, but the vast majority of the issues I covered in “Why Business Intelligence projects fail” hold as true today as they did back in 2009 when I wrote this piece.

 

 

Bumps in the Road

Bumps in the road

The above image appears in my updated [1] seminar deck Data Management, Analytics and People: An Eternal Golden Braid. It is featured on a slide titled “Why Data Management? – The negative case” [2]. So what was the point that I was so keen to make?

Well the whole slide looks like this…

Why Data Management? (Click to view a full-size version as a PDF in a new window).

…and the image on the left relates most directly to the last item of bulleted text on the right-hand side [3].
 
 
An Introductory Anecdote

Roadworks

Before getting into the meat of this article, an aside which may illuminate where I am coming from. I currently live in London, a city where I was born and to which I returned after a sojourn in Cambridge while my wife completed her PhD. Towards the end of my first period in London, we lived on a broad, but one-way road in West London. One day we received notification that the road was going to be resurfaced and moving our cars might be a useful thing to consider. The work was duly carried out and our road now had a deep black covering of fresh asphalt [4], criss-crossed by gleaming and well-defined dashed white lines demarking parking bays. Within what seemed like days, but was certainly no more than a few weeks, roadworks signs reappeared on our road, together with red and white fencing, a digger and a number of people with pneumatic drills [5] and shovels. If my memory serves me well, it was the local water company (Thames Water) who visited our road first.

The efforts of the Thames Water staff, while no doubt necessary and carried out professionally, rather spoiled our pristine road cover. I guess these things happen and coordination between local government, private firms and the sub-contractors that both employ cannot be easy [6]. However what was notable was that things did not stop with Thames Water. Over the next few months the same stretch of road was also dug up by both the Electricity and Gas utilities. There was a further set of roadworks on top of these, but my memory fails me on which organisation carried these out and for what purpose [7]; we are talking about events that occurred over eight years ago here.

More roadworks

The result of all this uncoordinated work was a previously pristine road surface now pock-marked by a series of new patches of asphalt, or maybe other materials; they certainly looked different and (as in the above photo) had different colours and grains. Several of these patches of new road covering overlapped each other; that is one hole redug sections previously excavated by earlier holes. Also the new patches of road surface were often either raised or depressed from the main run of asphalt, leading to a very uneven terrain. I have no idea how much it cost to repave the road in the first instance, but a few months of roadworks pretty much buried the repaving and led to a road whose surface was the opposite of smooth and consistent. I’d go so far as to say that the road was now in considerably worse condition than before the initial repaving. In any case, it could be argued that the money spent on the repaving was, for all intents and purposes, wasted.

After all this activity, our road was somewhat similar to the picture at the top of this article, but its state was much worse with more extensive patching and more overlapping layers. To this day I rather wish I had taken a photograph, which would also have saved me some money on stock photos!

I understand that each of the roadworks was in support of something that was probably desirable. For example, better sewerage, or maintenance to gas supplies which might otherwise have become dangerous. My assumption is that all of the work that followed on from the repaving needed to be done and that each was done at least as well as it had to be. Probably most of these works were completed on time and on budget. However, from the point of view of the road as a whole, the result of all these unconnected and uncoordinated works was a substantial deterioration in both its appearance and utility.
Lots of good can equal bad (for certain values of 'good')
In summary, the combination of a series of roadworks, each of which either needed to be done or led to an improvement in some area, resulted in the environment in which they were carried out becoming degraded and less fit-for-purpose. A series of things which could be viewed as beneficial in isolation were instead deleterious in aggregate. At this point, the issue that I wanted to highlight in the data world is probably swimming into focus for many readers.
 
 
The Entropy of a Data Asset exposed to Change tends to a Maximum [8]

Entropy

Returning to the slide I reproduce above, my assertion – which has been borne out during many years of observing the area – is that Change Programmes and Projects, if not subject to appropriately rigorous Data Governance, inevitably led to the degradation of data assets over time.

Here both my roadworks anecdote and the initial photograph illustrate the point that I am looking to make. Over the last decade or so, the delivery of technological change has evolved [9] to the point where many streams of parallel work are run independently of each other with each receiving very close management scrutiny in order to ensure delivery on-time and on-budget [10]. It should be recognised that some of this shift in modus operandi has been as a result of IT departments running projects that have spiralled out of control, or where delivery has been significantly delayed or compromised. The gimlet-like focus of Change on delivery “come Hell or High-water” represents the pendulum swinging to the other extreme.

Pendulum

What this shift in approach means in practice is that – as is often the case – when things go wrong or take longer than anticipated [11], areas of work are de-scoped to secure delivery dates. In my experience, 9 times out of 10 one of the things that gets thrown out is data-related work; be that not bothering to develop reporting on top of new systems, not integrating new data into existing repositories, not complying with data standards, or not implementing master data management.

As well as the danger of skipping necessary data related work, if some data-related work is actually undertaken, then corners may be cut to meet deadlines and budgets. It is not atypical for instance that a Change Programme, while adding their new capabilities to interfaces or ETL, compromises or overwrites existing functionality. This can mean that data-centric code is in a worse state after a Change Programme than before. My roadworks anecdote begins to feel all too apt a metaphor to employ.

Looking more broadly at Change Programmes, even without the curse of de-scopes, their focus is seldom data and the expertise of Change staff is not often in data matters. Because of this, such work can indeed seem to be analogous to continually digging up the same stretch of road for different purposes, combined with patching things up again in a manner that can sometimes be barely adequate. Extending our metaphor [12], the result of Change that is not controlled from a data point of view can be a landscape with lumps, bumps and pot-holes. Maybe the sewer was re-laid on time and to budget, but the road has been trashed in the process. Perhaps a new system was shoe-horned in to production, but rendered elements of an Analytical Repository useless in the process.

Data Governance (well actually Bank Governance, Data Governance involves less impressive facades)

Avoiding these calamities is the central role of Data Governance. What these examples also stress is that, rather than the dry, policy-based area that Data Governance is often assumed to be, it must be more dynamic and much more engaged in Change Portfolios. Such engagement should ideally be early and in a helpful manner, not late and in a policing role.

The analogy I have employed here also explains why leveraging existing Governance arrangements to add in a Data Governance dimension seldom works. This would be like asking the contractors engaged in roadworks to be extra careful to liaise with each other. This won’t work as there is no real incentive for such collaboration, the motivation of getting their piece of work done quickly and cheaply will trump other considerations. Instead some independent oversight is required. Like any good “regulator” this will work best if Data Governance professionals seek to be part of the process and focus on improving it. The alternative of simply pointing out problems after the fact adds much less business value.
 
 
And Finally

Sherlock

In A Study in Scarlet John Watson reads an article, which turns out to have been written by his illustrious co-lodger. A passage is as follows:

“From a drop of water,” said the writer, “a logician could infer the possibility of an Atlantic or a Niagara without having seen or heard of one or the other. So all life is a great chain, the nature of which is known whenever we are shown a single link of it.”

While I don’t claim to have the same acuity of mind as Conan-Doyle’s most famous creation, I can confirm that you can learn a lot about the need for Data Governance by simply closely observing the damage done by roadworks.
 


 Notes

 
[1]
 
I have updated my latest deck to use a different photo due to a dispute with the company I purchased the original photo from.
 
[2]
 
Which you may be glad to hear is followed directly by one titled “Why Data Management? – The positive case”.
 
[3]
 
It may be noted that I am going through a minimalist phase in my decks for public speaking. Indeed I did toy with having a deck consisting primarily of images before chickening out. Of course one benefit of being text-light is that you can focus on different elements and tell different stories for different audiences (see Presenting in Public).
 
[4]
 
Blacktop.
 
[5]
 
Jackhammers.
 
[6]
 
Indeed sometime in the late 1980s or early 1990s I was approached by one of the big consultancies about a job on a project to catalogue all proposed roadworks across London in an Oracle database. The objective of this was to better coordinate roadworks. I demurred and I believe that the project was unsuccessful, certainly by the evidence of what happened to our road.
 
[7]
 
It could well have been Thames Water again – the first time sewers, the second household water supply. It might have been British Telecom, but it probably wasn’t a cable company as they had been banned from excavations in Westminster after failing to make good after previous installations.
 
[8]
 
Rudolf Clausius in 1865, with reference to the Second Law of Thermodynamics.
 
[9]
 
As with the last time I used this word (see the notes section of Alphabet Soup) and also as applies with the phenomenon in the narual world, evolution implies change, but not necessarily always improvement.
 
[10]
 
Or perhaps more realistically to ensure that delays are minimised and cost overruns managed downwards.
 
[11]
 
Frequently it must be added because of either insufficient, or the wrong type of up-front analysis, or because a delivery timeframe was agreed based on some external factor rather than on what could practically be delivered in the time available. Oftentimes both factors are present and compound each other. The overall timetable is not based on any concrete understanding of what is to be done and analysis is either curtailed to meet timeframes, or – more insidiously – its findings are massaged to fit the desired milestones.
 
[12]
 
Hopefully not over-extending it.

 

 

The Chief Data Officer “Sweet Spot”

CDO "sweet spot"

I verbally “scribbled” something quite like the exhibit above recently in conversation with a longstanding professional associate. This was while we were discussing where the CDO role currently sat in some organisations and his or her span of responsibilities. We agreed that – at least in some cases – the role was defined sub-optimally with reference to the axes in my virtual diagram.

This discussion reminded me that I was overdue a piece commenting on November’s IRM(UK) CDO Executive Forum; the third in a sequence that I have covered in these pages [1], [2]. In previous CDO Exec Forum articles, I have focussed mainly on the content of the day’s discussions. Here I’m going to be more general and bring in themes from the parent event; IRM(UK) Enterprise Data / Business Intelligence 2016. However I will later return to a theme central to the Exec Forum itself; the one that is captured in the graphic at the head of this article.

As well as attending the CDO Forum, I was speaking at the umbrella event. The title of my talk was Data Management, Analytics, People: An Eternal Golden Braid [3].

Data Management, Analytics, People: An Eternal Golden Braid

The real book, whose title I had plagiarised, is Gödel, Escher and Bach, an Eternal Golden braid, by Pulitzer-winning American Author and doyen of 1970s pop-science books, Douglas R. Hofstadter [4]. This book, which I read in my youth, explores concepts in consciousness, both organic and machine-based, and their relation to recursion and self-reference. The author argued that these themes were major elements of the work of each of Austrian Mathematician Kurt Gödel (best known for his two incompleteness theorems), Dutch graphic artist Maurits Cornelis Escher (whose almost plausible, but nevertheless impossible buildings and constantly metamorphosing shapes adorn both art galleries and college dorms alike) and German composer Johann Sebastian Bach (revered for both the beauty and mathematical elegance of his pieces, particularly those for keyboard instruments). In an age where Machine Learning and other Artificial Intelligence techniques are moving into the mainstream – or at least on to our Smartphones – I’d recommend this book to anyone who has not had the pleasure of reading it.

In my talk, I didn’t get into anything as metaphysical as Hofstadter’s essays that intertwine patterns in Mathematics, Art and Music, but maybe some of the spirit of his book rubbed off on my much lesser musings. In any case, I felt that my session was well-received and one particular piece of post-presentation validation had me feeling rather like these guys for the rest of the day:

The cast and author / director of Serenity at Comic Con

What happened was that a longstanding internet contact [5] sought me out and commended me on both my talk and the prescience of my July 2009 article, Is the time ripe for appointing a Chief Business Intelligence Officer? He argued convincingly that this foreshadowed the emergence of the Chief Data Officer. While it is an inconvenient truth that Visa International had a CDO eight years earlier than my article appeared, on re-reading it, I was forced to acknowledge that there was some truth in his assertion.

To return to the matter in hand, one point that I made during my talk was that Analytics and Data Management are two sides of the same coin and that both benefit from being part of the same unitary management structure. By this I mean each area reporting into an Executive who has a strong grasp of what they do, rather than to a general manager. More specifically, I would see Data Compliance work and Data Synthesis work each being the responsibility of a CDO who has experience in both areas.

It may seem that crafting and implementing data policies is a million miles from data visualisation and machine learning, but to anyone with a background in the field, they are much more strongly related. Indeed, if managed well (which is often the main issue), they should be mutually reinforcing. Thus an insightful model can support business decision-making, but its authors would generally be well-advised to point out any areas in which their work could be improved by better data quality. Efforts to achieve the latter then both improve the usefulness of the model and help make the case for further work on data remediation; a virtuous circle.

CDO "sweet spot" vertical axis

Here we get back to the vertical axis in my initial diagram. In many organisations, the CDO can find him or herself at the extremities. Particularly in Financial Services, an industry which has been exposed to more new regulation than many in recent years, it is not unusual for CDOs to have a Risk or Compliance background. While this is very helpful in areas such as Governance, it is less of an asset when looking to leverage data to drive commercial advantage.

Symmetrically, if a rookie CDO was a Data Scientist who then progressed to running teams of Data Scientists, they will have a wealth of detailed knowledge to fall back on when looking to guide business decisions, but less familiarity with the – sometimes apparently thankless, and generally very arduous – task of sorting out problems in data landscapes.

Despite this, it is not uncommon to see CDOs who have a background in just one of these two complementary areas. If this is the case, then the analytics expert will have to learn bureaucratic and programme skills as quickly as they can and the governance guru will need to expand their horizons to understand the basics of statistical modelling and the presentation of information in easily digestible formats. It is probably fair to say that the journey to the centre is somewhat perilous when either extremity is the starting point.

CDO "sweet spot" vertical axis

Let’s now think about the second and horizontal axis. In some organisations, a newly appointed CDO will be freshly emerged from the ranks of IT (in some they may still report to the CIO, though this is becoming more of an anomaly with each passing year). As someone whose heritage is in IT (though also from very early on with a commercial dimension) I understand that there are benefits to such a career path, not least an in-depth understanding of at least some of the technologies employed, or that need to be employed. However a technology master who is also a business neophyte is unlikely to set the world alight as a newly-minted CDO. Such people will need to acquire new skills, but the learning curve is steep.

To consider the other extreme of this axis, it is undeniable that a CDO organisation will need to undertake both technical and technological work (or at least to guide this in other departments). Therefore, while an in-depth understanding of a business, its products, markets, customers and competitors will be of great advantage to a new CDO, without at least a reasonable degree of technical knowledge, they may struggle to connect with some members of their team; they may not be able to immediately grasp what technology tasks are essential and which are not; and they may not be able to paint an accurate picture of what good looks like in the data arena. Once more rapid assimilation of new information and equally rapid acquisition of new skills will be called for.

I couldn't find a good image of a cricket bat and so this will have to do

At this point it will be pretty obvious that my central point here is that the “sweet spot” for a CDO, the place where they can have greatest impact on an organisation and deliver the greatest value, is at the centre point of both of these axes. When I was talking to my friend about this, we agreed that one of the reasons why not many CDOs sit precisely at this nexus is because there are few people with equal (or at least balanced) expertise in the business and technology fields; few people who understand both data synthesis and data compliance equally well; and vanishingly few who sit in the centre of both of these ranges.

Perhaps these facts would also have been apparent from revewing the CDO job description I posted back in November 2015 as part of Wanted – Chief Data Officer. However, as always, a picture paints a thousand words and I rather like the compass-like exhibit I have come up with. Hopefully it conveys a similar message more rapidly and more viscerally.

To bring things back to the IRM(UK) CDO Executive Forum, I felt that issues around where delegates sat on my CDO “sweet spot” diagram (or more pertinently where they felt that they should sit) were a sub-text to many of our discussions. It is worth recalling that the mainstream CDO is still an emergent role and a degree of confusion around what they do, how they do it and where they sit in organisations is inevitable. All CxO roles (with the possible exception of the CEO) have gone through similar journeys. It is probably instructive to contrast the duties of a Chief Risk Officer before 2008 with the nature and scope of their responsibilities now. It is my opinion that the CDO role (and individual CDOs) will travel an analogous path and eventually also settle down to a generally accepted set of accountabilities.

In the meantime, if your organisation is lucky enough to have hired one of the small band of people whose experience and expertise already place them in the CDO “sweet spot”, then you are indeed fortunate. If not, then not all is lost, but be prepared for your new CDO to do a lot of learning on the job before they too can join the rather exclusive club of fully rounded CDOs.
 


 
Epilogue

As an erstwhile Mathematician, I’ve never seen a framework that I didn’t want to generalise. It occurs to me and – I assume – will also occur to many readers that the North / South and East / West diagram I have created could be made even more compass-like by the addition of North East / South West and North West / South East axes, with our idealised CDO sitting in the middle of these spectra as well [6].

Readers can debate amongst themselves what the extremities of these other dimensions might be. I’ll suggest just a couple: “Change” and “Business as Usual”. Given how organisations seem to have evolved in recent years, it is often unfortunately a case of never the twain shall meet with these two areas. However a good CDO will need to be adept at both and, from personal experience, I would argue that mastery of one does not exclude mastery of the other.
 


 Notes

 
[1]
 
See each of:

 
[2]
 
The main reasons for delay were a house move and a succession of illnesses in my family – me included – so I’m going to give myself a pass.
 
[3]
 
The sub-title was A Metaphorical Fugue On The Data ⇨ Information ⇨ Insight ⇨ Action Journey in The Spirt Of Douglas R. Hofstadter, which points to the inspiration behind my talk rather more explicity.
 
[4]
 
Douglas R. Hofstadter is the son of Nobel-wining physicist Robert Hofstadter. Prize-winning clearly runs in the Hofstadter family, much as with the Braggs, Bohrs, Curies, Euler-Chelpins, Kornbergs, Siegbahns, Tinbergens and Thomsons.
 
[5]
 
I am omitting any names or other references to save his blushes.
 
[6]
 
I could have gone for three or four dimensional Cartesian coordinates as well I realise, but sometimes (very rarely it has to be said) you can have too much Mathematics.

 

 

Themes from a Chief Data Officer Forum – the 180 day perspective

Tempus fugit

The author would like to acknowledge the input and assistance of his fellow delegates, both initially at the IRM(UK) CDO Executive Forum itself and later in reviewing earlier drafts of this article. As ever, responsibility for any errors or omissions remains mine alone.
 
 
Introduction

Time flies as Virgil observed some 2,045 years ago. A rather shorter six months back I attended the inaugural IRM(UK) Chief Data Officer Executive Forum and recently I returned for the second of what looks like becoming biannual meetings. Last time the umbrella event was the IRM(UK) Enterprise Data and Business Intelligence Conference 2015 [1], this session was part of the companion conference: IRM(UK) Master Data Management Summit / and Data Governance Conference 2016.

This article looks to highlight some of the areas that were covered in the forum, but does not attempt to be exhaustive, instead offering an impressionistic view of the meeting. One reason for this (as well as the author’s temperament) is that – as previously – in order to allow free exchange of ideas, the details of the meeting are intended to stay within the confines of the room.

Last November, ten themes emerged from the discussions and I attempted to capture these over two articles. The headlines appear in the box below:

Themes from the previous Forum:
  1. Chief Data Officer is a full-time job
  2. The CDO most logically reports into a commercial area (CEO or COO)
  3. The span of CDO responsibilities is still evolving
  4. Data Management is an indispensable foundation for Analytics, Visualisation and Statistical Modelling
  5. The CDO is in the business of driving cultural change, not delivering shiny toys
  6. While some CDO roles have their genesis in risk mitigation, most are focussed on growth
  7. New paradigms are data / analytics-centric not application-centric
  8. Data and Information need to be managed together
  9. Data Science is not enough
  10. Information is often a missing link between Business and IT strategies

One area of interest for me was how things had moved on in the intervening months and I’ll look to comment on this later.

By way of background, some of the attendees were shared with the November 2015 meeting, but there was also a smattering of new faces, including the moderator, Peter Campbell, President of DAMA’s Belgium and Luxembourg chapter. Sectors represented included: Distribution, Extractives, Financial Services, and Governmental.

The discussions were wide ranging and perhaps less structured than in November’s meeting, maybe a facet of the familiarity established between some delegates at the previous session. However, there were four broad topics which the attendees spent time on: Management of Change (Theme 5); Data Privacy / Trust; Innovation; and Value / Business Outcomes.

While clearly the second item on this list has its genesis in the European Commission’s recently adopted General Data Protection Regulation (GDPR [2]), it is interesting to note that the other topics suggest that some elements of the CDO agenda appear to have shifted in the last six months. At the time of the last meeting, much of what the group talked about was foundational or even theoretical. This time round there was both more of a practical slant to the conversation, “how do we get things done?” and a focus on the future, “how do we innovate in this space?”

Perhaps this also reflects that while CDO 1.0s focussed on remedying issues with data landscapes and thus had a strong risk mitigation flavour to their work, CDO 2.0s are starting to look more at value-add and delivering insight (Theme 6). Of course some organisations are yet to embark on any sort of data-related journey (CDO 0.0 maybe), but in the more enlightened ones at least, the CDO’s focus is maybe changing, or has already changed (Theme 3).

Some flavour of the discussions around each of the above topics is provided below, but as mentioned above, these observations are both brief and impressionistic:
 
 
Management of Change

Escher applies to most aspects of human endeavour

The title of Managing Change has been chosen (by the author) to avoid any connotations of Change Management. It was recognised by the group that there are two related issues here. The first is the organisational and behavioural change needed to both ensure that data is fit-for-purpose and that people embrace a more numerical approach to decision-making; perhaps this area is better described as Cultural Transformation. The second is the fact (also alluded to at the previous forum) that Change Programmes tend to have the effect of degrading data assets over time, especially where monetary or time factors lead data-centric aspects of project to be de-scoped.

On Cultural Transformation, amongst a number of issues discussed, the need to answer the question “What’s in it for me?” stood out. This encapsulates the human aspect of driving change, the need to engage with stakeholders [3] (at all levels) and the importance of sound communication of what is being done in the data space and – more importantly – why. These are questions to which an entire sub-section of this blog is devoted.

On the potentially deleterious impact of Change [4] on data landscapes, it was noted that whatever CDOs build, be these technological artefacts or data-centric processes, they must be designed to be resilient in the face of both change and Change.
 
 
Data Privacy / Trust

Data Privacy

As referenced above, the genesis of this topic was GDPR. However, it was interesting that the debate extended from this admittedly important area into more positive territory. This related to the observation that the care with which an organisation treats its customers’ or business partners’ data (and the level of trust which this generates) can potentially become a differentiator or even a source of competitive advantage. It is good to report an essentially regulatory requirement possibly morphing into a more value-added set of activities.
 
 
Innovation

Innovation

It might be expected that discussions around this topic would focus on perennials such as Big Data or Advanced Analytics. Instead the conversation was around other areas, such as distributed / virtualised data and the potential impact of Block Chain technology [5] on Data Management work. Inevitably The Internet of Things [6] also featured, together with the ethical issues that this can raise. Other areas discussed were as diverse as the gamification of Data Governance and Social Physics, so we cast the net widely.
 
 
Value / Business Outcomes

Business Value

Here we have the strongest link back into the original ten themes (specifically Theme 6). Of course the acme of data strategies is of little use if it does not deliver positive business outcomes. In many organisations, focus on just remediating issues with the current data landscape could consume a massive chunk of overall Change / IT expenditure. This is because data issues generally emanate from a wide variety of often linked and frequently long-standing organisational weaknesses. These can be architectural, integrational, procedural, operational or educational in nature. One of the challenges for CDOs everywhere is how to parcel up their work in a way that adds value, gets things done and is accretive to both the overall Business and Data strategies (which are of course intimately linked as per Theme 10). There is also the need to balance foundational work with more tactical efforts; the former is necessary for lasting benefits to be secured, but the latter can showcase the value of Data Management and thus support further focus on the area.
 
 
While the risk aspect of data issues gets a foot in the door of the Executive Suite, it is only by demonstrating commercial awareness and linking Data Management work to increased business value that any CDO is ever going to get traction. (Theme 6).
 


 
The next IRM(UK) CDO Executive Forum will take place on 9th November 2016 in London – if you would like to apply for a place please e-mail jeremy.hall@irmuk.co.uk.
 


 
Notes

 
[1]
 
I’ll be speaking at IRM(UK) ED&BI 2016 in November. Book early to avoid disappointment!
 
[2]
 
Wikipedia offers a digestible summary of the regulation here. Anyone tempted to think this is either a parochial or arcane area is encouraged to calculate what the greater of €20 million and 4% of their organisation’s worldwide turnover might be and then to consider that the scope of the Regulation covers any company (regardless of its domicile) that processes the data of EU residents.
 
[3]
 
I’ve been itching to use this classic example of stakeholder management for some time:

Rupert Edmund Giles - I'll be happy if just one other person gets it.

 
[4]
 
The capital “c” is intentional.
 
[5]
 
Harvard Business Review has an interesting and provocative article on the subject of Block Chain technology.
 
[6]
 
GIYF