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

 

 

Data Management as part of the Data to Action Journey

Data Information Insight Action (w700)

| Larger Version | Detailed and Annotated Version (as PDF) |

This brief article is actually the summation of considerable thought and reflects many elements that I covered in my last two pieces (5 Themes from a Chief Data Officer Forum and 5 More Themes from a Chief Data Officer Forum), in particular both the triangle I used as my previous Data Management visualisation and Peter Aiken’s original version, which he kindly allowed me to reproduce on this site (see here for more information about Peter).

What I began to think about was that both of these earlier exhibits (and indeed many that I have seen pertaining to Data Management and Data Governance) suggest that the discipline forms a solid foundation upon which other areas are built. While there is a lot of truth in this view, I have come round to thinking that Data Management may alternatively be thought of as actively taking part in a more dynamic process; specifically the same iterative journey from Data to Information to Insight to Action and back to Data again that I have referenced here several times before. I have looked to combine both the static, foundational elements of Data Management and the dynamic, process-centric ones in the diagram presented at the top of this article; a more detailed and annotated version of which is available to download as a PDF via the link above.

I have also introduced the alternative path from Data to Insight; the one that passes through Statistical Analysis. Data Management is equally critical to the success of this type of approach. I believe that the schematic suggests some of the fluidity that is a major part of effective Data Management in my experience. I also hope that the exhibit supports my assertion that Data Management is not an end in itself, but instead needs to be considered in terms of the outputs that it helps to generate. Pristine data is of little use to an organisation if it is not then exploited to form insights and drive actions. As ever, this need to drive action necessitates a focus on cultural transformation, an area that is covered in many other parts of this site.

This diagram also calls to mind the subject of where and how the roles of Chief Analytics Officer and Chief Data Officer intersect and whether indeed these should be separate roles at all. These are questions to which – as promised on several previous occasions – I will return to in future articles. For now, maybe my schematic can give some data and information practitioners a different way to view their craft and the contributions that it can make to organisational success.
 

 

5 More Themes from a Chief Data Officer Forum

A rather famous theme

This article is the second of two pieces reflecting on the emerging role of the Chief Data Officer. Each article covers 5 themes. You can read the first five themes here.

As with the first article, I would like to thank both Peter Aiken, who reviewed a first draft of this piece and provided useful clarifications and additional insights, and several of my fellow delegates, who also made helpful suggestions around the text. Again any errors of course remain my responsibility.
 
 
Introduction Redux

After reviewing a draft of the first article in this series and also scanning an outline of this piece, one of the other attendees at the inaugural IRM(UK) / DAMA CDO Executive Forum rightly highlighted that I had not really emphasised the strategic aspects of the CDO’s work; both data / information strategy and the close linkage to business strategy. I think the reason for this is that I spend so much of my time on strategic work that I’ve internalised the area. However, I’ve come to the not unreasonable conclusion that internalisation doesn’t work so well on a blog, so I will call out this area up-front (as well as touching on it again in Theme 10 below).

For more of my views on strategy formation in the data / information space please see my trilogy of articles starting with: Forming an Information Strategy: Part I – General Strategy.

With that said, I’ll pick up where we left off with the themes that arose in the meeting: 
 
Theme 6 – While some CDO roles have their genesis in risk mitigation, most are focussed on growth

Epidermal growth factor receptor

This theme gets to the CDO / CAO debate (which I will be writing about soon). It is true that the often poor state of data governance in organisations is one reason why the CDO role has emerged and also that a lot of CDO focus is inevitably on this area. The regulatory hurdles faced by many industries (e.g. Solvency II in my current area of Insurance) also bring a significant focus on compliance to the CDO role. However, in the unanimous view of the delegates, while cleaning the Augean Stables is important and equally organisations which fail to comply with regulatory requirements tend to have poor prospects, most CDOs have a growth-focussed agenda. Their primary objective is to leverage data (or to facilitate its leverage) to drive growth and open up new opportunities. Of course good data management is a prerequisite for achieving this objective in a sustainable manner, but it is not an end in itself. Any CDO who allows themself to be overwhelmed by what should just be part of their role is probably heading in the same direction as a non-compliant company.
 
 
Theme 7 – New paradigms are data / analytics-centric not application-centric

Applications & Data

Historically, technology landscapes used to be application-centric. Often there would be a cluster of systems in the centre (ideally integrated with each other in some way) and each with their own analytics capabilities; a CRM system with customer analytics “out-of-the-box” (whatever that really means in practice), an ERP system with finance analytics and maybe supply-chain analytics, digital estates with web analytics and so on. Even if there was a single-central system (those of us old enough will still remember the ERP vision), then this would tend to have various analytical repositories around it used by different parts of the organisation for different purposes. Equally some of the enterprise data warehouses I have built have included specialist analytical repositories, e.g. to support pricing, or risk, or other areas.

Today a new paradigm is emerging. Under this, rather than being at the periphery, data and analytics are in the centre, operating in a more joined-up manner. Many companies have already banked the automation and standardisation benefits of technology and are now looking instead to exploit the (often considerably larger) information and insight benefits [1]. This places information and insight assets at the centre of the landscape. It also means that finally information needs can start to drive system design and selection, not the other way round.
 
 
Theme 8 – Data and Information need to be managed together

Data and Information in harness

We see a further parallel with the CAO vs CDO debate here [2]. After 27 years with at least one foot in IT (though often in hybrid roles with dual business / IT reporting) and 15 explicitly in the data and information space, I really fail to see how data and information are anything other than two sides of the same coin.

To people who say that the CAO is the one who really understands the business and the CDO worries instead about back-end data governance, I would reply that an engine is only as good as the fuel that you put into it. I’d over-extend the analogy (as is my wont [3]) by saying that the best engineers will have a thorough understanding of:

  1. what purpose the engine will be applied to – racing car, or lorry (truck)
  2. the parameters within which it is required to perform
  3. the actual performance requirements
  4. what that means in terms of designing the engine
  5. what inputs the engine will have: petrol/diesel/bio-fuel/electricity
  6. what outputs it will produce (with no reference to poor old Volkswagen intended)

It may be that the engineering team has experts in various areas from metallurgy, to electronics, to chemistry, to machining, to quality control, to noise and vibration suppression, to safety, to general materials science and that these are required to work together. But whoever is in charge of overall design, and indeed overall production, would need to have knowledge spanning all these areas and would in addition need to ensure that specialists under their supervision worked harmoniously together to get the best result.

Data is the basic building block of information. Information is the embodiment of things that people want or need to know. You cannot generate information (let alone insight) without a very strong understanding of data. You can neither govern, nor exploit, data in any useful way without knowledge of the uses to which it will be put. Like the chief product engineer, there is a need for someone who understands all of the elements, all of the experts working on these and can bring them together just as harmoniously [4]).
 
 
Theme 9 – Data Science is not enough

If you don't understand  the notation, you've failed in your application to be a  Data Scientist

In Part One of this article I repeated an assertion about the typical productivity of data scientists:

“Data Scientists are only 10-20% productive; if you start a week-long piece of work on Monday, the actual statistical analysis will commence on Friday afternoon; the rest of the time is battling with the data”

While the many data scientists I know would attest to the truth of this, there is a broader point to be made. That is the need for what can be described as Data Interpreters. This role is complementary to the data science community, acting as an interface between those with PhDs in statistics and the rest of the world. At IRM(UK) ED&BI one speaker even went so far as to present a photo graph of two ladies who filled these ying and yang roles at a European organisation.

More broadly, the advent of data science, while welcome, has not obviated the need to pass from data through information to get to insight for most of an organisation’s normal measurements. Of course an ability to go straight from data to insight is also a valuable tool, but it is not suitable for all situations. There are also a number of things to be aware of before uncritically placing full reliance on statistical models [5].
 
 
Theme 10 – Information is often a missing link between Business and IT strategies

Business => Information => IT

This was one of the most interesting topics of discussion at the forum and we devoted substantial time to exploring issues and opportunities in this area. The general sense was that – as all agreed – IT strategy needs to be aligned with business strategy [6]. However, there was also agreement that this can be hard and in many ways is getting harder. With IT leaders nowadays often consumed by the need to stay abreast of both technology opportunities (e.g. cloud computing) and technology threats (e.g. cyber crime) as well as inevitably having both extensive business as usual responsibilities and significant technology transformation programmes to run, it could be argued that some IT departments are drifting away from their business partners; not through any desire to do so, but just because of the nature (and volume) of current work. Equally with the increasing pace of business change, few non-IT executives can spend as much time understanding the role of technology as was once perhaps the case.

Given that successful information work must have a foot in both the business and technology camps (“what do we want to do with our data?” and “what data do we have available to work with?” being just two pertinent questions), the argument here was that an information strategy can help to build a bridge these two increasingly different worlds. Of course this chimes with the feedback on the primacy of strategy that I got on my earlier article from another delegate; and which I reference at the beginning of this piece. It also is consistent with my own view that the data → information → insight → action journey is becoming an increasingly business-focused one.

A couple of CDO Forum delegates had already been thinking about this area and went so far as to present models pertaining to a potential linkage, which they had either created or adapted from academic journals. These placed information between business and IT pillars not just with respect to strategy but also architecture and implementation. This is a very interesting area and one which I hope to return to in coming weeks.
 
 
Concluding thoughts

As I mentioned in Part One, the CDO Forum was an extremely useful and thought-provoking event. One thing which was of note is that – despite the delegates coming from many different backgrounds, something which one might assume would be a barrier to effective communication – they shared a common language, many values and comparable views on how to take the areas of data management and data exploitation forward. While of course delegates at an such an eponymous Forum might be expected to emphasise the importance of their position, it was illuminating to learn just how seriously a variety organisations were taking the CDO role and that CDOs were increasingly becoming agents of growth rather than just risk and compliance tsars.

Amongst the many other themes captured in this piece and its predecessor, perhaps a stand-out was how many organisations view the CDO as a firmly commercial / strategic role. This can only be a positive development and my hope is that CDOs can begin to help organisations to better understand the asset that their data represents and then start the process of leveraging this to unlock its substantial, but often latent, business value.
 


 
Notes

 
[1]
 
See Measuring the benefits of Business Intelligence
 
[2]
 
Someone really ought to write an article about that!

UPDATE: They now have in: The Chief Data Officer “Sweet Spot” and Alphabet Soup

 
[3]
 
See Analogies for some further examples as well as some of the pitfalls inherent in such an approach.
 
[4]
 
I cover this duality in many places in this blog, for the reader who would like to learn more about my perspectives on the area, A bad workman blames his [Business Intelligence] tools is probably a good place to start; this links to various other resources on this site.
 
[5]
 
I cover some of these here, including (in reverse chronological order):

 
[6]
 
I tend to be allergic to the IT / Business schism as per: Business is from Mars and IT is from Venus (incidentally the first substantive article on I wrote for this site), but at least it serves some purpose in this discussion, rather than leading to unproductive “them and us” syndrome, that is sadly all to often the outcome.

 

 

5 Themes from a Chief Data Officer Forum

A rather famous theme

This article is the first of two pieces reflecting on the emerging role of the Chief Data Officer. Each article will cover 5 themes and the concluding chapter may be viewed here.

I would like to thank both Peter Aiken, who reviewed a first draft of this piece and provided useful clarifications and additional insights, and several of my fellow delegates, who also made helpful suggestions around the text. Any errors of course remain my responsibility.
 
 
Introduction

As previously trailed, I attended the IRM(UK) Enterprise Data & Business Intelligence seminar on 3rd and 4th November. On the first of these days I sat on a panel talking about approaches to leveraging data “beyond the Big Data hype”. This involved fielding some interesting questions, both from the Moderator – Mike Simons – and the audience; I’ll look to pen something around a few of these in coming days. It was also salutary that each one of the panellists cast themselves as sceptics with respect to Big Data (the word “Luddite” was first discussed as an appropriate description, only to then be discarded); feeling that it was a very promising technology but a long way from the universal panacea it is often touted to be.

However it is on the second day of the event that I wanted to focus in this article. During this I was asked to attend the inaugural Chief Data Officer Executive Forum, sponsored by long-term IRM partner DAMA, the international data management association. This day-long event was chaired by data management luminary Peter Aiken, Associate Professor of Information Systems at Virginia Commonwealth University and Founding Director of data management consultancy Data Blueprint.

The forum consisted of a small group of people working in the strongly-related arenas of data management, data governance, analytics, warehousing and information architecture. Some attendees formally held the title of CDO, some carried out functions overlapping or analogous to the CDO. This is probably not surprising given the emergent nature of the CDO role in many industries.

There was a fair mix of delegate backgrounds, including people who previously held commercial roles, or ones in each of finance, risk and technology (a spread that I referred to in my pre-conference article). The sectors attendees worked in ranged from banking, to manufacturing, to extractives, to government to insurance. A handful of DAMA officers made up the final bakers’ dozen of “wise men” [1].

Discussions were both wide-ranging and very open, so I am not going to go into specifics of what people said, or indeed catalogue the delegates or their organisations. However, I did want to touch on some of the themes which arose from our interchanges and I will leaven these with points made in Peter Aiken’s excellent keynote address, which started the day in the best possible way.
 
 
Theme 1 – Chief Data Officer is a full-time job

Not a part-time activity

In my experience in business, things happen when an Executive is accountable for them and things languish when either a committee looks at an area (= no accountability), or the work receives only middle-management attention (= no authority). If both being a guardian of an organisation’s data (governance) and caring about how this is leveraged to deliver value (exploitation) are important things, then they merit Executive ownership.

Equally it can be tempting to throw the data and information agenda to an existing Executive, maybe one who already plays in the information arena such as the CFO. The problem with this is that I don’t know many CFOs who have a lot of spare time. They tend to have many priorities already. Let’s say that your average CFO has 20 main things that they worry about. When they add data and information to this mix, then let’s be optimistic and say this slots in at number 15. Is this really going to lead to paradigm-shifting work on data exploitation or data governance?

For most organisations the combination of Data Governance and Data Exploitation is a huge responsibility in terms of both scope and complexity. It is not work to be approached lightly and definitively not territory where a part-timer will thrive.

Peter Aiken also emphasizes that a newly appointed CDO may well find him or herself looking to remediate years of neglect for areas such as data management. The need to address such issues suggests that focus is required.

To turn things round, how many organisations of at least a reasonable size have one of their executives act as CFO on a part time basis?
 
 
Theme 2 – The CDO most logically reports into a commercial area (CEO or COO)

Where does the CDO fit?

I’d echo Peter Aiken’s comments that IT departments and the CIOs who lead them have achieved great things in the past decades (I’ve often been part of the teams doing just this). However today (often as a result of just such successes) the CIO’s remit is vast. Even just care and feeding of the average organisation’s IT estate is a massive responsibility. If you add in typical transformation programmes as well, it is easy to see why most CIOs are extremely busy.

Another interesting observation is that the IT project mindset – while wholly suitable for the development, purchase and integration of transaction processing systems – is less aligned with data-centric work. This is because data evolves. Peter Aiken also talks about data operating at a different cadence, by which he means the flow or rhythm of events, especially the pattern in which something is experienced.

More prosaically, anyone who has seen the impact of a set of parallel and uncoordinated projects on a previously well-designed data warehouse will be able to attest to the project and asset mindsets not mingling too well in the information arena. Also, unlike much IT work, data-centric activities are not always ones that can be characterised by having a beginning, middle and end; then tend to be somewhat more open ended as an organisation’s data seldom is static and its information needs have similar dynamism.

Instead, the exploitation of an organisation’s data is essentially a commercial exercise which is 100% targeted at better business decision making. This work should be focussed on adding value (see also Theme 5 below). Both of these facts argue for the responsible function reporting outside of IT (but obviously with a very strong technical flavour). Logical reporting lines are thus into either the CEO or COO, assuming that the latter is charged with the day-to-day operations of the business [2].
 
 
Theme 3 – The span of CDO responsibilities is still evolving

Answers on a postcard...

While there are examples of CDOs being appointed in the early 2000s, the role has really only recently impinged on the collective corporate consciousness. To an extent, many organisations have struggled with the data → information → insight → action journey, so it is unsurprising that the precise role of the CDO is at present not entirely clear. Is CDO a governance-focussed role, or an information-generating role, or both? How does a CDO relate to a Chief Analytics Officer, or are they the same thing? [3]

It is evident that there is some confusion here. On the assumption (see Theme 2 above) that the CDO sits outside IT, then how does it relate to IT and where should data-centric development resource be deployed? How does the CDO relate to compliance and risk? [4]

The other way of looking at this is that there is a massive opportunity for embryonic CDOs to define their function and span of control. We have had CFOs and their equivalents for centuries (longer if you go back to early Babylonian Accounting), how exciting would it be to frame the role and responsibilities of an entirely new C-level executive?
 
 
Theme 4 – Data Management is an indispensable foundation for Analytics, Visualisation and Statistical Modelling

Look out for vases containing scorpions...

Having been somewhat discursive on the previous themes, here I will be brief. I’ve previously argued that a picture paints a thousand words [5] and here I’ll simply include my poor attempt at replicating an exhibit that I have borrowed from Peter Aiken’s deck. I think it speaks for itself:

Data Governance Triangle

You can view Peter’s original, which I now realise diverges rather a lot from my attempt to reproduce it, here.

I’ll close this section by quoting a statistic from the plenary sessions of the seminar: “Data Scientists are only 10-20% productive; if you start a week-long piece of work on Monday, the actual statistical analysis will commence on Friday afternoon; the rest of the time is battling with the data” [6].

CDOs should be focussed on increasing the productivity of all staff (Data Scientists included) by attending to necessary foundational work in the various areas highlighted in the exhibit above.
 
 
Theme 5 – The CDO is in the business of driving cultural change, not delivering shiny toys

When there's something weird on your board of dash / When there's something weird and it's kinda crass / Who you gonna call?

While all delegates agreed that a CDO needs to deliver business value, a distinction was made between style and substance. As an example, Big Data is a technology – an exciting one which allows us to do things we have not done before, but still a technology. It needs to be supported and rounded out by attention to process and people. The CDO should be concerned about all three of these dimensions (see also Theme 4 above).

I mentioned at the beginning of this article that some of the attendees at the CDO forum hailed from the extractive industries. We had some excellent discussions about how safety has been embedded in the culture of such organisations. But we also spoke about just how long this has taken and how much effort was required to bring about the shift in mindset. As always, changing human behaviour is not a simple or quick thing. If one goal of a CDO is to embed reliance on credible information (including robust statistical models) into an organisation’s DNA, then early progress is not to be anticipated; instead the CDO should be dug in for the long-term and have vast reserves of perseverance.

As regular readers will be unsurprised to learn, I’m delighted with this perspective. Indeed tranches of this blog are devoted precisely to the important area [7]. I am also somewhat allergic to a focus on fripperies at the expense of substance, something I discussed most directly in “All that glisters is not gold” – some thoughts on dashboards. These perspectives seem to be well-aligned with the stances being adopted by many CDOs.

As with any form of change, the group unanimously felt that good communication lay at the heart of success. A good CDO needs to be a consummate communicator.
 
 
Tune in next time…

I have hopefully already given some sense of the span of topics the CDO Executive Forum discussed. The final article in this short series covers a further 5 themes and then look to link these together with some more general conclusions about what a CDO should do and how they should do it.
 


 
Notes

 
[1]
 
Somewhat encouragingly three of these were actually wise women, then maybe I am setting the bar too low!
 
[2]
 
Though if reporting to a COO, the CDO will need to make sure that they stay close to wherever business strategy is developed; perhaps the CEO, perhaps a senior strategy or marketing executive.
 
[3]
 
I plan to write on the CDO / CAO dichotomy in coming weeks.

UPDATE: I guess it took more than a few weeks, but now see: The Chief Data Officer “Sweet Spot” and Alphabet Soup

 
[4]
 
I will expand on this area in Theme 6, which will be part of the second article in this series.
 
[5]
 
I actually have the cardinality wrong here as per my earlier article.
 
[6]
 
I will return to this point in Theme 9, which again will be part of the second article in the series.
 
[7]
 
A list of articles about cultural change in the context of information programmes may be viewed here.