The Chief Marketing Officer and the CDO – A Modern Fable

The Fox and the Grapes

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

— W.J.Linton (after Aesop)

Note:

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


 
Prelude…

The Decline and Fall of the CDO Empire?

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

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

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

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

  • Camelot – who run the UK National Lottery
     
  • Mumsnet – which is a web-site for UK parents
     
  • Flubit – a growing on-line marketplace aiming to compete with Amazon

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

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

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

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

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

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

Symmetry

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

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

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

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

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

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

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

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

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


 
…and Fugue

Electrification

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

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

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

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

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

  1. Addressing architectural debt that results from neglect of a Data Assets or the frequently deleterious impact of improperly governed change portfolios [14]. This is often a series of small to medium-sized changes, rather than a project with a discrete scope and start and end dates.
     
  2. More positively, engaging proactively in the change process in an attempt to act as a steward of Data Assets.
     
  3. Establishing a regular Data Audit.
     
  4. Regular Data Management activities.
     
  5. Providing tailored Analytics to help understand some unscheduled or unexpected event.
     
  6. Establishment of a data “SWAT team” to respond to urgent architecture, quality or reporting needs.
     
  7. Running a Data Governance committee and related activities.
     
  8. Creating and managing a Data Science capability.
     
  9. Providing help and advice to those struggling to use Data facilities.
     
  10. Responding to new Data regulations.
     
  11. Creating and maintaining a target operating model for Data and is use.
     
  12. Supporting Data Services to aid systems integration.
     
  13. Production of regular reports and refreshing self-serve Data Repositories.
     
  14. Testing and re-testing of Data facilities subject to change or change in source Data.
     
  15. Providing training in the use of Data facilities or the importance of getting Data right-first-time.

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

Data Strategy

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

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

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

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


 
Coda

Coda

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

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

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


 
Notes

 
[1]
 
I first wrote “knee-jerk reaction” and then thought that maybe I was being unkind. “When they go low, we go high” is a better maxim. Note: link opens a YouTube video.
 
[2]
 
I am sure that I read somewhere about the importance of the number of data points in any analysis, maybe I should ask a Data Scientist to remind me about this.
 
[3]
 
For a more balanced view of what real CDOs do, please take a look at my ongoing series of in-depth interviews.
 
[4]
 
As discussed in:

 
[5]
 
See Glimpses of Symmetry, Chapter 3 – Shifting Shapes for more on the properties of equilateral triangles.
 
[6]
 
As demonstrated by Emmy Noether in 1915.
 
[7]
 
At this point I think I am meant to say “Fake news! SAD!!!”
 
[8]
 
The [informal] proceedings of some of these may be viewed at:

 
[9]
 
Or Chief Electrical Officer, or Chief Electricity Officer.
 
[10]
 
I am doing some more digging and will of course update this piece should I find the evidence that has so far been elusive.
 
[11]
 
Self-driving electric cars come to mind of course. That or running a Starship.

Scotty

 
[12]
 
As an aside, where do Programme Managers go when (or should that be if) their Programmes finish?
 
[13]
 
It might be argued that some of these operational functions could be handed to IT. However, given that some elements of data functions have probably been carved out of IT in the past, this might be a retrograde step.
 
[14]
 
See Bumps in the Road.
 
[15]
 
Now Dogs Trust.

 


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

 

More Definitions in the Data and Analytics Dictionary

The Data and Analytics Dictionary

The peterjamesthomas.com Data and Analytics Dictionary is an active document and I will continue to issue revised versions of it periodically. Here are 20 new definitions, including the first from other contributors (thanks Tenny!):

  1. Artificial Intelligence Platform
  2. Data Asset
  3. Data Audit
  4. Data Classification
  5. Data Consistency
  6. Data Controls
  7. Data Curation (contributor: Tenny Thomas Soman)
  8. Data Democratisation
  9. Data Dictionary
  10. Data Engineering
  11. Data Ethics
  12. Data Integrity
  13. Data Lineage
  14. Data Platform
  15. Data Strategy
  16. Data Wrangling (contributor: Tenny Thomas Soman)
  17. Explainable AI (contributor: Tenny Thomas Soman)
  18. Information Governance
  19. Referential Integrity
  20. Testing Data (Training Data)

Remember that The Dictionary is a free resource and quoting contents (ideally with acknowledgement) and linking to its entries (via the buttons provided) are both encouraged.

People are now also welcome to contribute their own definitions. You can use the comments section here, or the dedicated form. Submissions will be subject to editorial review and are not guaranteed to be accepted.
 


 

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

 

In-depth with CDO Christopher Bannocks

In-depth with Christopher Bannocks


Part of the In-depth series of interviews

PJT Today I am talking to Christopher Bannocks, who is Group Chief Data Officer at ING. ING is a leading global financial institution, headquartered in the Netherlands. As stressed in other recent In-depth interviews [1], data is a critical asset in banking and related activities, so Christopher’s role is a pivotal one. I’m very glad that he has been able to find time in his busy calendar to speak to us.
PJT Hello Christopher, can you start by providing readers with a flavour of your career to date and perhaps also explain why you came to focus on the data arena.
CB Sure, it’s probably right to say I didn’t start out here, data was not my original choice, and for anyone of a similar age to me, data wasn’t a choice, when I started out, in that respect it’s a “new segment”. I started out on a management development programme in a retail bank in the UK, after which I moved to be an operations manager in investment banking. As part of that time in my career, post Euro migration and Y2K (yes I am genuinely that old, I also remember Vinyl records and Betamax video!) [2] I was asked to help solve the data problem. What I recognised very quickly was this was an area with under-investment, that was totally central the focus of that time – STP (Straight Through Processing). Equally it provided me with much broader perspectives, connections to all parts of the organisation that I previously didn’t have and it was at that point, some 20 years ago, that I decided this was the thing for me! I have since run and driven transformation in Reference Data, Master Data, KYC [3], Customer Data, Data Warehousing and more recently Data Lakes and Analytics, constantly building experience and capability in the Data Governance, Quality and data services domains, both inside banks, as a consultant and as a vendor.
PJT I am trying to get a picture of the role and responsibilities of the typical CDO (not that there appears to be such a thing), so would you mind touching on the span of your work at ING? I know you have a strong background in Enterprise Data Management, how does the CDO role differ from this area?
CB I guess that depends on how you determine the scope of Enterprise Data Management. However, in reality, the CDO role encompasses Enterprise Data Management, although generally speaking the EDM role includes responsibility for the day to day operations of the collection processes, which in my current role I don’t have. I have accountability for the governance and quality through those processes and for making the data available for downstream consumers, like Analytics, Risk, Finance and HR.

My role encompasses being the business driver for the data platform that we are rolling out across the organisation and its success in terms of the data going onto the platform and the curation of that data in a governed state, depending on the consumer requirements.

My role today boils down to 4 key objectives – data availability, data transparency, data quality and data control.

PJT I know that ING consists of many operating areas and has adopted a federated structure with respect to data matters. What are the strengths of this approach and how does it work on a day-to-day basis?
CB This approach ensures that the CDO role (I have a number of CDOs functionally reporting to me) remains close to the business and the local entity it supports, it ensures that my management team is directly connected to the needs of the business locally, and that the local businesses have a direct connection to the global strategy. What I would say is that there is no “one size fits all” approach to the CDO organisation model. It depends on the company culture and structure and it needs to fit with the stated objectives of the role as designed.

On a day to day basis, we are aligned with the business units and the functional units so we have CDOs in all of these areas. Additionally I have a direct set of reports who drive the standard solutions around tooling, governance, quality, data protection, Data Ethics, Metadata and data glossary and models.

PJT Helping organisations become “data-centric” is a key part of what you do. I often use this phrase myself; but was recently challenged to elucidate its meaning. What does a “data-centric” organisation look like to you? What sort of value does data-centricity release in your experience?
CB Data centric is a cultural shift, in the structures of the past where we have technology people and process, we now have data that touches all three. You know if you have reached the right place when data becomes part of the decision making process across the organisation, when decisions are only made when data is presented to support it and this is of the requisite quality. This doesn’t mean all decisions require data, some decisions don’t have data and that’s where leaderships decisions can be made, but for those decisions that have good data to support them, these can be made easily and at a lower level in the organisation. Hence becoming data centric supports an agile organisation and servant / leadership principles, utilising data makes decisions faster and outcomes better.
PJT I am on record multiple times [4] stating that technology choices are much less important than other aspects of data work. However, it is hard to ignore the impact that Big Data and related technologies have had. A few years into the cycle of Big Data adoption, do you see the tools and approaches yielding the expected benefits? Should I revisit my technology-agnostic stance?
CB I have also been on record multiple times saying that every data problem is a people problem in disguise. I still hold that this is true today although potentially this is changing. The problems of the past and still to this day originate with poor data stewardship, I saw it happening in front of my eyes last week in Heathrow when I purchased something in a well known electronics store. Because I have an overseas postcode the guy at the checkout put dummy data into all the fields to get through the process quickly and not impact my customer experience, I desperately wanted to stop him but also wanted to catch my plane. This is where the process efficiency impacts good data collection. If the software that supports the process isn’t flexible, the issue won’t be fixed without technology intervention, this is often true in data quality problems which have knock on effects to customers, which at the end of the day are why we are all here. This is a people problem (because who is taking responsibility here for fixing it, or educating that guy at the checkout) AND it’s a technology problem, caused by inflexible or badly implemented systems.

However, in the future, with more focus on customer driven checkout, digital channels and better customer experience, better interface driven data controls and robotics and AI, it may become further nuanced. People are still involved, communication remains critical but we cannot ignore technology in the digital age. For a long time, data groups have struggled with getting access to good tools and technology, now this technology domain is growing daily, and the tools are improving all the time. What we can do now with data at a significantly lower cost than ever before is amazing, and continues to improve all the time. Hence ignoring technology can be costly when extending capabilities to your stakeholders and could be a serious mistake, however focusing only on technology and ignoring people, process, communication etc is also a serious mistake. Data Leaders have to be multi-disciplinary today, and be able to keep up with the pace of change.

PJT I have heard you talk about “data platforms”, what do you mean by this and how do these contrast with another perennial theme, that of data democratisation? How does a “data platform” relate to – say – Data Science teams?
CB Data democratisation is enabled by the data platform. The data platform is the technology enablement of the four pillars I mentioned before, availability, transparency, quality and control. The platform is a collection of technologies that standardise the approach and access to well governed data across the organisation. Data Democratisation is simply making data available and abstracting away from siloed storage mechanisms, but the platform wraps the implementation of quality, controls and structure to the way that happens. Data Science teams then get the data they need, including data curation services to find the data they need quickly, for governed and structured data, Data Science teams can utilise the glossary to identify what they need and understand the level of quality based on consumer views, they also have access to metadata in standard forms. This empowers the analytics capability to move faster, spend less time on data discovery and curation, structure and quality and more time on building analytics.
PJT I mentioned the federated CDO team at ING above and assume this is reflected in the rest of the organisation structure. ING also has customers in 40 countries and I know first-hand that a global footprint adds complexity. What are the challenges in being a CDO in such an environment? Does this put a higher premium on influencing skills for example?
CB I am not sure it puts a higher premium on influencing skills, these have a high premium in any CDO role, even if you don’t have a federated structure, the reality is if you are in a data role you have more stakeholders than anyone else in the company, so influencing skills remain premium.

A global footprint means complexity for sure, it means differences in a world where you are trying to standardise and it means you have to be tuned in to cultural differences and boundaries. It also means a great deal of variety, opportunities to learn new cultures and approaches, it means you have to listen and understand and flex your style and it means pragmatism plays an important part in your decision making process.

At ING we have an amazing team of people who collaborate in a way I have never experienced before, supported by a strong attachment and commitment to the success of the business and our customers. This makes dealing with the complexity a team effort, with great energy and a fantastic working environment. In an organisation without the drive and passion we have here it would present challenges, with the support of the board and being a core part of the overall strategy, it ensures broad alignment to the goal, which makes the challenge easier for the organisation to solve, not easy, but easier and more fun.

PJT Building on the last point, every CDO I have interviewed has stressed the importance of relationships; something that chimes with my own experience. How do you go about building strong relationships and maintaining them when inevitable differences of opinion or clashes in interests arise?
CB I touched on this a little earlier. Pragmatism over purism. I see purist everywhere in data, with views that are so rigid that the execution of them is doomed because purism doesn’t build relationships. Relationships are built based on what you bring and give up, on what you can give, not on what you can get. I try every day to achieve this, but I am human too, so I don’t always get it right, I hope I get it right more than I get it wrong and where I get it wrong I hope I can be forgiven for my intention is pure. We owe it to our customers to work together for their benefit, where we have differences the customer outcomes should drive our decisions, in that we have a common goal. Disagreements can be helped and supported by identifying a common goal, this starts to align people behind a common outcome. Individual interests can be put aside in preference of the customer interest.
PJT I know that you are very interested in data ethics and feel that this is an important area for CDOs to consider. Can you tell the readers a bit more about data ethics and why they should be central to an organisation’s approach to data?
CB In an increasingly digital world, the use of data is becoming widespread and the pace at which it is used is increasing daily, our compute power grows exponentially as does the availability of data. Given this, we need an ethical framework to help us make good decisions with our customers and stakeholders in mind. How do you ensure that decisions in your organisation about how you use data are ethical? What are ethical decisions in your organisation and what are the guiding principles? If this isn’t clear and communicated to help all staff make good decisions, or have good discussions there is a real danger that decisions may not be properly socialised before all angles are considered.

Just meeting the bar of privacy regulation may not be enough, you can still meet that bar and do things that your customers may disagree with of find “creepy” so the correct thought needs to be applied and the organisation engaged to ensure the correct conversations take place, and there is a place to go to discuss ethics.

I am not saying that there is a silver bullet to solve this problem, but the conversation and the ability to have the conversation in a structured way helps the organisation understand its approach and make good decisions in this respect. That’s why CDOs should consider this an important part of the role and a critical engagement with users of data across the organisation.

PJT Finally, I have worked for businesses with a presence in the Netherlands on a number of occasions. As a Brit living abroad, how have you found Amsterdam. What – if any – adaptations have you had to make to your style to thrive in a somewhat different culture?
CB Having lived in India, I thought my move to the Netherlands could only be easy. I arrived thinking that a 45 minute flight could not possibly provide as many challenges as an 11 hour flight, especially from a cultural perspective. Of course I was wrong because any move to a different culture provides challenges you could never have expected and it’s the small adjustments that take you by surprise the most. It’s always a hugely enjoyable learning experience though. London is a more top down culture whereas in the Netherlands it’s a much flatter approach, my experience here is positive although it does require an adjustment. I work in Amsterdam but live in a small village, chosen deliberately to integrate faster. It’s harder, more of a challenge but helps you understand the culture as you make friends with local people and get closer to the culture. My wife and I have never been a fan of the expat scene, we prefer to integrate, however more difficult this feels at first, it’s worth it in the long run. I must admit though that I haven’t conquered the language yet, it’s a real work in progress!
PJT Christopher, I really enjoyed our chat, which I believe will also be of great interest to readers. Thank you.

Christoper Bannocks can be reached at via his LinkedIn profile.


Disclosure: At the time of publication, neither peterjamesthomas.com Ltd. nor any of its Directors had any shared commercial interests with Christopher Bannocks, ING or any entities associated with either of these.


If you are a Chief Data Officer, a Chief Analytics Officer, a Director of Data, or hold some other “Top Data Job” and would like to share your thoughts with the readers of this site in an interview like this one, please get in contact.

 
Notes

 
[1]
 
Specifically:

 
[2]
 
So does the interviewer.
 
[3]
 
Know your customer.
 
[4]
 
Most directly in: A bad workman blames his [Business Intelligence] tools

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

 

Version 2 of The Anatomy of a Data Function

Between November and December 2017, I published the three parts of my Anatomy of a Data Function. These were cunningly called Part I, Part II and Part III. Eight months is a long time in the data arena and I have now issued an update.

The Anatomy of a Data Function

Larger PDF version (opens in a new tab)

The changes in Version 2 are confined to the above organogram and Part I of the text. They consist of the following:

  1. Split Artificial Intelligence out of Data Science in order to better reflect the ascendancy of this area (and also its use outside of Data Science).
     
  2. Change Data Science to Data Science / Engineering in order to better reflect the continuing evolution of this area.

My aim will be to keep this trilogy up-to-date as best practice Data Functions change their shapes and contents.


 
If you would like help building or running your Data Function, or would just like to have an informal chat about the area, please get in touch
 


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

 

In-depth with CDO Jo Coutuer

In-depth with Jo Coutuer


Part of the In-depth series of interviews

PJT Today’s guest on In-depth is Jo Coutuer, Chief Data Officer and Member of the Executive Committee of BNP Paribas Fortis, a leading Belgian bank. Given the importance of the CDO role in Financial Services, I am very happy that Jo has managed to spare us some of his valuable time to talk.
PJT Jo, you have had an interesting career in a variety of organisations from consultancies to start-ups, from government to major companies. Can you give readers a pen-picture of the journey that has taken you to your current role?
JC For me, the variety of contexts has been the most rewarding. I started in an industry that has now sharply declined in Europe (Telco Manufacturing), continued in the consulting world of ERP tools, switched into a very interesting job for the government, became an entrepreneur and co-created a data company for 13 years, merged that data company into a big 4 consultancy and finally decided to apply my life’s learnings to the fascinating industry of banking. The most remarkable aspect of my career is the fact that my current role and the attention to data that goes with it, did not exist when I started my career. It illustrates how young people today can also build a future, without really knowing what lies ahead. All it takes is the mental flexibility to switch contexts when it is needed.
PJT At present – at least in Europe, and maybe further afield – there is no standard definition of a CDO’s role. Can you tell me a bit about the scope of your work at BNP Paribas Fortis? Are you most focussed on compliance, leverage of data, or a balance of both activities?
JC At BNP Paribas Fortis, the CEO and his executive committee made a courageous decision back in 2016 to create a specific department dedicated to Data. The move was courageous, not only because it defined a new leadership role and a budget, but also because it settled a debate between the businesses and the IT function. At the time of creation of the department, it was decided to carve out of IT the traditional function of “business intelligence and data warehousing” and to establish a central competence centre for “analytics and artificial intelligence“, which before was mostly scattered or non-existing. On top of that, the new department was tasked to assume the regulatory duties that relate to data. More and more, banking regulation focusses on reliable reporting, traceable data flows, systematic data quality measurement and well documented metadata, all embedded in a solid organisational governance. So yes, I would say our Data department is both “defensive” as well as “offensive”. As a CDO, I am privileged to be able to work with experts and leaders in the fields of regulation, data warehousing expertise and data science innovation. Without them, the breadth of the scope and the required depth, would not be manageable.
PJT Do you collaborate with other Executives in the data arena, or is the CDO primus inter pares when it comes to data matters?
JC I would not speak of a hierarchical order when it comes to data. It helps to distinguish three identities of a Data department.

The first one is the identity of the “Governor”. In that identity, peers accept that the CDO translates external duties into internal best practices, as long as this happens in a co-creation mode. We have established a “College of Data Managers”, who are 13 senior managers, representing each a specific “data perimeter”, which in its turn rather well maps to our fields of business or our internal functions. These senior managers intimately link the Data activities to the day-to-day business functions and their respective executives.

A second identity is that of the “Expert”. In that identity, we offer expertise in fields of data integration, data warehousing, reporting, visualisation, data science, … It means that I see my fellow executives as clients and partners and the Data department helps them achieve their business objectives. Mentally (and sometimes practically), we measure up to external professional services or IT companies.

A third identity is that of the “Integrator”. As an integrator, we actively make the link between the business of today, the technological and data potential of today and the business of tomorrow. We actively try to question existing practices and we introduce new concepts for a variety of business applications. And although we are more driving in this role than we are in the role of the “Expert”, we still are fully at the service of our clients.

PJT More generally, how do you see the CDO role changing in coming years, what would 2020’s CDO be doing? Will we even need CDOs in 2020?
JC Ahah! One of the most frequently asked questions on CDO related social media! If previous two years are any predictor of the future, I would say that the CDO of 2020 is one who has solidly matured the governance aspects of Data, just like the CFO and CRO have done that for financial management or risk management. Let’s say that Data has become “routine”.

At the same time, the 2020 CDO will need to offer to his peers, the technical and expert capabilities that are data centric and essential to running a digital business.

And on top of that, I believe that 2020 will be the timeframe in which data valorisation will become an active topic. I explicitly do not use the word “monetisation” because we currently associate data to often with “selling data for advertising purposes”. In our industry, PSD2 [1] will define our duties to be able to exchange data with third party service providers, at the explicit request of our clients. From that new reality, an API-driven ecosystem will surface in which data will be actively valorised, to the direct service of our clients, not to the indirect service of our marketing departments. The 2020 CDO will be instrumental in shaping his or her company’s ecosystem to make sure this happens in a well governed, trusted and safe way. Clients will seek that reassurance and will reward companies who take data management seriously.

PJT Of course, senior roles tend to exist because they add value to their organisations, what do you feel is the value that a CDO brings to the table?
JC I have already mentioned the CDO’s challenge to be schizophrenic ally split between his or her various identities. But it is exactly that breadth of scope that can add value. The CDO should be an “executive integrator”. He can employ “governors” and “experts”, but his or her role in the peer team of executives is to represent the transversality of data’s nature. Data “flows”, data “unites”. More than it is “oil”, data is “water”. It flows through the company’s ecosystem and it nourishes the business and the future business potential. As such, the CDO needs to keep the water clean and make sure it gets pumped across the organisation, so that others can benefit from the nutrients it. And while doing so, the CDO has a duty to add nutrients to the water, in the form of analytical or artificial intelligence induced insights.
PJT Focussing on Analytics, I know you have written about how to build the ideal Analytics team and have mentioned that “purple people” are the key. Can you explain more about this?
JC Purple people are people that integrate the skills of “red” people and “blue” people. Red people bring the scientific data methodologies to the table. Blue people bring the solid frameworks of the business. Data people as individuals and a Data department as an entity, must have as a mission to be “purple” and to actively bridge the gap between the fast growing set of data technologies and methodologies on the one hand and the rapidly evolving and transforming business challenges on the other hand. And of course, if you like Prince [2] as a musician, that can be an asset too!
PJT In my discussions with other CDOs [3] and indeed in my own experience, it seems that teamwork is crucial for a CDO. Of course, this is important for many senior roles, but it does seem central to what a CDO does. My perspective is that both a CDO’s own team and the virtual teams that he or she forms with colleagues are going to have a big say in whether things go well or not. What are your views on this topic?
JC You are absolutely right. A CDO or data function cannot exist in isolation. At some times, transversality feels a burden because it imposes a daily attention to stakeholders. However, in reality, it’s exactly the transversal effect that can generate the added value to an organisation. At the end of the day, the integration aspects between departments and people will generate positive side effects, above and beyond the techniques of data management.
PJT Artificial Intelligence in its various guises has been the topic of conversation recently. This is something with strong linkage to the data field. Obviously without divulging any commercial secrets, what role do you see AI playing in banking going forwards? What about in our lives in general?
JC It’s funny that AI is being discovered as a new topic. I remember writing my Master thesis on the topic a long time ago. Of course, things have evolved since the 90s, with a storage and computing capacity that is approximately 50,000 times stronger for the same price point. This capacity explosion, combined with the connectivity of the internet and the cloud, combined with the increased awareness that data and algorithms have become central elements in a many business strategies, has fundamentally re-calibrated the potential of AI.

In banking, AI and Analytics will soon help clients understand their finances better, will help them to take better and faster decisions, will generate a better (less friction) client experience for “the easy stuff” and it will allow the banks to put humans on “the hard stuff” or on those interactions with their clients that require true human interaction. Behind the scenes, Analytics and AI are already helping to prevent fraud, monitoring suspicious transactions to detect crime, money laundering and fraud. And even deeper inside the mechanics of a bank, Analytics and AI are helping prevent cyber-crimes and are monitoring the stability of the technological platforms onto which our modern financial and societal system is built.

I am convinced that the societal role of banks will continue to exists, despite innovative peer-to-peer or blockchain driven schemes. As such, Analytics and AI will contribute to society as a whole, through their contribution to a reliable and stable financial services system.

PJT With GDPR [4] coming into force only a couple of months ago, the subject of customer data and how it is used is a topical one. Taking BNP Paribas Fortis to one side, what are your thoughts on the balance between data privacy and the “free” services that we all pay for by allowing our data to be sold?
JC I believe that GDPR is both important legislation and brings benefits to customers. First of all, we have good historical reasons to care about our privacy. In times of societal crises or wars, it is the first weapon that is used against society and its citizens. So we should care for it deeply. Second, being in an industry for which “trust” is the most essential element of identity, protecting and respecting the data and the privacy of clients is a natural reflex. And putting the banking question aside for a moment, we should continue to educate aggressively about the fact that services never come for free. As long as consumers are well informed that they pay for their convenience with their data, there is no fundamental concern. But because there is still no real “paid” economy surfacing, the consumer does not really have a choice between “pay-for-service” or “give-data-for-service”. I believe that the market potential for paid services, that guarantee non-exploitation of personal data, is quietly growing. And when it finally appears, consumers will start making choices. Personally, I admit to having moved from being on all possible digital channels and tools, towards being much more selective. And I must admit that digital life with a privacy aware mind is still possible and still fun.
PJT It seems to me that a key capability of a CDO is as an influencer. Influence can take many shapes, from being an acknowledged expert in an area, to the softer skills of being someone that others can talk to openly. Do you agree about this observation? If so, how do you seek to be an influencer?
JC It’s a thin line to walk and it depends on the type of CDO that you are and the mandate that you have. If you have a mandate to do “governance only”, then you should have the confidence of delivering on your mandate, just like a CRO or a CFO does. For that I always revert to the phrase: “we agreed that data is a valuable asset, just like money or people or buildings, … so let’s then act like it.” If you have mandate to “change”, to “create value”, then you have to be an integrator and influencer because you can never change an organisation and its people on your own.
PJT Before letting you go, a quick personal question. I know you spent some time at the University of Cambridge. I lived in this town while my wife was working on her PhD. Like Cambridge, Leuven [5] is a historic town just outside of a major capital city. What parallels do you see between the two and what did you think of the locals?
JC Cambridge is famous for its “punts”, Leuven for its Stella Artois “pints”. And both central churches (or chapels) are home to iconic paintings by Flemish masters, Rubens in Cambridge and Bouts in Leuven. Visit both!
PJT Jo, thank you so much for talking to me and giving readers the benefit of your ideas and experience.

Jo Coutuer can be reached at via his LinkedIn profile.


Disclosure: At the time of publication, neither peterjamesthomas.com Ltd. nor any of its Directors had any shared commercial interests with Jo Coutuer, BNP Paribas Fortis or any entities associated with either of these.


If you are a Chief Data Officer, a Chief Analytics Officer, a Director of Data, or hold some other “Top Data Job” and would like to share your thoughts with the readers of this site in an interview like this one, please get in contact.

 
Notes

 
[1]
 
Payment Services Directive 2.
 
[2]
 
Prince Rogers Nelson.
 
[3]
 
Two recent examples include:

 
[4]
 
General Data Protection Regulation.
 
[5]
 
Leuven.

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

 

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

GDPR

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

Does it have to be like this?

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

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

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

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

  1. General Strategy
  2. Situational Analysis
  3. Completing the Strategy

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


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

 

An in-depth interview with experienced Chief Data Officer Roberto Maranca

In-depth with Roberto Maranca


Part of the In-depth series of interviews

PJT Today’s interview is with Roberto Maranca. Roberto is an experienced and accomplished Chief Data Officer, having held that role in GE Capital and Lloyds Banking Group. Roberto and I are both founder members of the IRM(UK) Chief Data Officer Executive Forum and I am delighted to be able to share the benefit of his insights with readers.
PJT Roberto, you have had a long and distinguished career in the data space, would you mind starting by giving readers a brief overview of this?
RM Certainly Peter, looking back now Data has been like a river flowing through all my career. But I can definitely recall that, at a certain point in my life in GE Capital (GEC), someone who I had worked with before called me to take a special assignment as IT lead for the Basel II implementation for the Bank owned by GEC in Europe. For the readers not in the Finance industry, Basel II, for most of us and certainly for me, was our Data baptism of fire because of its requirement to collect a lot of data across the organisation in order to calculate an “enterprise wide” set of risk metrics. So the usual ETL build and report generation wasn’t good enough if not associated to a common dictionary, validation of mappings, standardised referential integrity and quality management.

When Basel went in production in 2008, I was given the leadership of the European Business Intelligence team, where I consolidated my hunch that the reason that a 6 months dashboard build project would fail pre-production tests was mainly “data is not good enough” and not our lack of zeal. Even if was probably amongst the first in GEC to adopt a Data Quality tool, you had the feeling that IT could not be the proverbial tail shaking the dog in that space. A few years went by where I became much closer to operations in a regulated business, learning about security and operational risk frameworks, and then one day at the end of 2013, I saw it! GEC was to be regulated by the Federal Reserve as one entity, and that posed a lot of emphasis on data. The first ever job description of CDO in GEC was flashed in front of my eyes and I felt like I had just fallen on the way to Damascus. All those boxes that had been empty for years in my head got ticked just looking at it. I knew this was what I wanted to do, I knew I had to leave my career in IT to do it, I knew there was not a lot beyond that piece of paper, but I went for it. Sadly, almost two years into this new role, GE decided to sell GEC; you would not believe how much data you need to divest such a large business.

I found that Lloyds Banking Group was after a CDO and I could not let that opportunity go by. It has been a very full year where I led a complete rebuild of their Data Framework, while been deeply involved in the high-profile BCBS239 and GDPR initiatives.

PJT Can you perhaps highlight a single piece of work that was important to you, added a lot of value to the organisation, or which you were very proud of for some other reason?
RM I always had a thing about building things to last, so I have always tried to achieve a sustainable solution that doesn’t fall apart after a few months (in Six Sigma terms you will call it “minimising the long term sigma shift”, but we will talk about it another time). So trying to have change process to be mindful of “Data” has been my quest since day one, in the job of CDO. For this reason, my most important piece of work was probably the the creation of the first link between the PMO process in GEC and the Data Lineage and Quality Assurance framework, I had to insist quite a bit to introduce this, design it, test it and run it. Now of course, after the completion of the GEC sale, it has gone lost “like tears in the rain”, to cite one of the best movies ever [1].
PJT What was your motivation to take on Chief Data Officer roles and what do you feel that you bring to the CDO role?
RM I touched on some reasons in my introductory comments. I believe there is a serendipitous combination of acquired skills that allows me to see things in a different way. I spent most of my working life in IT, but I have a Masters in Aeronautical Engineering and a diploma in what we in Italy call “Classical Studies”, basically I have A levels in Latin, Greek, Philosophy, History. So for example, together with my pilot’s licence achieved over weekends, I have attended a drama evening school for a year (of course in my bachelor days). Jokes apart, the “art” of being a CDO requires a very rich and versatile background because it is so pioneering, ergo if I can draw from my study of flow dynamics to come up with a different approach to lineage, or use philosophy to embed a stronger data driven culture, I feel it is a marked plus.
PJT We have spoken about the CDO role being one whose responsibilities and main areas of focus are still sometimes unclear. I have written about this recently [2]. How do you think the CDO role is changing in organisations and what changes need to happen?
RM I mentioned the role being pioneering: compared to more established roles, CFO, COO and, even, CIO, the CDO is suffering from ambiguity, differing opinions and lack of clear career path. All of us in this space have to deal with something like inserting a complete new organ in a body that has got very strong immunological response, so although the whole body is dying for the function that the new organ provides (and with the new breed of regulation about, dying for lack of good and reliable data is not an exaggeration), there is a pernickety work of linking up blood vessels and adjusting every part of the organisation so that the change is harmonious, productive and lasting. But every company starts from a different level of maturity and a different status quo, so it is left to the CDO to come up with a modus operandi that would work and bring that specific environment to a recognisable standard.
PJT The Chief Data Officer has been described as having “the toughest job in the executive C-suite within many organizations” [3]. Do you agree and – if so – what are the major challenges?
RM I agree and it simply demonstrated: pick any Company’s Annual Report, do a word search for “data quality”, “data management“, “data science” or anything else relevant to our profession, you are not going to find many. IT has been around for a while more and yet technology is barely starting now to appear in the firm’s “manifesto”, mostly for things that are a risk, like cyber security. Thus the assumption is, if it is not seen as a differentiator to communicate to the shareholders and the wider world, why should it be of interest for the Board? It is not anyone’s fault and my gut feeling is that GDPR (or perhaps Cambridge Analytica) is going to change this, but we probably need another generational turnover to have CDOs “safely” sitting in executive groups. In the meantime, there is a lot we can do, maybe sitting immediately behind someone who is sitting in that crucial room.
PJT We both believe that cultural change has a central role in the data arena, can you share some thoughts about why this is important?
RM Data can’t be like a fad diet, it can’t be a program you start and finish. Companies have to understand that you have to set yourself on a path of “permanent augmentation”. The only way to do this is to change for good the attitude of the entire company towards data. Maybe starting from the first ambiguity, data is not the bits and bytes coming out of a computer screen, but it is rather the set of concepts and nouns we use in our businesses to operate, make products, serve our customers. If you flatten your understanding of data to its physical representation, you will never solve the tough enterprise problems, henceforth if it is not a problem of centralisation of data, but it is principally a problem of centralisation of knowledge and standardisation of behaviours, it is something inherently close to people and the common set of things in a company that we can call “culture”.
PJT Accepting the importance of driving a cultural shift, what practical steps can you take to set about making this happen?
RM In my keynotes, I often quote the Swiss philosopher (don’t tell me I didn’t warn you!) Henry Amiel:

Pure truth cannot be assimilated by the crowd: it must be communicated by contagion.

This is especially the case when you are confronted with large numbers of colleagues and small data teams. Creating a simple mantra that can be inoculated in many part of the organisation helps to create a more receptive environment. So CDOs should first be keen marketeers, able to create a simple brand and pursuing relentlessly a “propaganda” campaign. Secondly, if you want to bring change, you should focus where the change happens and make sure that wherever the fabric of the company changes, i.e. big programmes or transformations, data is top priority.

PJT What are the potential pitfalls that you think people need to be aware of when embarking on a data-centric cultural transformation programme?
RM First is definitely failing to manage your own expectations on speed and acceptance; it takes time and patience. Long-established organisations cannot leap into a brighter future just because an enlightened CDO shows them how. Second, and sort of related, it is a problem thinking that things can happen by management edicts and CDO policy compliance, there is a lot niftier psychology and sociology to weave into this.
PJT A two-part question. What do you see as the role of Data Governance in the type of cultural change you are recommending? Also, do you think that the nature of Data Governance has either changed or possibly needs to change in order to be more effective?
RM The CDO’s arrival at a discussion table is very often followed by statements like “…but we haven’t got resources for the Governance” or “We would like to, but Data Governance is such an aggro”. My simple definition for Data Governance is a process that allows Approved Data Consumers to obtain data that satisfies their consumption requirements, in accordance with Company’s approved standards of traceability, meaning, integrity and quality. Under this definition there is no implied intention of subjecting colleagues to gruelling bureaucratic processes, the issue is the status quo. Today, in the majority of firms, without a cumbersome process of checks and balances, it is almost impossible to fulfil such definition. The best Data Governance is the one you don’t see, it is the one you experience when you to get the data you need for your job without asking, this is the true essence of Data Democratisation, but few appreciate that this is achieved with a very strict and controlled in-line Data Governance framework sitting on three solid bastions of Metadata, User Access Controls and Data Classification.
PJT Can you comment on the relationship between the control of data and its exploitation; between Analytics and Governance if you will?Do these areas need to both be part of the CDO’s remit?
RM Oh… this is about the tale of the two tribes isn’t it? The Governors vs. the Experimenters, the dull CDOs vs the funky CAOs. Of course they are the yin and the yang of Data, you can’t have proper insight delivered to your customer or management if you have a proper Data Governance process, or should we call it “Data Enablement” process from the previous answer. I do believe that the next incarnation of the CDO is more a “Head of Data”, who has got three main pillars underneath, one is the previous CDOs all about governance, control and direction, the second is your R&D of data, but the third one that getting amassed and so far forgotten is the Operational side, the Head of Data should have business operational ownership of the critical Data Assets of the Company.
PJT The cultural aspects segues into thinking about people. How important is managing the people dimension to a CDO’s success?
RM Immensely. Ours is a pastoral job, we need to walk around, interact on internal social media, animate communities, know almost everyone and be known by everyone. People are very anxious about what we do, because all the wonderful things we are trying to achieve, they believe, will generate “productivity” and that in layman’s terms mean layoffs. We can however shift that anxiety to curiosity, reaching out, spreading the above-mentioned mantra but also rethinking completely training and reskilling, and subsequently that curiosity should transform in engagement which will deliver sustainable cultural change.
PJT I have heard you speak about “intelligent data management” can you tell me some more about what you mean by this? Does this relate to automation at all?
RM My thesis at Uni in 1993 was using AI algorithms and we all have been playing with MDM, DQM, RDM, Metadata for ages, but it doesn’t feel we cracked yet a Science of Data (NB this is different Data Science!) that could show us how to resolve our problems of managing data with 21st century techniques. I think our evolutionary path should move us from “last month you had 30k wrong postcodes in your database” to “next month we are predicting 20% fewer wrong address complaints”, in doing so there is an absolute need to move from fragmented knowledge around data to centralised harnessing of the data ecosystem, and that can only be achieved tuning in on the V.O.M. (Voice of the Machines), listening, deriving insight on how that ecosystem is changing, simulating response to external or internal factors and designing changes with data by design (or even better with everything by design). I yet have to see automated tools that do all of that without requiring man years to decide what is what, but one can only stay hopeful.
PJT Finally, how do you see the CDO role changing in coming years?
RM To the ones that think we are a transient role, I respond that Compliance should be everyone’s business, and yet we have Compliance Officers. I think that overtime the Pioneers will give way to the Strategists, who will oversee the making of “Data Products” that best suit the Business Strategist, and maybe one day being CEO will be the epitome of our career ladders one day, but I am not rushing to it, I love too much having some spare time to spend with my family and sailing.
PJT Roberto, it is always a pleasure to speak. Thank you for sharing your ideas with us today.

Roberto Maranca can be reached at r.maranca@outlook.com and has social media presence on LinkedIn and Twitter (@RobertoMaranca).


Disclosure: At the time of publication, neither peterjamesthomas.com Ltd. nor any of its Directors had any shared commercial interests with Roberto Maranca.


If you are a Chief Data Officer, a Chief Analytics Officer, a Director of Data, or hold some other “Top Data Job” and would like to share your thoughts with the readers of this site in an interview like this one, please get in contact.

 
Notes

 
[1]
 
 
[2]
 
The CDO – A Dilemma or The Next Big Thing?
 
[3]
 
Randy Bean of New Vantage Partners quoted in The CDO – A Dilemma or The Next Big Thing?

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

 

Link directly to entries in the Data and Analytics Dictionary

The Data and Analytics Dictionary

The peterjamesthomas.com Data and Analytics Dictionary has always had internal tags (anchors for those old enough to recall their HTML) which allowed me, as its author, to link to individual entries from other web-pages I write. An example of the use of these is my article, A Brief History of Databases.

I have now made these tags public. Each entry in the Dictionary is followed by the full tag address in a box. This is accompanied by a link icon as follows:

Data Dictionary excerpt

Clicking on the link icon will copy the tag address to your clipboard. Alternatively the tag URL may just be copied from the box containing it directly. You can then use this address in your own article to link back to the D&AD entry.

As with the vast majority of my work, the contents of the Data and Analytics Dictionary is covered by a Creative Commons Attribution 4.0 International Licence. This means you can include my text or images in your own web-pages, presentations, Word documents etc. You can even modify my work, so long as you point out that you have done this.

If you would like to link back to the Data and Analytics Dictionary to provide definitions of terms that you are using, this should now be very easy. For example:

Lorem ipsum dolor sit amet, consectetur adipiscing Big Data elit. Duis tempus nisi sit amet libero vehicula Data Lake, sed tempor leo consectetur. Pellentesque suscipit sed felisData Governance ac mattis. Fusce mattis luctus posuere. Duis a Spark mattis velit. In scelerisque massa ac turpis viverra, acLogistic Regression pretium neque condimentum.

Equally, I’d be delighted if you wanted to include part of all of the text of an entry in the Data and Analytics Dictionary in your own work, commercial or personal; a link back using this new functionality would be very much appreciated.

I hope that this new functionality will be useful. An update to the Dictionary’s contents will be published in the next couple of months.
 


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

 

The CDO – A Dilemma or The Next Big Thing?

Janus

It wasn’t so long ago that I last wrote about Forbes’s perspective on the data arena [1]. In this piece, I am going to compare and contrast two more recent Forbes articles. The first is 3 Reasons Why The Chief Data Officer Will Become The Next Big Thing by Lauren deLisa Coleman (@ultra_Lauren). The second is The Chief Data Officer Dilemma by Randy Bean (@RandyBeanNVP) [2].

While the contents of the two articles differ substantially – the first is positive about the future of the role, the second highlights some of its current challenges – there are interesting points made in each of them. In the midst of confusion about what a Chief Data Officer (CDO) is and what they do, it is perhaps not surprising that fundamentally different takes on the area can both contain seeds of truth.
 


 
Lauren deLisa Coleman

In the first piece, deLisa Coleman refers to the twin drivers of meeting increasingly stringent regulatory demands [3] and leveraging data to drive enhanced business outcomes; noting that:

Expertise and full dedication is needed particularly since data is threaded into nearly all facets of today’s businesses [4].

She states that appointing a CDO is the canonical response of Executive teams, while noting that there is not full consensus on all facets of this role. In covering the title’s “three reasons” why organisations need CDOs, deLisa Coleman references a survey by Infogix [5]. This highlights the increasing importance of each of the following areas: Metadata, Data Governance and the Internet of Things.

Expanding on these themes, deLisa Coleman adds:

Those who seize success within these new parameters will be companies that not only adapt most quickly but those that can also best leverage their company’s data in a strategic manner in innovative ways while continuing to gathering massive amounts under flawless methods of protection.

So far, so upbeat. To introduce a note of caution, I am aware that, in the last few years – and no doubt in part driven by articles in Forbes, Harvard Business Review and their ilk – most companies have set forth a vision for becoming a “data-driven organisation” [6]. However, the number that have actually achieved this objective – or even taken significant steps towards it – is of course much smaller. The central reason for this is that it is not easy to become a “data-driven organisation”. As with most difficult things, reaching this goal requires hard-work, focus, perseverance and, it has to be said, innate aptitude. Some experience of what is involved is of course also invaluable and, even in 2018, this is a rare commodity.

A sub-issue within this over-arching problem is miracle-worker syndrome; we’ll hire a great CDO and then we don’t need to worry about data any more [7]. Of course becoming a “data-driven organisation” requires the whole organisation to change. A good CDO will articulate the need for change, generate enthusiasm for moving forward and and coordinate the necessary metamorphosis. What they cannot do however is enact such a fundamental change without the active commitment of all tiers of the organisation.
 


 
Randy Bean

Of course this is where the second article becomes pertinent. Bean starts by noting the increasing prevalence of the CDO. He cites an annual study by his consultancy [8] which surveys Fortune 1000 companies. In 2012, this found that only 12% of the companies surveyed had appointed a CDO. By 2018, the figure has risen to over 63%, a notable trend [9].

However, he goes on to say that:

In spite of the common recognition of the need for a Chief Data Officer, there appears to be a profound lack of consensus on the nature of the role and responsibilities, mandate, and background that qualifies an executive to operate as a successful CDO. Further, because few organizations — 13.5% — have assigned revenue responsibility to their Chief Data Officers, for most firms the CDO role functions primarily as an influencer, not a revenue generator.

This divergence of opinion on CDO responsibilities, mandate, and importance of the role underscores why the Chief Data Officer may be the toughest job in the executive c-suite within many organizations, and why the position has become a hot seat with high turnover in a number of firms.

In my experience, while deLisa Coleman’s sunnier interpretation of the CDO environment both holds some truth and points to the future, Bean’s more gritty perspective is closer to the conditions currently experienced by many CDOs. This is reinforced by a later passage:

While 39.4% of survey respondents identify the Chief Data Officer as the executive with primary responsibility for data strategy and results within their firm, a majority of survey respondents – 60.6% — identify other C-Executives as the point person, or claim no single point of accountability. This is remarkable and highly significant, for it highlights the challenges that CDO’s face within many organizations.

Bean explains that some of this is natural, making a similar point to the one I advance above: the journey towards being “data-driven” is not a simple one and parts of organisations may both not want to take the trip and even dissuade colleagues from doing so. Passive or active resistance are things that all major transformations need to deal with. He adds that lack of clarity about the CDO role, especially around the involved / accountable question as it relates to strategy, planning and execution is a complicating factor.

Some particularly noteworthy points arose when the survey asked about the background and skills of a CDO. Findings included:

While 34% of executives believe the ideal CDO should be an external change agent (outsider) who brings fresh perspectives, an almost equivalent 32.1% of executives believe the ideal CDO should be an internal company veteran (insider) who understands the culture and history of the firm and knows how to get things done within that organization.

22.6% of executives […] indicated that the CDO must be either a data scientist or a technologist who is highly conversant with data. An additional 11.3% responded that a successful CDO must be a line-of-business executive who has been accountable for financial results.

The above may begin to sound somewhat familiar to some readers. It perhaps brings to mind the following figure [10]:

Expanded CDO Sweet Spot

As I pointed out last year in A truth universally acknowledged… organisations sometimes take a kitchen sink approach to experience and expertise, a lengthy list of requirements that will never been found in one person. From the above survey, it seems that this approach probably reflects the thinking of different executives.

I endorse one of Bean’s final points:

The lack of consensus on the Chief Data Officer role aptly mirrors the diversity of opinion on the value and importance of data as an enterprise asset and how it should be managed.

Back in my more technologically flavoured youth, I used to say that organisations get the IT that they deserve. The survey findings suggest that the same aphorism can be applied to both CDOs and the data landscapes that they are meant to oversee.
 


 
So two contrasting pieces from the same site. The first paints what I believe is an accurate picture of the importance of the CDO role in fulfilling corporate objectives. The second highlights some of the challenges with the CDO role delivering on its promise. Each perspective is valid. I would recommend readers take a look at both articles and then blend some of the insights with their own opinions and ideas.
 


 
Acknowledgements

I would like to thank Lauren deLisa Coleman and Randy Bean for both reviewing this article and allowing me to quote their work. Their openness and helpfulness are very much appreciated.
 


 
Notes

 
[1]
 
Draining the Swamp.
 
[2]
 
Text is reproduced with the kind permission of the authors.

Forbes has a limited free access policy for non-subscribers, this means that the number of articles you can view is restricted.

 
[3]
 
To which I would add both customer and business partner expectations about how their data is treated and used by organisations.
 
[4]
 
Echoing points from my two 2015 articles: 5 Themes from a Chief Data Officer Forum and 5 More Themes from a Chief Data Officer Forum, specifically:

It’s gratifying to make predictions that end up coming to be.

 
[5]
 
Infogix Identifies the Top Game Changing Data Trends for 2018.
 
[6]
 
It would be much easier to list those who do not share this aspiration.
 
[7]
 
Having been described as “the Messiah” in more than one organisation, I can empathise with the problems that this causes. Perhaps Moses – a normal man – leading his people out of the data dessert is a more apt Biblical metaphor, should you care for such things.
 
[8]
 
New Vantage Partners.
 
[9]
 
These are clearly figures for US companies and it is generally acknowledged that the US approach to data is more mature than elsewhere. In Europe, it may be that GDPR (plus, in my native UK, the dark clouds of Brexit) has tipped the compliance / leverage balance too much towards data introspection and away from revenue-generating data insights.
 
[10]
 
This first version of this image appeared in 2016’s The Chief Data Officer “Sweet Spot”, with the latest version being published in 2017’s A Sweeter Spot for the CDO?.

 

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

 

The Anatomy of a Data Function – Part III

Part I Part II Part III

Sepia's Anatomy

This is the third and final part of my review of the anatomy of a Data Function, Part I may be viewed here and Part II here.

Update:

The data arena is a fluid one. The original set of Anatomy of a Data Function articles dates back to November 2017. As of August 2018, the data function schematic has been updated to separate out Artificial Intelligence from Data Science and to change the latter to Data Science / Engineering. No doubt further changes will be made from time to time.

In the first article, I introduced the following Data Function organogram:

The Anatomy of a Data Function

Larger PDF version (opens in a new tab)

and went on to cover each of Data Strategy, Analytics & Insight and Data Operations & Technology. In Part II, I discussed the two remaining Data Function areas of Data Architecture and Data Management. In this final article, I wanted to cover the Related Areas that appear on the right of the above diagram. This naturally segues into talking about the practicalities of establishing a Data Function and highlighting some problems to be avoided or managed.

As in Parts I and II, unless otherwise stated, text indented as a quotation is excerpted from the Data and Analytics Dictionary.
 
 
Related Areas

Related Areas

I have outlined some of the key areas with which the Data Function will work. This is not intended to be a comprehensive list and indeed the boxes may be different in different organisations. Regardless of the departments that appear here, the general approach will however be similar. I won’t go through each function in great detail here. There are some obvious points to make however. The first is an overall one that clearly a collaborative approach is mandatory. While there are undeniably some police-like attributes of any Data Function, it would be best if these were carried out by friendly community policemen or women, not paramilitaries.

So rather more:

Community Police

and rather less:

Not quite so Community Police
 
Data Privacy and Information Security

Though strongly related, these areas do not generally fall under the Data Function. Indeed some legislation requires that they are separate functions. Data Privacy and Information Security are related, but also distinct from each other. Definitions are as follows:

[Data Privacy] pertains to data held by organisations about individuals (customers, counterparties etc.) and specifically to data that can be used to identify people (personally identifiable data), or is sensitive in nature, such as medical records, financial transactions and so on. There is a legal obligation to safeguard such information and many regulations around how it can be used and how long it can be retained. Often the storage and use of such data requires explicit consent from the person involved.

Data and Analytics Dictionary entry: Data Privacy

Information Security consists of the steps that are necessary to make sure that any data or information, particularly sensitive information (trade secrets, financial information, intellectual property, employee details, customer and supplier details and so on), is protected from unauthorised access or use. Threats to be guarded against would include everything from intentional industrial espionage, to ad hoc hacking, to employees releasing or selling company information. The practice of Information Security also applies to the (nowadays typical) situation where some elements of internal information is made available via the internet. There is a need here to ensure that only those people who are authenticated to access such information can do so.

Data and Analytics Dictionary entry: Information Security

 
Digital

Digital is not a box that would have necessarily have appeared on this chart 15, or even 10, years ago. However, nowadays this is often an important (and large) department in many organisations. Digital departments leverage data heavily; both what they gather themselves and and data drawn from other parts of the organisation. This can be to show customers their transactions, to guide next best actions, or to suggest potentially useful products or services. Given this, collaboration with the Data Function should be particularly strong.
 
Change Management

There are some specific points to make with respect to Change collaboration. One dimension of this was covered in Part II. Looking at things the other way round, as well as being a regular department, with what are laughingly referred to as “business as usual” responsibilities [1], the Data Function will also drive a number of projects and programmes. Depending on how this is approached in an organisation, this means either that the Data Function will need its own Project Managers etc., or to have such allocated from Change. This means that interactions with Change are bidirectional, which may be particularly challenging.

For some reason, Change departments have often ended up holding the purse strings for all projects and programmes (perhaps a less than ideal outcome), so a Data Function looking to get its own work done may run counter to this (see also the second section of this article).
 
IT

While the role of IT is perhaps narrower nowadays than historically [2], they are deeply involved in the world of data and the infrastructure that supports its movement around the organisation. This means that the Data Function needs to pay particular attention to its relationship with IT.
 
Embedded Analytics Teams

A wholly centralised approach to delivering Analytics is neither feasible, nor desirable. I generally recommend hybrid arrangements with a strong centralised group and affiliated analytical resource embedded in business teams. In some organisations such people may be part of the Data Function, or have a dotted line into it. In others the connection may be less formal. Whatever the arrangements, the best result would be if embedded analytical staff viewed themselves as part of a broader analytical and data community, which can share tips, work to standards and leverage each other’s work.
 
Data Stewards

Data Stewards are a concept that arises from a requirement to embed Data Governance policies and processes. Data Function Governance staff and Data Architects both need to work closely with Data Stewards. A definition is as follows:

This is a concept that arises out of Data Governance. It recognises that accountability for things like data quality, metadata and the implementation of data policies needs to be devolved to business departments and often locations. A Data Steward is the person within a particular part of an organisation who is responsible for ensuring that their data is fit for purpose and that their area adheres to data policies and guidelines.

Data and Analytics Dictionary entry: Data Steward

  
End User Computing

There are several good reasons for engaging with this area. First, the various EUCs that have been developed will embody some element (unsatisfied elsewhere) of requirements for the processing and or distribution of data; these needs probably need to be met. Second, EUCs can present significant risks to organisations (as well as delivering significant benefits) and ameliorating these (while hopefully retaining the benefits) should be on the list of any Data Function. Third, the people who have built EUCs tend to be knowledgeable about an organisation’s data, the sort of people who can be useful sources of information and also potential allies.

[End User Computing] is a term used to cover systems developed by people other than an organisation’s IT department or an approved commercial software vendor. It may be that such software is developed and maintained by a small group of people within a department, but more typically a single person will have created and cares for the code. EUCs may be written in mainstream languages such as Java, C++ or Python, but are frequently instead Excel- or Access-based, leveraging their shared macro/scripting language, VBA (for Visual Basic for Applications). While related to Microsoft Visual Basic (the precursor to .NET), VBA is not a stand-alone language and can only run within a Microsoft Office application, such as Excel.

Data and Analytics Dictionary entry: End User Computing (EUC)

 
Third Party Providers

Often such organisations may be contracted through the IT function; however the Data Function may also hire its own consultants / service providers. In either case, the Data Function will need to pay similar attention to external groups as it does to internal service providers.
 
 
Building a Data Function for the Practical Man [3]

Flag Planting for the Practical Man

When I published Part I of this trilogy, many people were kind enough to say that they found reading it helpful. However, some of the same people went on to ask for some practical advice on how to go about setting up such a Data Function and – in particular – how to navigate the inevitable political hurdles. While I don’t believe in recipes for success that are guaranteed to work in all circumstances, the second section of this article will cover three selected high-level themes that I think are helpful to bear in mind at the start of a Data Function journey. Here I am assuming that you are the leader of the nascent Data Function and it is your accountability to build the team while adding demonstrable business value [4].

Starting Small

It is a truth universally acknowledged, that a Leader newly in possession of a Data Function, must be in want of some staff [5]. However seldom will such a person be furnished with a budget and headcount commensurate with the task at hand; at least in the early days. Often instead, the mission, should you choose to accept it, is to begin to make a difference in the Data World with a skeleton crew at best [6]. Well no one can work miracles and so it is a question of judgement where to apply scarce resource.

My view is that this is best applied in shining a light on the existing data landscape, but in two ways. First, at the Analytics end of the spectrum, looking to unearth novel findings from an organisation’s data; the sort of task you give to a capable Data Scientist with some background in the industry sector they are operating in. Second, at the Governance end of the spectrum, documenting failures in existing data processing and reporting; in particular any that could expose the organisation to specific and tangible risks. In B2C organisations, an obvious place to look is in customer data. In B2B ones instead you can look at transactions with counterparties, or in the preparation of data for external reports, either Financial or Regulatory. Here the ideal person is a competent Data Analyst with some knowledge of the existing data landscape, in particular the compromises that have to be made to work with it.

In both cases, the objective is to tell the organisation things it does not know. Positively, a glimmer of what nuggets its data holds and the impact this could have. Negatively, examples of where a poor data landscape leads to legal, regulatory, or reputational risks.

These activities can add value early on and increase demand for more of this type of work. The first investigation can lead to the creation of a Data Science team, the second to the establishment of regular Data Audits and people to run these.

A corollary here is a point that I ceaselessly make, data exploitation and data control are two sides of the same coin. By making progress in areas that are at least superficially at antipodal locations within a Data Function, the connective tissue between them becomes more apparent.

BAU or Project?

There is a pernicious opinion held by an awful lot of people which goes as follows.

  1. We have issues with our data, its quality, completeness and fitness for purpose.
  2. We do not do a good enough job of leveraging our data to guide decision making.
  3. Therefore we need a data project / programme to sort this out once and for all.
  4. Where is the telephone number of the Change Director?

Well there is some logic to the above and setting up a data project (more likely programme) is a helpful thing to do. However, this is necessary, but not sufficient [7]. Let’s think of a comparison?

  1. We need to ensure that our Financial and Management accounts are sound.
  2. It would be helpful if business leaders had good Financial reports to help them understand the state of their business.
  3. Therefore we need a Finance project / programme to sort this out once and for all.
  4. Where is the telephone number of the Change Director?

Most CFOs would view the above as their responsibility. They have an entire function focussed on such matters. Of course they may want to run some Finance projects and Change will help with this, but a Finance Department is an ongoing necessity.

To pick another example one that illustrates just how quickly the make-up of organisations can change, just replace the word “Finance” with “Risk” in the above and “CFO” with “CRO”. While programmes may be helpful to improve either Risk or Finance, they do not run the Risk or Finance functions, the designated officers do and they have a complement of staff to assist them. It is exactly the same with data. Data programmes will enhance your use of data or control of it, but they will not ensure the day-to-day management and leverage of data in your organisation. Running “data” is the responsibility of the designated officer [8] and they should have a complement of staff to assist them as well.

The Data Function is a “business as usual” [9] function. Conveying this fact to a range of stakeholders is going to be one of the first challenges. It may be that the couple of examples I cite above can provide some ammunition for this task.

Demolishing Demoralising Demarcations

With Data Functions and their leaders both being relative emergent phenomena [10], the separation of duties between them and other areas of a business that also deal with data can be less than clear. Scanning down the Related Areas column of the overall Data Function chart, three entities stand out who may feel that they have a strong role to play in data matters: Digital, Change Management and IT.

Of course each is correct and collaboration is the best way forward. However, human nature is not always do benign and I have several times seen jockeying for position between Data, Digital, Change and IT. Route A to resolving this is of course having clarity as to everyone’s roles and a lead Executive (normally a CEO or COO) who ensures that people play nicely with each other. Back in the real world, it will be down to the leaders in each of these areas to forge some sort of consensus about who does what and why. It is probably best to realise this upfront, rather than wasting time and effort lobbying Executives to rule on things they probably have no intention of ruling on.

Nascent Data Function leaders should be aware that there will be a tendency for other teams to carve out what might be seen as the sexier elements of Data work; this can almost seem logical when – for example – a Digital team already has a full complement of web analytics staff; surely it is just a matter of pointing these at other internal data sets, right?

If we assume that the Data Function is the last of the above mentioned departments to form, then “zero sum game” thinking would dictate that whatever is accretive to the Data Function is deleterious to existing data staff in other departments. Perhaps a good place to start in combatting this mind-set is to first acknowledge it and second to take steps to allay people’s fears. It may well make sense for some staff to gravitate to the Data Function, but only if there is a compelling logic and only if all parties agree. Offering the leaders of other departments joint decision-making on such sensitive issues can be a good confidence-building step.

Setting out explicitly to help colleagues in other departments, where feasible to do so, can make very good sense and begin the necessary work of building bridges. As with most areas of human endeavour, forging good relationships and working towards the common good are both the right thing to do and put the Data Function leader in a good place as and when more contentious discussions arise.

To make this concrete, when people in another function appear to be stepping on the toes of the Data Function, instead of reacting with outrage, it may be preferable to embrace and fully understand the work that is being done. It may even make sense to support such work, even if the ultimate view is to do things a bit differently. Insisting on organisational purity and a “my way, or the highway” attitude to data matters are both steps towards a failed Data Function. Instead, engage, listen, support and – maybe over time – seek to nudge things towards your desired state.
 
 
Closing Thoughts

That's All Folks

So we have reached the end of our anatomical journey. While maybe the information contained in these three articles would pale into insignificance compared to an actual course in human anatomy, we have nevertheless covered five main work-areas within a Data Function, splitting these down into nineteen sub-areas and cataloguing eight functions with which collaboration will be key in driving success. I have also typed over 8,000 words to convey my ideas. For those who have read all of them, thank you for your perseverance; I hope that the effort has been worthwhile and that you found some of my opinions thought-provoking.

I would also like to thank the various people who have provided positive feedback on this series via LinkedIn and Facebook. Your comments were particularly influential in shaping this final chapter.

So what are the main takeaways? Well first the word collaboration has cropped up a lot and – because data is so pervasive in organisations – the need to collaborate with a wide variety of people and departments is strong. Second, extending the human anatomy analogy, while each human shares a certain basic layout (upright, bipedal, two arms, etc.), there is considerable variation within the basic parameters. The same goes for the organogram of a Data Function that I have presented at the beginning of each of these articles. The boxes may be rearranged in some organisations, some may not sit in the Data Function in others, the amount of people allocated to each work-area will vary enormously. As with human anatomy, grasping the overall shape is more important than focussing on the inevitable variations between different people.

Third, a central concept is of course that a Data Function is necessary, not just a series of data-centric projects. Even if it starts small, some dedicated resource will be necessary and it would probably be foolish to embark on a data journey without at least a skeleton crew. Fourth, in such straitened circumstances, it is important to point early and clearly to the value of data, both in reducing potentially expensive risks and in driving insights that can save money, boost market share or improve products or services. If the budget is limited, attend to these two things first.

A fifth and final thought is how little these three articles have focussed on technology. Hadoop clusters, data visualisation suites and data governance tools all have their place, but the success or failure of data-centric work tends to pivot on more human and process considerations. This theme of technology being the least important part of data work is one I have come back to time and time again over the nine years that this blog has been published. This observation remains as true today as back in 2008.
 

Part I Part II Part III

 
Notes

 
[1]
 
BAU should in general be filed along with other mythical creatures such as Unicorns, Bigfoot, The Kraken and The Loch Ness Monster.
 
[2]
 
Not least because of the rise of Data Functions, Digital Teams and stand-alone Change Organisations.
 
[3]
 
A title borrowed from J E Thompson’s Calculus for the Practical Man; a tome read by the young Richard Feynman in childhood. Today “Calculus for the Practical Person” might be a more inclusive title.
 
[4]
 
Also known as “pulling yourself up by your bootstraps”.
 
[5]
 
I seem to be channelling JA a lot at present – see A truth universally acknowledged….
 
[6]
 
Indeed I have stated on this particular journey with just myself for company on no fewer than for occasions (these three 1, 2, 3, plus at Bupa).
 
[7]
 
Once a Mathematician, always a Mathematician.
 
[8]
 
See Alphabet Soup for some ideas about what he or she might be called.
 
[9]
 
See note 1.
 
[10]
 
Despite early high-profile CDOs beginning to appear at the turn of the millennium – Joe Bugajski was appointed VP and Chief Data Officer at Visa International in 2001 (Wikipedia).

 

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