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.
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:
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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.
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:
and rather less:
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 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.
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 , 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).
While the role of IT is perhaps narrower nowadays than historically , 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 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 
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 .
It is a truth universally acknowledged, that a Leader newly in possession of a Data Function, must be in want of some staff . 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 . 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.
- We have issues with our data, its quality, completeness and fitness for purpose.
- We do not do a good enough job of leveraging our data to guide decision making.
- Therefore we need a data project / programme to sort this out once and for all.
- 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 . Let’s think of a comparison?
- We need to ensure that our Financial and Management accounts are sound.
- It would be helpful if business leaders had good Financial reports to help them understand the state of their business.
- Therefore we need a Finance project / programme to sort this out once and for all.
- 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  and they should have a complement of staff to assist them as well.
The Data Function is a “business as usual”  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 , 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.
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.
BAU should in general be filed along with other mythical creatures such as Unicorns, Bigfoot, The Kraken and The Loch Ness Monster.
Not least because of the rise of Data Functions, Digital Teams and stand-alone Change Organisations.
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.
Also known as “pulling yourself up by your bootstraps”.
I seem to be channelling JA a lot at present – see A truth universally acknowledged….
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).
Once a Mathematician, always a Mathematician.
See Alphabet Soup for some ideas about what he or she might be called.
See note 1.
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
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