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

 

Fact-based Decision-making

All we need is fact-based decision-making ma'am
This article is about facts. Facts are sometimes less solid than we would like to think; sometimes they are downright malleable. To illustrate, consider the fact that in 98 episodes of Dragnet, Sergeant Joe Friday never uttered the words “Just the facts Ma’am”, though he did often employ the variant alluded to in the image above [1]. Equally, Rick never said “Play it again Sam” in Casablanca [2] and St. Paul never suggested that “money is the root of all evil” [3]. As Michael Caine never said in any film, “not a lot of people know that” [4].

 
Up-front Acknowledgements

These normally appear at the end of an article, but it seemed to make sense to start with them in this case:

Recently I published Building Momentum – How to begin becoming a Data-driven Organisation. In response to this, one of my associates, Olaf Penne, asked me about my thoughts on fact-base decision-making. This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? Thanks to both Olaf and Neil for the inspiration.

Fact-based decision making. It sounds good doesn’t it? Especially if you consider the alternatives: going on gut feel, doing what you did last time, guessing, not taking a decision at all. However – as is often the case with issues I deal with on this blog – fact-based decision-making is easier to say than it is to achieve. Here I will look to cover some of the obstacles and suggest a potential way to navigate round them. Let’s start however with some definitions.

Fact NOUN A thing that is known or proved to be true.
(Oxford Dictionaries)
Decision NOUN A conclusion or resolution reached after consideration.
(Oxford Dictionaries)

So one can infer that fact-based decision-making is the process of reaching a conclusion based on consideration of things that are known to be true. Again, it sounds great doesn’t it? It seems that all you have to do is to find things that are true. How hard can that be? Well actually quite hard as it happens. Let’s cover what can go wrong (note: this section is not intended to be exhaustive, links are provided to more in-depth articles where appropriate):


 
Accuracy of Data that is captured

Data Accuracy

A number of factors can play into the accuracy of data capture. Some systems (even in 2018) can still make it harder to capture good data than to ram in bad. Often an issue may also be a lack of master data definitions, so that similar data is labelled differently in different systems.

A more pernicious problem is combinatorial data accuracy, two data items are both valid, but not in combination with each other. However, often the biggest stumbling block is a human one, getting people to buy in to the idea that the care and attention they pay to data capture will pay dividends later in the process.

These and other areas are covered in greater detail in an older article, Using BI to drive improvements in data quality.
 
 
Honesty of Data that is captured

Honesty of Data

Data may be perfectly valid, but still not represent reality. Here I’ll let Neil Raden point out the central issue in his customary style:

People find the most ingenious ways to distort measurement systems to generate the numbers that are desired, not only NOT providing the desired behaviors, but often becoming more dysfunctional through the effort.

[…] voluntary compliance to the [US] tax code encourages a national obsession with “loopholes”, and what salesman hasn’t “sandbagged” a few deals for next quarter after she has met her quota for the current one?

Where there is a reward to be gained or a punishment to be avoided, by hitting certain numbers in a certain way, the creativeness of humans often comes to the fore. It is hard to account for such tweaking in measurement systems.
 
 
Timing issues with Data

Timing Issues

Timing is often problematic. For example, a transaction completed near the end of a period gets recorded in the next period instead, one early in a new period goes into the prior period, which is still open. There is also (as referenced by Neil in his comments above) the delayed booking of transactions in order to – with the nicest possible description – smooth revenues. It is not just hypothetical salespeople who do this of course. Entire organisations can make smoothing adjustments to their figures before publishing and deferral or expedition of obligations and earnings has become something of an art form in accounting circles. While no doubt most of this tweaking is done with the best intentions, it can compromise the fact-based approach that we are aiming for.
 
 
Reliability with which Data is moved around and consolidated

Data Transcription

In our modern architectures, replete with web-services, APIs, cloud-based components and the quasi-instantaneous transmission of new transactions, it is perhaps not surprising that occasionally some data gets lost in translation [5] along the way. That is before data starts to be Sqooped up into Data Lakes, or other such Data Repositories, and then otherwise manipulated in order to derive insight or provide regular information. All of these are processes which can introduce their own errors. Suffice it to say that transmission, collation and manipulation of data can all reduce its accuracy.

Again see Using BI to drive improvements in data quality for further details.
 
 
Pertinence and fidelity of metrics developed from Data

Data Metric

Here we get past issues with data itself (or how it is handled and moved around) and instead consider how it is used. Metrics are seldom reliant on just one data element, but are often rather combinations. The different elements might come in because a given metric is arithmetical in nature, e.g.

\text{Metric X} = \dfrac{\text{Data Item A}+\text{Data Item B}}{\text{Data Item C}}

Choices are made as to how to construct such compound metrics and how to relate them to actual business outcomes. For example:

\text{New Biz Growth} = \dfrac{(\text{Sales CYTD}-\text{Repeat CYTD})-(\text{Sales PYTD}-\text{Repeat PYTD})}{(\text{Sales PYTD}-\text{Repeat PYTD})}

Is this a good way to define New Business Growth? Are there any weaknesses in this definition, for example is it sensitive to any glitches in – say – the tagging of Repeat Business? Do we need to take account of pricing changes between Repeat Business this year and last year? Is New Business Growth something that is even worth tracking; what will we do as a result of understanding this?

The above is a somewhat simple metric, in a section of Using historical data to justify BI investments – Part I, I cover some actual Insurance industry metrics that build on each other and are a little more convoluted. The same article also considers how to – amongst other things – match revenue and outgoings when the latter are spread over time. There are often compromises to be made in defining metrics. Some of these are based on the data available. Some relate to inherent issues with what is being measured. In other cases, a metric may be a best approximation to some indication of business health; a proxy used because that indication is not directly measurable itself. In the last case, staff turnover may be a proxy for staff morale, but it does not directly measure how employees are feeling (a competitor might be poaching otherwise happy staff for example).
 
 
Robustness of extrapolations made from Data

By the third trimester, there will be hundreds of babies inside you...

© Randall Munroe, xkcd.com

I have used the above image before in these pages [6]. The situation it describes may seem farcical, but it is actually not too far away from some extrapolations I have seen in a business context. For example, a prediction of full-year sales may consist of this year’s figures for the first three quarters supplemented by prior year sales for the final quarter. While our metric may be better than nothing, there are some potential distortions related to such an approach:

  1. Repeat business may have fallen into Q4 last year, but was processed in Q3 this year. This shift in timing would lead to such business being double-counted in our year end estimate.
     
  2. Taking point 1 to one side, sales may be growing or contracting compared to the previous year. Using Q4 prior year as is would not reflect this.
     
  3. It is entirely feasible that some market event occurs this year ( for example the entrance or exit of a competitor, or the launch of a new competitor product) which would render prior year figures a poor guide.

Of course all of the above can be adjusted for, but such adjustments would be reliant on human judgement, making any projections similarly reliant on people’s opinions (which as Neil points out may be influenced, conciously or unconsciously, by self-interest). Where sales are based on conversions of prospects, the quantum of prospects might be a more useful predictor of Q4 sales. However here a historical conversion rate would need to be calculated (or conversion probabilities allocated by the salespeople involved) and we are back into essentially the same issues as catalogued above.

I explore some similar themes in a section of Data Visualisation – A Scientific Treatment
 
 
Integrity of statistical estimates based on Data

Statistical Data

Having spent 18 years working in various parts of the Insurance industry, statistical estimates being part of the standard set of metrics is pretty familiar to me [7]. However such estimates appear in a number of industries, sometimes explicitly, sometimes implicitly. A clear parallel would be credit risk in Retail Banking, but something as simple as an estimate of potentially delinquent debtors is an inherently statistical figure (albeit one that may not depend on the output of a statistical model).

The thing with statistical estimates is that they are never a single figure but a range. A model may for example spit out a figure like £12.4 million ± £0.5 million. Let’s unpack this.

Example distribution

Well the output of the model will probably be something analogous to the above image. Here a distribution has been fitted to the business event being modelled. The central point of this (the one most likely to occur according to the model) is £12.4 million. The model is not saying that £12.4 million is the answer, it is saying it is the central point of a range of potential figures. We typically next select a symmetrical range above and below the central figure such that we cover a high proportion of the possible outcomes for the figure being modelled; 95% of them is typical [8]. In the above example, the range extends plus £0. 5 million above £12.4 million and £0.5 million below it (hence the ± sign).

Of course the problem is then that Financial Reports (or indeed most Management Reports) are not set up to cope with plus or minus figures, so typically one of £12.4 million (the central prediction) or £11.9 million (the most conservative estimate [9]) is used. The fact that the number itself is uncertain can get lost along the way. By the time that people who need to take decisions based on such information are in the loop, the inherent uncertainty of the prediction may have disappeared. This can be problematic. Suppose a real result of £12.4 million sees an organisation breaking even, but one of £11.9 million sees a small loss being recorded. This could have quite an influence on what course of action managers adopt [10]; are they relaxed, or concerned?

Beyond the above, it is not exactly unheard of for statistical models to have glitches, sometimes quite big glitches [11].

This segment could easily expand into a series of articles itself. Hopefully I have covered enough to highlight that there may be some challenges in this area.
 
 
And so what?

The dashboard has been updated, how thrilling...

Even if we somehow avoid all of the above pitfalls, there remains one booby-trap that is likely to snare us, absent the necessary diligence. This was alluded to in the section about the definition of metrics:

Is New Business Growth something that is even worth tracking; what will we do as a result of understanding this?

Unless a reported figure, or output of a model, leads to action being taken, it is essentially useless. Facts that never lead to anyone doing anything are like lists learnt by rote at school and regurgitated on demand parrot-fashion; they demonstrate the mechanism of memory, but not that of understanding. As Neil puts it in his article:

[…] technology is never a solution to social problems, and interactions between human beings are inherently social. This is why performance management is a very complex discipline, not just the implementation of dashboard or scorecard technology.


 
How to Measure the Unmeasurable

Measuring the Unmeasurable

Our dream of fact-based decision-making seems to be crumbling to dust. Regular facts are subject to data quality issues, or manipulation by creative humans. As data is moved from system to system and repository to repository, the facts can sometimes acquire an “alt-” prefix. Timing issues and the design of metrics can also erode accuracy. Then there are many perils and pitfalls associated with simple extrapolation and less simple statistical models. Finally, any fact that manages to emerge from this gantlet [12] unscathed may then be totally ignored by those whose actions it is meant to guide. What can be done?

As happens elsewhere on this site, let me turn to another field for inspiration. Not for the first time, let’s consider what Science can teach us about dealing with such issues with facts. In a recent article [13] in my Maths & Science section, I examined the nature of Scientific Theory and – in particular – explored the imprecision inherent in the Scientific Method. Here is some of what I wrote:

It is part of the nature of scientific theories that (unlike their Mathematical namesakes) they are not “true” and indeed do not seek to be “true”. They are models that seek to describe reality, but which often fall short of this aim in certain circumstances. General Relativity matches observed facts to a greater degree than Newtonian Gravity, but this does not mean that General Relativity is “true”, there may be some other, more refined, theory that explains everything that General Relativity does, but which goes on to explain things that it does not. This new theory may match reality in cases where General Relativity does not. This is the essence of the Scientific Method, never satisfied, always seeking to expand or improve existing thought.

I think that the Scientific Method that has served humanity so well over the centuries is applicable to our business dilemma. In the same way that a Scientific Theory is never “true”, but instead useful for explaining observations and predicting the unobserved, business metrics should be judged less on their veracity (though it would be nice if they bore some relation to reality) and instead on how often they lead to the right action being taken and the wrong action being avoided. This is an argument for metrics to be simple to understand and tied to how decision-makers actually think, rather than some other more abstruse and theoretical definition.

A proxy metric is fine, so long as it yields the right result (and the right behaviour) more often than not. A metric with dubious data quality is still useful if it points in the right direction; if the compass needle is no more than a few degrees out. While of course steps that improve the accuracy of metrics are valuable and should be undertaken where cost-effective, at least equal attention should be paid to ensuring that – when the metric has been accessed and digested – something happens as a result. This latter goal is a long way from the arcana of data lineage and metric definition, it is instead the province of human psychology; something that the accomploished data professional should be adept at influencing.

I have touched on how to positively modify human behaviour in these pages a number of times before [14]. It is a subject that I will be coming back to again in coming months, so please watch this space.
 


Further reading on this subject:


 
Notes

 
[1]
 
According to Snopes, the phrase arose from a spoof of the series.
 
[2]
 
The two pertinent exchanges were instead:

Ilsa: Play it once, Sam. For old times’ sake.
Sam: I don’t know what you mean, Miss Ilsa.
Ilsa: Play it, Sam. Play “As Time Goes By”
Sam: Oh, I can’t remember it, Miss Ilsa. I’m a little rusty on it.
Ilsa: I’ll hum it for you. Da-dy-da-dy-da-dum, da-dy-da-dee-da-dum…
Ilsa: Sing it, Sam.

and

Rick: You know what I want to hear.
Sam: No, I don’t.
Rick: You played it for her, you can play it for me!
Sam: Well, I don’t think I can remember…
Rick: If she can stand it, I can! Play it!
 
[3]
 
Though he, or whoever may have written the first epistle to Timothy, might have condemned the “love of money”.
 
[4]
 
The origin of this was a Peter Sellers interview in which he impersonated Caine.
 
[5]
 
One of my Top Ten films.
 
[6]
 
Especially for all Business Analytics professionals out there (2009).
 
[7]
 
See in particular my trilogy:

  1. Using historical data to justify BI investments – Part I (2011)
  2. Using historical data to justify BI investments – Part II (2011)
  3. Using historical data to justify BI investments – Part III (2011)
 
[8]
 
Without getting into too many details, what you are typically doing is stating that there is a less than 5% chance that the measurements forming model input match the distribution due to a fluke; but this is not meant to be a primer on null hypotheses.
 
[9]
 
Of course, depending on context, £12.9 million could instead be the most conservative estimate.
 
[10]
 
This happens a lot in election polling. Candidate A may be estimated to be 3 points ahead of Candidate B, but with an error margin of 5 points, it should be no real surprise when Candidate B wins the ballot.
 
[11]
 
Try googling Nobel Laureates Myron Scholes and Robert Merton and then look for references to Long-term Capital Management.
 
[12]
 
Yes I meant “gantlet” that is the word in the original phrase, not “gauntlet” and so connections with gloves are wide of the mark.
 
[13]
 
Finches, Feathers and Apples (2018).
 
[14]
 
For example:

 


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

 

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

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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

 

A Retrospective of 2017’s Articles

A Review of 2017

This article was originally intended for publication late in the year it reviews, but, as they [1] say, the best-laid schemes o’ mice an’ men gang aft agley…

In 2017 I wrote more articles [2] than in any year since 2009, which was the first full year of this site’s existence. Some were viewed by thousands of people, others received less attention. Here I am going to ignore the metric of popular acclaim and instead highlight a few of the articles that I enjoyed writing most, or sometimes re-reading a few months later [3]. Given the breadth of subject matter that appears on peterjamesthomas.com, I have split this retrospective into six areas, which are presented in decreasing order of the number of 2017 articles I wrote in each. These are as follows:

  1. General Data Articles
  2. Data Visualisation
  3. Statistics & Data Science
  4. CDO perspectives
  5. Programme Advice
  6. Analytics & Big Data

In each category, I will pick out two or three of pieces which I feel are both representative of my overall content and worth a read. I would be more than happy to receive any feedback on my selections, or suggestions for different choices.

 
 
General Data Articles
 
The Data & Analytics Dictionary
 
August
The Data and Analytics Dictionary
My attempt to navigate the maze of data and analytics terminology. Everything from Algorithm to Web Analytics.
 
The Anatomy of a Data Function
 
November & December
The Anatomy of a Data Function: Part I, Part II and Part III
Three articles focussed on the structure and components of a modern Data Function and how its components interact with both each other and the wider organisation in order to support business goals.
 
 
Data Visualisation
 
Nucleosynthesis and Data Visualisation
 
January
Nucleosynthesis and Data Visualisation
How one of the most famous scientific data visualisations, the Periodic Table, has been repurposed to explain where the atoms we are all made of come from via the processes of nucleosynthesis.
 
Hurricanes and Data Visualisation
 
September & October
Hurricanes and Data Visualisation: Part I – Rainbow’s Gravity and Part II – Map Reading
Two articles on how Data Visualisation is used in Meteorology. Part I provides a worked example illustrating some of the problems that can arise when adopting a rainbow colour palette in data visualisation. Part II grapples with hurricane prediction and covers some issues with data visualisations that are intended to convey safety information to the public.
 
 
Statistics & Data Science
 
Toast
 
February
Toast
What links Climate Change, the Manhattan Project, Brexit and Toast? How do these relate to the public’s trust in Science? What does this mean for Data Scientists?
Answers provided by Nature, The University of Cambridge and the author.
 
How to be Surprisingly Popular
 
February
How to be Surprisingly Popular
The wisdom of the crowd relies upon essentially democratic polling of a large number of respondents; an approach that has several shortcomings, not least the lack of weight attached to people with specialist knowledge. The Surprisingly Popular algorithm addresses these shortcomings and so far has out-performed existing techniques in a range of studies.
 
A Nobel Laureate’s views on creating Meaning from Data
 
October
A Nobel Laureate’s views on creating Meaning from Data
The 2017 Nobel Prize for Chemistry was awarded to Structural Biologist Richard Henderson and two other co-recipients. What can Machine Learning practitioners learn from Richard’s observations about how to generate images from Cryo-Electron Microscopy data?
 
 
CDO Perspectives
 
Alphabet Soup
 
January
Alphabet Soup
Musings on the overlapping roles of Chief Analytics Officer and Chief Data Officer and thoughts on whether there should be just one Top Data Job in an organisation.
 
A Sweeter Spot for the CDO?
 
February
A Sweeter Spot for the CDO?
An extension of my concept of the Chief Data Officer sweet spot, inspired by Bruno Aziza of AtScale.
 
A truth universally acknowledged…
 
September
A truth universally acknowledged…
Many Chief Data Officer job descriptions have a list of requirements that resemble Swiss Army Knives. This article argues that the CDO must be the conductor of an orchestra, not someone who is a virtuoso in every single instrument.
 
 
Programme Advice
 
Bumps in the Road
 
January
Bumps in the Road
What the aftermath of repeated roadworks can tell us about the potentially deleterious impact of Change Programmes on Data Landscapes.
 
20 Risks that Beset Data Programmes
 
February
20 Risks that Beset Data Programmes
A review of 20 risks that can plague data programmes. How effectively these are managed / mitigated can make or break your programme.
 
Ideas for avoiding Big Data failures and for dealing with them if they happen
 
March
Ideas for avoiding Big Data failures and for dealing with them if they happen
Paul Barsch (EY & Teradata) provides some insight into why Big Data projects fail, what you can do about this and how best to treat any such projects that head off the rails. With additional contributions from Big Data gurus Albert Einstein, Thomas Edison and Samuel Beckett.
 
 
Analytics & Big Data
 
Bigger and Better (Data)?
 
February
Bigger and Better (Data)?
Some examples of where bigger data is not necessarily better data. Provided by Bill Vorhies and Larry Greenemeier .
 
Elephants’ Graveyard?
 
March
Elephants’ Graveyard?
Thoughts on trends in interest in Hadoop and Spark, featuring George Hill, James Kobielus, Kashif Saiyed and Martyn Richard Jones, together with the author’s perspective on the importance of technology in data-centric work.
 
 
and Finally…

I would like to close this review of 2017 with a final article, one that somehow defies classification:

 
25 Indispensable Business Terms
 
April
25 Indispensable Business Terms
An illustrated Buffyverse take on Business gobbledygook – What would Buffy do about thinking outside the box? To celebrate 20 years of Buffy the Vampire Slayer and 1st April 2017.

 
Notes

 
[1]
 
“They” here obviously standing for Robert Burns.
 
[2]
 
Thirty-four articles and one new page.
 
[3]
 
Of course some of these may also have been popular, I’m not being masochistic here!

 

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

 

The Anatomy of a Data Function – Part II

Part I Part II Part III

Sepia's Anatomy

This is the second part of my review of the anatomy of a Data Function, the artfully named Part I may be viewed here. As seems to happen all too often to me, this series will now extend to having a Part III, which may be viewed 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 will consider the two remaining Data Function areas of Data Architecture and Data Management. Covering Related Areas, and presenting some thoughts on how to go about setting up a Data Function and the pitfalls to be faced along the way, together form the third and final part of this trilogy.

As in Part I, unless otherwise stated, text indented as a quotation is excerpted from the Data and Analytics Dictionary.
 
 
Data Architecture

Data Architecture

To be somewhat self-referential, this area acts a a cornerstone for the rest of the Data Function. While sometimes non-Data architects can seem to inhabit a loftier plane than most mere mortals, Data Architects (who definitively must be part of the Data Function and none of the Business, Enterprise or Solutions Architecture groups) tend to be more practical sorts with actual hands-on technical skills. Perhaps instead of the title “Architect”, “Structural Engineer” would be more appropriate. When a Data Architect draws a diagram with connected boxes, he or she generally understands how the connections work and could probably take a fair stab at implementing the linkages themselves. The other denizens of this area, such as Data Business Analysts, are also essentially pragmatic people, focused on real business outcomes. Data Architecture is a non-theoretical discipline and here I present some of the real-world activities that its members are often engaged in.
 
Change Portfolio Engagement

One of the most important services that a good Data Function can perform is to act as a moderator for the otherwise deleterious impact that uncontrolled (and uncoordinated) Change portfolios can have on even the best of data landscapes [1]. As I mention in another article:

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

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

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

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

Excerpted from: Bumps in the Road

A primary responsibility of a properly constituted Data Function is to lean hard against the prevailing winds of Change in order to protect existing data capabilities that would otherwise likely be blown away [2]. Given the gargantuan size of most current Change teams, it makes sense to have at least a reasonable amount of Data Function resource applied to this area. Hopefully early interventions in projects and programmes can mitigate any potentially adverse impacts and perhaps even lead to Change being accretive to data landscapes, as it really ought to be.

The best approach, as with most human endeavours is a collaborative one, with Data Function staff (probably Data Architects) getting involved in new Change projects and programmes at an early stage and shaping them to be positive from a Data dimension. However, there also needs to be teeth in the process; on occasion the Data Function must be able to prevent work that would cause true damage from going ahead; hopefully powers that are used more in breach than observance.
 
Data Modelling

It is in this area that the practical bent of Data Architects and Data Business Analysts is seen very clearly. Data modelling mirrors the realities of systems and databases the way that Theoretical Physicists use Mathematics to model the Natural World [3]. In both cases, while there may be a degree of abstraction, the end purpose is to achieve something more concrete. A definition is as follows:

[Data Modelling is] the process of examining data sets (e.g. the database underpinning a system) in order to understand how they are structured, the relationships between their various parts and the business entities and transactions they represent. While system data will have a specific Physical Data Model (the tables it contains and their linkages), Data Modelling may instead look to create a higher-level and more abstract set of pseudo-tables, which would be easier to relate to for non-technical staff and would more closely map to business terms and activities; this is known as a Conceptual Data Model. Sitting somewhere between the two may be found Logical Data Models. There are several specific documents produced by such work, one of the most common being an Entity-Relationship diagram, e.g. a sales order has a customer and one or more line items, each of which has a product.

Data and Analytics Dictionary entry: Data Modelling

 
Data Business Analysis

Another critical role. In my long experience of both setting up Data Functions and running Data Programmes, having good Data Business Analysts on board is often the difference between success and failure. I cannot stress enough how important this role is.

Data Business Analysts are neither regular Business Analysts, nor just Data Analysts, but rather a combination of the best of both. They do have all the requirement gathering skills of the best BAs, but complement these with Data Modelling abilities, always seeking to translate new requirements into expanded or refined Data Models. Also the way that they approach business requirements will be very specific. The optimal way to do this is by teasing out (and they collating and categorising) business questions and then determining the information needed to answer these. A good Data Business Analyst will also have strong Data Analysis skills, being able to work with unfamiliar and lightly-documented datasets to discern meaning and link this to business concepts. A definition is as follows:

A person who has extensive understanding of both business processes and the data necessary to support these. A Business Analyst is expert at discerning what people need to do. A Data Analyst is adept at working with datasets and extracting meaning from them. A Data Business Analyst can work equally happily in both worlds at the same time. When they talk to people about their requirements for information, they are simultaneously updating mental models of the data necessary to meet these needs. When they are considering how lightly-documented datasets hang together, they constantly have in mind the business purpose to which such resources may be bent.

Data and Analytics Dictionary entry: Data Business Analyst

 
 
Data Management

Data Management

Again, it is worth noting that I have probably defined this area more narrowly than many. It could be argued that it should encompass the work I have under Data Architecture and maybe much of what is under Data Operations & Technology. The actual hierarchy is likely to be driven by factors like the nature of the organisation and the seniority of Managers in the Data Function. For good or ill, I have focussed Data Management more on the care and feeding of Data Assets in my recommended set-up. A definition is as follows:

The day-to-day management of data within an organisation, which encompasses areas such as Data Architecture, Data Quality, Data Governance (normally on behalf of a Data Governance Committee) and often some elements of data provision and / or regular reporting. The objective is to appropriately manage the lifecycle of data throughout the entire organisation, which both ensures the reliability of data and enables it to become a valuable and strategic asset.

In some organisations, Data Management and Analytics are part of the same organisation, in others they are separate but work closely together to achieve shared objectives.

Data and Analytics Dictionary entry: Data Management

 
Data Governance

There is a clear link here with some of the Data Architecture activities, particularly the Change Portfolio Engagement work-area. Governance should represent the strategic management of the data component of Change (i.e. most of Change), day-to-day collaboration would sit more in the Data Architecture area.

The management processes and policies necessary to ensure that data captured or generated within a company is of an appropriate standard to use, represents actual business facts and has its integrity preserved when transferred to repositories (e.g. Data Lakes and / or Data Warehouses, General Ledgers etc.), especially when this transfer involves aggregation or merging of different data sets. The activities that Data Governance has oversight of include the operation of and changes to Systems of Record and the activities of Data Management and Analytics departments (which may be merged into one unit, or discrete but with close collaboration).

Data Governance has a strategic role, often involving senior management. Day-to-day tasks supporting Data Governance are often carried out by a Data Management team.

Data and Analytics Dictionary entry: Data Governance

 
Data Definitions & Metadata

This is a relatively straightforward area to conceptualise. Rigorous and consistent definitions of master data and calculated data are indispensable in all aspects of how a Data Function operates and how an organisation both leverages and protects its data. Focusing on Metadata, a definition would be as follows:

[Metadata is] data about data. So descriptions of what appears in fields, how these relate to other fields and what concepts bigger constructs like Tables embody. This helps people unfamiliar with a dataset to understand how it hangs together and is good practice in the same way that documentation of any other type of code is good practice. Metadata can be used to support some elements of Data Discovery by less technical people. It is also invaluable when there is a need for Data Migration.

Data and Analytics Dictionary entry: Metadata

 
Data Audit

One of the challenges in driving Data Quality improvements in organisations is actually highlighting the problems and their impacts. Often poor Data Quality is a hidden cost, spread across many people taking longer to do their jobs than is necessary, or specific instances where interactions with business counterparties (including customers) are compromised. Organisations obviously cope – at least in general – with these issues, but they are a drag on efficiency and, in extremis, can lead to incidents which can cause significant financial loss and/or reputational damage. A way to make such problems more explicit is via a regular Data Audit, a review of data in source systems and as it travels through various data repositories. This would include some assessment of the completeness and overall quality of data, highlighting areas of particular concern. So one component might include the percentage of active records which suffer from a significant data quality issue.

It is important that any such issues are categorised. Are they the result of less than perfect data entry procedures, which could be tightened up? Are they due to deficient validation in transactional systems, where this could be improved and there may be a role for Master Data Management? Are data interfaces between systems to blame, where these need to be reengineered or potentially replaced? Are there architectural issues with systems or repositories, which will require remedial work to address?

This information needs to be rolled up and presented in an accessible manner so that those responsible for systems and processes can understand where issues lie. Data Audits, even if partially automated, take time and effort, so it may be appropriate to carry them out quarterly. In this case, it is valuable to understand how the situation is changing over time and also to track the – hopefully positive – impact of any remedial action. Experienced Data Analysts with a good appreciation of how business is conducted in the organisation are the type of resource best suited to Data Audit work.
 
Data Quality

Much that needs to be said here is covered in the previous section about Data Audit. Data Quality can be defined as follows:

The characteristics of data that cover how accurately and completely it mirrors real world events and thereby how much reliance can be placed on it for the purpose of generating information and insight. Enhancing Data Quality should be a primary objective of Data Management teams.

Data and Analytics Dictionary entry: Data Quality

A Data Quality team, which would work closely with Data Audit colleagues, would be focussed on helping to drive improvements. The details of such work are covered in an earlier article, from which the following is excerpted:

There are a number of elements that combine to improve the quality of data:

  1. Improve how the data is entered
  2. Make sure your interfaces aren’t the problem
  3. Check how the data is entered / interfaced
  4. Don’t suppress bad data in your BI

As with any strategy, it is ideal to have the support of all four pillars. However, I have seen greater and quicker improvements through the fourth element than with any of the others.

Excerpted from: Using BI to drive improvements in data quality

 
Master Data Management

There is some overlap here with Data Definitions & Metadata as mentioned above. Master Data Management has also been mentioned here in the context of Data Quality initiatives. However this specialist area tends to demand dedicated staff. A definition is as follows:

Master Data Management is the term used to both describe the set of process by which Master Data is created, changed and deleted in an organisation and also the technological tools that can facilitate these processes. There is a strong relation here to Data Governance, an area which also encompasses broader objectives. The aim of MDM is to ensure that the creation of business transactions results in valid data, which can then be leveraged confidently to create Information.

Many of the difficulties in MDM arise from items of Master Data that can change over time; for example when one counterparty is acquired by another, or an organisational structure is changed (maybe creating new departments and consolidating old ones). The challenges here include, how to report historical transactions that are tagged with Master Data that has now changed.

Data and Analytics Dictionary entry: Master Data Management

 
 
At this point, we have covered all of the work-areas within our idealised Data Function. In the third and final piece, we will consider the right-hand column of Related Areas, ones that a Data Function must collaborate with. Having covered these, the trilogy will close by offering some thoughts on the challenges of setting up a Data Function and how these may be overcome.
 

Part I Part II Part III

 
Notes

 
[1]
 
I am old enough to recall a time before Change portfolios, I can recall no organisation in which I have worked over the last 20 years in which Change portfolios have had a positive impact on data assets; maybe I have just been unlucky, but it begins to feel more like a fundamental Physical Law.
 
[2]
 
I have clearly been writing about hurricanes too much recently!
 
[3]
 
As is seen, for example in, the Introduction to my [as yet unfinished] book on the role of Group Theory in Theoretical Physics, Glimpses of Symmetry.

 

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

 

The Anatomy of a Data Function – Part I

Part I Part II Part III

Back in Alphabet Soup, I presented a diagram covering what I think are good and bad approaches to organising Analytics and Data Management. I wanted to offer an expanded view [1] of the good organisation chart and to talk a bit about each of its components. Originally, I planned to address these objectives across two articles. As happens to me all too frequently, the piece has now expanded to become three parts. The second may be read here, and the third 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.

Let’s leap right in and look at my suggested chart:

The Anatomy of a Data Function

Larger PDF version (opens in a new tab)

I appreciate that the above is a lot of boxes! I can feel Finance and HR staff reaching for their FTE calculators as I write. A few things to note:

  1. I have avoided the temptation to add the titles of executives, managers or team leaders. Alphabet Soup itself pointed out how tough it can be to wrestle with the nomenclature. Instead I have just focussed on areas of work.
     
  2. The term “work areas” is intentional. In larger organisations, there may be teams or individuals corresponding to each box. In smaller ones Data Function staff will wear many hats and several work areas may be covered by one person.
     
  3. In some places, a number of work areas that I have tagged as Data Function ones may be performed in other parts of the organisation, though it is to be hoped with collaboration and coordination.

Having dealt with these caveats, let’s provide some colour on each of these progressing from top to bottom and left to right. In this first article we will consider the Data Strategy, Analytics & Insight and Data Operations & Technology areas. The second part will cover the remaining elements of Data Architecture and Data Management. The final article, considers Related Areas before also covering some of the challenges that may be faced in setting up a Data Function.

In what follows, unless otherwise stated, text indented as a quotation is excerpted from the Data and Analytics Dictionary.
 
 
Data Strategy

Data Strategy

A clear strategy is obviously most important to establish in the early days of a Data Function. Indeed a Data Strategy may well call for the creation of a Data Function where none currently exists. For anyone interested in this process, I recommend my series of three articles on this subject [2]. However a Data Strategy is not something carved in stone, it will need to be revisited and adapted (maybe significantly) as circumstances change (e.g. after an acquisition, a change in market conditions or potentially due to the emergence of some new technology). There is thus a need for ongoing work in this area. However, as demand for strategic work will tend to be lumpy, I suggest amalgamating Data Strategy with the following two sub-areas.
 
Data Comms & Education

Elsewhere on this site, I have highlighted the need for effective communication, education and assiduous follow-up in data programmes [3]. Education on data matters does not stop when a data quality drive is successfully completed, or when a new set of analytical capabilities are introduced, this is a need for an ongoing commitment here. Activities falling into this work area include: publishing regular data newsletters and infographics, designing and helping to deliver training programmes, providing follow-up and support to aid the embedded used of new capabilities or to ingrain new behaviours.
 
Relationship Management

There is a need for all Data Function staff to establish and maintain good working relations with any colleagues they come into contact with, regardless of their level or influence. However, the nature of, generally hierarchical, organisations is that it is often prudent to pay special attention to more senior staff, or to the type of person (common in many companies) who may not be that senior, but whose opinion is influential. In aggregate these two groups of people are often described as stakeholders. Providing regular updates to stakeholders and ensuring both that they are comfortable with Data Function work and that this is aligned with their priorities can be invaluable [4]. Having senior, business-savvy Data Function people available to do this work is the most likely path to success.
 
 
Analytics & Insight

Analytics & Insight

Broadly speaking the Analytics area and its sub-areas are focussed more on one-off analyses rather that the recurrent production of information [5], the latter being more the preserve of the Data Operations & Technology area. There is also more of a statistical flavour to the work carried out here.

[Analytics relates to] deriving insights from data which are generally beyond the purpose for which the data was originally captured – to be contrasted with Information which relates to the meaning inherent in data (i.e. the reason that it was captured in the first place). Analytics often employ advanced statistical techniques (logistic regression, multivariate regression, time series analysis etc.) to derive meaning from data.

Data and Analytics Dictionary entry: Analytics

 
Data Science / Engineering

I have Data Science / Engineering as a sub-area of analytics, as with most terminology used in the data arena and most organisational units that exist in Data Functions, some people might argue that I have this the wrong way round and that Data Science / Engineering should be preeminent. Reconciling different points of view is not my objective here, I think most people will agree that both work areas should be covered. This comment pertains to many other parts of this article. Here is a two-part definition of the area (or rather the people who populate it):

[Data Scientists are people who are] au fait with exploiting data in many formats from Flat Files to Data Warehouses to Data Lakes. Such individuals possess equal abilities in the data technologies (such as Big Data) and how to derive benefit from these via statistical modelling. Data Scientists are often lapsed actual scientists.

[Data Engineering is] essentially a support function for Data Science. If you consider the messy process of sourcing data, loading it into a repository, cleansing, filling in “holes”, combining disparate data and so on, this is a somewhat different skill set to then analysing the resulting data. Early in the history of Data Science, the whole process sat with Data Scientists. Increasingly nowadays, the part before actual analysis begins is carried out by Data Engineers. These people often also concern themselves with aspects of Master Data Management and Data Architecture.

Data and Analytics Dictionary entries: Data Scientist and Data Engineering

 
Artificial Intelligence

Artificial Intelligence (AI) has its roots in the academic discipline of both trying to understand cognition and reproduce it. In recent years, AI has come out of the lecture hall and lab and become an increasingly important aspect of the modern world, from driving our cars, to providing advice on financial products, to making recommendations for on-line purchases. Previously I had AI subsumed in the Data Science section. However, two trends have caused me to split it out in the current version of the Data Function Anatomy. First the importance of AI is increasing rapidly. Second it is no longer just Data Scientists who carry out this work. The advent of AI Platforms hides some of the complexity, while allowing a broader range of people to leverage the power of AI. The particular element of AI that has caught on to the greatest extent in business to date is Machine Learning.

Data and Analytics Dictionary entry: Artificial Intelligence

 
Data Visualisation

There is an overlap here with both the Data Science team within the Analytics & Insight area and the Business Intelligence team in the Data Operations & Technology area. Many of the outputs of a good Data Function will include graphs, charts and other such exhibits. However, here would be located the real specialists, the people who would set standards for the presentation of visual data across the Data Function and be the most able in leveraging visualisation tools. A definition of Data Visualisation is as follows:

Techniques – such as graphs – for presenting complex information in a manner in which it can be more easily digested by human observers. Based on the concept that a picture paints a thousand words (or a dozen Excel sheets).

Data and Analytics Dictionary entry: Data Visualisation

 
Predictive Analytics

Gartner refer to four types of Analytics: descriptive, diagnostic, predictive and prescriptive analytics. In an article I referred to these as:

  • What happened?
  • Why did it happen?
  • What is going to happen next?
  • What should we be doing?

Data and Analytics Dictionary entry: Analytics

Predictive analytics is that element of the Analytics function that aims to predict the future, “What is going to happen next?” in the above list. This can be as simple as extrapolating data based on a trend line, or can involve more sophisticated techniques such as Time Series Analysis. As with most elements of the Data Function, there is overlap between Predictive Analytics and both Data Science and Business Intelligence.
 
“Skunkworks”

As with Data Strategy, state-of-the-art in Analytics & Insight will continue to evolve. This part of the Data Function will aim to keep current with the latest developments and to try out new techniques and new technologies that may later be adopted more widely by Data Function colleagues. The “skunkworks” team would be staffed by capable programmers / data scientists / statisticians.
 
 
Data Operations & Technology

Data Operations & Technology

It could be reasonably argued that this area is part of Data Management; I probably would not object too strongly to this suggestion. However, there are some benefits to considering it separately. This is the most IT-like of the areas considered here. It recognises that data technology (being it the Hadoop suite, Data Warehouse technology, or combinations of both) is different to many other forms of technology and needs its own specialists to focus on it. It is likely that the staff in this area will also collaborate closely with IT (see the final work area in Part II) or, in some cases, supervise work carried out by IT. As well as directly creating data capabilities, Data Operations & Technology staff would be active in the day-to-day running of these; again in collaboration with colleagues from both inside and outside of the Data Function.
 
Business Intelligence

There is no ISO definition, but I use this term as a catch-all to describe the transformation of raw data into information that can be disseminated to business people to support decision-making.

Data and Analytics Dictionary entry: Business Intelligence

This sub-area focusses on the relatively mature task of providing Business Intelligence solutions to organisations and working with IT to support and maintain these. Good BI tools work best on a sound underlying information architecture and so there would need to also be close collaboration with Data Infrastructure staff within Data Operations & Technology as well as colleagues from Data Architecture and also Analytics & Insight.
 
Regular Reporting

If BI provides interactive capabilities to support decision making, Regular Reporting is about the provision of specific key reports to relevant parties on a periodic basis; daily, weekly, monthly etc. These may be burst out to people’s e-mail accounts, provided at some central location, or both. While this an area that is ideally automated, there will still be significant need for human monitoring and to support the inevitable changes.
 
Data Service

One of the things that any part of a Data Function will find itself doing on a very regular basis is crafting ad hoc data extracts for other departments, e.g. Marketing, Risk & Compliance etc. Sometimes such a need will be on an ongoing basis and a web-service or some other Data Integration mechanism will need to be set up. Rather than having this be something that is supported out of the general running costs of the Data Function, it makes sense to have a specific unit whose role is to fulfil these needs. Even so, there may be a need for queuing and prioritisation of requests
 
Data Infrastructure

This relates to the physical architecture of the data landscape (for various flavours of logical architectures, see Data Architecture in Part II). While some of the tasks here may be carried out by (or in collaboration with) IT, the Data Infrastructure team will be expert at the care and feeding of Hadoop and related technologies and have experience in the fine-tuning of Data Warehouses and Data Marts.
 
SWAT Team

While (as both mentioned above and also covered in Part III this article) some of the heavy lifting in data matters will be carried out by an organisation’s IT team and / or its external partners, the process for getting things done in this way can be slow, tortuous and expensive [6]. It is important that a Data Function has its own capability to make at least minor technological changes, or to build and deploy helpful data facilities without having to engage with the overall bureaucracy. The SWAT Team will have a small number of very capable and business-knowledgeable programmers, capable of quickly generating robust and functional code.
 
 
The second part of this piece picks up where I have left off here and first consider Data Architecture.
 

Part I Part II Part III

 
Notes

 
[1]
 
I have added some functions that were absent in the previous one, mostly as they were not central to the points I was making in the previous article.
 
[2]
 
My trilogy on Formatting a Data / Information Strategy has the following parts:

  1. Part I – General Strategy
  2. Part II – Situational Analysis
  3. Part III – Completing the Strategy
 
[3]
 
While this theme runs through most of my writing, it is most explicitly referenced in the following three articles:

  1. Marketing Change
  2. Education and Cultural Transformation
  3. Sustaining Cultural Change
 
[4]
 
It should be noted that the relationship management described here is not the same as a Project Manager covering progress against plan. This is more of a two way conversation to ensure that the Data Function remains cognisant of stakeholder needs
 
[5]
 
Though of course sometimes one-off analyses have value on an ongoing basis and so need to be productionised. In such cases the Analytics & Insight team would work with the Data Operations & Technology team to achieve this.
 
[6]
 
No citation needed.

 

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

 

Hurricanes and Data Visualisation: Part II(b) – Ooops!

Ooops!

The first half of my planned thoughts on Hurricanes and Data Visualisation, Rainbow’s Gravity and was published earlier back in September. Part two, Map Reading, joined it this month. In between, the first hurricane-centric article acquired an addendum, The Mona Lisa. With this post, the same has happened to the second article. Apparently you can’t keep a good hurricane story down.
 
 
One of our Hurricanes is missing

When I started writing about Hurricanes back in September of this year, it was in the aftermath of Harvey and Irma, both of which were safely far away from my native United Kingdom. Little did I think that in closing this mini-series Hurricane Ophelia (or at least the remnants of it) would be heading for these shores; I hope this is coincidence and not karma for me criticising the US National Weather Service’s diagrams!

As we batten down here, an odd occurrence was brought to my attention by Bill McKibben (@billmckibben), someone I connected with while working on this set of articles. Here is what he tweeted:

Ooops!

I am sure that inhabitants of both the Shetland Islands and the East Midlands will be breathing sighs of relief!

Clearly both the northward and eastward extent of Ophelia was outside of the scope of either the underlying model or the mapping software. A useful reminder to data professionals to ensure we set the boundaries of both modelling and visualisation work appropriately.

As an aside, this image is another for the Hall of Infamy, relying as it does on the less than helpful rainbow palette we critiqued all the way back in the first article.

I’ll hope to be writing again soon – hurricanes allowing!
 

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

 

A Nobel Laureate’s views on creating Meaning from Data

Image © MRC Laboratory of Molecular Biology, Cambridge, UK

Praise for the Praiseworthy

Today the recipients of the 2017 Nobel Prize for Chemistry were announced [1]. I was delighted to learn that one of the three new Laureates was Richard Henderson, former Director of the UK Medical Research Council’s Laboratory of Molecular Biology in Cambridge; an institute universally known as the LMB. Richard becomes the fifteenth Nobel Prize winner who worked at the LMB. The fourteenth was Venkatraman Ramakrishnan in 2009. Venki was joint Head of Structural Studies at the LMB, prior to becoming President of the Royal Society [2].

MRC Laboratory of Molecular Biology

I have mentioned the LMB in these pages before [3]. In my earlier article, which focussed on Data Visualisation in science, I also provided a potted history of X-ray crystallography, which included the following paragraph:

Today, X-ray crystallography is one of many tools available to the structural biologist with other approaches including Nuclear Magnetic Resonance Spectroscopy, Electron Microscopy and a range of biophysical techniques.

I have highlighted the term Electron Microscopy above and it was for his immense contributions to the field of Cryo-electron Microscopy (Cryo-EM) that Richard was awarded his Nobel Prize; more on this shortly.

First of all some disclosure. The LMB is also my wife’s alma mater, she received her PhD for work she did there between 2010 and 2014. Richard was one of two people who examined her as she defended her thesis [4]. As Venki initially interviewed her for the role, the bookends of my wife’s time at the LMB were formed by two Nobel laureates; an notable symmetry.

2017 Nobel Prize

The press release about Richard’s Nobel Prize includes the following text:

The Nobel Prize in Chemistry 2017 is awarded to Jacques Dubochet, Joachim Frank and Richard Henderson for the development of cryo-electron microscopy, which both simplifies and improves the imaging of biomolecules. This method has moved biochemistry into a new era.

[…]

Electron microscopes were long believed to only be suitable for imaging dead matter, because the powerful electron beam destroys biological material. But in 1990, Richard Henderson succeeded in using an electron microscope to generate a three-dimensional image of a protein at atomic resolution. This breakthrough proved the technology’s potential.

Electron microscopes [5] work by passing a beam of electrons through a thin film of the substance being studied. The electrons interact with the constituents of the sample and go on to form an image which captures information about these interactions (nowadays mostly on an electronic detector of some sort). Because the wavelength of electrons [6] is so much shorter than light [7], much finer detail can be obtained using electron microscopy than with light microscopy. Indeed electron microscopes can be used to “see” structures at the atomic scale. Of course it is not quite as simple as printing out the image snapped by you SmartPhone. The data obtained from electron microscopy needs to be interpreted by software; again we will come back to this point later.

Cryo-EM refers to how the sample being examined is treated prior to (and during) microscopy. Here a water-suspended sample of the substance is frozen (to put it mildly) in liquid ethane to temperatures around -183 °C and maintained at that temperature during the scanning procedure. The idea here is to protect the sample from the damaging effects of the cathode rays [8] it is subjected to during microscopy.
 
 
A Matter of Interpretation

On occasion, I write articles which are entirely scientific or mathematical in nature, but more frequently I bring observations from these fields back into my own domain, that of data, information and insight. This piece will follow the more typical course. To do this, I will rely upon a perspective that Richard Henderson wrote for the Proceedings of the National Academy of Science back in 2013 [9].

Here we come back to the interpretation of Cryo-EM data in order to form an image. In the article, Richard refers to:

[Some researchers] who simply record images, follow an established (or sometimes a novel or inventive [10]) protocol for 3D map calculation, and then boldly interpret and publish their map without any further checks or attempts to validate the result. Ten years ago, when the field was in its infancy, referees would simply have to accept the research results reported in manuscripts at face value. The researchers had recorded images, carried out iterative computer processing, and obtained a map that converged, but had no way of knowing whether it had converged to the true structure or some complete artifact. There were no validation tests, only an instinct about whether a particular map described in the publication looked right or wrong.

The title of Richard’s piece includes the phrase “Einstein from noise”. This refers to an article published in the Journal of Structural Biology in 2009 [11]. Here the authors provided pure white noise (i.e. a random set of black and white points) as the input to an Algorithm which is intended to produce EM maps and – after thousands of iterations – ended up with the following iconic mage:

Reprinted from the Journal of Structural Biology, Vol. 166. © Elsevier. Used under licence 4201981508561. Copyright Clearance Center.

Richard lists occurrences of meaning being erroneously drawn from EM data from his own experience of reviewing draft journal articles and cautions scientists to hold themselves to the highest standards in this area, laying out meticulous guidelines for how the creation of EM images should be approached, checked and rechecked.

The obvious correlation here is to areas of Data Science such as Machine Learning. Here again algorithms are applied iteratively to data sets with the objective of discerning meaning. Here too conscious or unconscious bias on behalf of the people involved can lead to the business equivalent of Einstein ex machina. It is instructive to see the level of rigour which a Nobel Laureate views as appropriate in an area such as the algorithmic processing of data. Constantly questioning your results and validating that what emerges makes sense and is defensible is just one part of what can lead to gaining a Nobel Prize [12]. The opposite approach will invariably lead to disappointment in either academia or in business.

Having introduced a strong cautionary note, I’d like to end this article with a much more positive tone by extending my warm congratulations to Richard both for his well-deserved achievement, but more importantly for his unwavering commitment to rolling back the bounds of human knowledge.
 
 
If you are interested in learning more about Cryo-Electron Microscopy, the following LMB video, which features Richard Henderson and colleagues, may be of interest:


 
Notes

 
[1]
 
The Nobel Prize in Chemistry 2017.
 
[2]
 
Both Richard and Venki remain Group Leaders at the LMB and are actively involved in new scientific research.
 
[3]
 
Data Visualisation – A Scientific Treatment.
 
[4]
 
Her thesis was passed without correction – an uncommon occurrence – and her contribution to the field was described as significant in the formal documentation.
 
[5]
 
More precisely this description applies to Transmission Electron Microscopes, which are the type of kit used in Cryo-EM.
 
[6]
 
The wave-particle duality that readers may be familiar with when speaking about light waves / photons also applies to all sub-atomic particles. Electrons have both a wave and a particle nature and so, in particular, have wavelengths.
 
[7]
 
This is still the case even if ultraviolet or more energetic light is used instead of visible light.
 
[8]
 
Cathode rays are of course just beams of electrons.
 
[9]
 
Henderson, R. (2013). Avoiding the pitfalls of single particle cryo-electron microscopy: Einstein from noise. PNAS This opens a PDF.
 
[10]
 
This is an example of Richard being very, very polite.
 
[11]
 
Shatsky, M., Hall, R.J., Brenner, S.E., Glaeser, R.M. (2009). A method for the alignment of heterogeneous macromolecules from electron microscopy. JSB This article is behind a paywall.
 
[12]
 
There are a couple of other things you need to do as well I believe.

 

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