cultural transformation

This Structure has Novel Features which are of Considerable Business Interest

Peter James Thomas cultural transformation, dashboards, jane doe ceo, management information, storytelling
A skilled practitioner, hard at work developing elements of a Structured Reporting FrameworkA skilled practitioner, hard at work developing elements of a Structured Reporting Framework
© Jennifer Thomas Photography – view full photo.
 
  For anyone who is unaware, the title of the article echoes a 1953 Nature paper [1], which was instead “of considerable biological interest” [2]  

 
Introduction

I have been very much focussing on the start of a data journey in a series of recent articles about Data Strategy [3]. Here I shift attention to a later stage of the journey [4] and attempt to answer the question: “How best to deliver information to an organisation in a manner that will encourage people to use it?” There are several activities that need to come together here: requirements gathering that is centred on teasing out the critical business questions to be answered, data repository [5] design and the overall approach to education and communication [6]. However, I want to focus on a further pillar in the edifice of easily accessible and comprehensible Insight and Information, a Structured Reporting Framework.

In my experience, Structured Reporting Frameworks are much misunderstood. It is sometimes assumed that they are a shiny, expensive and inconsequential trinket. Some espouse the opinion that the term is synonymous with Dashboards. Others claim that that immense effort is required to create one. I have even heard people suggesting that good training materials are an alternative to such a framework. In actual fact, for a greenfield site, a Structured Reporting Framework should mostly be a byproduct of taking a best practice approach to delivering data capabilities. Even for brownfield sites, layering at least a decent approximation to a Structured Reporting Framework over existing data assets should not be a prohibitively lengthy or costly exercise if approached in the right way.

But I am getting ahead of myself, what exactly is a Structured Reporting Framework? Let’s answer this question by telling a story, well actually two stories…

 
The New Job

 
Chapter One
In which we are introduced to Jane and she makes a surprising discovery.

Jane woke up. It was good to be alive. The sun was shining, the birds were singing and she had achieved one of her lifetime goals only three brief months earlier. Yes Jane was now the Chief Executive Officer of a major organisation: Jane Doe, CEO – how that ran off the tongue. Today was going to be a good day. Later she kissed her husband and one-year-old goodbye: “have a lovely day with Daddy, little boy!”, parcelled her six-year-old into the car and dropped her off at school, before heading into work. It was early January and, on the drive in, Jane thought about the poor accountants who had had a truncated Christmas break while they wrestled the annual accounts into submission. She must remember to write an email thanking them all for their hard work. As she swept into the staff car park and slotted into the closest bay to the entrance – that phrase again: “Jane Doe, CEO” in shiny black letters above her space – she felt a warm glow of pride and satisfaction.

Jane sunk into the padded leather chair in her spacious corner office, flipped open her MacBook Air and saw a note from her CFO. As she clicked, thoughts of pleasant meetings with investors crossed her mind. Thoughts of basking in the sort of market-beating results that the company had always posted. And then she read the mail…

… unprecedented deterioration in sales …

… many customers switched to a competitor …

… prices collapsed precipitously …

… costs escalated in Q4, the reasons are unclear …

… unexpected increase in bad debts …

… massive loss …

… capital erosion …

… issues are likely to continue and maybe increase …

… if nothing changes, potential bankruptcy …

… sorry Jane, nobody saw this coming!

Shaken, Jane wondered whether at least one person had seen this coming, her predecessor as CEO who had been so keen to take early retirement. Was there some insight as to the state of the business that he had been privy to and hidden from his fellow executives? There had been no sign, but maybe his gut had told him that bad things were coming.

Pushing such unhelpful thoughts aside, Jane began to ask herself more practical questions. How was she going to face the investors, and the employees? What was she going to do? And, she decided most pertinent of all, what exactly just happened and why?

 
In an Alternative Reality

Jane Doe - Happy CEO

 
Chapter One′
In which we have already met Jane and there are precious few surprises.
 
  Jane did some stuff before arriving at work which I won’t bore the reader with unnecessarily again. Cut to Jane opening an email from her CFO…  

… it’s not great, profit is down 10% …

… but our customer retention strategy is starting to work …

… we have been able to set a floor on prices …

… the early Q4 blip in expenses is now under control …

… I’m still worried about The Netherlands …

… but we are doing better than the competition …

… at least we saw this coming last year and acted!

Jane opened up her personal dashboard, which already showed the headline figures the CFO had been citing. She clicked a filter and the display changed to show the Netherlands operations. Still glancing at the charts and numbers, she dialled Amsterdam.

“Hi Luuk, I hope you had a good break.”

“Sure Jane, how about you?”

“Good Luuk, good thank you. How about you catch me up on how things are going?”

“Of course Jane, let me pull up the numbers… Now we both know that the turnaround has been poorer here than elsewhere. Let me show you what we think is the issue and explain what we are doing. If you can split the profit and loss figures by product first and order by ascending profit.”

“OK Luuk, I’ve done that.”

“Great. Now it’s obvious that a chunk of the losses, indeed virtually all of them, are to do with our Widget Q range. I’m sure you knew that anyway, but now let’s focus on Widget Q and break it down by territory. It’s pretty clear that the Rotterdam area is where we have a problem.”

“I see that Luuk, I did some work on these numbers myself over the weekend. What else can you tell me?”

“Well, hopefully I can provide some local colour Jane. Let’s look at the actual sales and then filter these by channel. Do you see what I see?”

“I do Luuk, what is driving this problem in sales via franchises?”

“Well, in my review of November, I mentioned a start-up competitor in the Widget Q sector. If you recall, they had launched an app for franchises which helps them to run their businesses and also makes it easy to order Widget Q equivalents from their catalogue. Well, I must admit that I didn’t envisage it having this level of impact. But at least we can see what is happening.

The app is damaging us, but it’s still early days and I believe we have a narrow window within which we can respond. When I discussed these same figures with my sales team earlier, they came up with what I think is a sound strategy to counterpunch.

Let me take you through what they suggested and link it back to these figures…”

The call with Luuk had assured Jane that the Netherlands would soon be back on track. She reflected that it was going to be tough to present the annual report to investors, but at least the early warning systems had worked. She had begun to see the problems start to build up in her previous role as EVP of UK and Ireland, not only in her figures, but in those of her counterparts around the world. Jane and her predecessor had jointly developed an evidence-based plan to address the emerging threats. The old CEO had retired, secure in the knowledge that Jane had the tools to manage what otherwise might have become a crisis. He also knew that, with Jane’s help, he had acted early and acted decisively.

Jane thought about how clear discussions about unambiguous figures had helped to implement the defensive strategy, calibrate it for local markets and allowed her and her team to track progress. She could only imagine what things would have been like if everybody was not using the same figures to flag potential problems, diagnose them, come up with solutions and test that the response was working. She shuddered to think how differently things might have gone without these tools…

 
The lie through which we tell the truth [7]

Schrödinger's Profitability

I know, I know! Don’t worry, I’m not going to give up my day job and instead focus on writing the next great British novel [8]. Equally I have no plans to author a scientific paper on Schrödinger’s Profitability, no matter how tempting. It may burst the bubble of those who have been marvelling at the depth of my creative skills, but in fact neither of the above stories are really entirely fictional. Instead they are based on my first hand experience of how access to timely, accurate and pertinent information and insight can be the difference between organisational failure and organisational success. The way that Jane and her old boss were able to identify issues and formulate a strategic response is a characteristic of a Structured Reporting Framework. The way that Jane and Luuk were able to discuss identical figures and to drill into the detail behind them is another such characteristic. Structured Reporting Frameworks are about making sure that everyone in an organisation uses the same figures and ensuring that these figures are easy to find and easy to understand.

To show how this works, let’s consider a schematic [9]:

A Structured Reporting Framework leads people logically and seamlessly from a high-level perspective of performance to more granular information exposing what factors are driving this performance. This functionality is canonically delivered by a series of tailored dashboards, each supported by lower-level dashboards, analysis facilities and reports (the last of which should be limited in number).

Busy Executives and Managers have their information needs best served via visual exhibits that are focussed on their areas of priority and highlight things that are of specific concern to them. Some charts or tables may be replicated across a number of dashboards, but others with be specific to a particular area of the business. If further attention is necessary (e.g. an indicator turns red) dashboard users should have the ability to investigate the causes themselves, if necessary drilling through to detailed transactional information. Symmetrically, more junior staff, engaged in the day-to-day operation of the organisation, need up-to-date (often real-time) information relating to their area, but may also need to set this within a broader business context. This means accessing more general exhibits. For example moving from a list of recent transactions to an historical perspective of the last two years.

Importantly, when a CEO like Jane Doe drills through from their dashboard all the way to a list report this would be the identical report with the identical figures as used by front-line staff day-to-day. When Jane picks up the ‘phone to ask a question of someone, regardless of whether they are a Country Manager, or an operations person, the figures that both see will be the same.

When not accessed from dashboards, reports and analysis facilities should be grouped into a simple menu hierarchy that allows users to navigate with ease and find what they need without having to trail through 30 reports, each with cryptic titles. As mentioned above, there should be a limited number of highly functional / customisable reports and analysis facilities, each of whose purpose is crystal clear.

The way that this consistency of figures is achieved is by all elements of the Structured Reporting Framework drawing their data from the same data repositories. In a modern Data Architecture, this tends to mean two repositories, an Analytical one delivering insight and an Operational one delivering information; these would obviously be linked to each other as well.

 
Banishing some Misconceptions

Banishing Misconceptions

I started by saying that some people make the mistake of thinking that a Structured Reporting Framework is an optional extra in a modern data landscape. In fact is is the crucial final link between an organisation’s data and the people who need to use it. In many ways how people experience data capabilities will be determined by this final link. Without paying attention to this, your shiny warehouse or data lake will be a technological curiosity, not an indispensable business tool. When the sadly common refrain of “we built state-of-the-art data capabilities, why is noone using them?” is heard, the lack of a Structured Reporting Framework is often the root cause of poor user adoption.

When building a data architecture from scratch, elements of your data repository should be so aligned with business needs that overlaying them with a Structured Reporting Framework should be a relatively easy task. But even an older and more fragmented data landscape can be improved at minimal cost by better organising current reports into more user-friendly menus [10] and by introducing some dashboards as alternative access points to them. Work is clearly required to do this, which might include some tweaks to the underlying repositories, but this is does not normally require re-writing all reports again from scratch. Such work can be approached pragmatically and incrementally, perhaps revamping reports for a given function, such as sales, before moving on to the next area. This way business value is also drip fed to the organisation.

 
I hope that this article will encourage some people to look at the idea of Structured Reporting Frameworks again. My experience is that attention paid to this concept can reap great returns at costs that can be much lower than you might expect.

It is worth thinking hard about which version of Jane Doe, CEO you want to be: the one in the dark reacting too late to events, or the one benefiting from the illumination provided by a Structured Reporting Framework.

 
If you would like to learn more about the impact that a Structured Reporting Framework can have on your organisation, or want to understand how to implement one, then you can get in contact via the form provided. You can also speak to us on +44 (0) 20 8895 6826.

Notes

 
[1]		  
WATSON, J., CRICK, F. Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid. Nature 171, 737–738 (1953).
 
[2]		  
From what I have gleaned from those who knew (know in Watson’s case) the pair, neither was (is) the most modest of men. I therefore ascribe this not insubstantial understatement to either the editors at Nature or common-all-garden litotes.
 
[3]		  
All of which are handily collected into our Data Strategy Hub.
 
[4]		  
Though not necessarily much later if you adopt an incremental approach to the delivery of Data Capabilities.
 
[5]		  
Be that Curated Data Lake or Conformed Data Warehouse.
 
[6]		  
See the Cultural Transformation section of my repository of Keynote Articles.
 
[7]		  
Albert Camus, referring to fiction in L’Étranger.
 
[8]		  
I still have my work cut out to finish my factual book, Glimpses of Symmetry.
 
[9]		  
This is a simplified version of one that I use in my own data consulting work.
 
[10]		  
Ideally rationalising and standardising look and feel and terminology at the same time.

peterjamesthomas.com

Another article from peterjamesthomas.com. The home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases.

5 Questions every CEO should ask before embarking on a Data Transformation

Peter James Thomas cultural transformation, data transformation ,

Questions, questions everywhere // For Big Company Inc. // Questions, questions everywhere // What does the CEO think?

The title of this article is borrowed from a piece published by recruitment consultants La Fosse Associates earlier in the year. As its content consisted of me being interviewed by their Senior Managing Consultant, Liam Grier, I trust that I won’t get accused of plagiarism. Liam and I have known each other for years and so I was happy to work with him on this interview.

La Fosse article

I am not going to rehash the piece here, instead please read the full article on La Fosse’s site. But the 5 questions I highlight are as follows:

  1. Why does my organisation need to embark on a Data Transformation – what will it achieve for us?
     
  2. What will be different for us / our customers / our business partners?
     
  3. Do I have the expertise and experience on hand to scope a Data Transformation and then deliver it?
     
  4. How long will a Data Transformation take and how much will it cost?
     
  5. Is there an end state to our Data Transformation, or do we need a culture of continuous data improvement?

As well as the CEO, I think that the above list merits consideration by any senior person looking to make a difference in the data arena.
 

peterjamesthomas.com

Another article from peterjamesthomas.com. The home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases.

 
 

A Retrospective of 2018’s Articles

Peter James Thomas big data, business analytics, change management, chief data officer, cultural transformation, data architecture, data science, data visualisation, in-depth, Mathematics, Physics, project management, Pure Mathematics

A Review of 2018

This is the second year in which I have produced a retrospective of my blogging activity. As in 2017, I have failed miserably in my original objective of posting this early in January. Despite starting to write this piece on 18th December 2018, I have somehow sneaked into the second quarter before getting round to completing it. Maybe I will do better with 2019’s highlights!

Anyway, 2018 was a record-breaking year for peterjamesthomas.com. The site saw more traffic than in any other year since its inception; indeed hits were over a third higher than in any previous year. This increase was driven in part by the launch of my new Maths & Science section, articles from which claimed no fewer than 6 slots in the 2018 top 10 articles, when measured by hits [1]. Overall the total number of articles and new pages I published exceeded 2017’s figures to claim the second spot behind 2009; our first year in business.

As with every year, some of my work was viewed by tens of thousands of people, while other pieces received less attention. This is my selection of the articles that I enjoyed writing most, which does not always overlap with the most popular ones. Given the advent of the Maths & Science section, there are now seven categories into which I have split articles. 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
  7. Maths & Science

In each category, I will pick out one or two 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
 
A Brief History of Databases		  
February
A Brief History of Databases
An infographic spanning the history of Database technology from its early days in the 1960s to the landscape in the late 2010s..
 
Data Strategy Alarm Bell		  
July
How to Spot a Flawed Data Strategy
What alarm bells might alert you to problems with your Data Strategy; based on the author’s extensive experience of both developing Data Strategies and vetting existing ones.
 
Just the facts...		  
August
Fact-based Decision-making
Fact-based decision-making sounds like a no brainer, but just how hard is it to generate accurate facts?
 
 
Data Visualisation
 
Comparative Pie Charts		  
August
As Nice as Pie
A review of the humble Pie Chart, what it is good at, where it presents problems and some alternatives.
 
 
Statistics & Data Science
 
Data Science Challenges – It’s Deja Vu all over again!		  
August
Data Science Challenges – It’s Deja Vu all over again!
A survey of more than 10,000 Data Scientists highlights a set of problems that will seem very, very familiar to anyone working in the data space for a few years.
 
 
CDO Perspectives
 
The CDO Dilemma		  
February
The CDO – A Dilemma or The Next Big Thing?
Two Forbes articles argue different perspectives about the role of Chief Data Officer. The first (by Lauren deLisa Coleman) stresses its importance, the second (by Randy Bean) highlights some of the challenges that CDOs face.
 
2018 CDO Interviews		  
May onwards
The “In-depth” series of CDO interviews
Rather than a single article, this is a series of four talks with prominent CDOs, reflecting on the role and its challenges.
 
The Chief Marketing Officer and the CDO – A Modern Fable		  
October
The Chief Marketing Officer and the CDO – A Modern Fable
Discussing an alt-facts / “fake” news perspective on the Chief Data Officer role.
 
 
Programme Advice
 
Building Momentum		  
June
Building Momentum – How to begin becoming a Data-driven Organisation
Many companies want to become data driven, but getting started on the journey towards this goal can be tough. This article offers a framework for building momentum in the early stages of a Data Programme.
 
 
Analytics & Big Data
 
Enterprise Data Marketplace		  
January
Draining the Swamp
A review of some of the problems that can beset Data Lakes, together with some ideas about what to do to fix these from Dan Woods (Forbes), Paul Barth (Podium Data) and Dave Wells (Eckerson Group).
 
Sic Transit Gloria Mundi		  
February
Sic Transit Gloria Magnorum Datorum
In a world where the word has developed a very negative connotation, what’s so bad about being traditional?
 
Convergent Evolution of Data Architectures		  
August
Convergent Evolution
What the similarities (and differences) between Ichthyosaurs and Dolphins can tell us about different types of Data Architectures.
 
 
Maths & Science
 
Euler's Number		  
March
Euler’s Number
A long and winding road with the destination being what is probably the most important number in Mathematics.
 The Irrational Ratio  
August
The Irrational Ratio
The number π is surrounded by a fog of misunderstanding and even mysticism. This article seeks to address some common misconceptions about π, to show that in many ways it is just like any other number, but also to demonstrate some of its less common properties.
 
Emmy Noether		  
October
Glimpses of Symmetry, Chapter 24 – Emmy
One of the more recent chapters in my forthcoming book on Group Theory and Particle Physics. This focuses on the seminal contributions of Mathematician Emmy Noether to the fundamentals of Physics and the connection between Symmetry and Conservation Laws.

 
Notes

 
[1]		  

The 2018 Top Ten by Hits
1. The Irrational Ratio
2. A Brief History of Databases
3. Euler’s Number
4. The Data and Analytics Dictionary
5. The Equation
6. A Brief Taxonomy of Numbers
7. When I’m 65
8. How to Spot a Flawed Data Strategy
9. Building Momentum – How to begin becoming a Data-driven Organisation
10. The Anatomy of a Data Function – Part I

 
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Another article from peterjamesthomas.com. The home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases.

 
 

Data Science Challenges – It’s Deja Vu all over again!

Peter James Thomas cultural transformation, data quality, data science , ,

The late Yogi Berra

The rather famous tautology, “It’s déjà vu all over again”, has of course been ascribed to that darling of malapropisms, baseball catcher Yogi Berra [1]. The phrase came to mind for me today when coming across the following exhibit:

Business Over Broadway - Kaggle Survey (Click to view a larger version in a new window)

© Business Over Broadway (2018). Based on Kaggle’s State of Data Science Survey 2017 (Sample size: 10,153).

The text in the above exhibit is not that clear [2], so here are the 20 top challenges [3] faced by those running Data Science teams in human-readable form:

# Challenge Cited by
1 Dirty Data 35.9%
2 Lack of Data Science talent in the organization 30.2%
3 Company politics / Lack of management/financial support for a Data Science team 27.0%
4 The lack of a clear question to be answering or a clear direction to go with available data 22.1%
5 Unavailability of/difficult access to data 22.0%
6 Data Science results not used by business decision makers 17.7%
7 Explaining Data Science to others 16.0%
8 Privacy issues 14.4%
9 Lack of significant domain expert input 14.2%
10 Organization is small and cannot afford a Data Science team 13.0%
11 Team using multiple ad hoc development environments such as Python/R/Java etc. 12.7%
12 Limitations of tools 12.0%
13 Need to coordinate with IT 11.8%
14 Maintaining responsible expectations about the potential impact of Data Science projects 11.5%
15 Inability to integrate findings into organization’s decision-making process 9.8%
16 Lack of funds to buy useful datasets from external sources 9.6%
17 Difficulties in deployment/scoring 8.6%
18 Scaling Data Science solution up to full database 8.4%
19 Limitations in the state of the art in machine learning 7.7%
20 Did not instrument data useful for scientific analysis and decision-making 6.5%
21 I prefer not to say 4.8%
22 Other 2.9%

The table above is a transcription of a transcription, so it would be remarkable if no Data Quality issues had crept in, however let’s assume that the figures are robust enough for our purposes. Of course the people surveyed will have reported multiple issues, so the percentages above are not additive. Nevertheless there are some very obvious comments to be made (some of the above items are pertinent to more than one of the points I would like to make):

  • Data Quality / Availability remain major issues – (1, 5, and 8)

    It is indeed true that Machine Learning can be quite good at dealing with some types or bad or missing data. But no technology or approach is going to be able to paper over all of the cracks if your data is essentially incomplete and of poor quality. This point (together with some others below) speaks to the need to not approach Data Science on a stand-alone basis, but as part of a more holistic approach to data matters [4].
     

  • The Human angle and a focus on Culture are imperative – (3, 6, 7, 14 and 15)

    Findings are one thing; using these to take action is quite another. At the end of the day, most ventures are successful or fail because of people; the people conducting the venture, the people receiving its intended benefits and so on. Ignore this dimension of data work (or any type of work) at your peril [5].
     

  • Business Questions amd Business Involvement matter – (4, 6, 9 and 15)

    While in some circumstances the data can indeed “speak for itself”, it makes a lot more sense for Data Scientists to partner with business colleagues to both get direction and to help ensure that their findings lead to action [6].
     

  • Tools & Technology typically Trumped – (11, 12 and 18)

    These first appear outside of the Top 10 (and 11 is a bit dubious to include here – it relates more to a proliferation of tools than to issues with any of them). I would never say that tools and technology are unimportant, but they are typically much less important than other considerations [7].

The overriding point is of course that – much as I noted out recently in Convergent Evolution – there is little new under the Sun. A survey of Business Intelligence / Data Warehousing professionals back in 2010 would have generated something very like the list above. A survey of EIS [8] professionals back in 2000 would have done the same.

The important things to do – regardless of the technologies and approaches employed – are to:

  1. Understand what questions are key to the running of an organisation [9]
     
  2. Determine what data is available to support decisions in these key areas
     
  3. Ensure that the data is in a “good enough” state, appropriately consolidated / made consistent, augmented / corrected by any useful external data and made available to the right people in a timely manner
     
  4. Focus on the human aspects of acting on what data is telling us and how to use data outputs to drive positive actions

Here too, little is new under the Sun. I have been referring to essentially these same four pillars of good practice since the mid 2000s. Some of our technological advances since then have been amazing. The prospect of leveraging the power of both Data Science and Artificial Intelligence in a business context is very exciting. But to truly succeed with these newer approaches, it helps to recall the eternal verities that have always underpinned good data-centric work [10]. The survey above makes this point crystal clear.

A final corollary to this observation is something I covered in A truth universally acknowledged…. The replies to the Kaggle survey highlight the fact that, much like the conductor of an orchestra does not need to be able to play the violin to a virtuoso level, people leading Data Science teams (and broader Data Functions) need a set of rounded skills, ones honed to address the types of issues appearing in the exhibits above. The skill-set that makes for an excellent Data Scientist does not necessarily help so much with many of the less technical issues that will determine the success or failure of Data Science teams.
 

 
Notes

 
[1]		  
Other Yogi-isms included, “Always go to other people’s funerals; otherwise they won’t go to yours”, “You can observe a lot by watching” and “If you can’t imitate him, don’t copy him”.
 
[2]		  
A Data Visualisation challenge to include that much text I realise. I think I might have been tempted to come up with pithier categories to aid legibility.
 
[3]		  
Ignoring “I prefer not to say” and “Other”.
 
[4]		  
As laid out in my many articles about the importance of Cultural Transformation.
 
[5]		  
See: Building Momentum – How to begin becoming a Data-driven Organisation.
 
[6]		  
I make precisely this point in my recent interview for Venturi Voice (starting just after 31:38).
 
[7]		  
I make this point most forcibly back in: A bad workman blames his [Business Intelligence] tools. The technology may be different, but the learnings are just as relevant today.
 
[8]		  
Executive Information Systems for those of tender years.
 
[9]		  
Machine learning techniques can clearly help here, but only if in concert with dialogue with people actually on the front-line and leading business areas.
 
[10]		  
In your search for such eternal verities, you could do much worse than starting with: 20 Risks that Beset Data Programmes.

 

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

 

Latest Interviews / Podcasts

Peter James Thomas business analytics, chief data officer, cultural transformation, data science ,

Interviews and Podcasts

The interviews that I conduct with leaders in their fields as part of my “In-depth” series have hopefully brought a new and interesting aspect to this site. However, often the boot is on the other foot and I am the person being interviewed about my experience and expertise in the data field and related matters [1]. Maybe interviewing other people helps me when I am in turn interviewed, maybe it’s the other way round. Whatever the case, I enjoyed recording the two conversations appearing below (thanks to the interviewers in both cases) and hope that the content is of interest to readers.

In both instances a link to the site originally publishing the interview is followed by a locally hosted version of the audio track and then a download option. I’d encourage readers to explore the other excellent interviews contained on both sites.


 
Enterprise Management 360° Podcast – 31st July 2018


 


 
Venturi Voice 3650° Podcast – 22nd April 2018


 

Downloadable link: Conducting a Data Orchestra

 
If you would like to interview me for your site or periodical, of if you are just interested in further exploring some of the themes I discuss in these two interviews, then please feel free to get in contact.
 

 
Notes

 
[1]		  
A list of other video interviews and podcasts I have taken part in can be viewed in the Media section of this site.

 

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

 

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

Peter James Thomas chief data officer, cultural transformation, data governance, data management, in-depth
In-depth with Roberto Maranca

Part of the In-depth series of interviews

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

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

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

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

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

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

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

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

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

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

 
Notes

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

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

 

A Retrospective of 2017’s Articles

Peter James Thomas astronomy, big data, business analytics, change management, chief data officer, data science, data visualisation, project management, Statistics

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

 

A truth universally acknowledged…

Peter James Thomas chief data officer, cultural transformation, data governance, data quality, data science, Mathematics & Science, outsourcing, Statistics

£10 note

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

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

  

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

CDO wishlist

Mandatory Requirements:

Highly Desirable Requirements:

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

Your common, all-garden CDO

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

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

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

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

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

Conducting the data orchestra

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

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

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

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

— Pride and Prejudice, by Jane Austen (1813)

  

 
Notes

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

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

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

 

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

 

20 Risks that Beset Data Programmes

Peter James Thomas cultural transformation, data management, data quality, education, outsourcing, project management

Data Programme Risks

This article draws extensively on elements of the framework I use to both highlight and manage risks on data programmes. It has its genesis in work that I did early in 2012 (but draws on experience from the years before this). I have tried to refresh the content since then to reflect new thinking and new developments in the data arena.
 
 
Introduction

What are my motivations in publishing this article? Well I have both designed and implemented data and information programmes for over 17 years. In the majority of cases my programme work has been a case of executing a data strategy that I had developed myself [1]. While I have generally been able to steer these programmes to a successful outcome [2], there have been both bumps in the road and the occasional blind alley, requiring a U-turn and another direction to be selected. I have also been able to observe data programmes that ran in parallel to mine in different parts of various organisations. Finally, I have often been asked to come in and address issues with an existing data programme; something that appears to happens all too often. In short I have seen a lot of what works and what does not work. Having also run other types of programmes [3], I can also attest to data programmes being different. Failure to recognise this difference and thus approaching a data programme just like any other piece of work is one major cause of issues [4].

Before I get into my list proper, I wanted to pause to highlight a further couple of mistakes that I have seen made more than once; ones that are more generic in nature and thus don’t appear on my list of 20 risks. The first is to assume that the way that an organisation’s data is controlled and leveraged can be improved in a sustainable way by just kicking off a programme. What is more important in my experience is to establish a data function, which will then help with both the governance and exploitation of data. This data function, ideally sitting under a CDO, will of course want to initiate a range of projects, from improving data quality, to sprucing up reporting, to establishing better analytical capabilities. Best practice is to gather these activities into a programme, but things work best if the data function is established first, owns such a programme and actively partakes in its execution.

Data is for life...

As well as the issue of ongoing versus transitory accountability for data and the undoubted damage that poorly coordinated change programmes can inflict on data assets, another driver for first establishing a data function is that data needs will always be there. On the governance side, new systems will be built, bought and integrated, bringing new data challenges. On the analytical side, there will always be new questions to be answered, or old ones to be reevaluated. While data-centric efforts will generate many projects with start and end dates, the broad stream of data work continues on in a way that, for example, the implementation of a new B2C capability does not.

The second is to believe that you will add lasting value by outsourcing anything but targeted elements of your data programme. This is not to say that there is no place for such arrangements, which I have used myself many times, just that one of the lasting benefits of gimlet-like focus on data is the IP that is built up in the data team; IP that in my experience can be leveraged in many different and beneficial ways, becoming a major asset to the organisation [5].

Having made these introductory comments, let’s get on to the main list, which is divided into broadly chronological sections, relating to stages of the programme. The 10 risks which I believe are either most likely to materialise, or which will probably have the greatest impact are highlighted in pale yellow.
 
 
Up-front Risks

In the beginning

Risk Potential Impact
1. Not appreciating the size of work for both business and technology resources. Team is set up to fail – it is neither responsive enough to business needs (resulting in yet more “unofficial” repositories and additional fragmentation), nor is appropriate progress is made on its central objective.
2. Not establishing a dedicated team. The team never escapes from “the day job” or legacy / BAU issues; the past prevents the future from being built.
3. Not establishing a unified and collaborative team. Team is plagued by people pursuing their own agendas and trashing other people’s approaches, this consumes management time on non-value-added activities, leads to infighting and dissipates energy.
4. Staff lack skills and prior experience of data programmes. Time spent educating people rather than getting on with work. Sub-optimal functionality, slippages, later performance problems, higher ongoing support costs.
5. Not establishing an appropriate management / governance structure. Programme is not aligned with business needs, is not able to get necessary time with business users and cannot negotiate the inevitable obstacles that block its way. As a result, the programme gets “stuck in the mud”.
6. Failing to recognise ongoing local needs when centralising. Local business units do not have their pressing needs attended to and so lose confidence in the programme and instead go their own way. This leads to duplication of effort, increased costs and likely programme failure.

With risk 2 an analogy is trying to build a house in your spare time. If work can only be done in evenings or at the weekend, then this is going to take a long time. Nevertheless organisations too frequently expect data programmes to be absorbed in existing headcount and fitted in between people’s day jobs.

We can we extend the building metaphor to cover risk 4. If you are going to build your own house, it would help that you understand carpentry, plumbing, electricals and brick-laying and also have a grasp on the design fundamentals of how to create a structure that will withstand wind rain and snow. Too often companies embark on data programmes with staff who have a bit of a background in reporting or some related area and with managers who have never been involved in a data programme before. This is clearly a recipe for disaster.

Risk 5 reminds us that governance is also important – both to ensure that the programme stays focussed on business needs and also to help the team to negotiate the inevitable obstacles. This comes back to a successful data programme needing to be more than just a technology project.
 
 
Programme Execution Risks

Programme execution

Risk Potential Impact
7. Poor programme management. The programme loses direction. Time is expended on non-core issues. Milestones are missed. Expenditure escalates beyond budget.
8. Poor programme communication. Stakeholders have no idea what is happening [6]. The programme is viewed as out of touch / not pertinent to business issues. Steering does not understand what is being done or why. Prospective users have no interest in the programme.
9. Big Bang approach. Too much time goes by without any value being created. The eventual Big Bang is instead a damp squib. Large sums of money are spent without any benefits.
10. Endless search for the perfect solution / adherence to overly theoretical approaches. Programme constantly polishes rocks rather than delivering. Data models reflect academic purity rather than real-world performance and maintenance needs.
11. Lack of focus on interim deliverables. Business units become frustrated and seek alternative ways to meet their pressing needs. This leads to greater fragmentation and reputational damage to programme.
12. Insufficient time spent understanding source system data and how data is transformed as it flows between systems. Data capabilities that do not reflect business transactions with fidelity. There is inconsistency with reports directly drawn from source systems. Reconciliation issues arise (see next point).
13. Poor reconciliation. If analytical capabilities do not tell a consistent story, they will not be credible and will not be used.
14. Inadequate approach to data quality. Data facilities are seen as inaccurate because of poor data going into them. Data facilities do not match actual business events due to either massaging of data or exclusion of transactions with invalid attributes.

Probably the single most common cause of failure with data programmes – and indeed or ERP projects and acquisitions and any other type of complex endeavour – is risk 7, poor programme management. Not only do programme managers have to be competent, they should also be steeped in data matters and have a good grasp of the factors that differentiate data programmes from more general work.

Relating to the other highlighted risks in this section, the programme could spend two years doing work without surfacing anything much and then, when they do make their first delivery, this is a dismal failure. In the same vein, exclusive focus on strategic capabilities could prevent attention being paid to pressing business needs. At the other end of the spectrum, interim deliveries could spiral out of control, consuming all of the data team’s time and meaning that the strategic objective is never reached. A better approach is that targeted and prioritised interims help to address pressing business needs, but also inform more strategic work. From the other perspective, progress on strategic work-streams should be leveraged whenever it can be, perhaps in less functional manners that the eventual solution, but good enough and also helping to make sure that the final deliveries are spot on [7].
 
 
User Requirement Risks

Dear Santa

Risk Potential Impact
15. Not enough up-front focus on understanding key business decisions and the information necessary to take them. Analytic capabilities do not focus on what people want or need, leading to poor adoption and benefits not being achieved.
16. In the absence of the above, the programme becoming a technology-driven one. The business gets what IT or Change think that they need, not what is actually needed. There is more focus on shiny toys than on actionable information. The programme forgets the needs of its customers.
17. A focus on replicating what the organisation already has but in better tools, rather than creating what it wants. Beautiful data visualisations that tell you close to nothing. Long lists of existing reports with their fields cross-referenced to each other and a new solution that is essentially the lowest common denominator of what is already in place; a step backwards.

The other most common reasons for data programme failure is a lack of focus on user needs and insufficient time spent with business people to ensure that systems reflect their requirements [8].
 
 
Integration Risk

Lego

Risk Potential Impact
18. Lack of leverage of new data capabilities in front-end / digital systems. These systems are less effective. The data team is jealous about its capabilities being the only way that users should get information, rather than adopting a more pragmatic and value-added approach.

It is important for the data team to realise that their work, however important, is just one part of driving a business forward. Opportunities to improve other system facilities by the leverage of new data structures should be taken wherever possible.
 
 
Deployment Risks

Education

Risk Potential Impact
19. Education is an afterthought, training is technology- rather than business-focused. People neither understand the capabilities of new analytical tools, nor how to use them to derive business value. Again this leads to poor adoption and little return on investment.
20. Declaring success after initial implementation and training. Without continuing to water the immature roots, the plant withers. Early adoption rates fall and people return to how they were getting information pre-launch. This means that the benefits of the programme not realised.

Finally excellent technical work needs to be complemented with equal attention to business-focussed education, training using real-life scenarios and assiduous follow up. These things will make or break the programme [9].
 
 
Summary.

Of course I don’t claim that the above list is exhaustive. You could successfully mitigate all of the above risks on your data programme, but still get sunk by some other unforeseen problem arising. There is a need to be flexible and to adapt to both events and how your organisation operates; there are no guarantees and no foolproof recipes for success [10].

My recommendation to data professionals is to develop your own approach to risk management based on your own experience, your own style and the culture within which you are operating. If just a few of the items on my list of risks can be usefully amalgamated into this, then I will feel that this article has served its purpose. If you are embarking on a data programme, maybe your first one, then be warned that these are hard and your reserves of perseverance will be tested. I’d suggest leveraging whatever tools you can find in trying to forge ahead.

It is also maybe worth noting that, somewhat contrary to my point that data programmes are different, a few of the risks that I highlight above could be tweaked to apply to more general programmes as well. Hopefully the things that I have learnt over the last couple of decades of running data programmes will be something that can be of assistance to you in your own work.
 

 
Notes

 
[1]		  
For my thoughts on developing data (or interchangeably) information strategies see:

  1. Forming an Information Strategy: Part I – General Strategy
  2. Forming an Information Strategy: Part II – Situational Analysis and
  3. Forming an Information Strategy: Part III – Completing the Strategy

or the CliffsNotes versions of these on LinkedIn:

  1. Information Strategy: 1) General Strategy
  2. Information Strategy: 2) Situational Analysis and
  3. Information Strategy: 3) Completing the Strategy
 
[2]		  
Indeed sometimes an award-winning one.
 
[3]		  
An abridged list would include:

  • ERP design, development and implementation
  • ERP selection and implementation
  • CRM design, development and implementation
  • CRM selection and implementation
  • Integration of acquired companies
  • Outsourcing of systems maintenance and support
 
[4]		  
For an examination of this area you can start with A more appropriate metaphor for Business Intelligence projects. While written back in 2008-9 the content of this article is as pertinent today as it was back then.
 
[5]		  
I cover this area in greater detail in Is outsourcing business intelligence a good idea?
 
[6]		  
Stakeholder

Probably a bad idea to make this stakeholder unhappy (see also Themes from a Chief Data Officer Forum – the 180 day perspective, note [3]).
 
[7]		  
See Vision vs Pragmatism, Holistic vs Incremental approaches to BI and Tactical Meandering for further background on this area.
 
[8]		  
This area is treated in the strategy articles appearing in note [1] above. In addition, some potential approaches to elements of effective requirements gathering are presented in Scaling-up Performance Management and Developing an international BI strategy.
 
[9]		  
Of pertinence here is my trilogy on the cultural transformation aspects of information programmes:

  1. Marketing Change
  2. Education and cultural transformation
  3. Sustaining Cultural Change
 
[10]		  
Something I stress forcibly in Recipes for Success?

 

 

Bumps in the Road

Peter James Thomas change management, data governance ,

Bumps in the road

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

Well the whole slide looks like this…

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

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

Roadworks

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

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

More roadworks

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

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

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

Entropy

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

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

Pendulum

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

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

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

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

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

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

Sherlock

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

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

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

 Notes

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