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:
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.
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.
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.
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.
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.
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).
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.
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.
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:
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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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:
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.
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…
…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
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.
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.
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]
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.
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.
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
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.
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.
The author would like to acknowledge the input and assistance of his fellow delegates, both initially at the IRM(UK) CDO Executive Forum itself and later in reviewing earlier drafts of this article. As ever, responsibility for any errors or omissions remains mine alone.
Introduction
Time flies as Virgil observed some 2,045 years ago. A rather shorter six months back I attended the inaugural IRM(UK) Chief Data Officer Executive Forum and recently I returned for the second of what looks like becoming biannual meetings. Last time the umbrella event was the IRM(UK) Enterprise Data and Business Intelligence Conference 2015 [1], this session was part of the companion conference: IRM(UK) Master Data Management Summit / and Data Governance Conference 2016.
This article looks to highlight some of the areas that were covered in the forum, but does not attempt to be exhaustive, instead offering an impressionistic view of the meeting. One reason for this (as well as the author’s temperament) is that – as previously – in order to allow free exchange of ideas, the details of the meeting are intended to stay within the confines of the room.
Last November, ten themes emerged from the discussions and I attempted to capture these over two articles. The headlines appear in the box below:
One area of interest for me was how things had moved on in the intervening months and I’ll look to comment on this later.
By way of background, some of the attendees were shared with the November 2015 meeting, but there was also a smattering of new faces, including the moderator, Peter Campbell, President of DAMA’s Belgium and Luxembourg chapter. Sectors represented included: Distribution, Extractives, Financial Services, and Governmental.
The discussions were wide ranging and perhaps less structured than in November’s meeting, maybe a facet of the familiarity established between some delegates at the previous session. However, there were four broad topics which the attendees spent time on: Management of Change (Theme 5); Data Privacy / Trust; Innovation; and Value / Business Outcomes.
While clearly the second item on this list has its genesis in the European Commission’s recently adopted General Data Protection Regulation (GDPR [2]), it is interesting to note that the other topics suggest that some elements of the CDO agenda appear to have shifted in the last six months. At the time of the last meeting, much of what the group talked about was foundational or even theoretical. This time round there was both more of a practical slant to the conversation, “how do we get things done?” and a focus on the future, “how do we innovate in this space?”
Perhaps this also reflects that while CDO 1.0s focussed on remedying issues with data landscapes and thus had a strong risk mitigation flavour to their work, CDO 2.0s are starting to look more at value-add and delivering insight (Theme 6). Of course some organisations are yet to embark on any sort of data-related journey (CDO 0.0 maybe), but in the more enlightened ones at least, the CDO’s focus is maybe changing, or has already changed (Theme 3).
Some flavour of the discussions around each of the above topics is provided below, but as mentioned above, these observations are both brief and impressionistic:
Management of Change
The title of Managing Change has been chosen (by the author) to avoid any connotations of Change Management. It was recognised by the group that there are two related issues here. The first is the organisational and behavioural change needed to both ensure that data is fit-for-purpose and that people embrace a more numerical approach to decision-making; perhaps this area is better described as Cultural Transformation. The second is the fact (also alluded to at the previous forum) that Change Programmes tend to have the effect of degrading data assets over time, especially where monetary or time factors lead data-centric aspects of project to be de-scoped.
On Cultural Transformation, amongst a number of issues discussed, the need to answer the question “What’s in it for me?” stood out. This encapsulates the human aspect of driving change, the need to engage with stakeholders [3] (at all levels) and the importance of sound communication of what is being done in the data space and – more importantly – why. These are questions to which an entire sub-section of this blog is devoted.
On the potentially deleterious impact of Change [4] on data landscapes, it was noted that whatever CDOs build, be these technological artefacts or data-centric processes, they must be designed to be resilient in the face of both change and Change.
Data Privacy / Trust
As referenced above, the genesis of this topic was GDPR. However, it was interesting that the debate extended from this admittedly important area into more positive territory. This related to the observation that the care with which an organisation treats its customers’ or business partners’ data (and the level of trust which this generates) can potentially become a differentiator or even a source of competitive advantage. It is good to report an essentially regulatory requirement possibly morphing into a more value-added set of activities.
Innovation
It might be expected that discussions around this topic would focus on perennials such as Big Data or Advanced Analytics. Instead the conversation was around other areas, such as distributed / virtualised data and the potential impact of Block Chain technology [5] on Data Management work. Inevitably The Internet of Things[6] also featured, together with the ethical issues that this can raise. Other areas discussed were as diverse as the gamification of Data Governance and Social Physics, so we cast the net widely.
Value / Business Outcomes
Here we have the strongest link back into the original ten themes (specifically Theme 6). Of course the acme of data strategies is of little use if it does not deliver positive business outcomes. In many organisations, focus on just remediating issues with the current data landscape could consume a massive chunk of overall Change / IT expenditure. This is because data issues generally emanate from a wide variety of often linked and frequently long-standing organisational weaknesses. These can be architectural, integrational, procedural, operational or educational in nature. One of the challenges for CDOs everywhere is how to parcel up their work in a way that adds value, gets things done and is accretive to both the overall Business and Data strategies (which are of course intimately linked as per Theme 10). There is also the need to balance foundational work with more tactical efforts; the former is necessary for lasting benefits to be secured, but the latter can showcase the value of Data Management and thus support further focus on the area.
While the risk aspect of data issues gets a foot in the door of the Executive Suite, it is only by demonstrating commercial awareness and linking Data Management work to increased business value that any CDO is ever going to get traction. (Theme 6).
The next IRM(UK) CDO Executive Forum will take place on 9th November 2016 in London – if you would like to apply for a place please e-mail jeremy.hall@irmuk.co.uk.
Notes
[1]
I’ll be speaking at IRM(UK) ED&BI 2016 in November. Book early to avoid disappointment!
[2]
Wikipedia offers a digestible summary of the regulation here. Anyone tempted to think this is either a parochial or arcane area is encouraged to calculate what the greater of €20 million and 4% of their organisation’s worldwide turnover might be and then to consider that the scope of the Regulation covers any company (regardless of its domicile) that processes the data of EU residents.
[3]
I’ve been itching to use this classic example of stakeholder management for some time:
[4]
The capital “c” is intentional.
[5]
Harvard Business Review has an interesting and provocative article on the subject of Block Chain technology.
Several weeks back now, I presented at IRM’s collocated European Master Data Management Summit and Data Governance Conference. This was my second IRM event, having also spoken at their European Data Warehouse and Business Intelligence Conference back in 2010. The conference was impeccably arranged and the range of speakers was both impressive and interesting. However, as always happens to me, my ability to attend meetings was curtailed by both work commitments and my own preparations. One of these years I will go to all the days of a seminar and listen to a wider variety of speakers.
Anyway, my talk – entitled Making Business Intelligence an Integral part of your Data Quality Programme – was based on themes I had introduced in Using BI to drive improvements in data quality and developed in Who should be accountable for data quality?. It centred on the four-pillar framework that I introduced in the latter article (yes I do have a fetish for four-pillar frameworks as per):
Given my lack of exposure to the event as a whole, I will restrict myself to writing about a comment that came up in the question section of my slot. As per my article on presenting in public, I try to always allow time at the end for questions as this can often be the most interesting part of the talk; for delegates and for me. My IRM slot was 45 minutes this time round, so I turned things over to the audience after speaking for half-an-hour.
There were a number of good questions and I did my best to answer them, based on past experience of both what had worked and what had been less successful. However, one comment stuck in my mind. For obvious reasons, I will not identify either the delegate, or the organisation that she worked for; but I also had a brief follow-up conversation with her afterwards.
She explained that her organisation had in place a formal data governance process and that a lot of time and effort had been put into communicating with the people who actually entered data. In common with my first pillar, this had focused on educating people as to the importance of data quality and how this fed into the organisation’s objectives; a textbook example of how to do things, on which the lady in question should be congratulated. However, she also faced an issue; one that is probably more common than any of us information professionals would care to admit. Her problem was not at the bottom, or in the middle of her organisation, but at the top.
In particular, though data governance and a thorough and consistent approach to both the entry of data and transformation of this to information were all embedded into the organisation; this did not prevent the leaders of each division having their own people take the resulting information, load it into Excel and “improve” it by “adjusting anomalies”, “smoothing out variations”, “allowing for the impact of exceptional items”, “better reflecting the opinions of field operatives” and the whole panoply of euphemisms for changing figures so that they tell a more convenient story.
In one sense this was rather depressing, someone having got so much right, but still facing challenges. However, it also chimes with another theme that I have stressed many times under the banner of cultural transformation; it is crucially important than any information initiative either has, or works assiduously to establish, the active support of all echelons of the organisation. In some of my most successful BI/DW work, I have had the benefit of the direct support of the CEO. Equally, it is is very important to ensure that the highest levels of your organisation buy in before commencing on a stepped-change to its information capabilities.
My experience is that enhanced information can have enormous payback. But it is risky to embark on an information programme without this being explicitly recognised by the senior management team. If you avoid laying this important foundation, then this is simply storing up trouble for the future. The best BI/DW projects are totally aligned with the strategic goals of the organisation. Given this, explaining their objectives and soliciting executive support should be all the easier. This is something that I would encourage my fellow information professionals to seek without exception.
Unfortunately one of the Ovum presenters, Madan Sheina, was ill, but Sarah did a great job running the session. The set up of the room and the number of delegates both encouraged interaction and there was a great atmosphere with lots of questions from the attendees and some interesting exchanges of ideas. Work commitments meant that I had to leave after lunch, which was a shame as I am sure that – based on what I saw in the morning – the afternoon workshops sessions would have been both entertaining and productive.
I certainly enjoyed my presentation – on Initiating and Developing a BI Strategy – which focussed on both my framework for success in Business Intelligence and, in particular, addressing the important cultural transformation aspects of these. Thank you also to the delegates both for the questions and observations and for kindly awarding my talk an 83% rating via the now ubiquitous seminar questionnaire.
Bouldering and Cultural Transformation
My partner bouldering the classic Boysen’s Groove in Snowdonia
As part of my section on change management, I covered some of the themes that I introduced in my article Perseverance. In this I spoke about one of the types of rock climbing that I enjoy; bouldering. Bouldering is regular rock climbing on steroids, it is about climbing ultra-hard, but short climbs; often on boulders – hence the name. I compared the level of commitment and persistence required for success in bouldering to the need for the same attributes in change management initiatives.
I spoke to a few different delegates about this analogy during a coffee break. One in particular came up with an interesting expansion on my rock climbing theme. He referred to how people engaged in mountaineering and multi-pitch rock climbing make progress in a series of stages, establishing a new base at a higher point before attempting the next challenge. He went on to link this to making incremental progress on IT projects. I thought this was an interesting observation and told the gentleman in question that he had provided the inspiration for a future blog article.
An introduction to lead climbing
The above video is excerpted from the introduction to Hard Grit a classic 1998 climbing film by Slackjaw productions. It features climbing on the Gritstone (a type of hard sandstone) edges of the UK’s Peak District. This famous sequence shows a pretty horrendous fall off of a Peak District test piece called Gaia at Black Rocks. Amazingly the climber received no worse injuries than a severely battered and lacerated leg. Despite its proximity to my home town of London, Gritstone climbing has never been my cup of tea – it is something of an acquired taste and one that I have never appreciated as much as its many devotees.
As an aside you can see a photo of a latter-day climber falling off the same route at the beginning of my article, Some reasons why IT projects fail. I’m glad to say in this photo, unlike the video above, the climber is wearing a helmet!
What the clip illustrates is the dangers inherent in the subject of this article; traditional lead climbing. OK the jargon probably needs some explanation. First of all climbing is a very broad church, in this piece I’ll be ignoring whole areas such as mountaineering, soloing and the various types of winter and ice climbing. I am going to focus on roped climbing on rock, something that generally requires dry weather (unless you are a masochist or the British weather changes on you).
In this activity, one person climbs (unsurprisingly the climber) and another holds the rope attached to them (the belayer). The belayer uses a mechanism called a belay device to do this, but we will elide these details. With my background in Business Intelligence, I’ll now introduce some dimensions with which you can “slice and dice” this activity:
multi-pitch / single pitch
Single-pitch climbs are shorter than a length of rope (typically 50-70m) and often happen on rock outcrops such as in the Peak District mentioned above. The climber completes the climb and then the belayer may follow them up if they want, or alternatively the climber might walk round to find an easy decent and the pair will then go and find another climb.
Multi-pitch climbs consist of at least two pitches; and sometimes many more. They tend to be in a mountain environment. One person may climb a pitch and then alternate with their partner, or the same person may climb each section first. It depends on the team.
top roping / leading
Top roping is not a very precise term (bottom roping might be more accurate) but is generally taken to mean that the rope runs from the belayer, to the top of the climb and then down to the climber.
As the climber ascends, the belayer (hopefully!) takes in the slack, but (again hopefully!) without hauling the climber up the route. This means that if the climber falls (and the belayer is both competent and attentive) they should be caught by the rope almost immediately. Obviously this arrangement only works on single-pitch climbs.
In lead climbing, or leading, the rope runs from the belayer up to the climber. As the climber ascends, they attach the rope to various points in the rock on the climb (for how they do this see the next bullet point).
Assuming that the climber is able to make a good attachment to the rock (again see next point) the issue here is how far they fall. If they climb 2m above their last attachment point, then a slip at this point will see them swinging 2m below this point – a total fall of 4m, much longer than when top roping. Also if the last attachment point is say 10m above the ground and the climber falls off say 8m above this, then slack in the system and rope stretch will probably see them hit the ground; something that should never happen in top roping.
[As an aside true top roping is what happens when the belayer climbs up after the climber. Here they are now belayed by the original climber from above. However no one uses the term top roping for this, instead they talk about bringing up the second, or seconding. Top roping is reserved for the practice of bottom roping described above, no one said that climbing was a logical sport!]
sport / traditionalIn the last point I referred to a lead climber mysteriously attaching themselves to the rock as they ascend. The way that they do this determines whether they are engaged in sport or traditional climbing (though there is some blurriness around the edges).
In sport climbing, holes are pre-drilled into the rock at strategic intervals (normally 3-5m apart, but sometimes more). Into these are glued either a metal staple or a single bolt with a metal hanger on it that has a hole in it.
The process of equiping a sport route in this way can take some time, particularly if it is overhanging and of course it needs to be done well if the bolts are to hold a climber’s fall. A single-pitch sport climb may have 10 or more of these bolts, plus generally a lower-off point at the top.
The climber will take with them at least the same number of quick draws as there are bolts. These are two spring-loaded carabiners joined by a section of strong tape. As the climber ascends, they clip one end of a quick-draw to the staple or hanger and the other end over the rope attaching them to their belayer.
So long as the person who drilled and inserted the bolts did a good job and so long as the climber is competent in clipping themselves into these; then sport climbing should be relatively safe. At this point I should stress that I know of good climbers who have died sport climbing, often by making a simple mistake, often after having completed a climb and looking to lower off. Sport climbing is a relatively safer form of climbing, but it is definitively not 100% safe; no form of climbing is.
Because of its [relative] safety, sport climbing has something of the ethos of bouldering, with a focus on climbing at your limit as the systems involved should prevent serious injury in normal circumstances.
In traditional climbing (uniformly called trad) the difference is that there are no pre-placed bolts, instead the climber has to take advantage of the nature of the rock to arrange their own attachment points. This means that you have to take the contents of a small hardware store with you on your climb. The assorted pieces of gear that you might use to protect yourself include: Nuts/wires (which you try to wedge into small cracks):
Hexes (which you try to wedge into large cracks):
Cams/Friends (spring-loaded mechanical devices that you place in parallel cracks – the latter name being a make of cams):
Slings (which you use to lasso spikes, or thread through any convenient holes in the rock):
Once you have secured any of the above into or around the rock, you clip in with a quick-draw as in Sport climbing and heave a sigh of relief.
In the video that started this section, Jean-minh Trin-thieu falls (a long way) on to a cam, which thankfully holds. The issue on this particular climb is that there are no more opportunities to place gear after the final cam at round about half-way up. The nature of the rock means that a lot of Gritsone climbing is like this; one of the reasons that it is not a favourite of mine.
In any case, having established the above dimensions, I am going to drill down via two of them to concentrate on just trad leading. My comments apply equally to multi- and single-pitch, but the former offers greater scope for getting yourself into trouble.
One of the major issues with trad climbing, particularly multi-pitch trad climbing in a mountain environment is that you are never quite sure what you need to take. The more gear you clip to your harness, the more likely you are to be able to deal with any eventuality, but the heavier you are going to be and the harder it will be to climb. Some one once compared trad leading to climbing wearing a metal skirt.
The issue here is that not only do you have to find somewhere to place this protective gear, you have to place it well so that it is not dislodged as you climb past, or pulls out if you fall. What adds to this problem is that you may have to try to place say a wire in a situation where you are holding on to a small hold with one hand, with only one foot on a hold and the other dangling. You may also be on an overhang and thus with all gravity’s force coming to bear on your tendons. At such moments thoughts like “how far below was my last piece of gear?”, “how confident am I that I placed it well?” and “what happens if I can’t fiddle this piece of metal into this crack before my fingers un-peal?” tend to come to mind with alarming ease.
It is not unheard of for a trad leader to climb up many metres, placing an assortment of gear en route, only to fall off and have all of it rip out, a phenomenon call “unzipping”, thankfully not something I have experienced directly; though I have seen it happen to other people.
These additional uncertainties tend to lead to a more cautious approach to trad leading, with many people climbing within their abilities on trad climbs. Some people push themselves on trad and some get away with it for a while. However there is a saying about there being old climbers and bold climbers, but no old bold climbers.
The links with business projects
I have written quite a few times before about the benefits of an incremental approach, so long as this bears the eventual strategic direction in mind (see for example: Tactical Meandering and Holistic vs Incremental approaches to BI). In rock climbing, even within a single pitch, it is often recommended to break this into sections, particularly if there are obvious places (e.g. ledges) where you can take a bit of a rest and consider the next section. This also helps with not being too daunted; often the biggest deal is to start climbing and once you are committed then things become easier (though of course this advice can also get you in over your head on occasion).
Splitting a climb into sections is a good idea, but – in the same way as with business projects – you need to keep your eye on your eventual destination. If you don’t you may be so focussed on the current moves that you go off route and then have to face potentially difficult climbing to get back where you need to be. The equivalent in business would be projects that do not advance the overall programme.
However the analogy doesn’t stop there. If we break a single-pitch trad lead climb into smaller sections, those between each piece of gear that you place, then it is obvious that you need to pay particular attention to the piece of equipment that you are about to employ. If you do this well, then you have minimised the distance that you will fall and this will bolster your confidence for the next piece of climbing. If you rush placing your gear, or assume that it is sort of OK, then at the best you will give yourself unnecessary concerns about your climbing for the next few metres. At worst a fall could lead to this gear ripping and a longer fall, or even hitting the ground.
In business projects, if you take an incremental approach, then in the same way you must remember that you will be judged on the success or failure of the most recent project. Of course if you have a track record of earlier success then this can act as a safety net; the same as when your highest piece of gear fails, but the next one catches you. However, it is not the most comfortable of things to take a really long leader fall and similarly it is best to build on the success of one project with further successes instead of resting on your laurels.
Of course the consequences of rushing your interim steps in rock climbing can be a lot more terminal than in business. Nevertheless failure in either activity is not welcome and it is best to take every precaution to avoid it.
I started my own feasibility study today, climbing [sadly only indoors] for the first time in the six, or so, weeks since I injured myself. Learning from my previous impetuousness I stuck to lowly V0s, working up only as far as V2 (for anyone interested an explanation of bouldering grades can be found here). My patience in forgoing climbing for a month and a half, together with my caution today seems to have paid off. Aside from a few tweaks, my damaged finger seems to have come through OK. I now need to remember to build things up very slowly and back-off at the first sign of any crunchiness whatsoever.
As per my previous analogy, it similarly takes time to turn round business or IT performance. Change is more of a marathon than a sprint (though often some basic things can be done a lot quicker). Staying with the area of rock climbing / business cross-overs, another previous article – Perseverance – highlighted the importance of this attribute in both areas. My aim is to take my own advice!
The discussion included the predictable references to GIGO, but conversation then moved on to who has responsibility for data quality, IT or the business.
As regular readers of this column will know, I view this as an unhelpful distinction. My belief is that IT is a type of business department, with specific skills, but engaged in business work and, in this, essentially no different to say the sales department or the strategy department. Looking at the question through this prism, it becomes tautological. However, if we ignore my peccadillo about this issue, we could instead ask whether responsibility for data quality should reside in IT or not-IT (I will manfully resist the temptation to write ~IT or indeed IT’); with such a change, I accept that this is now a reasonable question.
Answering a modified version of the question
My basic answer is that both groups will bring specific skills to the party and a partnership approach is the one that is most likely to end in success. There are however some strong arguments for IT playing a pivotal role and my aim is to expand on these in the rest of this article.
Before I enumerate these, one thing that I think is very important is that data quality is seen as a broad issue that requires a broad approach to remedy it. I laid out what I see as the four pillars of improving data quality in an earlier post: Using BI to drive improvements in data quality. This previous article goes into much more detail about the elements of a successful data quality improvement programme and its title provides a big clue as to what I see as the fourth pillar. More on this later.
1. The change management angle
Again, as with virtually all IT projects, the aim of a data quality initiative is to drive different behaviours. This means that change management skills are just as important in these types projects as in the business intelligence work that they complement. This is a factor to consider when taking decisions about who takes the lead in looking to improve data quality; who amongst the available resources have established and honed change management skills? The best IT departments will have a number of individuals who fit this bill, if not-IT has them as well, then the organisation is spoilt for choice.
2. The pan-organisational angle
Elsewhere I have argued that BI adds greatest value when it is all-pervasive. The same observations apply to data quality. If we assume that an organisation has a number of divisions, each with their own systems (due to the nature of their business and maybe also history), but also maybe sharing some enterprise applications. While it would undeniably be beneficial for Division A to get their customer files in order, it would be of even greater value if all divisions did this at the same time and with a consistent purpose. This would allow the dealings of Customer X across all parts of the business to be calculated and analysed. It could also drive cross-selling opportunities in particular market segments.
While it is likely that a number of corporate staff of different sorts will have a very good understanding about the high-level operations of each of the divisions, it is at least probable that only IT staff (specifically those engaged in collating detailed data from each division for BI purposes) will have an in-depth understanding of how transactions and master data are stored in different ways across the enterprise. This knowledge is a by-product of running a best practice BI project and the collateral intellectual property built up can be of substantial business value.
3. The BI angle
It was this area that formed the backbone of the earlier data quality article that I referenced above. My thesis was that you could turn the good data quality => good BI relationship on its head and use the BI tool to drive data quality improvements. The key here was not to sanitise data problems, but instead to expose them, also leveraging standard BI functionality like drill through to allow people to identify what was causing an issue.
One of the most pernicious data quality issues is of the valid, but wrong entry. For example a transaction is allocated a category code of X, which is valid, but the business event demands the value Y. Sometimes it is possible to guard against this eventuality by business rules, e.g. Product A can only be sold by Business Unit W, but this will not be possible for all such data. A variant of this issue is data being entered in the wrong field. Having spent a while in the Insurance industry, it was not atypical for a policy number to be entered as a claim value for example. Sometimes there is no easy systematic way to detect this type of occurrence, but exposing issues in a well-designed BI system is one way of noticing odd figures and then – crucially – being able to determine what is causing them.
4. The IT character angle
I was searching round for a way to put this nicely and then realised that Jim Harris had done the job for me in naming his excellent Obsessive-Compulsive Data Quality blog (OCDQ Blog). I’m an IT person, I may have general management experience and a reasonable understanding of many parts of business, but I remain essentially an IT person. Before that, I was a Mathematician. People in both of those lines of work tend to have a certain reputation; to put it positively, the ability to focus extremely hard on something for long periods is a common characteristic.
Aside: for the avoidance of doubt, as I pointed out in Pigeonholing – A tragedy, the fact that someone is good at the details does not necessarily preclude them from also excelling at seeing the big picture – in fact without a grasp on the details the danger of painting a Daliesque big picture is perhaps all too real!
Improving data quality is one of the areas where this personality trait pays dividends. I’m sure that there are some marketing people out there who have relentless attention to detail and whose middle name is “thoroughness”, however I suspect there are rather less of them than among the ranks of my IT colleagues. While leadership from the pertinent parts of not-IT is very important, a lot of the hard yards are going to be done by IT people; therefore it makes sense if they have a degree of accountability in this area.
In closing
Much like most business projects, improving data quality is going to require a cross-functional approach to achieve its goals. While you often hear the platitudinous statement that “the business must be responsible for the quality of its own data”, this ostensible truism hides the fact that one of the best ways for not-IT to improve the quality of an organisation’s data is to get IT heavily involved in all aspects of this work.
IT for its part can leverage both its role as one of the supra-business unit departments and its knowledge of how business transactions are recorded and move from one system to another to become an effective champion of data quality.
The context of the above bon mot was – as is often the case – a discussion on LinkedIn.com. I have been rather absent from the LinkedIn.com discussion groups for the same reasons that I have not been blogging and tweeting. In this case, my attention was drawn to the debate by a colleague.
The particular thread was posted by Andy McKnight and is entitled What’s missing from Business Intelligence? and at the time of writing has attracted nearly 60 responses (you have to be a member of the group to view the discussion). It referred to an article published by EMC2 which has the strap-line How CIOs can Reap the Benefits of BI Technology (note: this is a PDF document). Here is a pertinent quote:
The bad news is that only twenty-seven percent of respondents [to a survey of CIOs carried out by IDG Research] who use a BI solution report being extremely successful or very successful with it. Forty-five percent report being only somewhat successful, while seventeen percent say that they are not very, or not at all successful.
I’m not sure what happened to the other 11% of respondents, maybe they just hung up the ‘phone.
Blaming the users
Having stated that “BI has, too often, not lived up to expectations”, the paper goes on to list some reasons why. First on the list is the following:
lack of adoption by users
You don’t have to be Einstein to realise that this is the result of a BI project failing, not the cause of it. The equivalent in athletics terms would be to say that you came last in the race because everyone else was faster than you. While obviously true this observation doesn’t help a lot with how to do better next time.
Of course hidden in the comment is the plaintive whine heard emanating from many an unsuccessful project (or indeed product launch), “the problem is the users”. This is arrant nonsense, returning to the start of article if you write a book that is panned by the critics and not bought by the public, then there is at least some chance that the fault lies with you and not them. It is the job of the IT professional to know their users, understand their needs and provide systems that cause delight, not disillusion.
A more interesting observation later on is:
Many BI initiatives falter because the analytics capabilities that are at the core of the system aren’t even used. Many users simply pull data from the warehouse and dump it in a spreadsheet. […] A true BI implementation includes both reporting and analytics. CIOs indicate a much higher success rate with BI when users embrace both.
I think that there is some truth in this. Some of the BI failures I have seen have gone to the bother of building a warehouse only to front it with flat reports that are only marginally better than what they replaced.
In my career I have taken the opposite approach. While many people warn against analysis paralysis, I have deployed OLAP tools to all users, with fixed format reports de-emphasised, or used mostly for external purposes. This does mean that more effort needs to be put into training, but this is necessary anyway if you want your BI system to be an agent of change (and why else would you be building one if this is not the case?). I cover my general approach to driving user adoption in a series of three articles as follows:
This approach was very successful and we achieved user adoption of 92% – i.e. of those people who attended training, 92% remained active users (defined as using the BI system on average for at least two extended periods each week). We actually felt that the OLAP tools we were implementing were pretty intuitive and easy-to-use and so focussed mostly on how to use them in specific business scenarios. Overall we felt that training was 25% technical and 75% business-related.
Aiming for simplicity
Related to the above point, the EMC2 article also mentions the following reason for failure:
limited functionality/hard to use
This seems a little oxymoronic as normally it is depth of functionality that confuses people. I think I would disagree with both parts of this point. Out of the box, most BI tools have rich functionality and a reasonably intuitive to use. In one response to the LinkedIn.com thread I said the following:
I have been successful in getting users […] weaned […] off ad hoc reports, it wasn’t an easy process and required persistence and selling, but this paid off. […] It is illuminating seeing business managers (some of whom still dictate memos for their secretaries to type) “slicing and dicing”, drilling down/through and generally interacting away merrily and stating that if all IT was this easy to use and informative, they might have taken to it earlier.
My view here is that you can make the tool as complicated or a simple as you choose. Going back to my first warehouse project, in our somewhat naive early attempts at prototype cubes, we had all available dimensions and all available measures included. I think our idea is that the users could help us sift out the ones that were most important. Instead this approach caused the negative reactions that the article refers to.
We subsequently adopted a rule of having as few dimensions and measures as possible in a cube, without compromising the business need that the cube was trying to address. The second part of this rule was that every cube had to be focussed on answering business questions in at least one area and at most two.
Rather than having a small number of monolithic cubes, we went with the option of a slightly larger number of significantly clearer and simpler ones. I think that this was a factor in our success in driving business adoption.
Should the fact that some BI projects fail dissuade you from BI?
I won’t attempt to dissect the rest of the article, the areas that I comment on above are representative. There are some good points and some less good ones – just like any article, including of course my own. Take a look yourself and see whether the findings and recommendations chime with your own experience of success and failure. What I did want to do was to return to the context of the aphorism that starts this post.
The thesis of the original LinkedIn.com post was that because a significant number of organisations had failed to get enormous benefit from BI, BI itself was therefore somehow flawed. I think this is wrong-headed reasoning. If 1,000 people write a book, how many are likely to become acknowledged as great authors? How many are likely to have the lesser accolade of commercial success? The answer in both cases is “not many”. This is because writing well is a very difficult thing to do (I prove this myself with every blog post!). Not everyone who tries it will be successful. BI is also difficult to do well and a major cause of problems is underestimating this difficulty.
Maybe this is too recherché and example, and maybe if the chances of success with BI are as slim as winning the Purlitzer Prize then it is not worth the effort. So I’ll instead I’ll resort to my favourite area of the sporting analogy. Let’s take the same 1,000 people and say that they all take up a new sport – it is mostly immaterial what the sport is, let’s say tennis. How many of them will go on to become proficient in it? By this I don’t mean that they are the next Roger Federer, just that they become competent enough to serve adequately, master the dark arts of the backhand and sustain a few rallies. My feeling is that the stats would look something like those in the EMC2 report.
Is the prize worth it?
Given this, does it mean that some companies are just not cut out for BI and should ignore the area? Well the answer is “it depends”. Going back to tennis, if some one wants to be good, and has the determination to succeed, that is a necessary (though sadly not sufficient) condition. What may drive such a person on is the objective of achieving a goal, or maybe the pleasure of being able to perform at a certain level.
Focussing on business outcomes, I believe that BI can deliver substantial benefits. In fact I have argued elsewhere that BI can have the greatest payback of any IT project. Of course this presupposes that the BI project is done well. If the prize is potentially that great then maybe – like the aspiring tennis player who wants to become better – trying again makes sense. In recent recruitment I have heard frequent mention of organisations that were building their second warehouse as they didn’t get the first one quite right.
However the comparison with tennis breaks down in that business is a team game. If an organisation as a whole has struggled with BI, then this is not a question of simply accepting your genetic limitations. Companies can “evolve” capabilities by hiring people who have been successful in a field. They can also get benefit from targeted consultancy from practitioners who have a track record of success; this can help them to build an internal capability. This is an approach that I took advantage of myself in the initial six months of my first BI project [note: although I often seem to get mistaken for a BI consultant, I am not touting for business here!].
This means that if a company’s BI architecture is currently the equivalent of a Jeffrey Archer novel, it is still possible to transform it into Heart of Darkness. It will not be easy and will take time and effort, but there are people out there who have been successful and can act as guides.
In closing I should also mention that, if you take appropriate precautions, it is far from inevitable that the end of a BI journey will be finding your own version of Kurtz!
Standard note: You need to be a member of both LinkedIn.com and the group mentioned to view the discussions.
The case for a CBIO
I won’t republish all of John’s initial post, but for those who cannot access the thread these are the essential points that he raised:
There is an ever-increasing need for more and better information in organisations
Increasingly Business Intelligence is seen as a major source of competitive advantage
A CBIO would bring focus and (more importantly) accountability to this area
The CBIO should report directly to the CEO, with strong relations with the rest of the executive team
The CBIO’s team would be a hybrid business / technical one (as I strongly believe the best BI teams should be)
This team should also be at the forefront of driving change, based on the metrics that it generates
Now obviously creating a senior role with a portfolio spanning BI and change is going to be music that falls sweetly on my ears. I did however attempt to be objective in my response, which I reproduce in full below:
As someone who is (primarily) a BI professional, then of course my response could be viewed as entirely self-serving. Nevertheless, I’ll offer my thoughts.
In the BI programmes that I have run, I have had reporting lines into people such as the CIO, CFO or sometimes a combined IT / Operations lead. However (and I think that this is a big however), I have always had programme accountability to the CEO and have always had the entire senior leadership team (business and service departments) as my stakeholders. Generally my direction has come more from these dotted lines than from the solid ones – as you would hope would be the case in any customer-centric IT area.
I have run lots of different IT projects over the years. Things such as: building accounting, purchasing and sales systems; configuring and implementing ERP systems; building front-end systems for underwriters, marketing and executive teams; and so on. Given this background, there is definitely something about BI that makes it different.
Any IT system must be aligned to its users’ needs, that much is obvious. However with BI it goes a long way beyond alignment. In a very real sense, BI systems need to be the business. They are not there to facilitate business transactions, they are there to monitor the heartbeat of the organisation, to help it navigate the best way forward, to get early warning of problems, to check the efficacy of strategies and provide key input to developing them.
In short a good BI system should be focussed on precisely the things that the senior leadership team is focussed on, and in particular what the CEO is focussed on. In order to achieve this you need to understand what makes the business tick and you need to move very close to it. This proximity, coupled with the fact that good BI should drip business value means that I have often felt closer to the overall business leadership team than the IT team.
Please don’t misunderstand my point here. I have been an IT person for 20 years and I am not saying that BI should not be fully integrated with the overall IT strategy – indeed in my book it should be central to it as a major function of all IT systems is to gather information (as well as to support transactions and facilitate interactions with customers). However, there is something of a sense in which BI straddles the IT and business arenas (arenas that I have long argued should be much less distinct from each other than they are in many organisations).
The potentially massive impact of BI, the fact that it speaks the language of business leaders, the need for it to be aligned with driving cultural change and that the fact that the skills required for success in BI are slightly different for those necessary in normal IT projects all argue that something like a CBIO position is maybe not such a bad idea.
Indeed I have begun to see quite a few BI roles that are part of change directorates, or the office of the CEO or CFO. There are also some stand-alone BI roles out there, reporting directing to the board. Clearly there will always be a strong interaction with IT, but perhaps you have detected an emerging trend.
I suppose a shorter version of the above would run something like: my de facto reporting line in BI programmes has always been into the CEO and senior management team, so why not recognise this by making it a de jura reporting line.
BI is a weird combination of being both a specialist and generalist area. Generalist in needing to play a major role in running all aspects of the business, specialist in the techniques and technologies that are key to achieving this.
Over to the jury
Maybe the idea of a CBIO is one whose time has come. I would be interested in people’s views on this.
!["Users are the root of all evil" - anonymous [failed] BI Project Manager](https://i0.wp.com/peterjamesthomas.com/wp-content/uploads/2010/03/down-with-users.jpg?resize=200%2C288 "\"Users are the root of all evil\" - anonymous [failed] BI Project Manager")
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