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Titlepeter james thomas | expert in the data to information to insight to action journey

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H1
peter james thomas
bumps in the road
the big data universe
metamorphosis
alphabet soup
indiana jones and the anomalies of data
the chief data officer “sweet spot”
more statistics and medicine
post navigation
feedburner subscription
wordpress subscription
last 20 articles
categories
recommended sites
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expert in the data to information to insight to action journey
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an introductory anecdote
the entropy of a data asset exposed to change tends to a maximum
and finally
1 quadrillion = 1,000,000,000,000,000
15,000 pb = 15,000,000,000,000,000,000 bytes
before:
after:
breaking the code
before the flood
some other points of view
much cdo about nothing
more or less mandatory sporting analogy
in closing
epilogue
b
an introductory anecdote
the entropy of a data asset exposed to change tends to a maximum
and finally
1 quadrillion = 1,000,000,000,000,000
15,000 pb = 15,000,000,000,000,000,000 bytes
before:
after:
breaking the code
before the flood
some other points of view
much cdo about nothing
more or less mandatory sporting analogy
in closing
epilogue
i
an introductory anecdote
the entropy of a data asset exposed to change tends to a maximum
and finally
1 quadrillion = 1,000,000,000,000,000
15,000 pb = 15,000,000,000,000,000,000 bytes
before:
after:
breaking the code
before the flood
some other points of view
much cdo about nothing
more or less mandatory sporting analogy
in closing
epilogue
em an introductory anecdote
the entropy of a data asset exposed to change tends to a maximum
and finally
1 quadrillion = 1,000,000,000,000,000
15,000 pb = 15,000,000,000,000,000,000 bytes
before:
after:
breaking the code
before the flood
some other points of view
much cdo about nothing
more or less mandatory sporting analogy
in closing
epilogue
Bolds strong 14
b 14
i 14
em 14
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- http://jtonedm.com/
- http://www.robertmorison.com/
- https://jenstirrup.com/about/
james taylor http://jtonedm.com/
the proceedings of the 2015 chief analytics officer summit http://jtonedm.com/2015/10/27/chief-analytics-officer-summit-opening-keynotes/
robert morison http://www.robertmorison.com/
the chief data officer “sweet spot” https://peterjamesthomas.com/2016/12/22/the-chief-data-officer-sweet-spot/
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5 more themes from a chief data officer forum https://peterjamesthomas.com/2015/11/17/5-more-themes-from-a-chief-data-officer-forum/
the chief data officer “sweet spot” https://peterjamesthomas.com/2016/12/22/the-chief-data-officer-sweet-spot/
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indiana jones and the anomalies of data https://peterjamesthomas.com/2017/01/04/indiana-jones-and-the-anomalies-of-data/
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a more appropriate metaphor for business intelligence projects https://peterjamesthomas.com/2009/03/18/a-more-approprate-metaphor-for-business-intelligence-projects/
patterns patterns everywhere – the sequel https://peterjamesthomas.com/2014/01/26/patterns-patterns-everywhere-the-sequel/
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new adventures in wi-fi – track 1: blogging https://peterjamesthomas.com/2009/07/24/new-adventures-in-wi-fi-track-1-blogging/
business logic https://peterjamesthomas.com/2010/03/20/business-logic/
new adventures in wi-fi – track 2: twitter https://peterjamesthomas.com/2010/04/28/new-adventures-in-wi-fi-track-2-twitter/
using historical data to justify bi investments – part iii https://peterjamesthomas.com/2011/05/16/using-historical-data-to-justify-bi-investments-part-iii/
xkcd.com http://xkcd.com
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skip to content peter james thomas expert in the data to information to insight to action journey menu home about this site site map problems and browser compatibility please add my site to the recommended list career information executive biography experience validus holdings greene king element six (de beers) chubb international chubb insurance company of europe the emir project emir user feedback cedardata plc education professional education academic education awards financial sector technology award cognos uk award media & seminars media vendor case studies presss interviews & quotes videos cognos video testimonial informatica video testimonial computing / accountancy age “webinar” smart data collective – “podcast” microsoft bi video interview seminars keynote articles creative commons contact details bumps in the road 20 january 201720 january 2017 peter james thomas change management, data governance roadworks, sherlock holmes the above image appears in my seminar deck data management, analytics and people: an eternal golden braid. it is featured on a slide titled “why data management? – the negative case” [1]. given that i couldn’t find a better illustration for a particular point that i was trying to make in this part of my presentation, i ended up buying the road image from stock photo company alamy.com. being naturally parsimonious [2], i thought that i’d reuse my purchase here. 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] which you may be glad to hear is followed directly by one titled “why data management? – the positive case”. [2] the verity of this assertion is at best questionable. [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. follow @peterjthomas share this:click to print (opens in new window)click to email (opens in new window)click to share on twitter (opens in new window)click to share on linkedin (opens in new window)click to share on whatsapp (opens in new window)share on facebook (opens in new window)click to share on tumblr (opens in new window)click to share on reddit (opens in new window)click to share on pinterest (opens in new window)click to share on google+ (opens in new window)click to share on pocket (opens in new window)like this:like loading... leave a comment the big data universe 16 january 201715 january 2017 peter james thomas big data, google, infographics the royal society the above image is part of a much bigger infographic produced by the royal society about machine learning. you can view the whole image here. i felt that this component was interesting in a stand-alone capacity. the legend explains that a petabyte (pb) is equal to a million gigabytes (gb) [1], or 1 pb = 106 gb. a gigabyte itself is a billion bytes, or 1 gb = 109 bytes. recalling how we multiply indeces we can see that 1 pb = 106 × 109 bytes = 106 + 9 bytes = 1015 bytes. 1015 also has a name, it’s called a quadrillion. written out long hand: 1 quadrillion = 1,000,000,000,000,000 the estimate of the amount of data held by google is fifteen thousand petabytes, let’s write that out long hand as well: 15,000 pb = 15,000,000,000,000,000,000 bytes that’s a lot of zeros. as is traditional with big numbers, let’s try to put this in context. the average size of a photo on an iphone 7 is about 3.5 megabytes (1 mb = 1,000,000 bytes), so google could store about 4.3 trillion of such photos. stepping it up a bit, the average size of a high quality photo stored in cr2 format from a canon eos 5d mark iv is ten times bigger at 35 mb, so google could store a mere 430 billion of these. a high definition (1080p) movie is on average around 6 gb, so google could store the equivalent of 2.5 billion movies. if google employees felt that this resolution wasn’t doing it for them, they could upgrade to 150 million 4k movies at around 100 gb each. if instead they felt like reading, they could hold the equivalent of the library of congress print collections a mere 75 thousand times over [2]. rather than talking about bytes, 15,000 petametres is equivalent to about 1,600 light years and at this distance from us we find messier object 47 (m47), a star cluster which was first described an impressively long time ago in 1654. if instead we consider 15,000 peta-miles, then this is around 2.5 million light years, which gets us all the way to our nearest neighbour, the andromeda galaxy [3]. the fastest that humankind has got anything bigger than a handful of sub-atomic particles to travel is the 17 kilometres per second (11 miles per second) at which voyager 1 is currently speeding away from the sun. at this speed, it would take the probe about 43 billion years to cover the 15,000 peta-miles to andromeda. this is over three times longer than our best estimate of the current age of the universe. finally a more concrete example. if we consider a small cube, made of well concrete, and with dimensions of 1 cm in each direction, how big would a stack of 15,000 quadrillion of them be? well, if arranged into a cube, each of the sides would be just under 25 km (15 and a bit miles) long. that’s a pretty big cube. if the base was placed in the vicinity of new york city, it would comfortably cover manhattan, plus quite a bit of brooklyn and the bronx, plus most of jersey city. it would extend up to hackensack in the north west and almost reach jfk in the south east. the top of the cube would plough through the troposphere and get half way through the stratosphere before topping out. it would vie with mars’s olympus mons for the title of highest planetary structure in the solar system [4]. it is probably safe to say that 15,000 pb is an astronomical figure. google played a central role in the initial creation of the collection of technologies that we now use the term big data to describe the image at the beginning of this article perhaps explains why this was the case (and indeed why they continue to be at the forefront of developing newer and better ways of dealing with large data sets). as a point of order, when people start talking about “big data”, it is worth recalling just how big “big data” really is. notes [1] in line with the royal society, i’m going to ignore the fact that these definitions were originally all in powers of 2 not 10. [2] the size of the library of congress print collections seems to have become irretrievably connected with the figure 10 terabytes (10 × 1012 bytes) for some reason. no one knows precisely, but 200 tb seems to be a more reasonable approximation. [3] applying the unimpeachable logic of eminent pseudoscientist and numerologist erich von däniken, what might be passed over as a mere coincidence by lesser minds, instead presents incontrovertible proof that google’s pagerank algorithm was produced with the assistance of extraterrestrial life; which, if you think about it, explains quite a lot. [4] though i suspect not for long, unless we chose some material other than concrete. then i’m not a materials scientist, so what do i know? follow @peterjthomas share this:click to print (opens in new window)click to email (opens in new window)click to share on twitter (opens in new window)click to share on linkedin (opens in new window)click to share on whatsapp (opens in new window)share on facebook (opens in new window)click to share on tumblr (opens in new window)click to share on reddit (opens in new window)click to share on pinterest (opens in new window)click to share on google+ (opens in new window)click to share on pocket (opens in new window)like this:like loading... leave a comment metamorphosis 13 january 201716 january 2017 peter james thomas data science, data visualisation automattic, boris gorelik, python, wordpress no neither my observations on the work of kafka, nor that of escher [1]. instead some musings relating on how to transform a bare bones and unengaging chart into something that both captures the attention of the reader and better informs them of the message that the data displayed is relaying. let’s consider an example: before: after: the two images above are both renderings of the same dataset, which tracks the degree of fragmentation of the israeli parliament – the knesset – over time [2]. they are clearly rather different and – i would argue – the latter makes it a lot easier to absorb information and thus to draw inferences. both are the work of boris gorelik a data scientist at automattic, a company that is most well-known for creating freemium saas blogging platform, wordpress.com and open source blogging software, wordpress [3]. i have been a contented wordpress.com user since the inception of this blog back in november 2008, so it was with interest that i learnt that automattic have their own data-focussed blog, data for breakfast, unsurprisingly hosted on wordpress.com. it was on data for breakfast that i found boris’s article, evolution of a plot: better data visualization, one step at a time. in this he takes the reader step by step through what he did to transform his data visualisation from the ugly duckling “before” exhibit to the beautiful swan “after” exhibit. boris is using python and various related libraries to do his data visualisation work. given that i stopped commercially programming sometime around 2009 (admittedly with a few lapses since), i typically use the much more quotidian excel for most of the charts that appear on peterjamesthomas.com [4]. sometimes, where warranted, i enhance these using visio and / or paintshop pro. for example, the three [5] visualisations featured in a tale of two [brexit] data visualisations were produced this way. despite the use of calibri, which is probably something of a giveaway, i hope that none of these resembles a straight-out-of-the-box excel graph [6]. uk referendum on eu membership – percentage voting by age bracket (see notes) uk referendum on eu membership – numbers voting by age bracket (see notes) uk referendum on eu membership – number voting by age bracket (see notes) while, in the above, i have not gone to the lengths that boris has in transforming his initial and raw chart into something much more readable, i do my best to make my excel charts look at least semi-professional. my reasoning is that, when the author of a chart has clearly put some effort into what their chart looks like and has at least attempted to consider how it will be read by people, then this is a strong signal that the subject matter merits some closer consideration. next time i develop a chart for posting on these pages, i may take boris’s lead and also publish how i went about creating it. notes [1] though the latter’s work has adorned these pages on several occasions and indeed appears in my seminar decks. [2] boris has charted a metric derived from how many parties there have been and how many representatives of each. see his article itself for further background. [3] you can learn more about the latter at wordpress.org. [4] though i have also used graphpad prism for producing more scientific charts such as the main one featured in data visualisation – a scientific treatment. [5] yes i can count. i have certificates which prove this. [6] indeed the final one was designed to resemble a fractured british flag. i’ll leave readers to draw their own conclusions here. follow @peterjthomas share this:click to print (opens in new window)click to email (opens in new window)click to share on twitter (opens in new window)click to share on linkedin (opens in new window)click to share on whatsapp (opens in new window)share on facebook (opens in new window)click to share on tumblr (opens in new window)click to share on reddit (opens in new window)click to share on pinterest (opens in new window)click to share on google+ (opens in new window)click to share on pocket (opens in new window)like this:like loading... leave a comment alphabet soup 10 january 201716 january 2017 peter james thomas business analytics, chief data officer football manager, james taylor, jen stirrup, pass, robert morison this article is about the latest consumer product from the google stable, something which will revolutionise your eating experience by combining a chicken-broth base with a nanotechnology garnish and a soupçon of deep learning techniques to create a warming meal that also provides a gastro-intestinal health-check. wait… …i may have got my wires crossed a bit there. no, i mis-spoke, the article is actually about ever increasing number of cxo titles [1], which has made a roster of many organisations’ executives come to resemble a set of scrabble tiles. specifically i will focus on two values of x, a and d, so the cao and cdo roles [2]. what do these tlas [3] stand for, what do people holding these positions do and can we actually prove that, for these purposes only, “a” ≡ “d”? breaking the code the starting position is not auspicious. what might cao stand for? existing roles that come to mind include: chief accounting officer and chief administrative officer. however, in our context, it actually stands for chief analytics officer. there is no iso definition of analytics, as i note in one of my recent seminar decks [4] (quoting the gartner it glossary, but with my underlining): analytics has emerged as a catch-all term for a variety of different business intelligence and application-related initiatives. in particular, bi vendors use the ‘analytics’ moniker to differentiate their products from the competition. increasingly, ‘analytics’ is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. i should of course mention here that my current role incorporates the word “analytics” [5], so i may be making a point against myself. but before i start channeling my 2009 article, business analytics vs business intelligence [6], i’ll perhaps instead move on to the second acronym. how to decode cdo? well an equally recent translation would be chief digital officer, but you also come across chief development officer and sometimes even chief diversity officer. our meaning will however be chief data officer. you can read about what i think a cdo does here. a observation that is perhaps obvious to make at this juncture is that when the acronym of a role is not easy to pin down, the content of the role may be equally amorphous. it is probably fair to say that this is true of both cao and cdo job descriptions. both are emerging roles in the majority of organisations. before the flood one thing that both roles have in common is that – in antediluvian days – their work used to be the province of another cxo, the cio. this was before many cios became people who focus on solution architecture, manage relationships with outsourcers and have their time consumed by running service desks and heading off infrastructure issues [7]. where organisations may have had just a cio, they may well now have a cio, a cao and a cdo (and also a cto perhaps which splits one original “c” role into four). aside from being a job creation scheme, the reasons for such splits are well-documented. the prevalence of outsourcing (and the complexity of managing such arrangements); the pervasiveness and criticality of technology leading to many cios focussing more on the care and feeding of systems than how businesses employ them; the relentless rise of change organisations; and (frequnetly related to the last point) the increase in size of it departments (particularly if staff in external partner organisations are included). all of these have pushed cios into more business as usual / back-room / engineering roles, leaving a vacuum in the nexus between business, technology and transformation. the fact that data processing is very different to data collation and synthesis has been another factor in caos and / or cdos filling this vacuum. some other points of view as trailed in some previous articles [8], i have been thinking about the potential cao / cdo dichotomy for some time. towards the beginning of this period i read some notes that decision management luminary james taylor had published based on the proceedings of the 2015 chief analytics officer summit. in the first part of these he cites comments made by robert morison as follows: practically speaking organizations need both roles [cao and cdo] filled – either by one person or by two working closely together. this is hard because the roles are both new and evolving – role clarity was not the norm creating risk. in particular if both roles exist they must have some distinction such as demand v supply, offense v defense – adding value to data with analytics v managing data quality and consistency. but enterprises need to be ready – in particular when data is being identified as an asset by the ceo and executive team. cdos tend to be driven by fragmented data environments, regulatory challenges, customer centricity. cao tends to be driven by a focus on improving decision-making, moving to predictive analytics, focusing existing efforts. where cao and cdo roles are separate, the former tends to work on exploiting data, the latter on data foundations / compliance. these are precisely the two vertical extremities of the spectrum i highlighted in the chief data officer “sweet spot”. as robert points out, in order for both to be successful, the cao and cdo need to collaborate very closely. around the same time, another take on the same general question was offered by jen stirrup in her 2015 pass diary [9] article, why are pass doing business analytics at all?. here jen cites the gartner distinctions between descriptive, diagnostic, predictive and prescriptive analytics adding that: business intelligence and business analytics are a continuum. analytics is focused more on a forward motion of the data, and a focus on value. channeling douglas adams, this model can be rehashed as: what happened? why did it happen? what is going to happen next? what should we be doing? as well as providing a finer grain distinguishing different types of analytics, the steps necessary to answer these questions also tend to form a bridge between what might be regarded as definitively cdo work and what might be regarded as definitively cao work. as jen notes, it’s a continuum. answering “what happened?” with any accuracy requires solid data foundations and decent data quality, working out “what is going to happen next?” requires each of solid data foundations, decent data quality and a statistical approach. much cdo about nothing in some organisations, particularly the type where headcount is not a major factor in determining overall results, separate cao and cdo departments can coexist; assuming of course that their leaders recognise their mutual dependency, park their egos at the door and get on with working together. however, even in such organisations, the question arises of to whom should the cao and cdo report, a single person, two different people, or should one of them report to the other? in more cost-conscious organisations entirely separate departments may feel like something of a luxury. my observation is that cao staff generally end up doing data collation and cleansing, while cdo staff often get asked to provide data and carry out data analysis. this blurs what is already a fairly specious distinction between the two areas and provides scope for both duplication of work and – more worryingly – different answers to the same business questions. as i have mentioned in earlier articles, to anyone engaged in the fields, analytics and data management are two sides of the same coin and both benefit from being part of the same unitary management structure. if we consider the arrangements on the left-hand side of the above diagram, the two departments may end up collaborating, but the structure does not naturally lead to this. indeed, where the priorities of the people that the cao and cdo report in to differ, then there is scope for separate agendas, unhealthy competition and – again – duplication and waste. it is my assertion that the arrangements on the right-hand side are more likely to lead to a cohesive treatment of the spectrum of data matters and thus superior business outcomes. in the right-hand exhibit, i have intentionally steered away from cao and cdo titles. i recognise that there are different disciplines within the data world, but would expect virtual teams to form, disband and reform as required drawing on a variety of skills and experience. i have also indicated that the whole area should report into a single person, here given the monicker of tdj (or top data job [10]). you could of course map analytics lead to cao and data management lead to cdo if you chose. equally you could map one or other of these to the tdj, with the other subservient. to an extent it doesn’t really matter. what i do think matters is that the tdj goes to someone who understands the whole data arena; both the cao and cdo perspectives. in my opinion this rules out most ceos, coos and cfos from this role. more or less mandatory sporting analogy [11] an analogy here comes from robert morison’s mention of “offense v defense” [12]. this puts me in mind of an [association] football manager. in soccer (to avoid further confusion), there are not separate offensive and defensive teams, whose presence on the field of play are mutually exclusive. instead your defenders and attackers are different roles within one team; also sometimes defenders have to attack and attackers have to defend. the arrangements in the left-hand organogram are as if the defenders in a soccer team were managed by one person, the attackers by another and yet they were all expected to play well together. of course there are specialist coaches, but there is one manager of a soccer team who has overall accountability for tactics, selection and style of play (they also manage any specialist coaches). it is generally the manager who lives or dies according to their team’s success. equally, in the original right-hand organogram, if the tdj is held by someone who understands just analytics or just data management, then it is like a soccer manager who only understands attack, but not defence. the point i am trying to make is probably more readily apprehended via the following diagram: on the assumption that the manager on the right knows a lot about both attack and defence in soccer, whereas the team owner is at best an interested amateur, then is the set up on the left or on the right likely to be a more formidable footballing force? even in american football the analogy still holds. there are certainly offensive and defensive coaches, each of whom has “their” team on the park for a period. however, it is the head coach who calls the shots and this person needs to understand all of the nuances of the game. in closing so, my recommendation is that – in data matters – you similarly have someone in the top data job, with a broad knowledge of all aspects of data. they can be supported by specialists of course, but again someone needs to be accountable. to my mind, we already have a designation for such as person, a chief data officer. however, to an extent this is semantics. a chief analytics officer who is knowledgeable about data governance and data management could be the head data honcho [13], but one who only knows about analytics is likely to have their work cut out for them. equally if cao and cdo functions are wholly separate and only come together in an organisation under someone who has no background in data matters, then nothing but problems is going to arise. the top data job – or cdo in my parlance – has to be au fait with the span of data activities in an organisation and accountable for all work pertaining to data. if not then they will be as useful as a soccer manager who only knows about one aspect of the game and can only direct a handful of the 11 players on the field. do organisations want some chance of winning the game, or to tie their hands behind their backs and don a blindfold before engaging in data activities? the choice should not really be a difficult one. notes [1] ∀ x : 65 ≤ ascii(x) ≤ 90. [2] “c”, “a”, “o” + “c”, “d”, “o” + (for no real reason save expediency) “r” allows you to spell accord, which scores 11 in executive scrabble. [3] three letter acronyms. [4] data management, analytics, people: an eternal golden braid – a metaphorical fugue on the data ⇒ information ⇒ insight ⇒ action journey in the spirit of douglas r. hofstadter – irm(uk) enterprise data / business intelligence 2016 [5] i hasten to add that it also contains the phrase “data management” – see here. [6] probably not a great idea for any of those involved. [7] whether or not this evolution (or indeed regression) of the cio role has proved to be a good thing is perhaps best handled in a separate article. [8] including: wanted – chief data officer 5 themes from a chief data officer forum 5 more themes from a chief data officer forum and the chief data officer “sweet spot” [9] pass was co-founded by ca technologies and microsoft corporation in 1999 to promote and educate sql server users around the world. since its founding, pass has expanded globally and diversified its membership to embrace professionals using any microsoft data technology. [10] with acknowledgement to peter aiken. [11] a list of my articles that employ sporting analogies appears – appropriately enough – at the beginning of analogies. [12] that’s “offence vs defence” in case any readers were struggling. [13] maybe organisations should consider adding hdh to their already very crowded executive alphabet soup. follow @peterjthomas share this:click to print (opens in new window)click to email (opens in new window)click to share on twitter (opens in new window)click to share on linkedin (opens in new window)click to share on whatsapp (opens in new window)share on facebook (opens in new window)click to share on tumblr (opens in new window)click to share on reddit (opens in new window)click to share on pinterest (opens in new window)click to share on google+ (opens in new window)click to share on pocket (opens in new window)like this:like loading... 1 comment indiana jones and the anomalies of data 4 january 201712 january 2017 peter james thomas project management, statistics indiana jones, xkcd.com one of an occasional series [1] highlighting the genius of randall munroe. randall is a prominent member of the international data community and apparently also writes some sort of web-comic as a side line [2]. copyright xkcd.com data and indiana jones, these are a few of my favourite things… [3] indeed i must confess to having used a variant of the image below in each of my seminar deck and – on this site back in 2009 – a previous article, a more appropriate metaphor for business intelligence projects. in both cases i was highlighting that data-centric work is sometimes more like archaeology than the frequently employed metaphor of construction. to paraphrase myself, you never know what you will find until you start digging. the image suggested the unfortunate results of not making this distinction when approaching data projects. so, perhaps i am arguing for less data architects and more data archaeologists; the whip and fedora are optional of course! notes [1] well not that occasional as, to date, the list extends to: patterns patterns everywhere – the sequel an inconvenient truth analogies, the whole article is effectively an homage to xkcd.com a single version of the truth? especially for all business analytics professionals out there new adventures in wi-fi – track 1: blogging business logic [my adaptation] new adventures in wi-fi – track 2: twitter using historical data to justify bi investments – part iii [2] xkcd.com if you must ask. [3] though in this case, my enjoyment would have been further enhanced by the use of “artefacts” instead. follow @peterjthomas share this:click to print (opens in new window)click to email (opens in new window)click to share on twitter (opens in new window)click to share on linkedin (opens in new window)click to share on whatsapp (opens in new window)share on facebook (opens in new window)click to share on tumblr (opens in new window)click to share on reddit (opens in new window)click to share on pinterest (opens in new window)click to share on google+ (opens in new window)click to share on pocket (opens in new window)like this:like loading... leave a comment the chief data officer “sweet spot” 22 december 201616 january 2017 peter james thomas business analytics, chief data officer, data governance, data management, data quality, statistics cast of serenity, firefly, irm uk i verbally “scribbled” something quite like the exhibit above recently in conversation with a longstanding professional associate. this was while we were discussing where the cdo role currently sat in some organisations and his or her span of responsibilities. we agreed that – at least in some cases – the role was defined sub-optimally with reference to the axes in my virtual diagram. this discussion reminded me that i was overdue a piece commenting on november’s irm(uk) cdo executive forum; the third in a sequence that i have covered in these pages [1], [2]. in previous cdo exec forum articles, i have focussed mainly on the content of the day’s discussions. here i’m going to be more general and bring in themes from the parent event; irm(uk) enterprise data / business intelligence 2016. however i will later return to a theme central to the exec forum itself; the one that is captured in the graphic at the head of this article. as well as attending the cdo forum, i was speaking at the umbrella event. the title of my talk was data management, analytics, people: an eternal golden braid [3]. the real book, whose title i had plagiarised, is gödel, escher and bach, an eternal golden braid, by pulitzer-winning american author and doyen of 1970s pop-science books, douglas r. hofstadter [4]. this book, which i read in my youth, explores concepts in consciousness, both organic and machine-based, and their relation to recursion and self-reference. the author argued that these themes were major elements of the work of each of austrian mathematician kurt gödel (best known for his two incompleteness theorems), dutch graphic artist maurits cornelis escher (whose almost plausible, but nevertheless impossible buildings and constantly metamorphosing shapes adorn both art galleries and college dorms alike) and german composer johann sebastian bach (revered for both the beauty and mathematical elegance of his pieces, particularly those for keyboard instruments). in an age where machine learning and other artificial intelligence techniques are moving into the mainstream – or at least on to our smartphones – i’d recommend this book to anyone who has not had the pleasure of reading it. in my talk, i didn’t get into anything as metaphysical as hofstadter’s essays that intertwine patterns in mathematics, art and music, but maybe some of the spirit of his book rubbed off on my much lesser musings. in any case, i felt that my session was well-received and one particular piece of post-presentation validation had me feeling rather like these guys for the rest of the day: what happened was that a longstanding internet contact [5] sought me out and commended me on both my talk and the prescience of my july 2009 article, is the time ripe for appointing a chief business intelligence officer? he argued convincingly that this foreshadowed the emergence of the chief data officer. while it is an inconvenient truth that visa international had a cdo eight years earlier than my article appeared, on re-reading it, i was forced to acknowledge that there was some truth in his assertion. to return to the matter in hand, one point that i made during my talk was that analytics and data management are two sides of the same coin and that both benefit from being part of the same unitary management structure. by this i mean each area reporting into an executive who has a strong grasp of what they do, rather than to a general manager. more specifically, i would see data compliance work and data synthesis work each being the responsibility of a cdo who has experience in both areas. it may seem that crafting and implementing data policies is a million miles from data visualisation and machine learning, but to anyone with a background in the field, they are much more strongly related. indeed, if managed well (which is often the main issue), they should be mutually reinforcing. thus an insightful model can support business decision-making, but its authors would generally be well-advised to point out any areas in which their work could be improved by better data quality. efforts to achieve the latter then both improve the usefulness of the model and help make the case for further work on data remediation; a virtuous circle. here we get back to the vertical axis in my initial diagram. in many organisations, the cdo can find him or herself at the extremities. particularly in financial services, an industry which has been exposed to more new regulation than many in recent years, it is not unusual for cdos to have a risk or compliance background. while this is very helpful in areas such as governance, it is less of an asset when looking to leverage data to drive commercial advantage. symmetrically, if a rookie cdo was a data scientist who then progressed to running teams of data scientists, they will have a wealth of detailed knowledge to fall back on when looking to guide business decisions, but less familiarity with the – sometimes apparently thankless, and generally very arduous – task of sorting out problems in data landscapes. despite this, it is not uncommon to see cdos who have a background in just one of these two complementary areas. if this is the case, then the analytics expert will have to learn bureaucratic and programme skills as quickly as they can and the governance guru will need to expand their horizons to understand the basics of statistical modelling and the presentation of information in easily digestible formats. it is probably fair to say that the journey to the centre is somewhat perilous when either extremity is the starting point. let’s now think about the second and horizontal axis. in some organisations, a newly appointed cdo will be freshly emerged from the ranks of it (in some they may still report to the cio, though this is becoming more of an anomaly with each passing year). as someone whose heritage is in it (though also from very early on with a commercial dimension) i understand that there are benefits to such a career path, not least an in-depth understanding of at least some of the technologies employed, or that need to be employed. however a technology master who is also a business neophyte is unlikely to set the world alight as a newly-minted cdo. such people will need to acquire new skills, but the learning curve is steep. to consider the other extreme of this axis, it is undeniable that a cdo organisation will need to undertake both technical and technological work (or at least to guide this in other departments). therefore, while an in-depth understanding of a business, its products, markets, customers and competitors will be of great advantage to a new cdo, without at least a reasonable degree of technical knowledge, they may struggle to connect with some members of their team; they may not be able to immediately grasp what technology tasks are essential and which are not; and they may not be able to paint an accurate picture of what good looks like in the data arena. once more rapid assimilation of new information and equally rapid acquisition of new skills will be called for. at this point it will be pretty obvious that my central point here is that the “sweet spot” for a cdo, the place where they can have greatest impact on an organisation and deliver the greatest value, is at the centre point of both of these axes. when i was talking to my friend about this, we agreed that one of the reasons why not many cdos sit precisely at this nexus is because there are few people with equal (or at least balanced) expertise in the business and technology fields; few people who understand both data synthesis and data compliance equally well; and vanishingly few who sit in the centre of both of these ranges. perhaps these facts would also have been apparent from revewing the cdo job description i posted back in november 2015 as part of wanted – chief data officer. however, as always, a picture paints a thousand words and i rather like the compass-like exhibit i have come up with. hopefully it conveys a similar message more rapidly and more viscerally. to bring things back to the irm(uk) cdo executive forum, i felt that issues around where delegates sat on my cdo “sweet spot” diagram (or more pertinently where they felt that they should sit) were a sub-text to many of our discussions. it is worth recalling that the mainstream cdo is still an emergent role and a degree of confusion around what they do, how they do it and where they sit in organisations is inevitable. all cxo roles (with the possible exception of the ceo) have gone through similar journeys. it is probably instructive to contrast the duties of a chief risk officer before 2008 with the nature and scope of their responsibilities now. it is my opinion that the cdo role (and individual cdos) will travel an analogous path and eventually also settle down to a generally accepted set of accountabilities. in the meantime, if your organisation is lucky enough to have hired one of the small band of people whose experience and expertise already place them in the cdo “sweet spot”, then you are indeed fortunate. if not, then not all is lost, but be prepared for your new cdo to do a lot of learning on the job before they too can join the rather exclusive club of fully rounded cdos. epilogue as an erstwhile mathematician, i’ve never seen a framework that i didn’t want to generalise. it occurs to me and – i assume – will also occur to many readers that the north / south and east / west diagram i have created could be made even more compass-like by the addition of north east / south west and north west / south east axes, with our idealised cdo sitting in the middle of these spectra as well [6]. readers can debate amongst themselves what the extremities of these other dimensions might be. i’ll suggest just a couple: “change” and “business as usual”. given how organisations seem to have evolved in recent years, it is often unfortunately a case of never the twain shall meet with these two areas. however a good cdo will need to be adept at both and, from personal experience, i would argue that mastery of one does not exclude mastery of the other. notes [1] see each of: 5 themes from a chief data officer forum 5 more themes from a chief data officer forum and themes from a chief data officer forum – the 180 day perspective [2] the main reasons for delay were a house move and a succession of illnesses in my family – me included – so i’m going to give myself a pass. [3] the sub-title was a metaphorical fugue on the data ⇨ information ⇨ insight ⇨ action journey in the spirt of douglas r. hofstadter, which points to the inspiration behind my talk rather more explicity. [4] douglas r. hofstadter is the son of nobel-wining physicist robert hofstadter. prize-winning clearly runs in the hofstadter family, much as with the braggs, bohrs, curies, euler-chelpins, kornbergs, siegbahns, tinbergens and thomsons. [5] i am omitting any names or other references to save his blushes. [6] i could have gone for three or four dimensional cartesian coordinates as well i realise, but sometimes (very rarely it has to be said) you can have too much mathematics. follow @peterjthomas share this:click to print (opens in new window)click to email (opens in new window)click to share on twitter (opens in new window)click to share on linkedin (opens in new window)click to share on whatsapp (opens in new window)share on facebook (opens in new window)click to share on tumblr (opens in new window)click to share on reddit (opens in new window)click to share on pinterest (opens in new window)click to share on google+ (opens in new window)click to share on pocket (opens in new window)like this:like loading... 3 comments more statistics and medicine 8 november 20161 january 2017 peter james thomas mathematics & science, statistics diagnostic tests, false positives, medical profession, risk assessment i wrote last on the intersection of these two disciplines back in march 2011 (medical malpractice). what has prompted me to return to the subject is some medical tests that i was offered recently. if the reader will forgive me, i won’t go into the medical details – and indeed have also obfuscated some of the figures i was quoted – but neither are that relevant to the point that i wanted to make. this point relates to how statistics are sometimes presented in medical situations and – more pertinently – the disconnect between how these may be interpreted by the man or woman in the street, as opposed to what is actually going on. rather than tie myself in knots, let’s assume that the test is for a horrible disease called pjt syndrome [1]. let’s further assume that i am told that the test on offer has an accuracy of 80% [2]. this in and of itself is a potentially confusing figure. does the test fail to detect the presence of pjt syndrome 20% of the time, or does it instead erroneously detect pjt syndrome, when the patient is actually perfectly healthy, 20% of the time? in this case, after an enquiry, i was told that a negative result was a negative result, but that a positive one did not always mean that the subject suffered from pjt syndrome; so the issue is confined to false positives, not false negatives. this definition of 80% accuracy is at least a little clearer. so what is a reasonable person to deduce from the 80% figure? probably that if they test positive, that there is an 80% certainty that they have pjt syndrome. i think that my visceral reaction would probably be along those lines. however, such a conclusion can be incorrect, particularly where the incidence of pjt syndrome is low in a population. i’ll try to explain why. if we know that pjt syndrome occurs in 1 in every 100 people on average, what does this mean for the relevance of our test results? let’s take a graphical look at a wholly representative population of exactly 100 people. the pjt syndrome sufferer appears in red at the bottom right. now what is the result of the 80% accuracy of our test, remembering that this means that 20% of people taking it will be falsely diagnosed as having pjt syndrome? well 20% of 100 is – applying a complex algorithm – approximately 20 people. let’s flag these up on our population schematic in grey. so 20 people have the wrong diagnosis. one is correctly identified as having pjt syndrome and 79 are correctly identified as not having pjt syndrome; so a total of 80 have the right diagnosis. what does this mean for those 21 people who have been unfortunate enough to test positive for pjt syndrome (the one person coloured red and the 20 coloured grey)? well only one of them actually has the malady. so, if i test positive, my chances of actually having pjt syndrome are not 80% as we originally thought, but instead 1 in 21 or 4.76%. so my risk is still low having tested positive. it is higher than the risk in the general population, which is 1 in 100, or 1%, but not much more so. the problem arises if having a condition is rare (here 1 in 100) and the accuracy of a test is low (here it is wrong for 20% of people taking it). if you consider that the condition that i was being offered a test for actually has an incidence of around 1 in 20,000 people, then with an 80% accurate test we would get the following: in a population of 20,000 one 1 person has the condition in the same population a test with our 80% accuracy means that 20% of people will test positive for it when they are perfectly healthy, this amounts to 4,000 people so in total, 4,001 people will test positive, 1 correctly, 4,000 erroneously which means that a positive test tells me my odds of having the condition being tested for are 1 in 4,001, or 0.025%; still a pretty unlikely event low accuracy tests and rare conditions are a very bad combination. as well as causing people unnecessary distress, the real problem is where the diagnosis leads potential suffers to take actions (e.g. undergoing further diagnosis, which could be invasive, or even embarking on a course of treatment) which may themselves have the potential to cause injury to the patient. i am not of course suggesting that people ignore medical advice, but doctors are experts in medicine and not statistics. when deciding what course of action to take in a situation similar to one i recently experienced, taking the time to more accurately assess risks and benefits is extremely important. humans are well known to overestimate some risks (and underestimate others), there are circumstances when crunching the numbers and seeing what they tell you is not only a good idea, it can help to safeguard your health. for what it’s worth, i opted out of these particular tests. notes [1] a terrible condition which renders sufferers unable to express any thought in under 1,000 words. [2] not the actual figure quoted, but close to it. follow @peterjthomas share this:click to print (opens in new window)click to email (opens in new window)click to share on twitter (opens in new window)click to share on linkedin (opens in new window)click to share on whatsapp (opens in new window)share on facebook (opens in new window)click to share on tumblr (opens in new window)click to share on reddit (opens in new window)click to share on pinterest (opens in new window)click to share on google+ (opens in new window)click to share on pocket (opens in new window)like this:like loading... leave a comment post navigation ← older posts welcome to: peterjamesthomas.com, a site which covers my thoughts on the confluence of business, technology and change. search for: feedburner subscription subscribe in a reader subscribe by e-mail wordpress subscription enter your email address to receive notifications of new posts by email. join 87 other followers follow me on: view my profile on: last 20 articles bumps in the road the big data universe metamorphosis alphabet soup indiana jones and the anomalies of data the chief data officer “sweet spot” more statistics and medicine curiouser and curiouser – the limits of brexit voting analysis how age was a critical factor in brexit a tale of two [brexit] data visualisations showing uncertainty in a data visualisation themes from a chief data officer forum – the 180 day perspective data management as part of the data to action journey 5 more themes from a chief data officer forum 5 themes from a chief data officer forum an inconvenient truth wanted – chief data officer forming an information strategy: part iii – completing the strategy the need for collaboration between teams using the same data in different ways forming an information strategy: part ii – situational analysis categories business intelligence (133) bi and the economic crisis (15) business analytics (23) business intelligence competency centres (2) dashboards (4) data visualisation (9) data warehousing (26) enterprise performance management (31) infographics (5) management information (41) sas bi / ba controversy (7) chief data officer (21) data governance (9) data management (5) data quality (12) data science (6) cultural transformation (45) change management (34) education (8) general (183) business (109) management (13) site update (25) social media (32) blogging (15) linkedin (5) twitter (13) strategy (4) technology (135) big data (6) cloud computing (1) industry commentary (40) amazon (1) balanced insight (1) erp (2) google (9) ibm (7) cognos (4) informatica (1) microsoft (12) oracle (8) hyperion (3) sap (3) businessobjects (3) sas (7) sun (4) oracle and sun (4) systems integration (1) text analytics (2) it business alignment (55) mathematics & science (29) biology (7) mathematics (21) pure mathematics (5) statistics (14) physics (4) project management (32) international projects (4) outsourcing (2) recommended sites acuate data quality beyenetwork bogorad on business breakthrough analysis by seth grimes at intelligent enterprise business intelligence news by marcus borba dale roberts' business intelligence now and the future george tomko’s cio rant information management inside the biz with jill dyché it business alignment (it2b) james taylor’s decision management on ebizq.net judith hurwitz’s blog knowledge works market strategies for it suppliers – merv adrian michael sandberg's data visualization blog neil raden’s blog at intelligent enterprise obsessive-compulsive data quality by jim harris phil simon's virtual soapbox sarah burnett’s blog shawn rogers – the business intelligence brief smartdatacollective the boulder bi brain trust the business intelligence blog the data warehousing information center the it-finance connection if you would like your site to be added to my recommended site list, please submit your details on this form. contact details report problems blog at wordpress.com. twitter linkedin tumblr peter james thomas blog at wordpress.com. post to cancel send to email address your name your email address cancel post was not sent - check your email addresses! email check failed, please try again sorry, your blog cannot share posts by email. %d bloggers like this:


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