The revised and expanded Data and Analytics Dictionary

The Data and Analytics Dictionary

Since its launch in August of this year, the Data and Analytics Dictionary has received a welcome amount of attention with various people on different social media platforms praising its usefulness, particularly as an introduction to the area. A number of people have made helpful suggestions for new entries or improvements to existing ones. I have also been rounding out the content with some more terms relating to each of Data Governance, Big Data and Data Warehousing. As a result, The Dictionary now has over 80 main entries (not including ones that simply refer the reader to another entry, such as Linear Regression, which redirects to Model).

The most recently added entries are as follows:

  1. Anomaly Detection
  2. Behavioural Analytics
  3. Complex Event Processing
  4. Data Discovery
  5. Data Ingestion
  6. Data Integration
  7. Data Migration
  8. Data Modelling
  9. Data Privacy
  10. Data Repository
  11. Data Virtualisation
  12. Deep Learning
  13. Flink
  14. Hive
  15. Information Security
  16. Metadata
  17. Multidimensional Approach
  18. Natural Language Processing (NLP)
  19. On-line Transaction Processing
  20. Operational Data Store (ODS)
  21. Pig
  22. Table
  23. Sentiment Analysis
  24. Text Analytics
  25. View

It is my intention to continue to revise this resource. Adding some more detail about Machine Learning and related areas is probably the next focus.

As ever, ideas for what to include next would be more than welcome (any suggestions used will also be acknowledged).


From:, home of The Data and Analytics Dictionary


The Data and Analytics Dictionary

The Data and Analytics Dictionary

I find myself frequently being asked questions around terminology in Data and Analytics and so thought that I would try to define some of the more commonly used phrases and words. My first attempt to do this can be viewed in a new page added to this site (this also appears in the site menu):

The Data and Analytics Dictionary

I plan to keep this up-to-date as the field continues to evolve.

I hope that my efforts to explain some concepts in my main area of specialism are both of interest and utility to readers. Any suggestions for new entries or comments on existing ones are more than welcome.


Forming an Information Strategy: Part III – Completing the Strategy

Forming an Information Strategy
I – General Strategy II – Situational Analysis III – Completing the Strategy

Maybe we could do with some better information, but how to go about getting it? Hmm...

This article is the final of three which address how to formulate an Information Strategy. I have written a number of other articles which touch on this subject [1] and have also spoken about the topic [2]. However I realised that I had never posted an in-depth review of this important area. This series of articles seeks to remedy this omission.

The first article, Part I – General Strategy, explored the nature of strategy, laid some foundations and presented a framework of questions which will need to be answered in order to formulate any general strategy. The second, Part II – Situational Analysis, explained how to adapt the first element of this general framework – The Situational Analysis – to creating an Information Strategy. In Part I, I likened formulating an Information Strategy to a journey, Part III – Completing the Strategy sees us reaching the destination by working through the rest of the general framework and showing how this can be used to produce a fully-formed Information Strategy.

As with all of my other articles, this essay is not intended as a recipe for success, a set of instructions which – if slavishly followed – will guarantee the desired outcome. Instead the reader is invited to view the following as a set of observations based on what I have learnt during a career in which the development of both Information Strategies and technology strategies in general have played a major role.
A Recap of the Strategic Framework

Forth Rail Bridge

I closed Part I of this series by presenting a set of questions, the answers to which will facilitate the formation of any strategy. These have a geographic / journey theme and are as follows:

  1. Where are we?
  2. Where do we want to be instead and why?
  3. How do we get there, how long will it take and what will it cost?
  4. Will the trip be worth it?
  5. What else can we do along the way?

Part II explained the process of answering question 1 through the medium of a Situational Analysis. It is worth pointing out at this juncture that the Situational Analysis will also naturally form the first phase of the more lengthy process of gathering and analysing business requirements. For the purposes of the rest of this article, when such requirements are mentioned, they are taken as being the embryonic ones captured as part of the Situational Analysis.

In this final article I will focus on how to approach obtaining answers to questions 2 to 5. Having spent quite some time considering question 1 in the previous chapter, the content here will be somewhat briefer for the remaining questions; not least as I have covered some of this territory in earlier articles [3].
2. Where do we want to be instead and why?

My thoughts here split into two sub-sections. The second, What does Good look like?, is (as will be obvious from the title) more forward looking than backward. It covers reasons why the destination may be worth the journey. The first is more to do with why staying in the current location may not be a great idea [4]. However, one motivation for not staying put is that somewhere else may well be better. For this reason, there is not definitive border between these two sub-sections and it will be evident from the text that they instead bleed into each other.

2a. Drivers for Change

Change Next Exit

People often say that the gains that result from Information Programmes are intangible. Of course some may indeed be fairly intangible, but even the most ephemeral of these will not be entirely immune from some sort of valuation. Other benefits, when examined closely enough, can turn out to be surprisingly tangible [5]. In making a case for change (and of course the expenditure associated with this) it is good to try to have a balance of tangible and intangible factors. Here is a selection which may be applicable:

Internal IT drivers

  • These often centre around both the cost and confusion associated with a fragmented and inconsistent Information Landscape; something which, even as we head in to 2015, is still not atypical.
  • Opportunity costs may arise from an inability to combine data from different repositories or to roll up data to cover an entire organisation.
  • There is also a case to be made here around things like the licensing costs that result from having too many information repositories and too many tools being used to access them.
  • However, the cost of remediating such fragmentation can often appear in the shape of additional IT headcount devoted to maintaining a complex landscape and additional business headcount devoted to remediating information shortcomings.

Productivity gains

  • Less number crunching, more business-focussed analysis. Often an organisation’s most highly qualified (and highly paid) staff can spend much of their time repeating quotidian tasks that computers could do far more reliably. Freeing up such able and creative people to add more business value should be an objective and should have benefits.
  • At one company I estimated that teams would spend 5-7 days assembling the information necessary to support a meeting with one of a number of key business partners or a major client; our goal became to provide the same information effectively instantaneously; these types of benefits can be costed and also tend to resonate with business stakeholders.

Increasing sales / improving profitability

  • All information programmes (indeed most any business activity) should be dedicated to increasing profitability of course. In some specific industries the leverage of high-quality information is more readily associated with profitability than others. However, with enough time spent understanding the dynamics of an organisation, I would suggest that it is possible to make this linkage in a credible manner in pretty much any industry sector.
  • With respect to sales, sometimes if you want to increase say cross-selling, a very effective way is simply to measure it, maybe by department and salesperson. If there is some reliable way to track this, improvements in cross-selling will inevitably follow.

Mitigating operational risk

  • More reliable, unbiased and transparent production of information can address a number of operational risks; what these are specifically will vary from organisation to organisation.
  • However, most years see some organisation or another have to restate their results – there have been cases where adding two figures rather than subtracting them has led to a later restatement. Cases can often be built around the specific pain points in an organisation, or sometimes even near misses that were caught at the 11th hour.
  • Equally the cost of checking and re-checking figures before publication can be extremely high.

It is also generally worth asking business users what value they would ascribe to improved information, for example what things could they do under new arrangements that they cannot do now? It is important here that any benefits – and in particular any ones which prove to be intangible – are expressed in business language, not technical jargon.

2b. What does Good look like?

OK this dates me - I don't care!

Answering this question is predicated on both experience of successful information improvement programmes and a degree of knowledge about the general information market. There are two main elements here, what does good look like technically and what does it look like from a process / people perspective.

To cover the technical first, this is the simpler area, not least as we have understood how to develop robust, flexible and highly-performing information architectures for at least 15 years.

Integrated Information Architecture (click to view a larger version in a new tab)

The basics are shown in the diagram above [6]. Questions to consider here include:

  • What would a new information architecture look like?
  • What are the characteristics of the new which would indicate that it is an improvement on the old, can these be articulated to non-technical people?
  • What are required elements and how do they relate to the high-level needs captured in the Situational Analysis?
  • How does the proposed architecture relate to incumbent technologies and current staff skills?
  • Can any elements of existing information provision be leveraged, either temporarily or on an ongoing basis?
  • What has worked for other organisations and why would this be pertinent to the organisation in question?
  • Are any new developments in technology pertinent?

Arguably the more important area is the non-technical. Here there is a range of items to consider, some of which are captured in the following exhibit [7]:

Information Process (click to view a larger version  in a new tab)

I could spend an separate set of articles commenting on the elements of the above diagram; indeed I already have and interested readers are directed to the footnotes for links to some of these [8]. However it is worth pointing out the critical role to be played by both user education (a more apt phrase than training) and formal Data Governance. Also certain elements of information tend to work well when they sit within a regular business process; such as a monthly or quarterly review of specific aspects of results and future projections.
3. How do we get there, how long will it take and what will it cost?

Tube ticket machines

3a. Outline an Indicative Programme of Work

I am not going to offer Programme Planning 101 here, but briefly the first step in putting together an indicative programme of work is to decompose the overall journey into chunks, each of which can then be estimated. Each chunk should cover a group of reports / analyses and include activities from requirements gathering through to testing and finally deployment [9]. For the purposes of an indicative programme within a strategy document, the strategist can rely upon both information gathered in the Situational Analysis and their own experience of how to best decompose such work. Ultimately the size and number of the chunks should be dictated by business need, but at this stage estimates can be based upon experience and reasonable assumptions.

It is important that each chunk (or sub-chunk) delivers value and offers an opportunity for the approach and progress to be reviewed. A further factor to consider when estimating these chunks is that they should be delivered at a pace which allows them to be properly digested by users; resource allocations should reflect this. For each chunk the strategist should consider the type and quantum of resource required and the timing with which these are applied.

The indicative programme plan should also include a first phase which relates to reviewing the plan itself. Forming a strategy involves less people than running a programme. Even if initial estimation is carried out very diligently, it is likely that further issues will emerge once more detailed work later commences. As the information programme team ramps up, it is important that time is allocated for new team members to kick the tyres on the plan and make recommendations for improvement.

3b. How much will it cost?

Coins on scales

A big element of cost estimates will be a by-product of the indicative programme plan, which will cover programme duration and the amount of resource required at different points. Some further questions to consider when looking to catalogue costs include the following:

  • What are baseline costs for current information provision?
  • To what degree to these need to be incurred in parallel to an information improvement programme, are there ways to reduce these legacy costs to free up funds for the central programme?
  • What transitional costs are needed to execute the Information Strategy?
    • Hardware and software: is change necessary?
    • People: what is the best balance between internal, contract and outsourced resources, to what degree can existing staff be leveraged without compromising their current responsibilities?
    • How will costs vary by programme phase, will these taper as elements of older information systems are replaced by new facilities?
    • Can costs be reduced by having people play different roles at different points in the programme?
  • What costs will be ongoing once the strategy has been executed?
  • How do these compare to the current baseline?
  • Sometimes one aim of an Information Strategy will be to reduce to cost of ongoing support and maintenance, if so, how will this be achieved and how will any transition be managed?

A consideration here is whether the most important thing is to maximise speed of delivery or minimise risk? Things that will reduce risk could include: initial exploratory phases; starting with a small number of programme resources and increasing these based only on success; and instigating appropriate governance processes. However each of these will also increase duration and therefore cost. In some areas a trade off will be necessary and which side of these equations is more important will vary from organisation to organisation.
4. Will the trip be worth it?

Pros and cons

Answering parts of question 2 will help with getting a handle on potential benefits of executing an Information Strategy. Work on question 3 will get us an idea of the timeframes and costs involved. There is a need to combine the two of these into a cost / benefit analysis. This should be an honest and transparent assessment of the potential payback of adopting the Information Strategy. Given that most Information Strategies will take more than a year to implement and that benefits may equally be realised on an ongoing basis, it will generally make sense to look at figures over a 3-5 year period. It may be possible to draw up a quasi-P&L statement showing the impact of adopting the strategy, such an approach can resonate with senior stakeholders.

Points to recall and questions to consider here include:

  • Costs will emerge from the Indicative Programme Plan, but remember the ongoing costs of maintaining existing information capabilities.
  • As with most initiatives, the benefits of information programmes split into tangible and intangible components:
    • Where possible make benefits tangible even if this requires a degree of guesstimation [10].
    • Remember that many supposed intangibles can be estimated with some thought.
  • What benefits have other companies seen from similar programmes, particularly ones in the same industry sector?
  • Is it possible to perform “what if?” scenarios with current and future capabilities; could better information could have led to better outcomes? [11]
  • Ask business people to estimate the impact of better information.
  • Intangible benefits resonate where they are expressed in clear business language, not IT speak.

It should be borne in mind here that the cost / benefit analysis may not add up. If this is the case, then either a less expensive approach is more suitable for the company, or the potential benefits need to be looked at again. Where progress can genuinely not be made on either of these areas, the responsible strategist will acknowledge that doing nothing may well be the logical approach for the organisation in question.
5. What else can we do along the way?

Here be elephants

Finally, it is worth noting that short-term tactical deliveries can strongly support a strategy [12]. Interim work can meet urgent business needs in a timely manner. This is a substantial benefit in itself and also evidences progress in the area of improving information capabilities. It also demonstrates that that the programme team understands commercial pressures. This type of work is also complementary in that it can be used to:

  • Validate some elements of the cost / benefit analysis.
  • Round out requirements gathering.
  • Highlight any areas which have been overlooked.
  • Provide invaluable deployment and training experience, which can be leveraged for the implementation of more strategic capabilities.

It can also be useful make mistakes early and with small deliverables, not later with major ones. For these reasons, it is suggested that any Information Strategy should embrace “throw away” work. However this should be reflected in the overall programme plan and resources should be specifically allocated to this area. If this is not done, then tactical work can easily overwhelm the team and prevent progress on more strategic areas from being made; generally a death knell for a programme.
A Recap of the Main Points

  1. Carry out a Situational Analysis.
  2. As part of this, start the process of capturing High-level Business Requirements.
  3. Establish Drivers for Change, what benefits can be realised by better information, or by producing information in a better way?
  4. Ask “What Does Good Look Like?”, from both a technical and a process / people point of view.
  5. Develop an Indicative Programme of Work with realistic resource estimates and durations.
  6. Estimate Current, Transitional and Ongoing Costs.
  7. Itemise some of the major Interim Deliverables.
  8. Create a Cost / Benefits Analysis.

Bringing everything together

Chickie in dee Basget! Ing vurn spuur dee Chickie, Uun yeh vurn spay dee Basget!

There is a need to take the detailed work described over the course of the last three articles and the documentation which has been created as part of the process and to distill these down into a format that is digestible by senior management. There is no silver bullet here, summarising screeds of detail in a way that preserves the main points and presents them in a way that resonates is not easy. It takes judgement, an understanding of how businesses operate and strong analytical, writing and often diagrammatic skills. These will not be acquired by reading a blog article, but by honing experience and expertise over many years of work. To an extent, producing relevant and cogent summaries is where good IT professionals earn their money.

Unfortunately, at the time of writing, there is no book entitled Summarising Complex Issues for Dummies [13], [14].

This article and its two predecessors have been akin to listing the ingredients required to make a complex meal. While it is difficult to make great food without good ingredients or with some key spice missing, these things are not sufficient to ensure culinary excellence; what is also needed is a competent chef [15]. I cook a lot myself and, whenever I try a recipe for the first time, it can be a bit fraught. Sometimes I don’t get all of the elements of the meal ready at the same time, sometimes while I’m paying attention to reading the instructions for one part, another part boils over, or gets burnt. These problems with cooking tend dissipate with repetition. In the same way, what is generally needed in developing a sound Information Strategy is the equivalents great ingredients, a competent chef and an experienced one as well.

Forming an Information Strategy
I – General Strategy II – Situational Analysis III – Completing the Strategy


These include (in chronological order):

IRM European Data Warehouse and Business Intelligence Conference
– November 2012
Where this is the case, I will of course provide links back to my previous work.
Some of the factors here may come to light as a result of the previous Situational Analysis of course.
I grapple with estimating the potential payback of Information Programmes in a series of earlier articles:

This is an expanded version of the diagram I posted as part of Using multiple business intelligence tools in an implementation – Part I back in May 2009. I have elided details such as the fine structure of the warehouse (staging, relational, multidimensional etc.), master data sources and also which parts of it are accessed by different tools and different types of users. In a severe breach with the traditional IT approach, I have also left some arrows out.
This is an updated version of an exhibit I put together working with an actuarial colleague back in 2001, early in my journey into information improvement programmes.
These include my trilogy on the change management aspects of information programmes:

and a number of articles relating to Data Governance / Data Quality, notably:

Sometimes the first level of decomposition will need to be broken up into further and smaller chunks with this process iterating until the strategist reaches tasks which they are happy to estimate with a degree of certainty.
It may make sense to have different versions of the cost / benefit analysis, more conservative ones including only the most tangible benefits and more aggressive ones taking in to account benefits which have to be somewhat less certain.
Again see the series of three articles starting with Using historical data to justify BI investments – Part I.
For further thoughts on the strategic benefits of tactical work see:

Given both the two interpretations of this phrase and the typical audience for summaries of strategies, perhaps this is a fortunate thing.
I did however find the following title:

I can't however seem to find either Quantum Chromodynamics or Brain Surgery for Dummies

Contrary to the image above, a muppet (in the English sense of the word) won’t suffice.



The need for collaboration between teams using the same data in different ways

The Data Warehousing Institute

This article is based on conversations that took place recently on the TDWI LinkedIn Group [1].

The title of the discussion thread posted was “Business Intelligence vs. Business Analytics: What’s the Difference?” and the original poster was Jon Dohner from Information Builders. To me the thread topic is something of an old chestnut and takes me back to the heady days of early 2009. Back then, Big Data was maybe a lot more than just a twinkle in Doug Cutting and Mike Cafarella‘s eyes, but it had yet to rise to its current level of media ubiquity.

Nostalgia is not going to be enough for me to start quoting from my various articles of the time [2] and neither am I going to comment on the pros and cons of Information Builders’ toolset. Instead I am more interested in a different turn that discussions took based on some comments posted by Peter Birksmith of Insurance Australia Group.

Peter talked about two streams of work being carried out on the same source data. These are Business Intelligence (BI) and Information Analytics (IA). I’ll let Peter explain more himself:

BI only produces reports based on data sources that have been transformed to the requirements of the Business and loaded into a presentation layer. These reports present KPI’s and Business Metrics as well as paper-centric layouts for consumption. Analysis is done via Cubes and DQ although this analysis is being replaced by IA.


IA does not produce a traditional report in the BI sense, rather, the reporting is on Trends and predictions based on raw data from the source. The idea in IA is to acquire all data in its raw form and then analysis this data to build the foundation KPI and Metrics but are not the actual Business Metrics (If that makes sense). This information is then passed back to BI to transform and generate the KPI Business report.

I was interested in the dual streams that Peter referred to and, given that I have some experience of insurance organisations and how they work, penned the following reply [3]:

Hi Peter,

I think you are suggesting an organisational and technology framework where the source data bifurcates and goes through two parallel processes and two different “departments”. On one side, there is a more traditional, structured, controlled and rules-based transformation; probably as the result of collaborative efforts of a number of people, maybe majoring on the technical side – let’s call it ETL World. On the other a more fluid, analytical (in the original sense – the adjective is much misused) and less controlled (NB I’m not necessarily using this term pejoratively) transformation; probably with greater emphasis on the skills and insights of individuals (though probably as part of a team) who have specific business knowledge and who are familiar with statistical techniques pertinent to the domain – let’s call this ~ETL World, just to be clear :-).

You seem to be talking about the two of these streams constructively interfering with each other (I have been thinking about X-ray Crystallography recently). So insights and transformations (maybe down to either pseudo-code or even code) from ~ETL World influence and may be adopted wholesale by ETL World.

I would equally assume that, if ETL World‘s denizens are any good at their job, structures, datasets and master data which they create (perhaps early in the process before things get multidimensional) may make work more productive for the ~ETLers. So it should be a collaborative exercise with both groups focused on the same goal of adding value to the organisation.

If I have this right (an assumption I realise) then it all seems very familiar. Given we both have Insurance experience, this sounds like how a good information-focused IT team would interact with Actuarial or Exposure teams. When I have built successful information architectures in insurance, in parallel with delivering robust, reconciled, easy-to-use information to staff in all departments and all levels, I have also created, maintained and extended databases for the use of these more statistically-focused staff (the ~ETLers).

These databases, which tend to be based on raw data have become more useful as structures from the main IT stream (ETL World) have been applied to these detailed repositories. This might include joining key tables so that analysts don’t have to repeat this themselves every time, doing some basic data cleansing, or standardising business entities so that different data can be more easily combined. You are of course right that insights from ~ETL World often influence the direction of ETL World as well. Indeed often such insights will need to move to ETL World (and be produced regularly and in a manner consistent with existing information) before they get deployed to the wider field.

Now where did I put that hairbrush?

It is sort of like a research team and a development team, but where both “sides” do research and both do development, but in complementary areas (reminiscent of a pair of entangled electrons in a singlet state, each of whose spin is both up and down until they resolve into one up and one down in specific circumstances – sorry again I did say “no more science analogies”). Of course, once more, this only works if there is good collaboration and both ETLers and ~ETLers are focussed on the same corporate objectives.

So I suppose I’m saying that I don’t think – at least in Insurance – that this is a new trend. I can recall working this way as far back as 2000. However, what you describe is not a bad way to work, assuming that the collaboration that I mention is how the teams work.

I am aware that I must have said “collaboration” 20 times – your earlier reference to “silos” does however point to a potential flaw in such arrangements.


PS I talk more about interactions with actuarial teams in: BI and a different type of outsourcing

PPS For another perspective on this area, maybe see comments by @neilraden in his 2012 article What is a Data Scientist and what isn’t?

I think that the perspective of actuaries having been data scientists long before the latter term emerged is a sound one.

I couldn't find a suitable image from Sesame Street :-o

Although the genesis of this thread dates to over five years ago (an aeon in terms of information technology), I think that – in the current world where some aspects of the old divide between technically savvy users [4] and IT staff with strong business knowledge [5] has begun to disappear – there is both an opportunity for businesses and a threat. If silos develop and the skills of a range of different people are not combined effectively, then we have a situation where:

| ETL World | + | ~ETL World | < | ETL World ∪ ~ETL World |

If instead collaboration, transparency and teamwork govern interactions between different sets of people then the equation flips to become:

| ETL World | + | ~ETL World | ≥ | ETL World ∪ ~ETL World |

Perhaps the way that Actuarial and IT departments work together in enlightened insurance companies points the way to a general solution for the organisational dynamics of modern information provision. Maybe also the, by now somewhat venerable, concept of a Business Intelligence Competency Centre, a unified team combining the best and brightest from many fields, is an idea whose time has come.

A link to the actual discussion thread is provided here. However You need to be a member of the TDWI Group to view this.
Anyone interested in ancient history is welcome to take a look at the following articles from a few years back:

  1. Business Analytics vs Business Intelligence
  2. A business intelligence parable
  3. The Dictatorship of the Analysts
I have mildly edited the text from its original form and added some new links and new images to provide context.
Particularly those with a background in quantitative methods – what we now call data scientists
Many of whom seem equally keen to also call themselves data scientists



Ten Million Aliens – More musings on BI-ology


Ten Million Aliens by Simon Barnes

This article relates to the book Ten Million Aliens – A Journey Through the Entire Animal Kingdom by British journalist and author Simon Barnes, but is not specifically a book review. My actual review of this entertaining and informative work appears on Amazon and is as follows:

Having enjoyed Simon’s sport journalism (particularly his insightful and amusing commentary on Test Match cricket) for many years, I was interested to learn about this new book via his web-site. As an avid consumer of pop-science literature and already being aware of Simon’s considerable abilities as a writer, I was keen to read Ten Million Aliens. To be brief, I would recommend the book to anyone with an enquiring mind, an interest in the natural world and its endless variety, or just an affection for good science writing. My only sadness was that the number of phyla eventually had to come to an end. I laughed in places, I was better informed than before reading a chapter in others and the autobiographical anecdotes and other general commentary on the state of our stewardship of the planet added further dimensions. I look forward to Simon’s next book.

Instead this piece contains some general musings which came to mind while reading Ten Million Aliens and – as is customary – applies some of these to my own fields of professional endeavour.
Some Background

David Ivon Gower

Regular readers of this blog will be aware of my affection for Cricket[1] and also my interest in Science[2]. Simon Barnes’s work spans both of these passions. I became familiar with Simon’s journalism when he was Chief Sports Writer for The Times[3] an organ he wrote for over 32 years. Given my own sporting interests, I first read his articles specifically about Cricket and sometimes Rugby Union, but began to appreciate his writing in general and to consume his thoughts on many other sports.

There is something about Simon’s writing which I (and no doubt many others) find very engaging. He manages to be both insightful and amusing and displays both elegance of phrase and erudition without ever seeming to show off, or to descend into the overly-florid prose of which I can sometimes (OK often) be guilty. It also helps that we seem to share a favourite cricketer in the shape of David Gower, who appears above and was the most graceful bastman to have played for England in the last forty years. However, it is not Simon’s peerless sports writing that I am going to focus on here. For several years he also penned a wildlife column for The Times and is a patron of a number of wildlife charities. He has written books on, amongst other topics, birds, horses, his safari experiences and conservation in general.

Green Finch, Great Tit, Lesser Spotted Woodpecker, Tawny Owl, Magpie, Carrion Crow, Eurasian Jay, Jackdaw

My own interest in science merges into an appreciation of the natural world, perhaps partly also related to the amount of time I have spent in remote and wild places rock-climbing and bouldering. As I started to write this piece, some welcome November Cambridge sun threw shadows of the Green Finches and Great Tits on our feeders across the monitor. Earlier in the day, my wife and I managed to catch a Lesser Spotted Woodpecker, helping itself to our peanuts. Last night we stood on our balcony listening to two Tawny Owls serenading each other. Our favourite Corvidae family are also very common around here and we have had each of the birds appearing in the bottom row of the above image on our balcony at some point. My affection for living dinosaurs also extends to their cousins, the herpetiles, but that is perhaps a topic for another day.

Ten Million Aliens has the modest objectives, revealed by its sub-title, of saying something interesting about about each of the (at the last count) thirty-five phyla of the Animal Kingdom[4] and of providing some insights in to a few of the thousands of familes and species that make these up. Simon’s boundless enthusiasm for the life he sees around him (and indeed the life that is often hidden from all bar the most intrepid of researchers), his ability to bring even what might be viewed as ostensibly dull subject matter[5] to life and a seemingly limitless trove of pertinent personal anecdotes, all combine to ensure not only that he achieves these objectives, but that he does so with some élan.
Classifications and Hierarchies

Biological- Classification

Well having said that this article wasn’t going to be a book review, I guess it has borne a striking resemblance to one so far. Now to take a different tack; one which relates to three of the words that I referenced and provided links to in the last paragraph of the previous section: phylum, family and species. These are all levels in the general classification of life. At least one version of where these three levels fit into the overall scheme of things appears in the image above[6]. Some readers may even be able to recall a related mnemonic from years gone by: Kings Play Chess on Fine Green Sand[7].

The father of modern taxonomy, Carl Linnaeus, founded his original biological classification – not unreasonably – on the shared characteristics of organisms; things that look similar are probably related. Relations mean that like things can be collected together into groups and that the groups can be further consolidated into super-groups. This approach served science well for a long time. However when researchers began to find more and more examples of convergent evolution[8], Linnaeus’s rule of thumb was seen to not always apply and complementary approaches also began to be adopted.


One of these approaches, called Cladistics, focuses on common ancestors rather than shared physical characteristics. Breakthroughs in understanding the genetic code provided impetus to this technique. The above diagram, referred to as a cladogram, represents one school of thought about the relationship between avian dinosaurs, non-avian dinosaurs and various other reptiles that I mentioned above.

It is at this point that the Business Intelligence professional may begin to detect something somewhat familiar[9]. I am of course talking about both dimensions and organising these into hierarchies. Dimensions are the atoms of Business Intelligence and Data Warehousing[10]. In Biological Classification: H. sapiens is part of Homo , which is part of Hominidae, which is part of Primates, which is part of Mammalia, which is part of Chordata, which then gets us back up to Animalia[11]. In Business Intelligence: Individuals make up Teams, which make up Offices, which make up Countries and Regions.

Above I references different approaches to Biological Classification, one based on shared attributes, the other on homology of DNA. This also reminds me of the multiple ways to roll-up dimensions. To pick the most obvious, Day rolls up to Month, Quarter, Half-Year and Year; but also in a different manner to Week and then Year. Given that the aforementioned DNA evidence has caused a reappraisal of the connections between many groups of animals, the structures of Biological Classification are not rigid and instead can change over time[12]. Different approaches to grouping living organisms can provide a range of perspectives, each with its own benefits. In a similar way, good BI/DW design practices should account for both dimensions changing and the fact that different insights may well be provided by parallel dimension hierarchies.

In summary, I suppose what I am saying is that BI/DW practitioners, as well as studying the works of Inmon and Kimball, might want to consider expanding their horizons to include Barnes; to say nothing of Linnaeus[13]. They might find something instructive in these other taxonomical works.


Articles from this blog in which I intertwine Cricket and aspects of business, technology and change include (in chronological order):

Articles on this site which reference either Science or Mathematics are far too numerous to list in full. A short selection of the ones I enjoyed writing most would include (again in chronological order):

Or perhaps The London Times for non-British readers, despite the fact that it was the first newspaper to bear that name.
Here “Aninal Kingdom” is used in the taxonomical sense and refers to Animalia.
For an example of the transformation of initially unpromising material, perhaps check out the chapter of Ten Million Aliens devoted to Entoprocta.
With acknowledgment to The Font.
Though this elides both Domains and Johny-come-latelies like super-families, sub-genuses and hyper-orders [I may have made that last one up of course].
For example the wings of Pterosaurs, Birds and Bats.
No pun intended.
This metaphor becomes rather cumbersome when one tries to extend it to cover measures. It’s tempting to perhaps align these with fundamental forces, and thus bosons as opposed to combinations of fermions, but the analogy breaks down pretty quickly, so let’s conveniently forget that multidimensional data structures have fact tables at their hearts for now.
Here I am going to strive manfully to avoid getting embroiled in discussions about domains, superregnums, superkingdoms, empires, or regios and instead leave the interested reader to explore these areas themselves if they so desire. Ten Million Aliens itself could be one good starting point, as could the following link.
Science is yet to determine whether these slowly changing dimensions are of Type 1, 2, 3 or 4 (it has however been definitively established that they are not Type 6 / Hybrid).
Interesting fact of the day: Linnaeus’s seminal work included an entry for The Kraken, under Cephalopoda



A Dictionary of the Business Intelligence Language

Software Advice article

Michael Koploy of on-line technology consulting company Software Advice recently asked me, together with four other people from the Business Intelligence / Data Warehousing community, to contribute some definitions of commonly-used technology jargon pertinent to our field. The results can be viewed in his article, BI Buzzword Breakdown. Readers may be interested in the differing, but hopefully complementary, definitions that were offered.

In jockeying for space with my industry associates, only one of my definitions (that relating to Data Mining) was used. Here are two others, which were left on the cutting room floor. Maybe they’ll make it to the DVD extras.
The equivalent of the Unicorn dream sequence in Bladerunner, but imbued with greater dramatic meaning...

Big Data Rather than having the entirely obvious meaning, has come to be associated with a set of technologies, some of them open source, that emerged from the needs of several of the major on-line businesses (Google, Yahoo, Facebook and Amazon) to analyse the large amount of data they had relating to how people interact with their web-sites. The area is often linked to Apache Hadoop, a low-cost technology that allows commodity servers to be combined to collectively to store large amounts of data, particularly where the structure of these varies considerably and particularly where there is a need to support unpredictably-growing volumes.
Data Warehouse A collection of data, generally emanating from a number of different systems, which is combined to form a consistent structure suitable for the support of a variety of reporting and analytical needs. Most warehouses will have an element of data stored in a multi-dimensional format; i.e. one that is intended to support pivot-table like slicing and dicing. This is achieved using specific data structures: Fact tables, which hold figures, or measures (like profit, or sales, or growth); and dimension tables, which hold business entities, or dimensions (like countries, weeks, product lines, salesman etc.). The dimensions are often nested into hierarchies, such as Region => Country => City => Area. Warehouse data is generally leveraged using traditional reports, On-Line Analytical Processing (OLAP) and more advanced analytical approaches, such as data mining.

Approximately 5.5 cm isn't THAT big is it?

The above comments are perhaps most notable for representing my first reference to the latest information hot topic, the rather misleadingly named Big Data. To date I have rather avoided the rampaging herd in this area – maybe through fear of being crushed in the stampede – but it is probably a topic to which I will return once there is less hype and more substance to comment on.

I will be presenting at the IRM European Data Warehouse and Business Intelligence Conference

IRM UK - European Data Warehousing and Business Intelligence Conference - 2011

This IRM UK event will be taking place in central London from the 7th to 9th November 2011. It is co-located with the IRM Data Management & Information Quality Conference. Full details may be obtained from the IRM conference web-site here. I am speaking on the morning of the 9th and will be building on themes introduced in my previous artcile: A Single Version of the Truth?