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?
1 Comment | 
business intelligence, data warehousing | Tagged: IRM UK | 
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Posted by Peter Thomas
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Business Intelligence Group |
The following is a lightly edited transcript of a reply I posted to a question asked on the LinkedIn.com Business Intelligence Group. This was entitled What should companies consider before investing in a BI solution?.
I suggest some of the following:
3 Comments | business intelligence, linkedin | Permalink
Posted by Peter Thomas
The last in a series of mini-posts, normal (aka more prolix) service will be resumed soon.
Although tinyurl.com was a pioneer in URL shortening, it seems to have been overtaken by a host of competing services. For example I tend to use bit.ly most of the time. However I still rather like the tinyurl.com option to create your own bespoke shortened URLs.
This feature rather came into its own recently when I was looking for a concise way to share my recent trilogy focusing on the use of historical data to justify BI/DW investments in Insurance. There is something satisfying in thinking that the following will persist as long as tinyurl.com does:
| • | http://tinyurl.com/bi-insurance-part-1 |
| • | http://tinyurl.com/bi-insurance-part-2 |
| • | http://tinyurl.com/bi-insurance-part-3 |
It is nice to make a [semi-] permanent mark from time to time!
2 Comments | business intelligence, technology | Tagged: bitly, tinyurl, url shortening | Permalink
Posted by Peter Thomas
Introduction
The internet is full of articles claiming to transform the reader into the Social Media equivalent of Charles Atlas. I have written some of them myself (though hopefully while highlighting that that things are seldom as simple as ticking a set of boxes). Bearing in mind the old adage that you learn more from your mistakes than your successes, here are some thoughts on Social Media failures; the first three are mine and the fourth a failure that seems very widespread. Lest this article becomes too depressing, I will close with a more positive piece of Social Media news.
Failure 1 – Thinking that you can dip in and out of Social Media
I recently came across Ken Mueller’s blog via a LinkedIn Group (see the segment of New Adventures in WiFi that relates to LinkedIn for some thoughts on groups). In one of his articles he lays out what he sees as the factors that have led to him tripling his blog traffic. Foremost amongst these is consistency:
I’ve been doing this every day for about 2 years now. Some of the growth that I’m seeing is due to just plugging away and forcing myself to blog every day, hopefully creating good, relevant content that people want to read. If I take a day off, I notice a drop in traffic. In fact, I always see a drop in my November traffic because I go away for Thanksgiving to an area with no Internet access.
A quick look at the above chart, which shows the number of articles I have published each month since founding this blog back in November 2008, will reveal that consistency hasn’t been my middle name.
For a variety of reasons, I have had periods where I have sustained a high output of articles (without, it is to be hoped, quantity compromising quality) and periods where my writing has slowed to a barely perceptible trickle. To take an ultra-prosaic example, I started writing this piece while commuting by train and my recent output is highly correlated with my method of transportation.
Coming out of some of the troughs in writing, I have sometimes felt that I could simply pick up where I left off. This is probably the case with some niche readers who may visit this site; this is precisely because at least some of my content is directly pertinent to them from time to time. However, after a while, even they may have looked elsewhere for their regular fix of the topics I cover here. Beyond this, there is equally likely to be a second cohort of casual readers who will quickly move on to pastures new if the grass here does not re-grow apace [note to self, I am meant to be restraining myself from overly liberal use of analogies, must try harder!].
Even if an author has written several articles that have proved popular with a number of people; after anything more than a few weeks’ lay-off, it can almost be like starting again from scratch. To employ a too widely-used phrase, you are only as good as your last month’s (or maybe week’s, or maybe day’s) output.
Disregarding for the moment my own parenthetic advice from the end of the paragraph before last, this feels rather familiar. It seems to be very like what it feels like trying to get fit again after an injury or time away from a sport. It doesn’t really matter if you had attained a certain level of fitness a year ago; what is relevant today is your current level of fitness and the gap between the two. Sometimes recalling just how long it took them to achieve a previous standard can be quite de-motivating to an athlete returning from a break. Once fit, it is a lot easier to stay fit than is is to regain lost fitness. The same applies to audiences and this is why – as Kevin suggests in his article – at least periodic blogging (assuming that it is of a standard) is essential.
My learning here is both to make time to write and also to re-engage with my readers.
[Perhaps ironically this article itself has been in gestation for a few weeks]
Failure 2 – Assuming that what has worked before will work again
I have a specific example in mind here and it relates to a blog post that precedes this one. In turn this goes back to a survey of senior IT people that I carried out predominantly via LinkedIn back in January 2009. This related to their view on the top priorities that they faced in their jobs. Recently I thought that it would be interesting to update this and – no doubt naturally – I also though that I would adopt the same modus operandi; i.e. LinkedIn. I even targeted the same Group – that of CIO Magazine.
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CIO Magazine forum | |
Sad to say, while I had dozens of responses last time round, there was been little or no response at all when I attempted to refresh the findings. I have been thinking about why this might be. Of course my musings are pure speculation, but a few ideas come to mind:
By way of contrast – and perhaps tapping into my thoughts about variants of the same question having been asked many times before – the same Group has a thread asking members to state in one word what their key challenge is. Although many of the replies are somewhat trite and there is a limit to how much information a single word can convey, it is instructive to think that an innovative approach (and one that requires little time typing a response) has been successful where my attempt to repeat a previous exercise has failed.
My learning here is to think of new ways to approach old material, rather than simply believing that your can repeat past successes.
[UPDATE: I posted on the original CIO Magazine Group threads to change its status to publicly available and started to receive new thoughts on this. Another thought - perhaps people are just more comfortable contributing to discussions that others have already engaged in, rather than being the first to comment?]
Failure 3 – Ascribing [as yet] unwarranted maturity to Social Media
I religiously refrain from blogging about current work projects, however the following was 100% in the public domain of its very nature.
I have recently been doing some recruitment and – given both the increasing use of LinkedIn by recruitment firms in their work and that I have a pretty extensive network – thought that it would be worth trying to leverage Social Media to reach out to potential candidates. I did this via a status update, rather than taking the perhaps more obvious path of using the various job sections. My logic here was that I would potentially reach a wider audience in one go than via several postings within pertinent groups. I was also pursuing my recruitment through more traditional channels, so this idea could simply be viewed as a Social Media experiment.
As with any honest scientist, it is important that I state my negative results as well as positive. In this case, though I was contacted by many recruitment agencies, I didn’t get any feedback from actual candidates themselves at all. It could be argued that the failure was in the way I approached the experiment, or the narrowness of the channel that I selected. While both of these are true observations, the whole point of Social Media in business (if there is one) is to make either organisation-to-person, or person-to-person contact ridiculously easy and immediate. Regardless of my level of ineptitude, it wasn’t easy to achieve what I wanted to achieve and I abandoned my experiment after a week or so.
My learning here is to not to refrain from business / Social Media experimentation, but not to expect too much from what is after all an emerging area.
Failure 4 – Vendor employees not “getting” Social Media
I have often used this column to talk about my opinion that your choice of Business Intelligence tool is one of the least important factors in a BI/DW project. In the article I link to in the previous sentence, I quote from an interview I gave in which I compare the market for BI tools with that for cars. There is no definitive answer to the question “what is the best car?” and in the same way there is no “best BI tool”. Going further than this, there are many other areas of a BI/DW project which, if done well, will come close to guaranteeing your success regardless of which BI tool you select; but, if done badly, will come close to guaranteeing your failure with any BI tool.
I have also previously contrasted my opinion with the surprisingly large number of discussion threads on LinkedIn that have as a title some variant of “Please, please, please, please, please tell me which is the best BI tool”. I worry about people making quite significant purchasing decisions based on replies posted in an internet forum, but that is perhaps a topic for another day. The particular failure I wanted to highlight is of people posting on these types of thread who work for Big BI Corporation Inc. Of course everyone is entitled to their opinion, but I am not sure that many readers would be swayed by:
I highly recommend Object Explorer Studio+ for all your BI needs
- Joe Blogs
Particularly where one click reveals that Joe Blogs is either employed by the owners of OES+ or a consultant whose company seems to exclusively do OES+ implementations. I hate to single out one vendor, but a particularly egregious reply to one of these “Which BI Tool?” threads that I saw recently consisted of one word:
Microsoft
- Jimmy Blogs
As I say, on the very same thread there were examples of employees of many other big and small BI vendors doing just the same, but most of them at least provided more than one word. In the cause of balance, the same thread also contained some thoughts along the lines of:
I can heartily recommend Oracle BI, OBIEE+ is great because [sales pitch deleted]. If you would like to know more drop me a line at jeff.blogs@oracle.com
- Jeff Blogs
I still wonder whether Jeff got any e-mails. At least he flagged his connection with Oracle, I don’t recall many other vendor employees being honest enough to do the same.
Lest I be accused of bias there were also not too dissimilar postings from people strongly associated with SAP, IBM, QlikTech, Pentaho and a sprinkling of BI start-ups. I should perhaps also note that SAS was not a culprit (at least to date), but then maybe this was because the question was about BI, something they abjure. Microstrategy was also honourably notable for its lack of replies containing naive self-promotion, but perhaps this was simply an oversight.
The above rather bizarre behaviour leads to two questions:
Maybe here again we have an issue of social media maturity. Many people are perhaps struggling as much to get their message across effectively as they did with say the advent of television advertising.
My learning here is that I should curb my rather obsessive compulsion to “out” vendors promoting their own products under the guise of neutral advice-giving.
[not sure that I am going to take much notice of this one however]
Success – The Accidental Search Engine Optimiser
After covering three of my own failures and one of the BI vendor community (though I am sure the phenomenon is not restricted to BI or even technology vendors), I will close with one of my successes, albeit an unintentional one. I noticed a strange result the other day when looking at the following (I was actually looking for something else believe it or not):
I believe that my elevated ranking is probably correlated to recent changes in Google’s algorithms that take greater account of social media. Certainly I don’t recall placing on the first page for any Google search before, let alone rank #1. I suppose that I might have a degree of technical satisfaction if this was as the result of months of assiduous search engine optimisation. However the truth is that the result appears to be the unintended by-product of doing lots of things that I wanted to do anyway, like writing about topics I am interested in and trying to engage with a wide group of people in a number of different ways. In a sense the fact that this achievement was accidental (or at least collateral) makes it more pleasing. Maybe the secret to Social Media success is simply to not worry about it and just get on with expressing yourself.
My learning here is that providing content that is of interest to your target audience and being clear about who you are and what you do is going to be an approach that trumps any more mechanistic approach to SEO.
Closing thoughts
I believe that I have leant something from my three failures above (and that vendors should learn something from the fourth), but the single success encourages me to persevere. My aim in sharing these experiences is to hopefully also similarly encourage other Social Media ingénues like myself. I hope that I have at least partially achieved this.
8 Comments | blogging, business intelligence, google, linkedin | Tagged: failure, Ken Mueller, search engine optimisation, SEO, success | Permalink
Posted by Peter Thomas
Note: In the following I have used the abridgement Maths when referring to Mathematics, I appreciate that this may be jarring to US readers, omitting the ‘s’ is jarring to me, so please accept my apologies in advance.
Introduction
Regular readers of this blog will be aware of my penchant for analogies. Dominant amongst these have been sporting ones, which have formed a major part of articles such as:
| Rock climbing: | Perseverance A bad workman blames his [BI] tools Running before you can walk Feasibility studies continued… Incremental Progress and Rock Climbing |
| Cricket: | Accuracy The Big Picture |
| Mountain Biking: | Mountain Biking and Systems Integration |
| Football (Soccer): | “Big vs. Small BI” by Ann All at IT Business Edge |
I have also used other types of analogy from time to time, notably scientific ones such as in the middle sections of Recipes for Success?, or A Single Version of the Truth? – I was clearly feeling quizzical when I wrote both of those pieces! Sometimes these analogies have been buried in illustrations rather than the text as in:
| Synthesis | RNA Polymerase transcribing DNA to produce RNA in the first step of protein synthesis |
| The Business Intelligence / Data Quality symbiosis | A mitochondria, the possible product of endosymbiosis of proteobacteria and eukaryots |
| New Adventures in Wi-Fi – Track 2: Twitter | Paul Dirac, the greatest British Physicist since Newton |
On other occasions I have posted overtly Mathematical articles such as Patterns, patterns everywhere, The triangle paradox and the final segment of my recently posted trilogy Using historical data to justify BI investments.
Jim Harris (@ocdqblog) frequently employs analogies on his excellent Obsessive Compulsive Data Quality blog. If there is a way to form a title “The X of Data Quality”, and relate this in a meaningful way back to his area of expertise, Jim’s creative brain will find it. So it is encouraging to feel that I am not alone in adopting this approach. Indeed I see analogies employed increasingly frequently in business and technology blogs, to say nothing of in day-to-day business life.
However, recently two things have given me pause for thought. The first was the edition of Randall Munroe’s highly addictive webcomic, xkcd.com, that appeared on 6th May 2011, entitled “Teaching Physics”. The second was a blog article I read which likened a highly abstract research topic in one branch of Theoretical Physics to what BI practitioners do in their day job.
An homage to xkcd.com
Let’s consider xkcd.com first. Anyone who finds some nuggets of interest in the type of – generally rather oblique – references to matters Mathematical or Scientific that I mention above is likely to fall in love with xkcd.com. Indeed anyone who did a numerate degree, works in a technical role, or is simply interested in Mathematics, Science or Engineering would as well – as Randall says in a footnote:
“this comic occasionally contains [...] advanced mathematics (which may be unsuitable for liberal-arts majors)”
Although Randall’s main aim is to entertain – something he manages to excel at – his posts can also be thought-provoking, bitter-sweet and even resonate with quite profound experiences and emotions. Who would have thought that some stick figures could achieve all that? It is perhaps indicative of the range of topics dealt with on xkcd.com that I have used it to illustrate no fewer than seven of my articles (including this one, a full list appears at the end of the article). It is encouraging that Randall’s team of corporate lawyers has generally viewed my requests to republish his work favourably.
The example of Randall’s work that I wanted to focus on is as follows.
© xkcd.com (adapted from the original to fit the dimensions of this page)
It is worth noting that often the funniest / most challenging xkcd.com observations appear in the mouse-over text of comic strips (alt or title text for any HTML heads out there – assuming that there are any of us left). I’ll reproduce this below as it is pertinent to the discussion:
Space-time is like some simple and familiar system which is both intuitively understandable and precisely analogous, and if I were Richard Feynman I’d be able to come up with it.
If anyone needs some background on the science referred to then have a skim of this article if you need some background on the scientist mentioned (who has also made an appearance on peterjamesthomas.com in Presenting in Public) then glance through this second one.
Here comes the Science…
Randall points out the dangers of over-extending an analogy. While it has always helped me to employ the rubber-sheet analogy of warped space-time when thinking about the area, it is rather tough (for most people) to extrapolate a 2D surface being warped to a 4D hyperspace experiencing the same thing. As an erstwhile Mathematician, I find it easy enough to cope with the following generalisation:
| S(1) = | The set of all points defined by one variable (x1) – i.e. a straight line |
| S(2) = | The set of all points defined by two variables (x1, x2) – i.e. a plane |
| S(3) = | The set of all points defined by three variables (x1, x2, x3) – i.e. “normal” 3-space |
| S(4) = | The set of all points defined by four variables (x1, x2, x3, x4) – i.e. 4-space |
| ” ” ” “ | |
| S(n) = | The set of all points defined by n variables (x1, x2, … , xn) – i.e. n-space |
As we increase the dimensions, the Maths continues to work and you can do calculations in n-space (e.g. to determine the distance between two points) just as easily (OK with some more arithmetic) as in 3-space; Pythagoras still holds true. However, actually visualising say 7-space might be rather taxing for even a Field’s Medallist or Nobel-winning Physicist.
… and the Maths
More importantly while you can – for example – use 3-space as an analogue for some aspects of 4-space, there are also major differences. To pick on just one area, some pieces of string that are irretrievably knotted in 3-space can be untangled with ease in 4-space.
To briefly reference a probably familiar example, starting with 2-space we can look at what is clearly a family of related objects:
| 2-space: | A square has 4 vertexes, 4 edges joining them and 4 “faces” (each consisting of a line – so the same as edges in this case) |
| 3-space: | A cube has 8 vertexes, 12 edges and 6 “faces” (each consisting of a square) |
| 4-space: | A tesseract (or 4-hypercube) has 16 vertexes, 32 edges and 8 “faces” (each consisting of a cube) |
| Note: The reason that faces appears in inverted commas is that the physical meaning changes, only in 3-space does this have the normal connotation of a surface with two dimensions. Instead of faces, one would normally talk about the bounding cubes of a tesseract forming its cells. |
Even without any particular insight into multidimensional geometry, it is not hard to see from the way that the numbers stack up that:
| n-space: | An n-hypercube has 2n vertexes, 2n-1n edges and 2n “faces” (each consisting of an (n-1)-hypercube) |
Again, while the Maths is compelling, it is pretty hard to visualise a tesseract. If you think that a drawing of a cube, is an attempt to render a 3D object on a 2D surface, then a picture of a tesseract would be a projection of a projection. The French (with a proud history of Mathematics) came up with a solution, just do one projection by building a 3D “picture” of a tesseract.
As aside it could be noted that the above photograph is of course a 2D projection of a 3D building, which is in turn a projection of a 4D shape; however recursion can sometimes be pushed too far!
Drawing multidimensional objects in 2D, or even building them in 3D, is perhaps a bit like employing an analogy (this sentence being of course a meta-analogy). You may get some shadowy sense of what the true object is like in n-space, but the projection can also mask essential features, or even mislead. For some things, this shadowy sense may be more than good enough and even allow you to better understand the more complex reality. However, a 2D projection will not be good enough (indeed cannot be good enough) to help you understand all properties of the 3D, let alone the 4D. Hopefully, I have used one element of the very subject matter that Randall raises in his webcomic to further bolster what I believe are a few of the general points that he is making, namely:
Why BI is not [always] like Theoretical Physics
Having hopefully supported these points, I’ll move on to the second thing that I mentioned reading; a BI-related blog also referencing Theoretical Physics. I am not going to name the author, mention where I read their piece, state what the title was, or even cite the precise area of Physics they referred to. If you are really that interested, I’m sure that the nice people at Google can help to assuage your curiosity. With that out of the way, what were the concerns that reading this piece raised in my mind?
Well first of all, from the above discussion (and indeed the general tone of this blog), you might think that such an article would be right up my street. Sadly I came away feeling that the connection made was, tenuous at best, rather unhelpful (it didn’t really tell you anything about Business Intelligence) and also exhibited a lack of anything bar a superficial understanding of the scientific theory involved.
The analogy had been drawn based on a single word which is used in both some emerging (but as yet unvalidated) hypotheses in Theoretical Physics and in Business Intelligence. While, just like the 2D projection of a 4D shape, there are some elements in common between the two, there are some fundamental differences. This is a general problem in Science and Mathematics, everyday words are used because they have some connection with the concept in hand, but this does not always imply as close a relationship as the casual reader might infer. Some examples:
Part of the blame for what I viewed as an erroneous connection between things that are not actually that similar lies with something that is, in general, I view more positively; the popular science book. The author of the BI/Physics blog post referred to just such a tome in making his argument. I have consumed many of these books myself and I find them an interesting window into areas in which I do not have a background. The danger with them lies when – in an attempt to convey meaning that is only truly embodied (if that is the word) in Mathematical equations – our good friend the analogy is employed again. When done well, this can be very powerful and provide real insight for the non-expert reader (often the writers of pop-science books are better at this kind of thing than the scientists themselves). When done less well, this can do more than fail to illuminate, it can confuse, or even in some circumstances leave people with the wrong impression.
© Jean-François Colonna
During my MSc, I spent a year studying the Riemann Hypothesis and the myriad of results that are built on the (unproven) assumption that it is true. Before this I had spent three years obtaining a Mathematics BSc. Before this I had taken two Maths A-levels (national exams taken in the UK during and at the end of what would equate to High School in the US), plus (less relevantly perhaps) Physics and Chemistry. One way or another I had been studying Maths for probably 15 plus years before I encountered this most famous and important of ideas.
So what is the Riemann Hypotheis? A statement of it is as follows:
The real part of all non-trivial zeros of the Riemann Zeta function is equal to ½
There! Are you any the wiser? If I wanted to explain this statement to those who have not studied Pure Mathematics at a graduate level, how would I go about it? Maybe my abilities to think laterally and be creative are not well-developed, but I struggle to think of an easily accessible way to rephrase the proposal. I could say something gnomic such as, “it is to do with the distribution of prime numbers” (while trying to avoid the heresy of adding that prime numbers are important because of cryptography – I believe that they are important because they are prime numbers!).
I spent a humble year studying this area, after years of preparation. Some of the finest Mathematical minds of the last century (sadly not a set of which I am a member) have spent vast chunks of their careers trying to inch towards a proof. The Riemann Hypothesis is not like something from normal experience; it is complicated. Some things are complicated and not easily susceptible to analogy.
Equally – despite how interesting, stimulating, rewarding and even important Business Intelligence can be – it is not Theoretical Physics and n’er the twain shall meet.
And so what?
So after this typically elliptical journey through various parts of Science and Mathematics, what have I learnt? Mainly that analogies must be treated with care and not over-extended lest they collapse in a heap. Will I therefore stop filling these pages with BI-related analogies, both textual and visual? Probably not, but maybe I’ll think twice before hitting the publish key in future!
6 Comments | Biology, business intelligence, Physics, Pure Mathematics | Tagged: jim harris, multidimensional geometry, ocdq blog, riemann zeta function, space-time, xkcd.com | Permalink
Posted by Peter Thomas
This article completes the three-part series which started with Using historical data to justify BI investments – Part I and continued (somewhat inevitably) with Using historical data to justify BI investments – Part II. Having presented a worked example, which focused on using historical data both to develop a profit-enhancing rule and then to test its efficacy, this final section considers the implications for justifying Business Intelligence / Data Warehouse programmes and touches on some more general issues.
The Business Intelligence angle
In my experience when talking to people about the example I have just shared, there can be an initial “so what?” reaction. It can maybe seem that we have simply adopted the all-too-frequently-employed business ruse of accentuating the good and down-playing the bad. Who has not heard colleagues say “this was a great month excluding the impact of X, Y and Z”? Of course the implication is that when you include X, Y and Z, it would probably be a much less great month; but this is not what we have done.
One goal of business intelligence is to help in estimating what is likely to happen in the future and guiding users in taking decisions today that will influence this. What we have really done in the above example is as follows:
![Look out Morlocks, here I come... [alumni of Imperial College London are so creative aren't they?]](http://peterthomas.files.wordpress.com/2011/05/the-time-machine.jpg?w=450 "Look out Morlocks, here I come... [alumni of Imperial College London are so creative aren't they?]")
For the avoidance of doubt, in the previously attached example, the losses incurred in 2009 – 2010 have absolutely no influence on the rule we adopt, this is based solely on 2006 – 2008 losses. All the 2009 – 2010 losses are used for is to validate our rule.
We have therefore achieved two things:
From a Business Intelligence / Data Warehousing perspective, the general pitch is then something like:
The example also says something else – although we may already have reporting tools, analysis capabilities and even people dabbling in statistical modelling, it appears that there is room for improvement in our approach. The 2009 – 2010 loss ratio was 54% and it could have been closer to 40%. Thus what we are doing now is demonstrably not as good as it could be and the monetary value of making a stepped change in information capabilities can be estimated.
In the example, we are talking about £1m of biannual premium and £88k of increased profit. What would be the impact of better information on an annual book of £1bn premium? Assuming a linear relationship and using some advanced Mathematics, we might suggest £44m. What is more, these gains would not be one-off, but repeatable every year. Even if we moderate our projected payback to a more conservative figure, our exercise implies that we would be not out of line to suggest say an ongoing annual payback of £10m. These are numbers and concepts which are likely to resonate with Executive decision-makers.
To put it even more directly an increase of £10m a year in profits would quickly swamp the cost of a BI/DW programme in very substantial benefits. These are payback ratios that most IT managers can only dream of.
As an aside, it may have occurred to readers that the mechanistic rule is actually rather good and – if so – why exactly do we need the underwriters? Taking to one side examples of solely rule-based decision-making going somewhat awry (LTCM anyone?) the human angle is often necessary in messy things like business acquisition and maintaining relationships. Maybe because of this, very few insurance organisations are relying on rules to take all decisions. However it is increasingly common for rules to play some role in their overall approach. This is likely to take the form of triage of some sort. For example:
In this way process efficiencies are gained. Staff time is only applied where it is necessary and the most expensive resources are applied to those cases that most merit their abilities. |
Correlation
© xkcd.com
Let’s pause for a moment and consider the Insurance example a little more closely. What has actually happened? Well we seem to have established that performance of policies in 2006 – 2008 is at least a reasonable predictor of performance of the same policies in 2009 – 2010. Taking the mutual fund vendors’ constant reminder that past performance does not indicate future performance to one side, what does this actually mean?
What we have done is to establish a loose correlation between 2006 – 2008 and 2009 – 2010 loss ratios. But I also mentioned a while back that I had fabricated the figures, so how does that work? In the same section, I also said that the figures contained an intentional bias. I didn’t adjust my figures to make the year-on-year comparison work out. However, at the policy level, I was guilty of making the numbers look like the type of results that I have seen with real policies (albeit of a specific type). Hopefully I was reasonably realistic about this. If every policy that was bad in 2006 – 2008 continued in exactly the same vein in 2009 – 2010 (and vice versa) then my good segment would have dropped from an overall loss ratio of 54% to considerably more than 40%. The actual distribution of losses is representative of real Insurance portfolios that I have analysed. It is worth noting that only a small bias towards policies that start bad continuing to be bad is enough for our rule to work and profits to be improved. Close scrutiny of the list of policies will reveal that I intentionally introduced several counter-examples to our rule; good business going bad and vice versa. This is just as it would be in a real book of business.
Rather than continuing to justify my methodology, I’ll make two statements:
Closing thoughts
Having gone into a lot of detail over the course of these three articles, I wanted to step back and assess what we have covered. Although the worked-example was drawn from my experience in Insurance, there are some generic learnings to be made.
Broadly I hope that I have shown that – at least in Insurance, but I would argue with wider applicability – it is possible to use the past to infer what actions we should take in the future. By a slight tweak of timeframes, we can even take some steps to validate approaches suggested by our information. It is important that we remember that the type of basic analysis I have carried out is not guaranteed to work. The same can be said of the most advanced statistical models; both will give you some indication of what may happen and how likely this is to occur, but neither of them is foolproof. However, either of these approaches has more chance of being valuable than, for example, solely applying instinct, or making decisions at random.
In Patterns, patterns everywhere, I wrote about the dangers associated with making predictions about events are essentially unpredictable. This is another caveat to be born in mind. However, to balance this it is worth reiterating that even partial correlation can lead to establishing rules (or more sophisticated models) that can have a very positive impact.
While any approach based on analysis or statistics will have challenges and need careful treatment, I hope that my example shows that the option of doing nothing, of continuing to do things how they have been done before, is often fraught with even more problems. In the case of Insurance at least – and I suspect in many other industries – the risks associated with using historical data to make predictions about the future are, in my opinion, outweighed by the risks of not doing this; on average of course!
6 Comments | business analytics, business intelligence, data warehousing, Statistics | Tagged: bi benefits, correlation, insurance, predictive modelling, xkcd.com | Permalink
Posted by Peter Thomas
This article is the second in what has now expanded from a two-part series to a three-part one. This started with Using historical data to justify BI investments – Part I and finishes with Using historical data to justify BI investments – Part III (once again exhibiting my talent for selecting buzzy blog post titles).
Introduction and some belated acknowledgements
The intent of these three pieces is to present a fairly simple technique by which existing, historical data can be used to provide one element of the justification for a Business Intelligence / Data Warehousing programme. Although the specific example I will cover applies to Insurance (and indeed I spent much of the previous, introductory segment discussing some Insurance-specific concepts which are referred to below), my hope is that readers from other sectors (or whose work crosses multiple sectors) will be able to gain something from what I write. My learnings from this period of my career have certainly informed my subsequent work and I will touch on more general issues in the third and final section.
This second piece will focus on the actual insurance example. The third will relate the example to justifying BI/DW programmes and, as mentioned above, also consider the area more generally.
Before starting on this second instalment in earnest, I wanted to pause and mention a couple of things. At the beginning of the last article, I referenced one reason for me choosing to put fingertip to keyboard now, namely me briefly referring to my work in this area in my interview with Microsoft’s Bruno Aziza (@brunoaziza). There were a couple of other drivers, which I feel rather remiss to have not mentioned earlier.
First, James Taylor (@jamet123) recently published his own series of articles about the use of BI in Insurance. I have browsed these and fully intend to go back and read them more carefully in the near future. I respect James and his thoughts brought some of my own Insurance experiences to the fore of my mind.
Second, I recently posted some reflections on my presentation at the IRM MDM / Data Governance seminar. These focussed on one issue that was highlighted in the post-presentation discussion. The approach to justifying BI/DW investments that I will outline shortly also came up during these conversations and this fact provided additional impetus for me to share my ideas more widely.
Winners and losers
The main concept that I will look to explain is based on dividing sheep from goats. The idea is to look at a set of policies that make up a book of insurance business and determine whether there is some simple factor that can be used to predict their performance and split them into good and bad segments.
In order to do this, it is necessary to select policies that have the following characteristics:
The reason for this is that we are going to divide this five-year term into two pieces (the first three and the final two years) and treat these differently.
This is so that the associated loss ratios better reflect current market conditions.
I explained this concept last time round. Short-tail policies (or lines or business) are ones in which any claims are highly likely to be reported as soon as they occur (for example property or accident insurance).
These policies tend to have a low contribution from IBNR (again see the previous piece for a definition). In practice this means that we can use the simplest of the Insurance ratios, paid loss-ratio (i.e. simply Claims divided by Premium), with some confidence that it will capture most of the losses that will be attached to the policy, even if we are talking about say 2010.
Another way of looking at this is that (borrowing an idea discussed last time round) for this type of policy the Underwriting Year and Calendar Year treatments are closer than in areas where claims may be reported many years after the policy was in force.
Before proceeding further, it perhaps helps to make things more concrete. To achieve this, you can download a spreadsheet containing a sample set of Insurance policies, together with their premiums and losses over a five-year period from 2006 to 2010 by clicking here (this is in Office 97-2003 format – if you would prefer, there is also a PDF version available here). Hopefully you will be able to follow my logic from the text alone, but the figures may help.
A few comments about the spreadsheet. First these are entirely fabricated policies and are not even loosely based on any data set that I have worked with before. Second I have also adopted a number of simplifications:
The sheet also calculates the policy paid loss ratio for each year and figures for the whole portfolio appear at the bottom. While the in-year performance of any particular policy can gyrate considerably, it may be seen from the aggregate figures that overall performance of this rather small book of business is relatively consistent:
| Year | Paid Loss Ratio |
| 2006 | 53% |
| 2007 | 59% |
| 2008 | 54% |
| 2009 | 53% |
| 2010 | 54% |
| Total | 54% |
Above I mentioned looking at the five years in two parts. At least metaphorically we are going to use our right hand t cover the results from years 2009 and 2010 and focus on the first three years on the left. Later – after we have established a hypothesis based on 2006 to 2008 results – we can lift our hand and check how we did against the “real” figures.
For the purposes of this illustration, I want to choose a rather mechanistic way to differentiate business that has performed well and badly. In doing this I have to remember that a policy may have a single major loss one year and then run free of losses for the next 20. If I was simply to say any policy with a large loss is bad, I am potentially drastically and unnecessarily culling my book (and also closing the stable door after the horse has bolted). Instead we need to develop a rule that takes this into account.
In thinking about overall profitability, while we have greatly reduced the impact of both reported but unpaid claims and IBNR by virtue of picking a short-tail business, it might be prudent to make say a 5% allowance for these. If we also assume an expense ratio of 35%, then we have a total of non-underwriting-related outgoings of 40%. This means that we can afford to have a paid loss ratio of up to 60% (100% – 40%) and still turn a profit.
Using this insight, my simple rule is as follows:
A policy will be tagged as “bad” if two things occur:
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This rule roughly splits the book 75 / 25; with 74% of policies being good. Other choices of parameters may result in other splits and it would be advisable spending a little time optimising things. Perhaps 26% of policies being flagged as bad is too aggressive for example (though this rather depends on what you do about them – see below). However in the simpler world of this example, I’ll press on to the next stage with my first pick.
Well all we have done so far is to tag policies that have performed badly – in the parlance of Analytics zealots we are being backward-looking. Now it is time to lift our hand on 2009 to 2010 and try to be forward-looking. While these figures are obviously also backward looking (the day that someone comes up with future data I will eat my hat), from the frame of reference of our experimental perspective (sitting at the close of 2008), they can be thought of as “the future back then”. We will use the actual performance of the policies in 2009 – 2010 to validate our choice of good and bad that was based on 2006 – 2008 results.
Overall the 50 policies had a loss ratio of 54% in 2009 – 2010. However those flagged as bad in our above exercise had a subsequent loss ratio of 92%. Those flagged as good had a subsequent loss ratio of 40%. The latter is a 14 point improvement on the overall performance of the book.
So we can say with some certainly that our rule, though simplistic, has produced some interesting results. The third part of this series will focus more closely on why this has worked. For now, let’s consider what actions the split we have established could drive.
What to do with the bad?
We were running a 54% paid ratio in 2009-2010. Using the same assumptions as above, this might have equated to a 94% combined ratio. Our book of business had an annual premium of £0.5m so we received £1m over the two years. The 94% combined would have implied making a £60k profit if we had done nothing different. So what might have happened if we had done something?
There are a number of options. The most radical of these would have been to not renew any of the bad policies; to have carried out a cull. Let us consider what would have been the impact of such an approach. Well our book of business would have shrunk to £740k over the two years at a combined of 40% (the ratio of the good book) + 40% (other outgoing) = 80%, which implies a profit of £148k, up £88k. However there are reasons why we might not have wanted to so drastically shrink our business. A smaller pot of money for investment purposes might have been one. Also we might have had customers with policies in both the good and bad segments and it might have been tricky to cancel the bad while retaining the good. And so on…
Another option would have been to have refined our rule to catch fewer policies. Inevitably, however, this would have reduced the positive impact on profits.
At the other extreme, we might have chosen to take less drastic action relating to the bad policies. This could have included increasing the premium we charged (which of course could also have resulted in us losing the business but via the insured’s choice), raising the deductible payable on any losses, or looking to work with insureds to put in place better risk management processes. Let’s be conservative and say that if the bad book was running at 92% and the overall book at 54% then perhaps it would have been feasible to improve the bad book’s performance to a neutral figure of say 60% (implying a break-even combined of 100%). This would have enabled the insurance organisation to maintain its investment base, to have not lost good business as a result of culling related bad and to have preserved the profit increase generated by the cull.
In practice of course it is likely that some sort of mixed approach would have been taken. The general point is that we have been able to come up with a simple strategy to separate good and bad business and then been able to validate how accurate our choices were. If, in the future, we possessed similar information, then there is ample scope for better decisions to be taken, with potentially positive impact on profits.
Next time…
In the final part of what is now a trilogy, I will look more deeply at what we have learnt from the above example, tie these learnings into how to pitch a BI/DW programme in Insurance and make some more general observations.
4 Comments | business, business analytics, business intelligence, data warehousing | Tagged: bi benefits, bruno aziza, insurance, james taylor | Permalink
Posted by Peter Thomas
This is the first of what was originally a two part piece that has now expanded into three. In the initial chapter, I provide some background on Insurance industry concepts and practices. These are built on in the second chapter (Using historical data to justify BI investments – Part II), in which I offer an Insurance-based worked example. In the final piece, which is cunningly named Part III, I will explain how such an approach to analysing historical data can be used to justify BI investments.
Readers who are already au fait with insurance may choose to wait for the next instalment.
Introduction
Quite some time ago, when I wrote Measuring the Benefits of Business Intelligence, I mentioned that, in some circumstances, I had been able to leverage historical data (is there any other kind?) to justify Business Intelligence investments. I briefly touched on this area in my recent interview with Microsoft’s Bruno Aziza (@brunoaziza) and thought that it was well past time me writing more fully on the topic.
My general approach applies where there are periodic decisions to be made about a business relationship and where how that relationship has performed in the past informs these decisions. These criteria particularly pertain to the industry in which I ran my first BI / DW project; commercial property and casualty insurance. While I hope that users from other sectors may be able to extrapolate my example to apply to them, it is to insurance that I will turn to explain what I did.
An insurance primer
![I have always wanted to launch a '[...] for Pacifiers' series in the US](http://peterthomas.files.wordpress.com/2011/05/insurance-for-dummies.jpg "I have always wanted to launch a '[...] for Pacifiers' series in the US")
My previous article, The Specific Benefits of Business Intelligence in Insurance, starts with a widely used and pig-related (no typo) explanation of how insurance works, both for the insurer and the insured. I won’t repeat this here, but if you are unfamiliar with the area I recommend you taking a look first.
Although of course there are exceptions (event related insurance for example), many commercial insurance policies – just like those that most of us purchase in our personal lives to cover cars and property – have an annual term after which either party can decide whether or not to renew the cover. At renewal, as in the pig example, the insurer will first of all want to assess whether or not they have received more money than they have paid out over the past year. However, the entire point of insurance is that sometimes an event occurs which requires the insurer to give the insured a sum in excess of the premium that they have paid in a given year (or indeed over many years). The insurer is therefore less interested in whether a particular year has been bad – from their perspective – than whether the overall relationship has been, or will become, bad. Perhaps I am over simplifying, but if in most years the insurer pays out less in settling claims than they receive in premium (or ideally there are no claims at all) and if one bad year’s claims are unlikely to negate the benefits accrued in the normal years, then this is good business for the insurer.
Some rational comments
I have bandied about a number of rather woolly concepts in the previous section which include: how much money the insured has paid out and how much they have taken in. Of course these things tend to be more complicated. On the simpler side of the equation, broadly speaking, money coming in is from the insurance premiums paid by customers (but see also the box appearing below).
| Investment income
Some insurers are actually relatively relaxed about paying out more in claims that they receive in premium over the life of a policy. This is because of timing differences. So long as the claims are settled some time after premium is received and so long as there are relatively lucrative investment opportunities (remember that?), it may be that the investment income that the insurer can generate while it has use of the insured’s premium will more than compensate for what might be termed an operating loss on the policy. Equally some insurers will have the business goal of – at least in aggregate – always having premiums exceeding claims and thus making a profit on their core underwriting activities. In this case any investment income is added to the underwriting-related profits, rather than compensating for underwriting-related losses. I won’t complicate this article any further by including investment income, but it is a factor in the profitability of insurance companies. |
Equally broadly speaking, money going out is normally in six categories:
In the cause of clarity, I will lump commission, reinsurance and the general expense of being in business into Other Expenses for what follows. However please bear in mind that, as is often the case in life, things are not as simple as I will make them out to be.
Rather than dealing in monetary units, insurance companies like percentages; though they then insist on referring to these as ratios. Taking the above categories of money flowing in and out of an insurance company, the main ratios that they consider are then:
| Ratio | Calculation | ||||
| Paid Loss | = |
|
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| Reported Loss | = |
|
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| Incurred Loss | = |
|
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| Expense | = |
|
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| Combined | = |
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This concept requires a short diversion as later on I will exclude it from our discussions and will need to explain why. There are some interesting time lags in insurance. Take the sad case of asbestosis (also mentioned in my previous article). Here those unfortunately exposed developed symptoms of the disease in some cases many years later. However if their exposure was in say 1972, they would be covered by whatever Employers Liability policy their organisation held or whatever personal policy they held in the case of the self-employed. An asbestosis sufferer may have changed insurance company ten times since their exposure, but it is the insurance company who provided cover at the time who is liable for any claims.
Rather than waiting for such claims to emerge, insurance companies follow the best practise of recognising liabilities at the earliest point. Because of this, they set up estimated reserves for claims that they may receive in future years (or decades) and apply these to the year in which the policy was in force. Of course in some lines of business, say Property cover, most claims are reported as soon as they occur and so IBNR reserves are low. However in others, say Directors and Officers Liability, or the Employers Liability mentioned above, claims may arise many years hence and IBNR can be a big factor in results.
It should be stressed that IBNR is seldom calculated for a single policy (though it is conceivable that this would happen on a very large risk). Instead it is estimated for classes of policies, often grouped into lines of business, and the same “rate” of IBNR is applied across the board. Of course IBNR is calculated based on experience of losses in the same baskets of policies in previous years, adjusted to take account of current differences (e.g. more or less favourable economic conditions for Directors and Officers Liability, or maybe rising or falling property indeces for Property).
For reasons that are probably obvious, lines of business where most claims are promptly reported (i.e. low IBNR) are called short-tail lines. Those where claims may emerge some time after the period covered by the policy (i.e. high IBNR) are called long-tail lines. Later on I will be focussing just on short-tail business.
[Incidentally, improving this process of estimation is one of the specific benefits of Business Intelligence in insurance that I highlighted in my previous article.]
Underwriting Year
Something else may have occurred to readers when considering the time lags that I reference in the previous section, namely that while a policy may last from say 1st January 2006 to 31st December 2006, claims against this may occur either during this period, or after it. The financial statements of an insurance company will place claims in the period that they are notified or settled. So in the above example, a claim paid on 23rd April 2008 (assuming the financial and calendar years coincide) will be reflected in the 2008 report and accounts.
However it is often useful for analysis purposes to lump together all of the claims relating to a policy and associate these with the year in which it was written. Again in our example this would mean our 23rd April 2008 claim would be recorded in the Underwriting Year of 2006. So an Underwriting Year report comparing 2006 and 2007 say would have the premium for all policies written in 2006 and all the claims against these policies – regardless of when they occur – compared to the premium for 2007 and all the claims against these policies, whenever they occur.
Because of this, Underwriting Year reports provide a good measure of the performance of policies (or books of business) over time, regardless of how associated losses are dispersed. By contrast Calendar Year (i.e. financial) reports will often have premium from policies written in say 2010 combined with losses from policies written in say 2000 – 2010.
Tune in next time…
Having laid some foundations, in the next article, I will draw on the various concepts that I have introduced above to offer a worked example. In the closing chapter, I will explain how I such an example to justify a major, multi-year Business Intelligence / Data Warehousing programme within the insurance industry.
7 Comments | business analytics, business intelligence, data warehousing | Tagged: bi benefits, ibnr, insurance, loss ratio, underwriting year | Permalink
Posted by Peter Thomas
Several weeks back now, I presented at IRM’s collocated European Master Data Management Summit and Data Governance Conference. This was my second IRM event, having also spoken at their European Data Warehouse and Business Intelligence Conference back in 2010. The conference was impeccably arranged and the range of speakers was both impressive and interesting. However, as always happens to me, my ability to attend meetings was curtailed by both work commitments and my own preparations. One of these years I will go to all the days of a seminar and listen to a wider variety of speakers.
Anyway, my talk – entitled Making Business Intelligence an Integral part of your Data Quality Programme – was based on themes I had introduced in Using BI to drive improvements in data quality and developed in Who should be accountable for data quality?. It centred on the four-pillar framework that I introduced in the latter article (yes I do have a fetish for four-pillar frameworks as per):
Given my lack of exposure to the event as a whole, I will restrict myself to writing about a comment that came up in the question section of my slot. As per my article on presenting in public, I try to always allow time at the end for questions as this can often be the most interesting part of the talk; for delegates and for me. My IRM slot was 45 minutes this time round, so I turned things over to the audience after speaking for half-an-hour.
There were a number of good questions and I did my best to answer them, based on past experience of both what had worked and what had been less successful. However, one comment stuck in my mind. For obvious reasons, I will not identify either the delegate, or the organisation that she worked for; but I also had a brief follow-up conversation with her afterwards.
She explained that her organisation had in place a formal data governance process and that a lot of time and effort had been put into communicating with the people who actually entered data. In common with my first pillar, this had focused on educating people as to the importance of data quality and how this fed into the organisation’s objectives; a textbook example of how to do things, on which the lady in question should be congratulated. However, she also faced an issue; one that is probably more common than any of us information professionals would care to admit. Her problem was not at the bottom, or in the middle of her organisation, but at the top.
In particular, though data governance and a thorough and consistent approach to both the entry of data and transformation of this to information were all embedded into the organisation; this did not prevent the leaders of each division having their own people take the resulting information, load it into Excel and “improve” it by “adjusting anomalies”, “smoothing out variations”, “allowing for the impact of exceptional items”, “better reflecting the opinions of field operatives” and the whole panoply of euphemisms for changing figures so that they tell a more convenient story.
In one sense this was rather depressing, someone having got so much right, but still facing challenges. However, it also chimes with another theme that I have stressed many times under the banner of cultural transformation; it is crucially important than any information initiative either has, or works assiduously to establish, the active support of all echelons of the organisation. In some of my most successful BI/DW work, I have had the benefit of the direct support of the CEO. Equally, it is is very important to ensure that the highest levels of your organisation buy in before commencing on a stepped-change to its information capabilities.
My experience is that enhanced information can have enormous payback. But it is risky to embark on an information programme without this being explicitly recognised by the senior management team. If you avoid laying this important foundation, then this is simply storing up trouble for the future. The best BI/DW projects are totally aligned with the strategic goals of the organisation. Given this, explaining their objectives and soliciting executive support should be all the easier. This is something that I would encourage my fellow information professionals to seek without exception.
2 Comments | business intelligence, change management, cultural transformation, data quality | Tagged: IRM UK, seminars | Permalink
Posted by Peter Thomas
A friend and fellow information professional is currently responsible for both building a new data warehouse and supporting its predecessor, which is based on a different technology platform. In these times of ever-increasing focus on costs, she had been asked to port the old warehouse to the new platform, thereby avoiding some licensing payments. She asked me what I thought about this idea and we chatted for a while. For some reason, our conversation went off at a bit of a tangent and I started to tell her the story of an acquaintance of mine and his recent sad experiences.
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My acquaintance, let’s call him Jim to avoid causing any embarassment, had always been interested in cars; driving them, maintaining them, souping them up, endlessly reading car magazines and so on. His dream had always been to build his own car and his eye had always been on a Caterham kit. I suppose for him the pleasure of making a car was at least as great, if not more, as the pleasure of driving one.
Jim saved his pennies and eventually got together enough cash to embark on his dream project. Having invested his money, he started to also invest his time and effort. However, after a few weeks of toil, he hit a snag. It was nothing to do with his slowly emerging Caterham, but to do with the more quotidian car he used for his daily commute to work. Its engine had developed a couple of niggles that had been resistant to his own attempts to fix them and he had reluctantly decided that it was in need of some new parts and quite expensive ones at that. Jim had already spent quite a bit of cash on the Caterham and more on some new tools that he needed to assemble it. The last thing he wanted to do now was to have a major outlay on his old car; particularly because, once the Caterham was finished, he had planned to trade it for its scrap-metal worth.
But now things got worse, Jim’s current car failed its MOT (vehicle safety inspection for any non-UK readers) because the faulty engine did not meet emission standards. However, one of his friends came up with a potential solution. He said, “As you have already assembled the Caterham engine, why not put this into your current car and use this instead? You can then swap it out into the Caterham chassis and body when you have built this.”
This sounded like a great idea to Jim, but there were some issues with it. His Cateham was supplied with a Cosworth-developed 2.3-litre Ford Duratec engine. This four-cylinder twin cam unit was the wrong size and shape to fit into the cavity left by removing the worn-out engine from his commuting car. Well as I had mentioned at the start, Jim was a pretty competent amateur mechanic and he thought that he had a good chance of rising to the challenge. He was motivated by the thought of not having to shell out extra cash and in any case he loved tinkering with cars.
So he put in some new brackets to hold the Caterham engine. He then had to grind-down a couple of protruding pieces of the Duratec block to gain the extra 5 mm necessary to squeeze it in. The fuel feeds were in the wrong place, but a bit of plumbing and that was also sorted. Perhaps this might cause an issue with efficiency of the engine burn cycle, but Jim figured that it would probably be OK. Next the vibration dampers were not really up to the job of dealing with the more powerful engine and neither was the exhaust system. No worries, thought Jim, a tap of a hammer here, a bend of a pipe here and he could also add in a couple of components that had been sitting at the back of his garage rusting for years as well. Eventually everything seemed fine.
Jim ventured out of his garage in his old car, with its new engine. He was initially a bit trepidatious, but his work seemed to be hanging together. Sure the car was making a bit of a noise, shaking a bit and the oil temperature seems a bit high, but Jim felt that these were only minor problems. He told himself that all his handiwork had to do was to hang together for a few more months until he finished the rest of the Caterham.
With these nice thoughts in mind, Jim approached a bend. The car flew off the road at a tangent as he realised – too late – that he had been travelling at Caterham speeds into the corner and didn’t have a Caterham chassis, a Caterham suspension, or Caterham brakes. His old car was not up to dealing with the forces created in the turn. His tyres failed to grip and, after what seemed like an eternity of slow-motion spinning and screeching and panic, he find himself in a ditch; healthy, but with a wheel sheared off and smoke coming out of the front of the car. A later inspection confirmed that his commuting car was a write-off, and his insurance policy didn’t fully cover the cost of a new vehicle.
Jim ended up having to buy another day-to-day car, which delayed him from spending the additional money necessary to get the Caterham on the road for quite some time. However, after scrimping and saving for a while, he eventually got back to his dream project, only to find that combination of the modifications he had to make to the Duratec engine, plus the after effects of the crash meant that it was now useless and he needed to purchase a replacement.
So because Jim didn’t want to run to the expense of maintaining his old car while he built his new one, he would instead have to buy a new temporary car plus a new engine for the Caterham. Jim was just as far off from finishing the Caterham as when he had started, despite wasting a lot of time and money along the way. A very sad story.
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Suddenly I realised that I had been wittering on about a wholly unrelated subject to my friend’s data warehousing problem. I apologised and turned the conversation back to this. To my astonishment, she told me that she had already made up her mind. I suppose she had taken advantage of the length of time I had spent telling Jim’s story to more profitably weigh the pros and cons of different approaches in her mind and thereby had reached her decision. Anyway, she thanked me for my help, I protested that I hadn’t really offered her any and we each went our separate ways.
I found out later she had decided to pay the maintenance costs on the old data warehouse.
I would like to apologise in advance if anyone at Caterham, Cosworth, Ford, or indeed Peugeot, takes offence to any of the content of the above story or its illustrations. I’m sure that you make very fine products and this article isn’t really about any of them.
1 Comment | data warehousing, project management | Tagged: caterham, cosworth | Permalink
Posted by Peter Thomas
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