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.
The discussion included the predictable references to GIGO, but conversation then moved on to who has responsibility for data quality, IT or the business.
As regular readers of this column will know, I view this as an unhelpful distinction. My belief is that IT is a type of business department, with specific skills, but engaged in business work and, in this, essentially no different to say the sales department or the strategy department. Looking at the question through this prism, it becomes tautological. However, if we ignore my peccadillo about this issue, we could instead ask whether responsibility for data quality should reside in IT or not-IT (I will manfully resist the temptation to write ~IT or indeed IT’); with such a change, I accept that this is now a reasonable question.
Answering a modified version of the question
My basic answer is that both groups will bring specific skills to the party and a partnership approach is the one that is most likely to end in success. There are however some strong arguments for IT playing a pivotal role and my aim is to expand on these in the rest of this article.
Before I enumerate these, one thing that I think is very important is that data quality is seen as a broad issue that requires a broad approach to remedy it. I laid out what I see as the four pillars of improving data quality in an earlier post: Using BI to drive improvements in data quality. This previous article goes into much more detail about the elements of a successful data quality improvement programme and its title provides a big clue as to what I see as the fourth pillar. More on this later.
1. The change management angle
Again, as with virtually all IT projects, the aim of a data quality initiative is to drive different behaviours. This means that change management skills are just as important in these types projects as in the business intelligence work that they complement. This is a factor to consider when taking decisions about who takes the lead in looking to improve data quality; who amongst the available resources have established and honed change management skills? The best IT departments will have a number of individuals who fit this bill, if not-IT has them as well, then the organisation is spoilt for choice.
2. The pan-organisational angle
Elsewhere I have argued that BI adds greatest value when it is all-pervasive. The same observations apply to data quality. If we assume that an organisation has a number of divisions, each with their own systems (due to the nature of their business and maybe also history), but also maybe sharing some enterprise applications. While it would undeniably be beneficial for Division A to get their customer files in order, it would be of even greater value if all divisions did this at the same time and with a consistent purpose. This would allow the dealings of Customer X across all parts of the business to be calculated and analysed. It could also drive cross-selling opportunities in particular market segments.
While it is likely that a number of corporate staff of different sorts will have a very good understanding about the high-level operations of each of the divisions, it is at least probable that only IT staff (specifically those engaged in collating detailed data from each division for BI purposes) will have an in-depth understanding of how transactions and master data are stored in different ways across the enterprise. This knowledge is a by-product of running a best practice BI project and the collateral intellectual property built up can be of substantial business value.
3. The BI angle
It was this area that formed the backbone of the earlier data quality article that I referenced above. My thesis was that you could turn the good data quality => good BI relationship on its head and use the BI tool to drive data quality improvements. The key here was not to sanitise data problems, but instead to expose them, also leveraging standard BI functionality like drill through to allow people to identify what was causing an issue.
One of the most pernicious data quality issues is of the valid, but wrong entry. For example a transaction is allocated a category code of X, which is valid, but the business event demands the value Y. Sometimes it is possible to guard against this eventuality by business rules, e.g. Product A can only be sold by Business Unit W, but this will not be possible for all such data. A variant of this issue is data being entered in the wrong field. Having spent a while in the Insurance industry, it was not atypical for a policy number to be entered as a claim value for example. Sometimes there is no easy systematic way to detect this type of occurrence, but exposing issues in a well-designed BI system is one way of noticing odd figures and then – crucially – being able to determine what is causing them.
4. The IT character angle
I was searching round for a way to put this nicely and then realised that Jim Harris had done the job for me in naming his excellent Obsessive-Compulsive Data Quality blog (OCDQ Blog). I’m an IT person, I may have general management experience and a reasonable understanding of many parts of business, but I remain essentially an IT person. Before that, I was a Mathematician. People in both of those lines of work tend to have a certain reputation; to put it positively, the ability to focus extremely hard on something for long periods is a common characteristic.
Aside: for the avoidance of doubt, as I pointed out in Pigeonholing – A tragedy, the fact that someone is good at the details does not necessarily preclude them from also excelling at seeing the big picture – in fact without a grasp on the details the danger of painting a Daliesque big picture is perhaps all too real!
Improving data quality is one of the areas where this personality trait pays dividends. I’m sure that there are some marketing people out there who have relentless attention to detail and whose middle name is “thoroughness”, however I suspect there are rather less of them than among the ranks of my IT colleagues. While leadership from the pertinent parts of not-IT is very important, a lot of the hard yards are going to be done by IT people; therefore it makes sense if they have a degree of accountability in this area.
Much like most business projects, improving data quality is going to require a cross-functional approach to achieve its goals. While you often hear the platitudinous statement that “the business must be responsible for the quality of its own data”, this ostensible truism hides the fact that one of the best ways for not-IT to improve the quality of an organisation’s data is to get IT heavily involved in all aspects of this work.
IT for its part can leverage both its role as one of the supra-business unit departments and its knowledge of how business transactions are recorded and move from one system to another to become an effective champion of data quality.
As might be inferred from my last post, certain sporting matters have been on my mind of late. However, as is becoming rather a theme on this blog, these have also generated some business-related thoughts.
On Friday evening, the Australian cricket team finished the second day of the second Test Match on a score of 152 runs for the loss of 8 (out of 10) first innings wickets. This was still 269 runs behind the England team‘s total of 425.
In scanning what I realise must have been a hastily assembled end-of-day report on the web-site of one of the UK’s leading quality newspapers, a couple are glaring errors stood out. First, the Australian number 4 batsman Michael Hussey was described as having “played-on” to a delivery from England’s shy-and-retiring Andrew Flintoff. Second, the journalist wrote that Australia’s number six batsman, Marcus North, had been “clean-bowled” by James Anderson.
I appreciate that not all readers of this blog will be cricket aficionados and also that the mysteries of this most complex of games are unlikely to be made plain by a few brief words from me. However, “played on” means that the ball has hit the batsman’s bat and deflected to break his wicket (or her wicket – as I feel I should mention as a staunch supporter of the all-conquering England Women’s team, a group that I ended up meeting at a motorway service station just recently).
By contrast, “clean-bowled” means that the ball broke the batsman’s wicket without hitting anything else. If you are interested in learning more about the arcane rules of cricket (and let’s face it, how could you not be interested) then I suggest taking a quick look here. The reason for me bothering to go into this level of detail is that, having watched the two dismissals live myself, I immediately thought that the journalist was wrong in both cases.
It may be argued that the camera sometimes lies, but the cricinfo.com caption (whence these images are drawn) hardly ever does. The following two photographs show what actually happened:
As hopefully many readers will be able to ascertain, Hussey raised his bat aloft, a defensive technique employed to avoid edging the ball to surrounding fielders, but misjudged its direction. It would be hard to “play on” from a position such as he adopted. The ball arced in towards him and clipped the top of his wicket. So, in fact he was the one who was “clean-bowled”; a dismissal that was qualified by him having not attempted to play a stroke.
North on the other hand had been at the wicket for some time and had already faced 13 balls without scoring. Perhaps in frustration at this, he played an overly-ambitious attacking shot (one not a million miles from a baseball swing), the ball hit the under-edge of his horizontal bat and deflected down into his wicket. So it was North, not Hussey, who “played on” on this occasion.
So, aside from saying that Hussey had been adjudged out “handled the ball” and North dismissed “obstructed the field” (two of the ten ways in which a batsman’s innings can end – see here for a full explanation), the journalist in question could not have been more wrong.
As I said, the piece was no doubt composed quickly in order to “go to press” shortly after play had stopped for the day. Maybe these are minor slips, but surely the core competency of a sports journalist is to record what happened accurately. If they can bring insights and colour to their writing, so much the better, but at a minimum they should be able to provide a correct description of events.
Everyone makes mistakes. Most of my blog articles contain at least one typographical or grammatical error. Some of them may include errors of fact, though I do my best to avoid these. Where I offer my opinions, it is possible that some of these may be erroneous, or that they may not apply in different situations. However, we tend to expect professionals in certain fields to be held to a higher standard.
For a molecular biologist, the difference between a 0.20 micro-molar solution and a 0.19 one may be massive. For a team of experimental physicists, unbelievably small quantities may mean the difference between confirming the existence of the Higgs Boson and just some background noise.
In business, it would be unfortunate (to say the least) if auditors overlooked major assets or liabilities. One would expect that law-enforcement agents did not perjure themselves in court. Equally politicians should never dissemble, prevaricate or mislead. OK, maybe I am a little off track with the last one. But surely it is not unreasonable to expect that a cricket journalist should accurately record how a batsman got out.
Twitter and Truth
I made something of a leap from these sporting events to the more tragic news of Michael Jackson’s recent demise. I recall first “hearing” rumours of this on twitter.com. At this point, no news sites had much to say about the matter. As the evening progressed, the self-styled celebrity gossip site TMZ was the first to announce Jackson’s death. Other news outlets either said “Jackson taken to hospital” or (perhaps hedging their bets) “US web-site reports Jackson dead”.
By this time the twitterverse was experiencing a cosmic storm of tweets about the “fact” of Jackson’s passing. A comparably large number of comments lamented how slow “old media” was to acknowledge this “fact”. Eventually of course the dinosaurs of traditional news and reporting lumbered to the same conclusion as the more agile mammals of Twitter.
In this case social media was proved to be both quick and accurate, so why am I now going to offer a defence of the world’s news organisations? Well I’ll start with a passage from one of my all-time favourite satires, Yes Minister, together with its sequel Yes Prime Minister.
In the following brief excerpt Sir Geoffrey Hastings (the head of MI5, the British domestic intelligence service) is speaking to The Right Honourable James Hacker (the British Prime Minister). Their topic of conversation is the recently revealed news that a senior British Civil Servant had in fact been a Russian spy:
Things might get out. We don’t want any more irresponsible ill-informed press speculation.
Even if it’s accurate?
Especially if it’s accurate. There is nothing worse than accurate irresponsible ill-informed press speculation.
Was the twitter noise about Jackson’s death simply accurate ill-informed speculation? It is difficult to ask this question as, sadly, the tweets (and TMZ) proved to be correct. However, before we garland new media with too many wreaths, it is perhaps salutary to recall that there was a second rumour of a celebrity death circulating in the febrile atmosphere of Twitter on that day. As far as I am aware, Pittsburgh’s finest – Jeff Goldblum – is alive and well as we speak. Rumours of his death (in an accident on a New Zealand movie set) proved to be greatly exaggerated.
The difference between a reputable news outlet and hordes of twitterers is that the former has a reputation to defend. While the average tweep will simply shrug their shoulders at RTing what they later learn is inaccurate information, misrepresenting the facts is a cardinal sin for the best news organisations. Indeed reputation is the main thing that news outlets have going for them. This inevitably includes annoying and time-consuming things such as checking facts and validating sources before you publish.
With due respect to Mr Jackson, an even more tragic set of events also sparked some similar discussions; the aftermath of the Iranian election. The Economist published an interesting artilce comparing old and new media responses to this entitiled: Twitter 1, CNN 0. Their final comments on this area were:
[…]the much-ballyhooed Twitter swiftly degraded into pointlessness. By deluging threads like Iranelection with cries of support for the protesters, Americans and Britons rendered the site almost useless as a source of information—something that Iran’s government had tried and failed to do. Even at its best the site gave a partial, one-sided view of events. Both Twitter and YouTube are hobbled as sources of news by their clumsy search engines.
Much more impressive were the desk-bound bloggers. Nico Pitney of the Huffington Post, Andrew Sullivan of the Atlantic and Robert Mackey of the New York Times waded into a morass of information and pulled out the most useful bits. Their websites turned into a mish-mash of tweets, psephological studies, videos and links to newspaper and television reports. It was not pretty, and some of it turned out to be inaccurate. But it was by far the most comprehensive coverage available in English. The winner of the Iranian protests was neither old media nor new media, but a hybrid of the two.
Aside from the IT person in me noticing the opportunity to increase the value of Twitter via improved text analytics (see my earlier article, Literary calculus?), these types of issues raise concerns in my mind. To balance this slightly negative perspective it is worth noting that both accurate and informed tweets have preceded several business events, notably the recent closure of BI start-up LucidEra.
Also main stream media seem to have swallowed the line that Google has developed its own operating system in Chrome OS (rather than lashing the pre-existing Linux kernel on to its browser); maybe it just makes a better story. Blogs and Twitter were far more incisive in their commentary about this development.
Considering the pros and cons, on balance the author remains something of a doubting Thomas (by name as well as nature) about placing too much reliance on Twitter for news; at least as yet.
Accuracy an Business Intelligence
Some business thoughts leaked into the final paragraph of the Introduction above, but I am interested more in the concept of accuracy as it pertains to one of my core areas of competence – business intelligence. Here there are different views expressed. Some authorities feel that the most important thing in BI is to be quick with information that is good-enough; the time taken to achieve undue precision being the enemy of crisp decision-making. Others insist that small changes can tip finely-balanced decisions one way or another and so precision is paramount. In a way that is undoubtedly familiar to regular readers, I straddle these two opinions. With my dislike for hard-and-fast recipes for success, I feel that circumstances should generally dictate the approach.
There are of course different types of accuracy. There is that which insists that business information reflects actual business events (often more a case for work in front-end business systems rather than BI). There is also that which dictates that BI systems reconcile to the penny to perhaps less functional, but pre-existing scorecards (e.g. the financial results of an organisation).
A number of things can impact accuracy, including, but not limited to: how data has been entered into systems; how that data is transformed by interfaces; differences between terminology and calculation methods in different data sources; misunderstandings by IT people about the meaning of business data; errors in the extract transform and load logic that builds BI solutions; and sometimes even the decisions about how information is portrayed in BI tools themselves. I cover some of these in my previous piece Using BI to drive improvements in data quality.
However, one thing that I think differentiates enterprise BI from departmental BI (or indeed predictive models or other types of analytics), is a greater emphasis on accuracy. If enterprise BI is to aspire to becoming the single version of the truth for an organisation, then much more emphasis needs to be placed on accuracy. For information that is intended to be the yardstick by which a business is measured, good enough may fall short of the mark. This is particularly the case where a series of good enough solutions are merged together; the whole may be even less than the sum of its parts.
A focus on accuracy in BI also achieves something else. It stresses an aspiration to excellence in the BI team. Such aspirations tend to be positive for groups of people in business, just as they are for sporting teams. Not everyone who dreams of winning an Olympic gold medal will do so, but trying to make such dreams a reality generally leads to improved performance. If the central goal of BI is to improve corporate performance, then raising the bar for the BI team’s own performance is a great place to start and aiming for accuracy is a great way to move forward.
A final thought: England went on to beat Australia by precisely 115 runs in the second Test at Lord’s; the final result coming today at precisely 12:42 pm British Summer Time. The accuracy of England’s bowling was a major factor. Maybe there is something to learn here.
Browsing through my WordPress Tag Surfer today (a really nice feature by the way), I came across an interesting list of problems that can occur in a data warehousing / business intelligence project, together with suggestions for managing these. A link appears below:
The author, Raphael Klebanov, has clearly lived the data warehousing process and a lot of what he says chimes closely with my own experience.
Some of his themes around business engagement, the alignment of BI delivery with business needs and the importance of education are echoed throughout my own writing. This article is definitely worth a read in my opinion.