A bad workman blames his [Business Intelligence] tools

Tools
 
Introduction

This is a proverb with quite some history to it. Indeed its lineage has been traced to 13th Century France in: mauvés ovriers ne trovera ja bon hostill (les mauvais ouvriers ne trouveront jamais un bon outil being a rendition in more contemporary French). To me this timeless observation is applicable to present-day Business Intelligence projects. Browsing through on-line forums, it is all too typical to see discussions that start “What is the best BI software available on the market?”, “Who are the leaders in SaaS BI?” and (rather poignantly in my opinion) “Please help me to pick the best technology for a dashboard.” I feel that these are all rather missing the point. Before I explain why, I am going to offer another of my sporting analogies, which I believe is pertinent. Indeed sporting performace is an area to which the aphorism appearing in the title is frequently applied.

If you would like to skip the sporting analogy and cut to the chase, please click here.
 
 
The importance of having the right shoes

Rock climbing is a sport that certainly has its share of machismo; any climbing magazine or web-site will feature images of testosterone-infused youths whose improbable physiques (often displayed to full advantage by the de rigueur absence of any torso-encumbering clothing) propel them to the top of equally improbable climbs.

Given this, many commentators have noted the irony of climbing being conducted by people wearing the equivalent of rubber-covered ballet slippers. The fact that one of the most iconic rock climbing shoes of all time was a fetching shade of pink merely adds piquancy to this observation. Examples of these, the classic FiveTen Anasazi Lace-ups, are featured in the following photo of top British climber, Steve McClure (yes it is the right way up).

The UK's finest sport climer - Steve McClure - sports the Pink'uns

When I started rock climbing, my first pair of shoes were Zephyrs from Spanish climbing firm Boreal. They looked something like this:

The Zephyr by Boreal - $87 - £67
The Zephyr by Boreal - $87 - £67

Although it might not be apparent from the above image, these are intended to be comfortable shoes. Ones to be worn by more experienced climbers on long mountain days, or suitable for beginners, like myself at the time, on shorter climbs. Although not exactly cheap, they are not prohibitively expensive and the rubber on the soles is quite hard-wearing as well.

The Zephyrs worked well for me, but inevitably over time you begin to notice the shoes worn by better climbers at the crag or at the wall. You also cannot fail to miss the much sexier shoes worn by professional climbers in films, climbing magazine articles and (no coincidence here) advertisements. These other shoes also cost more (again no coincidence) and promise better performance. When you are looking to get better at something, it is tempting to take any advantage that you can get. Also, perhaps especially when you are looking to break into a new area, there is some pressure to conform, to look like the “in-crowd”, maybe even simply to distance yourself from the beginner that you were only a few months previously.

This is very shallow behaviour of course, but it is also the rock on which the advertising industry is founded. I wanted to get better as a climber, but would have to admit that other, less noble, motives also drove me to wanting to purchase new rock shoes.

The Galileo by FiveTen - $130 - £85
The Galileo by FiveTen - $130 - £85

The Galileos shown above are made by US company FiveTen and are representative of the type of shoes that I have worn for most my climbing career. FiveTen shoes have been worn by many top climbers over the years (though there have recently been some quite high-profile defections to start-up brand Evolv, who can never seem to decide whether to append a final ‘e’ to their name or not).

Amongst other things, FiveTens are noted for the stickiness of their rubber, which is provided by an organisation called Stealth Rubber and appears on no other rock climbing shoes. Generally the greater the adhesion between your foot and the rock, the greater the force that you can bring to bear on it to drive yourself upwards. Also it helps to have confidence that your foot has a good chance of staying in place, no matter how glassy the rock may be (and no matter how long the fall may be should this not happen). I have worn FiveTen shoes on all of my hardest climbs (none of which have actually been very hard in the grand scheme of things sad to say).

The Solution by La Sportiva - $155 - £120 (link goes to the Sportiva site)
The Solution by La Sportiva - $155 - £120
(the link loads a Flash page on the Sportiva site)

Nevertheless, with what I admit was rather a sense of guilt, I have recently embarked on a dalliance with another rock shoe manufacturer, La Sportiva of Italy. The Sportiva Solutions which are shown above are both the most expensive rock shoes I have ever owned and the most technical. If NASA made a rock shoe, they would probably not be a million miles away from the Solutions. The radical nature of their design can perhaps best be appreciated in three dimensions and you can do this by clicking on the above image.

The Solutions are very, very good rock shoes. I recently had the opportunity to carry out a before and after comparison on the following climb, A Miller’s Tale:

A Miller's Tale (V4/Font 6b+) - Rubicon Wall, Derbyshire, England
A Miller's Tale (V4/Font 6b+)
Rubicon Wall, Derbyshire, England
© http://77jenn.blogspot.com

My partner, who appears in the photo (incidentally sporting FiveTen shoes), climbed this on her second go. By contrast, I had many fruitless attempts wearing my own pair of FiveTens (that, to be fair to FiveTen, were much less technical than the Galileo’s above and were also probably past the end of their useful life). I frequently found my feet skittering off of the highly polished limestone, which resulted in me rapidly returning to terra firma.

A couple of weeks later, equipped with my shiny new Sportivas, my feet did not slip once. Of course the perfect end to this story would have been to say that I then climbed the problem (for an explanation of why some types of climbs are called problems see my earlier article Perseverance). Sadly, though I made much more progress during my second session, I need to go back to finally tick it off of my list.

So here surely is an example of the tool making a difference, or is it? My partner had climbed A Miller’s Tale quite happily without having the advantage of my new footwear. She is 5’3″ (160cm) compared to my 5’11” (180cm) and the taller you are the easier it is to reach the next hold. Strength is a factor in climbing and I am also stronger in absolute terms than she is. The reason that she succeeded where I failed is simply that she is a better climber than I am. It is an oft-repeated truism in the climbing world that many females have better techniques than men. This, together with the “unfair” advantage of smaller fingers, is the excuse often offered by muscle-bound men who fail to complete a climb that a female then dances her way up. However in my partner’s case, she is also very strong, with her power-to-weight ratio being the key factor. You don’t need to lift massive weights in climbing, just your own body.

So I didn’t really need better rock shoes to prevent my feet from slipping. If I got my body into a better balanced position, then this would have had the same impact. Equally, if my abdominal muscles were stronger, I could have squeezed my feet harder onto the rock, increasing their adhesion (this type of strength, known as core strength for obvious reasons, is crucial to progressing in many types of climbing). What the Solutions did was not to make me a better climber, but to make up for some of my inadequacies. In this way, by allowing me the luxury of not focussing on increasing my strength or improving my technique, you could even argue that they might be bad for my climbing in the long run. I probably protest too much in this last comment, but hopefully the reader can appreciate the point that I am trying to make.

Campus board training

In order to become a better climber I need to do lots of things. I need to strengthen the tendons in my fingers (or at least in nine of them as I ruptured the tendon in my right ring finger playing rugby years ago) so that I can hold on to smaller edges and grasp larger ones for longer. I need to develop my abdominal muscles to hold me onto the rock face better and put more pressure on my feet; particularly when the climb is overhanging. I need to build up muscles in my back, shoulders and arms to be able to move more assuredly between holds that are widely spaced. I must work on my endurance, so that I do not fail climbs because I am worn out by a long series of lower moves. Finally I need to improve my technique: making my footwork more precise; paying more attention to the shape of my body and how this affects my centre of gravity and the purchase I have on holds; getting more comfortable with the tricks of the trade such as heel- and toe-hooks; learning when to be aggressive in my climbing and when to be slow and deliberate; and finally better visualising how my body fits against the rock and the best way to flow economically from one position to the next.

If I can get better in all of these areas, then maybe I will have earned my new technical rock shoes and I will be able to take advantage of the benefits that they offer. Having the right shoes can undoubtedly improve your climbing, but it is no substitute for focussing on the long list in the previous paragraph. There is no real short-cut to becoming a better climber, it just takes an awful lot of work.

A final thing to add in this section is that the Solutions offer advantages to the climber on certain types of climbs. On any overhanging, pocketed rock, they are brilliant. But the way that they shape your foot into a down-turned claw would be a positive disadvantage when trying to pad up a slab. In this second scenario, something like my worn out FiveTens (now sadly consigned to the rubbish tip) would be the tool of choice. It is important to realise that the right tool is often dictated by the task in hand and one that excels in area A may be an also-ran in area B.

Notes:

  1. Lest it be thought that the above manufacturers play only in narrow niches, I should explain that each of Boreal, FiveTen and La Sportiva produce a wide range of rock shoes catering to virtuially every type of climber from the neophyte to the world’s best.
  2. If you think that the pound dollar rates are rather strange in the above exhibits, then a few things are at play. Some are genuine differences, but others are because they are historical rates. for example, I struggled to find a US web-site that still sells Boreal Zephyrs.
  3. If you are interested in finding out more about my adventures in rock climbing, then take a look at my partner’s blog.

 
 
The role of technology in Business Intelligence

I hope that I have established that at least in the world of rock climbing, the technology that you have at your disposal is only one of many factors necessary for success; indeed it is some way from being the most important factor.

Having really poor, or worn out, rock shoes can dent your confidence and even get you into bad habits (such as not using your feet enough). Having really good rock shoes can bring some incremental benefits, but these are not as great as those to be gained by training and experience. Most of the technologically-related benefits will be realised by having reasonably good and reasonably new shoes.

While the level of a professional rock climber’s performance will be undoubtedly be improved by using the best equipment available, a bad climber with $150 rock shoes will still be a bad climber (note this is not intended to be a self-referential comment).

Requirements - Data Analysis - Information - Manage Change
Requirements - Data Analysis - Information - Manage Change

Returning to another of my passions, Business Intelligence, I see some pertinent parallels. In a series of previous articles (including BI implementations are like icebergs, “All that glisters is not gold” – some thoughts on dashboards and Short-term “Trouble for Big Business Intelligence Vendors” may lead to longer-term advantage) , I have laid out my framework for BI success and explained why I feel that technology is not the most important part of a BI programme.

My recommended approach is based on four pillars:

  1. Determine what information is necessary to drive key business decisions.
  2. Understand the various data sources that are available and how they relate to each other.
  3. Transform the data to meet the information needs.
  4. Manage the embedding of BI in the corporate culture.

Obviously good BI technology has a role to play across all of these areas, but it is not the primary concern in any of them. Let us consider what is often one of the most difficult areas to get right, embedding BI in an organisation’s DNA. What is the role of the BI tool here?

Well if you want people to actually use the BI system, it helps if the way that the BI technology operates is not a hindrance to this. Ideally the ease-of-use and intuitiveness of the BI technology deployed should be a plus point for you. However, if you have the ultimate in BI technology, but your BI system does not highlight areas that business people are interested in, does not provide information that influences actual decision-making, or contains numbers that are inaccurate, out-of-date, or unreconciled, then it will not be used. I put this a little more succinctly in a recent article: Using multiple business intelligence tools in an implementation – Part II (an inspired title I realise), which I finished by saying:

If your systems do not have credibility with your users, then all is already lost and no amount of flashy functionality will save you.

Similar points can be made about all of the other pillars. Great BI technology should be the icing on your BI cake, not one of the main ingredients.
 
 
The historical perspective

What Car?

Ajay Ohri from the DecisionStats web-site recently interviewed me in some depth about a range of issues. He specifically asked me about what differentiated the various BI tools and I reproduce my reply here:

The really important question in BI is not which tool is best, but how to make BI projects successful. While many an unsuccessful BI manager may blame the tool or its vendor, this is not where the real issues lie. I firmly believe that successful BI rests on four mutually reinforcing pillars: understand the questions the business needs to answer, understand the data available, transform the data to meet the business needs and embed the use of BI in the organisation’s culture. If you get these things right then you can be successful with almost any of the excellent BI tools available in the marketplace. If you get any one of them wrong, then using the paragon of BI tools is not going to offer you salvation.

I think about BI tools in the same way as I do the car market. Not so many years ago there were major differences between manufacturers. The Japanese offered ultimate reliability, but maybe didn’t often engage the spirit. The Germans prided themselves on engineering excellence, slanted either in the direction of performance or luxury, but were not quite as dependable as the Japanese. The Italians offered out-and-out romance and theatre, with mechanical integrity an afterthought. The French seemed to think that bizarrely shaped cars with wheels as thin as dinner plates were the way forward, but at least they were distinctive. The Swedes majored on a mixture of safety and aerospace cachet, but sometimes struggled to shift their image of being boring. The Americans were still in the middle of their love affair with the large and the rugged, at the expense of convenience and value-for-money. Stereotypically, my fellow-countrymen majored on agricultural charm, or wooden-panelled nostalgia, but struggled with the demands of electronics.

Nowadays, the quality and reliability of cars are much closer to each other. Most manufacturers have products with similar features and performance and economy ratings. If we take financial issues to one side, differences are more likely to related to design, or how people perceive a brand. Today the quality of a Ford is not far behind that of a Toyota. The styling of a Honda can be as dramatic as an Alfa Romeo. Lexus and Audi are playing in areas previously the preserve of BMW and Mercedes and so on. To me this is also where the market for BI tools is at present. It is relatively mature and the differences between product sets are less than before.

Of course this doesn’t mean that the BI field will not be shaken up by some new technology or approach (in-memory BI or SaaS come to mind). This would be the equivalent of the impact that the first hybrid cars had on the auto market. However, from the point of view of implementations, most BI tools will do at least an adequate job and picking one should not be your primary concern in a BI project.

If you are interested, you can read the full interview here.
 
 
The current reality

IBM to acquire SPSS

As my comments to Ajay suggest, maybe in past times there were greater differences between BI vendors and the tools that they supplied. One benefit of the massive consolidation that has occurred in recent years is that the five biggest players: IBM/Cognos, Oracle/Hyperion, SAP/BusinessObjects, Microsoft and (the as yet still independent) SAS all have product portfolios that are both wide and deep. If there is something that you want your BI tool to do, it is likely that any of these organisations can provide you with the software; assuming that your wallet allows it. Both the functionality and scope of offerings from smaller vendors operating in the BI arena have also increased greatly in recent times. Finding a technology that fits your specific needs for functionality, ease-of-use, scalability and reliability should not be a problem.

This general landscape is one against which it is interesting to view the recent acquisition of business analytics firm SPSS by IBM. According to Reuters, IBM’s motivations are as follows:

IBM plans to buy business analytics company SPSS Inc for $1.2 billion in cash to better compete with Oracle Corp and SAP AG in the growing field of business intelligence

Full story here.

As an aside, should both Microsoft and SAS be worried that they are omitted from this list?

Whatever the corporate logic for IBM, to me this is simply more evidence that BI technology is becoming a utility (it should however be noted that this is not the same as BI itself becoming a utility). I believe that this trend will lead to a greater focus on the use of BI technology as part of broad-based BI programmes that drive business value. Though BI has the potential of releasing massive benefits for organisations, the track record has been somewhat patchy. Hopefully as people start to worry less about BI technology and more about the factors that really drive success in BI programmes, this will begin to change.

A precursor to Business Intelligence

As with any technical innovation over the centuries, it is only when the technology itself becomes invisible that the real benefits flow.
 

The Dictatorship of the Analysts

Lest it be thought that I am wholly obsessed by the Business Intelligence vs Business Analytics issue (and to be honest I have a whole lot of other ideas for articles that I would rather be working on), I should point out that this piece is not focussed on SAS. In my last correspondence with that organisation (which was in public and may be viewed here) I agreed with Gaurav Verma’s suggestion that SAS customers be left to make up their own minds about the issue.

CIO Magazine

However the ripples continue to spread from the rock that Jim Davis threw into the Business Intelligence pond. The latest mini-tsunami is in an article on CIO.com by Scott Staples, President and Co-CEO of IT Services at MindTree. [Incidentally, I’d love to tell you more about MindTree’s expertise in the area of Business Intelligence, but unfortunately I can’t get their web-site’s menu to work in either Chrome or IE8; I hope that you have better luck.]

Scott’s full article is entitled Analytics: Unlocking Value in Business Intelligence (BI) Initiatives. In this, amongst other claims, Scott states the following:

To turn data into information, companies need a three-step process:

  1. Data Warehouse (DW)—companies need a place for data to reside and rules on how the data should be structured.
  2. Business Intelligence—companies need a way to slice and dice the data and generate reports.
  3. Analytics—companies need to extract the data, analyze trends, uncover opportunities, find new customer segments, and so forth.

Most companies fail to add the third step to their DW and BI initiatives and hence fall short on converting data into information.

He goes on to say:

[…] instead of companies just talking about their DW and BI strategies, they must now accept analytics as a core component of business intelligence. This change in mindset will solve the dilemma of data ≠ information:

Current Mindset: DW + BI = Data

Future Mindset: DW + (BI + Analytics) = Information

Now in many ways I agree with a lot of what Scott says, it is indeed mostly common sense. My quibble comes with his definitions of BI and Analytics above. To summarise, he essentially says “BI is about slicing and dicing data and generating reports” and “Analytics is about extracting data, analysing trends, uncovering opportunities and finding new customer segments”. To me Scott has really just described two aspects of exactly the same thing, namely Business Intelligence. What is slicing and dicing for if not to achieve the aims ascribed above to Analytics?

Let me again – and for the sake of this argument only – accept the assertion that Analytics is wholly separate from BI (rather than a subset). As I have stated before this is not entirely in accordance with my own views, but I am not religious about this issue of definition and can happily live with other people’s take on it. I suppose that one way of thinking about this separation is to call the bits of BI that are not Analytics by the older name of OLAP (possibly ignoring what the ‘A’ stands for, but I digress). However, even proponents of the essential separateness of BI and Analytics tend to adopt different definitions to Scott.

To me what differentiates Analytics from other parts of BI is statistics. Applying advanced (or indeed relatively simple) statistical methods to structured, reliable data (such as one would hope to find in data warehouses more often than not) would clearly be the province of Analytics. Thus seeking to find attributes of customers (e.g. how reliably they pay their bills, or what areas they live in) or events in their relationships with an organisation (e.g. whether a customer service problem arose and how it was dealt with) that are correlated with retention/repeat business would be Analytics.

Maybe discerning deeply hidden trends in data would also fall into this camp, but what about the rather simpler “analysing trends” that Scott ascribes to Analytics? Well isn’t that just another type of slice and dice that he firmly puts in the BI camp?

Trend analysis in a multidimensional environment is simply using time as one of the dimensions that you are slicing and dicing your measures by. If you want to extrapolate from data, albeit in a visual (and possibly non-rigorous manner) to estimate future figures, then often a simple graph will suffice (something that virtually all BI tools will provide). If you want to remove the impact of outlying values in order to establish a simple correlation, then most BI tools will let you filter, or apply bands (for example excluding large events that would otherwise skew results and mask underlying trends).

Of course it is maybe a little more difficult to do something like eliminating seasonality from figures in these tools, but then this is pretty straightforward to do in Excel if it is an occasional need (and most BI tools support one-click downloading to Excel). If such adjustments are a more regular requirement, then seasonally adjusted measures can be created in the Data Mart with little difficulty. Then pretty standard BI facilities can be used to do some basic analysis.

Of course paid-up statisticians may be crying foul at such loose analysis, of course correlation does not imply causation, but here we are talking about generally rather simple measures such as sales, not the life expectancy of a population, or the GDP of a country. We are also talking about trends that most business people will already have a good feeling for, not phenomena requiring the application of stochastic time series to model them.

So, unlike Scott, I would place “back-of-an-envelop” and graphical-based analysis of figures very firmly in the BI camp. To me proper Analytics is more about applying rigorous statistical methods to data in order to either generate hypotheses, or validate them. It tends to be the province of specialists, whereas BI (under the definition that I am currently using where it is synonymous with OLAP) is carried out profitably by a wider range of business managers.

So is an absence of Analytics – now using my statistically-based definition – a major problem in “converting data into information” as Scott claims? I would answer with a very firm “no”. If we take information as being that which is generated and consumed by a wide range of managers in an organisation, then if this is wrong then the problem is much earlier on and most likely centred on how the data warehousing and BI parts have been implemented (or indeed in a failure to manage the concomitant behavioural change). I covered what I believe are often the reasons that BI projects fail to live up to their promise in my response to a Gartner report. This earlier article may be viewed here.

In fact I think that what happens is that when broader BI projects fail in an organisation, people fall back on two things: a) their own data (Excel and Access) and b) the information developed by the same statistical experts who are the logical users of Analytic tools. The latter is characterised by a reliance on Finance, or Marketing reports produced by highly numerate people with Accounting qualifications or MBAs, but which are often unconnected to business manager’s day-to-day experiences. The phrase “democratisation of information” has been used in relation to BI. Where BI fails, or does not exist, then the situation I have just described is maybe instead the dictatorship of the analysts.

I have chosen the word “dictatorship” with all of its negative connotations advisedly. I do not think that the situations that I have described above is a great position for a company to be in. The solution is not more Analytics, which simply entrenches the position of the experts to the detriment of the wider business community, but getting the more mass-market disciplines of the BI (again as defined above) and data warehousing pieces right and then focussing on managing the related organisational change. In the world of business information, as in the broader context, more democracy is indeed the antidote to dictatorship.

I have penned some of my ideas about how to give your BI projects the greatest chance of success in many places on this blog. But for those interested, I suggest maybe starting with: Scaling-up Performance Management, “All that glisters is not gold” – some thoughts on dashboards, The confluence of BI and change management and indeed the other blog articles (both here and elsewhere) that these three pieces link to.

Also for those with less time available, and although the article is obviously focussed on a specific issue, the first few sections of Is outsourcing business intelligence a good idea? pull together many of these themes and may be a useful place to start.

If your organisation is serious about adding value via the better use of information, my recommendation is to think hard about these areas rather than leaping into Analytics just because it is the latest IT plat du jour.
 

The Apologists

A whole mini industry has recently been created in SAS based on justifying Jim Davis’ comments to the effect that: Business Intelligence is dead, long live Business Analytics. An example is a blog post by Alison Bolen, sascom Editor-in-Chief, entitled: More notes on naming. While such dedication to creating jobs in the current economic climate is to be lauded, I’m still not sure what SAS is trying to achieve.

The most recent article is by Gaurav Verma, Global Marketing Manager for Business Analytics at SAS. He calls his piece: Business Analytics vs. Business Intelligence – it’s more than just semantics or marketing hyperbole. In this Gaurav asks the question:

Given that I have been evangelizing BI for more than 12 years as practitioner, analyst, consultant and marketer, I should be leading the calls of blasphemy. Instead, I’m out front leading global marketing for the SAS Business Analytics framework. Why?

One answer that immediately comes to mind is contained in the question, it is of course: “because Gaurav is the head of global marketing for Business Analytics at SAS”.

Later in his argument, by sleight of hand, Gaurav associates business intelligence with:

Traditional and rapidly commoditizing query and reporting

Of course everything that is not “query and reporting” must be called something else, presumably business analytics is an apt phrase in Gaurav’s mind. To me, despite Gaurav’s headline, this is just yet more wordsmithery. No other commentators seem to see BI as primarily “query and reporting” and if you remove this plank from Gaurav’s aregument, the rest of it falls to pieces.

The choice of words is interesting. Recent pieces by SASers have applied adjectives such as “traditional”, “classic” and even “little” to the noun-phrase “business intelligence” in order to explain exactly what Jim Davis actually meant by his remarks. Whether any of these linguistic qualifications of the area of BI are required, separate from the task of supporting Mr Davis’ arguments, remains something of a mystery to me.

I for one would heartily like to move beyond these silly tit-for-tat discussions. My recommendations for the course that SAS should take appear here – albeit in lightly coded form.

Short of retracting Mr Davis’ ill-thought-out comments, the second best idea for SAS might be to be very quiet about the area for a while and hope that people slowly forget about it. For some reason, it is SAS themselves who seem to want to keep this sorry episode alive. They do this by continuing to publish artciles such as Gaurav’s. While this trend continues, I’ll continue to publish my rebuttals, boring as it may become for everyone else.
 

A business intelligence parable

Once upon a time there were two technology companies, both operating in the Corporate On-Line Analysis market. One was called Credible Organisational KPI Enterprise (IT people love acronyms so much that they sometimes even nest them) and the other was known as Predictive Enlightenment Powered by Statistical Inference. However, both companies were generally better known by their respective acronyms; as was the market in which they competed.

Credible Organisational KPI Enterprise and Predictive Enlightenment Powered by Statistical Inference had parts of their respective product sets that overlapped with each other, but also had some more distinctive offerings. In the places where their portfolios diverged, each was seen as a market leader. In the shared areas, things were less clear-cut; some users preferring Credible Organisational KPI Enterprise and others Predictive Enlightenment Powered by Statistical Inference. Often those who expressed a preference did so in very strong terms, but not always with much evidence to back this up.

Well none of this mattered too much to most regular people until one day the head of marketing of Predictive Enlightenment Powered by Statistical Inference made a speech in which he claimed – contrary to all previous industry thinking – that the usefulness of general Corporate On-Line Analysis had been overstated and that only Predictive Enlightenment Powered by Statistical Inference could really offer users any benefits.

The deep insight underpinning the claims of Predictive Enlightenment Powered by Statistical Inference’s Chief Marketing Officer was that while Credible Organisational KPI Enterprise’s products relied on mostly water and sugar to make their customers happy, the revolutionary tools provided by his company had a secret, special ingredient, code-named only hydrated-C12H22O11.

These claims caused rather a furore in the Corporate On-Line Analysis world, with many commentators strongly disputing them. Several of the colleagues of the Predictive Enlightenment Powered by Statistical Inference CMO rushed to his defence. Some indeed went on to claim that Corporate On-Line Analysis was merely a subset of Predictive Enlightenment Powered by Statistical Inference, this despite most people having previously thought of both Credible Organisational KPI Enterprise and Predictive Enlightenment Powered by Statistical Inference as being different types of Corporate On-Line Analysis vendors.

While this move by Predictive Enlightenment Powered by Statistical Inference was probably intended to highlight the strengths of their product set and to better differentiate themselves from Credible Organisational KPI Enterprise, instead it just confused most people working in the area of Corporate On-Line Analysis and made them wonder whether the people at Predictive Enlightenment Powered by Statistical Inference understood their own products and market.

In the end, the people at Predictive Enlightenment Powered by Statistical Inference came to their senses, realising that what had initially seemed like a great marketing idea was actually counterproductive and even making them look slightly ridiculous. They issued a statement saying that their CMO’s comments had been taken out of context but nevertheless unequivocally retracting them.

After this outbreak of sensible behaviour, things in the Corporate On-Line Analysis world started to settle down again and everyone lived happily ever after.

BI versus SAS?
 


 
Before the legal teams of any beverage companies start issuing writs, I should point out that any similarity between the above fable and their products is wholly coincidental. Any similarity to the recent behaviour of other commercial organisations may be somewhat less of a coincidence.
 

Neil Raden’s thoughts on Business Analytics vs Business Intelligence

neil-raden

Industry luminary Neil Raden, founder of Hired Brains, has weighed into the ongoing debate about Business Analytics vs Business Intelligence on his Intelligent Enterprise blog. The discussions were spawned by comments made by Jim Davis, Chief Marketing Officer of SAS, at a the recent SAS Global Forum. Neil was in the audience when Jim spoke and both his initial reaction and considered thoughts are worth reading.

Neil’s blog article is titled From ‘BI’ to ‘Business Analytics,’ It’s All Fluff.
 


 
Neil Raden is an “industry influencer” – followed by technology providers, consultants and even industry analysts. His skill at devising information assets and decision services from mountains of data is the result of thirty years of intensive work. He is the founder of Hired Brains, a provider of consulting and implementation services in business intelligence and analytics to many Global 2000 companies. He began his career as a casualty actuary with AIG in New York before moving into predictive modeling services, software engineering and consulting, with experience in delivering environments for decision making in fields as diverse as health care to nuclear waste management to cosmetics marketing and many others in between. He is the co-author of the book Smart (Enough) Systems and is widely published in magazines and online media. He can be reached at nraden@hiredbrains.com.

I also have featured an earlier piece that Neil wrote for BeyeNETWORK in “Can You Really Manage What You Measure?” by Neil Raden. You can find Neil’s thoughts on a wide range of technology issues in many places on the web and they are always recommended reading. 

Other Intelligent Enterprise articles referenced on this blog may be viewed here.
 

Irony and WordPress.com advertising

After the response that I posted yesterday to comments by Jim Davis, SVP and Chief Marketing Officer at SAS Institute, I suspect that the following advert is eveidence of the new irony module in WordPress.com‘s advertisng engine!

Ironic advertising
Ironic advertising

 

Business Analytics vs Business Intelligence

  “Business intelligence is an over-used term that has had its day, and business analytics is now the differentiator that will allow customers to better forecast the future especially in this current economic climate.”
 
Jim Davis SVP and Chief Marketing Officer, SAS Institute Inc.
 

The above quote is courtesy of an article reported on Network World, the full piece may be viewed here.

Analytics vs Intelligence

In the same article, Mr Davis went on to add:

I don’t believe [BI is] where the future is, the future is in business analytics. Classic business intelligence questions, support reactive decision-making that doesn’t work in this economy because it can only provide historical information that can’t drive organizations forward. Business intelligence doesn’t make a difference to the top or bottom line, and is merely a productivity tool like e-mail.

The first thing to state is that the comments of this SVP put me more in mind of AVP, should we be anticipating a fight to the death between two remorseless and implacably adversarial foes? Maybe a little analysis of these comments about analytics is required. Let’s start with SAS Institute Inc. who describe themseleves thus on their web-site [with my emphasis]:

SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market.

It is also worth noting that the HTML title of sas.com is [again with my emphasis]:

SAS | Business Intelligence Software and Predictive Analytics

Is SAS’s CMO presaging a withdrawal from the BI market, or simply trashing part of the company’s business, it is hard to tell. But what are the differences between Business Intelligence and Business Analytics and are the two alternative approaches, or merely different facets of essentially the same thing?

To start with, let’s see what the font of all knowledge has to say about the subject:

Business Intelligence (BI) refers to skills, technologies, applications and practices used to help a business acquire a better understanding of its commercial context. Business intelligence may also refer to the collected information itself.

BI applications provide historical, current, and predictive views of business operations. Common functions of business intelligence applications are reporting, OLAP, analytics, data mining, business performance management, benchmarks, text mining, and predictive analytics.

http://en.wikipedia.org/wiki/Business_intelligence

and also:

Business Analytics is how organizations gather and interpret data in order to make better business decisions and to optimize business processes. […]

Analytics are defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based decision-making. […] In businesses, analytics (alongside data access and reporting) represents a subset of business intelligence (BI).

http://en.wikipedia.org/wiki/Business_analytics

Rather amazingly for WikiPedia, I seem to have found two articles that are consistent with each other. Both state that business analytics is a subset of the wider area of business intelligence. Of course we are not in the scientific realm here (and WikiPedia is not a peer-reviewed journal) and the taxonomy of technologies and business tools is not set by some supranational body.

I tend to agree with the statement that business analytics is part of business intelligence, but it’s not an opinion that I hold religiously. If the reader feels that they are separate disciplines, I’m unlikely to argue vociferously with them. However if someone makes a wholly inane statement such as BI “can only provide historical information that can’t drive organizations forward”, then I may be a little more forthcoming.

Let’s employ the tried and test approach of reductio ad absurdum by initially accepting the statement:

  Business intelligence is valueless as it is only ever backward-looking because it relies upon historical information  

Where does a logical line of reasoning take us? Well what type of information does business analytics rely upon to work its magic? Presumably the answer is historical information, because unless you believe in fortune-telling, there really is no other kind of information. In the first assertion, we have that the reason for BI being valueless is its reliance on historical information. Therefore any other technology or approach that also relies upon historical information (the only kind of information as we have agreed) must be similarly compromised. We therefore arrive at a new conclusion:

  Business analytics is valueless as it is only ever backward-looking because it relies upon historical information  

Now presumably this is not the point that Mr Davis was trying to make. It is safe to say that he would probably disagree with this conclusion. Therefore his original statement must be false: Q.E.D.

Maybe the marketing terms business intelligence and business analytics (together with Enterprise Performance Management, Executive Information Systems and Decision Support Systems) should be consigned to the scrap heap and replaced by the simpler Management Information.

All areas of the somewhat splintered discipline that I work in use the past to influence the future, be that via predictive modelling or looking at whether last week’s sales figures are up or down. Pigeon-holing one element or another as backward-looking and another as forward-looking doesn’t even make much marketing sense, let alone being a tenable intellectual position to take. I think it is not unreasonable to expect more cogent commentary from the people at SAS than Mr Davis’ recent statements.
 

 
Continue reading about this area in: A business intelligence parable and The Apologists.