The Dictatorship of the Analysts

14 April 2009

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.
 

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The Apologists

7 April 2009

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.
 

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A business intelligence parable

4 April 2009

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.
 

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Neil Raden’s thoughts on Business Analytics vs Business Intelligence

30 March 2009

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.
 

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Irony and WordPress.com advertising

29 March 2009

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

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Business Analytics vs Business Intelligence

28 March 2009
  “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.
 

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Trends in Business Intelligence

9 March 2009

trends

This year, as in every year since the phrase “Business Intelligence” first came to prominence, there have been a rash of predictions about what will happen with the area in 2009. I have linked to and commented on some of these myself on this blog. This article is my take on the area. I should however explain that there is a twist. These are not the trends that I expect to see in 2009, just my thoughts on what ought to be trends in BI over the next few years. I cannot claim to have any prescience about whether they will come to pass, but I will be encouraged if they do.
 

  1. A more holistic approach to BI in organisations that have already invested
  2. Operational BI
  3. Consolidation of the number of BI platforms with organisations
  4. An increase in the prevalence of BI competency centres
  5. BI teams being jointly owned and managed by business divisions and IT
  6. Data from central warehouses being served to front-end applications
  7. BI data being used to automate some business decisions
  8. Further emphasis on predictive analytics
  9. BI having an increasing role in compliance and risk management
  10. Incorporation of external data into BI platforms
  11. Provision of BI to business partners and/or customers
  12. The most creative users of BI employing it to thrive, even in the current climate

 
1. A more holistic approach to BI in organisations that have already invested

Often BI solutions have started in a single area. Typically – given its direct link to better managing overall performance – this has been around analysing an organisation’s financial results, though implementations starting in the sales and even operational arenas are also not uncommon. BI systems tend to add more value as they bring in more information, particularly where a high-level phenomenon – e.g. a decrease in expenditure – can be attributed to lower level ones – e.g. greater repeat business (business acquisition being more expensive than repeat business in most industries), plus enhanced productivity, plus reduced staff turnover (resulting in lower recruitment and training costs). To do this, BI needs to widen its scope to take on new data sources and combine them in creative ways. Where BI platforms have already added value, there will be pressure from users to expand them to deliver even more utility and provide more sophisticated insights. This in turn will require BI practitioners to take a more holistic view of the area and to develop roadmaps explaining how new data sources will be brought on stream and what business value will result from this.
 
 
2. Operational BI

I think that there is a particular argument for BI to make a greater contribution in the area of operational management, particularly tying this to overall performance. Often BI implementations have focussed on the more stately world of monthly (or weekly) results and steered clear of the daily or hourly demands of operational information. The BI projects that I have seen in the Operational sphere have been more focussed at producing weekly status reports or year-to-date trend analyses. Related to BI platforms expanding to new areas, I see increasing demand for information about the internal effectiveness of an organisation; quickly identifying bottlenecks or areas of inefficiency and tracking efforts to address these. Supporting this type of reporting will often require BI practitioners to rethink their architectures which are often set up to refresh on longer timeframes. This may in turn require the warehouse to support multiple environments with different periodicities.
 
 
3. Consolidation of the number of BI platforms with organisations

It is all too common for organisations to have separate BI implementations. Maybe the ERP system comes with some shrink-wrapped cubes, as does the CRM system. Perhaps Finance have invested in some budget analysis tools and different business units have started dabbling in predictive modelling (see 8. below). Maybe certain power users or numerate departments have their own, much cherished databases. Clearly this approach is sub-optimal. If it was ever acceptable to have such an ad hoc approach to the area, the current economic climate means that there is no longer room for a multitude of platforms, each supported by their own ETL, servers, IT teams and (hopefully) training programmes. While transitional costs may make it impractical for some organisations to move to a single platform in the short term, the reductions in licensing, internal support, data centre and training costs that come from standardising BI tools are compelling. This is to say nothing about reducing the level of confusion in users faced with multiple reporting and analysis systems, each with their own terminology, vagaries of functionality and often un-reconciled to each other. Arguments about certain BI tools being best of breed for particular tasks were never wholly convincing, with the breadth of functionality offered by all of the major players today, any of them will be at least good enough for all but the most exacting of applications.
 
 
4. An increase in the prevalence of BI competency centres

If this is partly as a result of the previous three trends, it also has some drivers all of its own. Even in multinational organisations with highly devolved structures and local accountability, most of the management information that is needed to run businesses will be relatively consistent from country to country and business unit to business unit. Perhaps the sources will vary, but the business transactions that they support are surprisingly consistent. Many organisations are waking up to this fact and pulling BI provision into the centre. There are obvious benefits to this in terms of cost savings, not reinventing the wheel, increasing the consistency of reporting and enabling corporate roll-ups. However, my own feeling is that the best organisations will supplement a strong central resource with a (probably much smaller) virtual component located close to business needs who can better respond to these in a timely manner, but within an overall information architecture. This hybrid approach offers the best of both worlds.
 
 
5. BI teams being jointly owned and managed by business divisions and IT

It is an oft-repeated aphorism that all IT projects are business projects. While clearly true in theory, there are enough counterexamples to suggest that practise is rather different. However, BI projects have often stayed much closer to this maxim than other IT efforts. This is because BI systems serve no purpose unless they are closely entwined with business goals. Natural selection should weed out any non-business-focussed BI projects eventually; even in the sleepiest of organisations. It is likely that this close relationship will deepen. Whilst many aspects of BI are firmly in the IT arena (managing the regular refresh of multiple terabytes of data effectively clearly being one), I see a joint stewardship of the area developing. By this I mean something deeper than business oversight or steering committees. It is also different to business BI liaison managers being appointed – these roles often have the unintended consequence of isolating IT from its customers. What I see emerging in more enlightened organisations is true co-ownership of BI with business and IT management closely collaborating to run the area.
 
 
6. Data from central warehouses being served to front-end applications

Of course it is not uncommon for transaction processing systems to have buttons or links that call up a given report. What I am talking about here is a closer coupling, one that does not require users to leave their current system and where information from the warehouse is presented in a seamless manner to users. While such information will have all the BI benefits of being reconciled, consistent and accurate, its provenance will increasingly become invisible to the user (who shouldn’t really have to worry where figures come from so long as they are accurate). This is an area in which web-services have some real potential to leverage the investments that organisations have made in warehouses.
 
 
7. BI data being used to automate some business decisions

Stepping a little further along the path from the previous point, even before users of front-end systems have a need to review information about a transaction, it may well be that the information itself has been what determines whether a human is involved or not. Already some organisations perform triage on business transactions. Ones that meet all of a number of requirements get processed automatically. Ones that meet some of them, but not all, may get routed to junior staff, whose role is to determine whether they require further review or can proceed. Finally, those with a major variance to requirements may get sent straight to more senior staff. This approach means that a larger volume of business can be handled and that expensive senior resource is only applied where it is necessary. Of course a prerequisite to this type of approach is having reliable information on which to perform the triage.
 
 
8. Further emphasis on predictive analytics

While the best BI implementations encourage users to extrapolate from past figures to estimate future ones, the degree to which the analytical skills of the user plays a part varies from implementation to implementation. Here by analytics I mean the use of larger data sets to identify trends or exceptions, mostly at a portfolio level. Again, having invested in developing data warehouses, it makes sense to utilise the many and sophisticated tools that are available to carry out advanced statistical analysis on these; the aim being to deduce trends that would be beyond even the most skilled of human analysts. A by-product of successful BI implementations is that they often free the more numerate of people in organisations from the burden of repetitive number crunching and allow them to focus on more added-value work such as this.
 
 
9. BI having an increasing role in compliance and risk management

Sometimes BI projects may have had their genesis in these areas. However, even when this is not the case a pleasing result of having consolidated much of the organisation’s data in one place is that this then forms a valuable resource for compliance and risk management. Indeed, taking an external perspective, regulators tend to view the existence of an enterprise data warehouse as a sign that an organisation takes managing its risks seriously. Although business managers and risk managers may have slightly different (though hopefully complementary) perspectives and want to answer different types of questions, the data that they need to do this is not that dissimilar. Producing compliance suites from a well-designed warehouse is probably not one of the more taxing BI problems. Again expansion in this area is a further example of point 1. in action.
 
 
10. Incorporation of external data into BI platforms

I have spoken above about the remit of BI systems expanding internally and becoming more consistent geographically. A further trend in expansion is to meld internal information with that from external providers. The type of external information would range from industry to industry, but might include market data, information about specific companies or individuals (e.g. credit scores, where the use of these is admissible). For example, in insurance, where I have spent the last twelve years of my career, incorporating information from externally produced flood, or windstorm models is often a priority.
 
 
11. Provision of BI to business partners and/or customers

Sometimes one objective of BI implementations is to better understand the relationships with business partners and customers. I have seen this develop to the degree where output from BI systems is mailed to such counterparties. A logical extension of this is to allow such organisations direct access to “their information”. Of course as with any e-commerce initiative, there would have be to strict controls on what is viewed and who can view it, but these are problems that are regularly addressed in other areas of IT and the tools to do this are readily available.
 
 
12. The most creative users of BI employing it to thrive, even in the current climate

There have been many articles (including ones written by me) which have spoken about good BI being a great defence in times of economic stress. I would go beyond this and state that the real BI pioneers will take advantage of these capabilities to capture markets from their less well-informed competitors and to steer a course away from areas of business that may bring other less-foresighted organisations down. I look forward to seeing case studies bearing this out appear over the next few years.
 

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