One area that people seem agreed upon is the importance of Java to Oracle’s application strategy, so it makes sense – as a defensive move if nothing else – for them to seek to prevent influence over its future direction falling into the hands of a competitor (which in turn raises the question of when exactly Oracle and Sun started talking and how much overlap there was with the IBM negotiations).
The future of MySQL seems less clear. Some commentators feel that Oracle will support it and allow it to continue to thrive as one of their products. At the other extreme, I have seen suggestions that it will be killed off. Of course as an open source database, this might be easier said than done. There seems to have been a steady trickle of MySQL people out of Sun, pre-acquisition and I would have thought that there is enough expertise and ownership outside of Oracle/Sun for MySQL to have some sort of future regardless of Oracle’s strategy for it.
A bit of a dark horse is OpenOffice.org. A lot of commentary has focused on Oracle positioning themselves to compete with IBM via the acquisition. Perhaps OpenOffice.org offers Larry Ellison another chance to cross swords with his old adversaries at Microsoft.
Moving from software to operating systems, Sun’s Solaris has probably suffered more than most from the rise of Linux, but there have been rumours about Solaris offering Oracle a better route to the current technology Nirvana of cloud computing. Whether this is really the case, I’ll leave to more technically competent authorities to discuss.
But beneath Solaris beats the SPARC chips and other components of Sun’s hardware. Is Oracle’s real aim to offer a complete solution: ERP, CRM, BI and DW in a box? Sun’s hardware has not exactly been flying off the shelf in recent months, but perhaps the sales team at Oracle have other ideas. Maybe their feeling is that all that Sun’s boxes need is to be part of a more alluring overall package. Leveraging Sun’s hardware and operating system is what many people assume is behind Oracle’s strategy. This is certainly the path that would lead to challenging IBM as a company that can meet many of an organisation’s needs as a one-stop-shop.
However, this segues into another observation. If Oracle really has IBM in its sights, then it lacks one crucial piece of ammunition, a global services organisation; the sort of outfit that IBM acquired from the hiving off of PwC’s consulting arm. Maybe now is a good time to but stock in CSC?
But to return to some of the points I made earlier, there is a further possibility. Perhaps Oracle don’t want to move into the fiercely competitive and low-margin arena of hardware sales after all. Perhaps it was Sun’s software assets that were the real goal. Does Oracle really want to position itself as a hardware vendor, no doubt poisoning strong relationships with people such as HP in the process? Maybe not. If this is indeed the case then maybe there will be a spin-off of Sun’s hardware assets, or indeed a sale to someone like HP – assuming that they wanted them.
One of the most intriguing aspects of Oracle’s proposed acquisition of Sun is just how many balls have been thrown up into the air by it. It will be really interesting to see how they fall over the next few months.
Some of the blogs that I have read on this subject are acknowledged at the end of my earlier article.
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.
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.]
To turn data into information, companies need a three-step process:
Data Warehouse (DW)—companies need a place for data to reside and rules on how the data should be structured.
Business Intelligence—companies need a way to slice and dice the data and generate reports.
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.
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.
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.
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.
Nic’s film is epic in scope, his aim is to cover the entire sweep of not just business intelligence, but data and business systems as well. It is amazing that he manages to fit this War and Peace-like task into only 10 minutes 36 seconds. However lest the reader expects Bergman-esque earnestness, it is worth pointing out that the mood is enlivened by the type of pop-culture references that are likely to appeal to a 40-something geek like your reviewer.
I’ll try to avoid giving too much of the plot away, however Nic’s initial aim is to answer the following four questions about BI:
Where have we been?
Where are we now?
Where are we going? and
Why should you care?
It is recommended that anyone wishing to avoid spoilers clicks here now!
Having failed to get a satisfactory definition of BI from Wikipedia (I trod the same path looking for a definition of IT-Business Alignment in the presentation appearing here), the director embarks on a personal quest to find the answer himself. Along the way, he comes to the realisation that BI is about decisions and that people take these decisions. In trying to explore this area further, Nic takes a journey from the advent of databases in the late 1960s; through the creation of the business systems to populate them, and the silo-based reports they generated, in the 1970s; to the arrival of the data warehouse in the 1980s – a stage he tags BI 1.0.
As the profile and importance of BI increased during the 1990s and the amount of data, both structured and unstructured, increased exponentially – notably with the growth of the web – the number and type of BI tools also proliferated. Because of the variety of tools, their complexity and cost, the market then consolidated, with many of the BI tools finding new homes in the same organisations that had previously brought you business systems. The resulting menu of broad-based and functional BI platforms is Nic’s definition of BI 2.0.
Nevertheless, the director felt that there was still something not quite right in the world of BI; namely the single version of the truth was about as likely to be pinned down as a Snark. The problem in his mind was that people were still left out of the equation (Nic likes equations and includes lots of them in his film). This realisation in turn leads to the denouement in which Nic brings together all of the threads of his previous detective work to state that “BI is about providing the right data at the right time to the right people so that they can take the right decisions” (a definition I wholeheartedly endorse).
The film ends with a cliffhanger, presaging a new approach to BI that will enable collaboration and drive innovation. I suspect the resolution to this punctuated narrative will soon be playing at all good Microsoft multiplexes along with the other summer blockbusters.
Nic Smith joined the Microsoft team in December of 2006, bringing a deep knowledge base of the Business Intelligence space. Prior to joining Microsoft, Nic spent time with Business Objects, a pure play BI company, where he was responsible for the vision of BI and performance management. Nic also spent time with former BI company Crystal Decisions, where he helped bring an enterprise reporting BI platform to market. Nic brings a unique blend of market knowledge, brand development and a solution orientated focus as an evangelist for BI. In addition to his business initiatives, Nic is involved in elite athletic development for youth. He holds a Bachelors Degree in Marketing and Communications from Simon Fraser University in Vancouver, British Columbia.
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 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 email@example.com.
“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 DavisSVP 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.
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.
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).
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.
The conversation on the thread turned to the fact that, in the current economic climate, there may be less focus on major, strategic BI initiatives and more on quick, tactical ones that address urgent business needs.
My take on this is that it is a perfectly respectable approach, indeed it is one that works pretty well in my experience regardless of the economic climate. There is however one proviso, that the short-term work is carried out with one eye on a vision of what the future BI landscape will look like. Of course this assumes that you have developed such a vision in the first place, but if you haven’t why are you talking about business intelligence when report writing is probably what you are engaged in (regardless of how fancy the tools may be that you are using to deliver these).
I talked about this specific area extensively in my earlier article, Holistic vs Incremental approaches to BI and also offered some more general thoughts in Vision vs Pragmatism. In keeping with the latter piece, and although the initial discussions referred to above related to BI, I wanted to use this article to expand the scope to some other sorts of IT projects (and maybe to some non-IT projects as well).
Some might argue (as people did on the LinkedIn.com thread) that all tactical work has to be 100% complementary to you strategic efforts. I would not be so absolute. To me you can wander quite some way from your central goals if it makes sense to do so in order to meet pressing business requirements in a timely and cost-effective manner. The issue is not so much how far you diverge from your established medium-term objectives, but that you always bear these in mind in your work. Doing something that is totally incompatible with your strategic work and even detracts from it may not be sensible (though it may sometimes still be necessary), but delivering value by being responsive to current priorities demonstrates your flexibility and business acumen; two characteristics that you probably want people to associate with you and your team.
Tactical meandering sums up the approach pretty well in my opinion. A river can wander a long way from a line drawn from its source to its mouth. Sometimes it can bend a long way back on itself in order to negotiate some obstacle. However, the ultimate destination is set and progress towards it continues, even if this is sometimes tortuous.
Expanding on the geographic analogy, sometimes meanders become so extreme that the river joins back to its main course, cutting off the loop and leaving an oxbow lake on one side. This is something that you will need to countenance in your projects. Sometimes an approach, or a technology, or a system was efficacious at a point in time but now needs to be dropped, allowing the project to move on. These eventualities are probably inevitable and the important thing is to flag up their likelihood in advance and to communicate clearly when they occur.
My experience is that, if you keep you strategic direction in mind, the sum of a number of tactical meanders can advance you quite some way towards your goals; importantly adding value at each step. The quickest path from A to B is not always a straight line.
Insurance – specifically Property Casualty Insurance – is the industry that I have worked within for the last twelve years. During this time, I managed teams spanning IT, Finance and Operations. However the successes that I am most proud of have been in the related fields of Business Intelligence and Cultural Transformation that appear in the title of this blog.
Insure/insho′or/ v.tr.1 secure the payment of a sum of money in the event of loss or damage to property, life a person etc. (O.E.D.)
Insurance is all about risk; evaluating risk, transferring risk, reducing risk. The essentials of the industry can be appreciated via a rather colourful fable provided in Success in Insurance (S.R. Diacon and R.L. Carter). This tale was originally told by someone at The Association of British Insurers:
Once upon a time there were 11 men; each of them owned a pig.
Unexpectedly one of the pigs died. The owner could not afford £90 for a new pig and so he had to leave the country and go to work in the town instead. The remaining 10 men went to see a wise man. ‘It could happen to any of us,’ they said. ‘What can we do?’
‘Could you each afford £10 for a new pig if your pig died?’ asked the wise man. They all agreed that they could manage that. ‘Very well,’ said the wise man. ‘If you each give me £10, I’ll buy you a pig if yours dies this year.’ They all agreed.
That year one pig did die. The price of pigs had gone up to £95 by now, but the wise man replaced the pig, so none of the men suffered and the wise man had £5 left for the trouble and risk he had taken.
Pricing Insurance products
Of course in the above example, there were two crucial factors for the wise man. First the outcome that only one pig actually died; if instead there had been two pig-related fatalities, the perhaps less-wise man would have been out-of-pocket by £90. Second, the related issue of him setting the price of the pig Insurance policy at £10; if it had been set at £9 he would again have suffered a loss. It is clear that it takes a wise man to make accurate predictions about future events and charge accordingly. In essence this is one thing that makes Insurance different to many other areas of business.
If you work in manufacturing, your job will of course have many challenges, but determining how much it costs to make one of your products should not be one of them. The constituent costs are mostly known and relatively easy to add up. They might include things such as: raw materials and parts; factory space and machinery; energy; staff salaries and benefits; marketing and advertising; and distribution. Knowing these amounts, it should be possible to price a product in such a way that revenue from sales normally exceeds costs of production.
In Insurance a very large part of the cost of production is, by definition, not known at the point at which prices are set. This is the amount that will eventually be paid out in claims; how many new pigs will need to be bought in the example above. If you consider areas such as asbestosis, it can immediately be seen that the cost of Insurance policies may be spread over many years or even decades. The only way to predict the eventual costs of an Insurance product with any degree of confidence, and thereby set its price, is to rely upon historical information to make informed predictions about future claims activity.
By itself, this aspect of Insurance places enormous emphasis on the availability of quality information to drive decisions, but there are other aspects of Insurance that reinforce this basic need.
In most areas of commerce the issue of how you get your product to market is a very important one. In Insurance, there are a range of questions in this area. Do you work with brokers or direct with customers? Do you partner with a third party – e.g. a bank, a supermarket or an association – to reach their customers?
Even for Insurance companies that mostly or exclusively work with brokers, which brokers? The broker community is diverse ranging from the large multinational brokers; to middle-sized organisations, that are nevertheless players in a given country or line of business; and to small independent brokers, with a given specialism or access to a niche market. Which segment should an Insurance company operate with, or should it deal with all sectors, but in different ways?
The way to determine an effective broker strategy is again through information about how these relationships have performed and in which ways they are trending. Sharing elements of this type of high-quality information with brokers (of course just about the business placed with them) is also a good way to deepen business relationships and positions the Insurer as a company that really understands the risks that it is underwriting.
At the beginning of this article I stated that Insurance is all about risk. As in the pig fable, it is about policy holders reducing their risk by transferring this to an Insurance company that pools these with other risks. External factors can impinge on this risk transfer. Hurricane season is is always a time of concern for Insurance companies with US property exposures, but over the last few years we have had our share of weather-related problems in Europe as well. The area of climate change is one that directly impinges upon Insurers and better understanding its potential impact is a major challenge for them.
With markets, companies, supply-chains and even labour becoming more global, Insurance programmes increasingly cover multiple countries and Insurance companies need to be present in more places (generally a policy covering risks in a country has to be written by a company – or subsidiary – based in that country). This means that Insurance professionals can depend less on first-hand experience of risks that may be on the other side of the world and instead need reliable and consistent information about trends in books of business.
The increasingly global aspect of Insurance also brings into focus different legal and regulatory regimes, which both directly impinge on Insurers and change the profile of risks faced by their customers. As we are experiencing in the current economic crisis, legal and regulatory regimes can sometimes change rapidly, altering exposures and impacting on pricing.
The present economic situation affects Insurance in the same ways that it does all companies, but there are also some specific Insurance challenges. First of all, with the value of companies declining in most markets, there is likely to be an uptick in litigation, leading to an increase in claims against Directors and Officers policies. Also falling property values mean that less Insurance is required to cover houses and factories, leading to a contraction in the market. Declining returns in equity and fixed income markets mean that one element of Insurance income – the return on premiums invested in the period between them being received and any claims being paid out – has become much less.
So shifts in climate, legal and regulatory regimes and economic conditions all present challenges in how risk is managed; further stressing the importance of excellent business intelligence in Insurnace.
The Insurance Cycle
If this litany of problems was not enough to convince the reader of the necessity of good information in Insurance, there is one further issue which makes managing all of the above issues even more complex. This is the fact that Insurance is a cyclical industry.
The above chart (which I put together based on data from Tillinghast) shows the performance of the London Marine Insurance market as a whole between 1985 to 2002. If you picked any other market in any other location, you would get a similar sinusoidal curve, though there might well be phase differences as the cycles for different types of Insurance are not all in lock-step.
To help readers without a background in Insurance, the ratio displayed is essentially a measure of the amount of money going out of an Insurance Company (mostly its operating expenses plus claims) divided by the amount of money coming in (mostly Insurance premiums). This is called the combined ratio. A combined ratio less than 100% broadly indicates a profit and one above 100% broadly indicates a loss.
It may be seen that the London Marine market as a whole has swung from profit to loss, to profit, to loss and back to profit over these 18 years. This article won’t cover the drivers of this phenomenon in any detail, but one factor is that when profits are being made, more capital is sucked into the market, which increases capacity, drives down costs and eventually erodes profitability. As with many things in life rather than stopping at break-even, this process overshoots resulting in losses and the withdrawal of capital. Prices then rise and profitability returns, starting a new cycle.
Given this environmental background to the Insurance business, it is obvious that it is very important to an Insurance company to work out its whereabouts in the cycle at any time. It is particularly crucial to anticipate turning points because this is when corporate strategies may need to change very rapidly. There may be a great opportunity for defence to change to attack, alternatively a previously expansionary strategy may need to be reined in order to weather a more trying business climate.
In order to make predictions about the future direction of the cycle, there is no substitute for having good information and using this to make sound analyses.
I hope that the article has managed to convey some of the special challenges faced by Insurance companies and why many of these dramatically increase the value of good business intelligence.
Essentially Insurance is all about making good decisions. Should I underwrite this newly presented risk? Should I renew an existing policy or not? What price should I set for a policy? When should I walk away from business? When should I aggressively expand? All of these decisions are wholly dependent on having high-quality information and because of this business intelligence can have an even greater leverage in Insurance than in other areas of industry.
Given this it is not unreasonable to state in closing that while good information is essential to any organisation, it is the very lifeblood of an Insurance company. My experience is that Business Intelligence offers the best way to meet these pressing business needs.
You can read more about my thoughts on Business Intelligence and Insurance in:
Nigel speaks about issues that he sees related to the consolidation of BI vendors. In his opinion this has led to the big players paying more attention to integrating acquisitions and rationalising product lines instead of focusing on customer needs. In one passage, he says:
Within product development, the main theme moved from innovation to integration. So, instead of delivering previously promised product enhancements to existing customers, product releases came out late and the highlights were the new connections to other products owned by the vendor, but which were probably not used by the existing customers. In other words, product development was driven by the priorities of the vendor, not the customer.
Whilst there is undoubtedly truth in Nigel’s observations, I have a slightly different slant on them, which I offered in my comments:
It is my very strong opinion that what the users of BI need to derive value is not the BI vendors “delivering previously promised product enhancements” but using the already enormously extensive capabilities of their existing BI tools better. BI should not be a technology-driven area, the biggest benefits come from BI departments getting to know their users’ needs better and focusing on these rather than the latest snazzy tool.
If this does happen, it may mean less than brilliant news for the BI vendors’ sales in the short-term, but successful BI implementations are going to be a better advert for them than some snazzy BI n.0 feature. The former is more likely to drive revenues for them in the medium term as companies build on successes and expand the scope of their existing BI systems.
While some people see large potential downsides in the acquisition of such companies as BusinessObjects, Hyperion and Cognos by large, non-BI companies, you could argue that their new owners are the sort of organisations that will aim to use BI to drive real-world business success. Who knows whether they will be successful, but if they are and this is at the expense of technological innovation, then I think that this is a reasonable sacrifice.
As to whose vision of the future is right, I guess only time will tell.