Automating the business intelligence process?

Balanced Insight Merv Adrian - IT Market Strategies for Suppliers
Balanced Insight Merv Adrian

 
 
Introduction

I enjoy reading the thoughts of vastly experienced industry analyst Merv Adrian on his blog, Market Strategies for IT Suppliers, and also on twitter via @merv. Merv covers industry trends and a wide variety of emerging and established technologies and companies. I would encourage you to subscribe to his RSS feed.

In a recent artcile, Balanced Insight – Automating BI Design to Deployment, Merv reviews the Consensus tool and approach developed by Ohio-based outfit Balanced Insight. I suggest that you read Merv’s thoughts first as I won’t unnecessarily repeat a lot of what he says here. His article also has links to a couple of presentations featuring the use of Consensus to build both Cognos 8 and Proclarity prototypes, which are interesting viewing.
 
 
An overview of Balanced Insight

Disclaimer: I haven’t been the beneficiary of a briefing from Balanced Insight, and so my thoughts are based solely on watching their demos, some information from their site and – of course – Merv’s helpful article.

The company certainly sets expectations high with the strap line of their web site:

Agile & Aligned Business Intelligence - With Balanced Insight Consensus® deliver in half the time without compromising cross project alignment.

Promising to “deliver in half the time without compromising cross project alignment” is a major claim and something that I will try to pay close attention to later.

The presentations / demonstrations start with a set-up of a fictional company (different ones in different demos) who want to find out more about issues in their business: outstanding receivables, or profit margins [Disclosure: the fact that the second demo included margins on mountain bikes initially endeared me to the company]. In considering these challenges, Balanced Insight offers the following slide contrasting IT’s typical response with the, presumably superior, one taken by them:

IT's approach to information problems vs Balanced Insight's
IT's approach to information problems vs Balanced Insight's

I agree with Balanced Insight’s recommendation, but rather take issue with the assumption that IT always starts by looking exclusively at data when asked to partake in information-based initiatives. I have outlined what I see as the four main pillars of a business intelligence project at many places on this blog, most recently in the middle of my piece on Business Intelligence Competency Centres. While of course it is imperative to understand the available data (what would be the alternative?), the first step in any BI project is to understand the business issues and, in particular, the questions that the business wants an answer to. If you search the web for BI case studies or methodologies, I can’t imagine many of these suggesting anything other than Balanced Insight’s recommended approach.

Moving on, the next stage of both the demos introduces the company’s “information packages”. These are panes holding business entities and have two parts; the upper half contains “Topics and Categories” (things such as date or product), the bottom half contains measurements. The “Topics and Categories” can be organised into hierarchies, for example: day is within week, which is within month, quarter and year. At this point most BI professionals will realise that “Topics and Categories” are what we all call “Dimensions” – but maybe Balanced Insight have a point picking a less technical-sounding name. So what the “information package” consists of is a list of measures and dimensions pertaining to a particular subject area – it is essentially a loose specification for a data mart.

The interesting point is what happens next, the Consensus Integrator uses the “information package” to generate what the vendor claims is an optimised star-schema database (in a variety of databases). It then creates a pre-built prototype that references the schema; this can be in a selection of different BI tools. From what I can tell from the demos, the second stage appears to consist of creating an XML file that is then read by the BI tool. In the first example, the “Topics and Categories” become dimensions in Cognos AnalysisStudio and the measures remain measures. In both demos sample data is initially used, but in the ProClarity one a version with full data is also shown – it is unclear whether this was populated via Consensus or not. The “information package” can also be exported to data modelling tools such as ERwin.

One of the Balanced Insight presentations then mentions that “all that’s left to do is then to develop your ETL”. I appreciate that it is difficult to go into everything in detail in a short presentation, but this does rather seem to be glossing over a major area, indeed one of my four pillars of BI projects referred to above. Such rather off-hand comments do not exactly engender confidence. If there is a better story to tell here, then Balanced Insight’s presentations should try to tell it.
 
 
The main themes

There are a few ideas operating here. First that Balanced Insight’s tools can support a process which will promote best practice in defining and documenting the requirements of a BI project and allow a strong degree of user interaction. Second that the same tools can quickly and easily produce functioning prototypes that can be used to refine these same requirements and also make discussions with business stakeholders more concrete. Finally that the prototypes can employ a variety of database and BI tools – so maybe you prototype on a cheap / free database and BI tool, then implement on a more expensive, and industrial strength, combination later.

Balanced Insight suggest that their product helps to address “the communication gap between IT and the business”. I think it is interesting using the “information package” as a document repository, which may be helpful at other stages of the project. But there are other ways of achieving this as well. How business friendly these are probably depends on how the BI team set them up. I have seen Excel and small Access databases work well without even buying a specific tool. Also I think that if a BI team needs a tool to ensure it sticks to a good process, then there is probably a bigger problem to worry about.

Of course, the production of regular prototypes is a key technique to employ in any BI project and it seems that Balanced Insight may be on to something here, particularly if the way that their “information package” presents subject areas makes it easier for the BI team and business people to discuss things. However, it is not that arduous to develop prototypes directly in most BI tools. To put this in a context drawn from my own experience, building Cognos cubes to illustrate the latest iteration of business requirement gathering was often a matter of minutes, compared to business analysts putting in many days of hard work before this stage.

Having decided to use Consensus to capture information about measures and dimensions, the ability to then transfer these to a range of BI tools in interesting. This may offer the opportunity to change tools during the initial stages of the project and to try out different tools with the same schema and data to assess their effectiveness. This may also be something that is a useful tool when negotiating with BI vendors. However, again I am not sure exactly how big of a deal this is. I would be interested in better understanding how users have taken advantage of this feature.
 
 
A potential fly in the ointment

It would be easy to offer a couple of other criticisms of the approach laid out in the demos; namely that it seems to be targeted at developing point solutions rather than a pervasive BI architecture and that (presumably related to this) the examples shown are very basic. However, I’m willing to given them the benefit of the doubt, a sales pitch is probably not the place for a lengthy exploration of broad and complex issues. So I think my overall response to Balanced Insight’s Consensus product could be summed up as guardedly positive.

Nevertheless, there is one thing that rather worries me and this can best be seen by looking at the picture below. [As per the disclaimer above, the following diagram is based on my own understanding of the product and has not been provided by Balanced Insight.]

My perception of how Balanced Insight addresses needs for information
My perception of how Balanced Insight addresses needs for information

I think I understand the single black arrow on the right of the diagram, I’m struggling to work out what Consensus offers (aside from documentation) for the two black arrows on the left hand side. Despite the fact that Balanced Insight disparaged the approach of looking at available data in their presentation, there is no escaping the fact that some one will have to do this at some point. Connections will then have to be made between the available data and the business questions that need answering.

In both demos Consensus is pre-populated with dimensions, measures and linkages of these to sample data. How this happens is not covered, but this is a key area for any BI project. Unless Balanced Insight have some deus ex machina that helps to cut the length of this stage, then I begin to become a little sceptical about their claim to halve the duration of BI work.

Of course my concerns could be unfounded. It will be interesting to see how things develop for the company and whether their bold claims stand the test of time.
 

Maureen Clarry stresses the need for change skills in business intelligence on BeyeNetwork

The article

beyenetwork2

Maureen Clarry begins her latest BeyeNETWORK article, Leading Change in Business Intelligence, by stating:

If there was a standard list of core competencies for leaders of business intelligence (BI) initiatives, the ability to manage complex change should be near the top of the list.

I strongly concur with Maureen’s observation and indeed the confluence of BI and change management is a major theme of this blog; as well as the title of one of my articles on the subject. Maureen clearly makes the case that “business intelligence is central to supporting […] organizational changes” and then spends some time on Prosci’s ADKAR model for leading change; bringing this deftly back into the BI sphere. Her closing thoughts are that such a framework can help a lot in driving the success of a BI project.
 
 
My reflections

I find it immensely encouraging that an increasing number of BI professionals and consultants are acknowledging the major role that change plays in our industry and in the success of our projects. In fact it is hard to find some one who has run a truly successful BI project without paying a lot of attention to how better information will drive different behaviour – if it fails to do this, then “why bother?” as Maureen succinctly puts it.

Without describing it as anything so grand as a framework, I have put together a trilogy of articles on the subject of driving cultural transformation via BI. These are as follows:

Marketing Change
Education and cultural transformation
Sustaining Cultural Change

However the good news about many BI professionals and consultants embracing change management as a necessary discipline does not seem to have filtered through to all quarters of the IT world. Many people in senior roles still seem to see BI as just another technology area. This observation is born out of the multitude of BI management roles that request an intimate knowledge of specific technology stacks. These tend to make only a passing reference to experience of the industry in question and only very infrequently mention the change management aspects of BI at all.

Of course there are counterexamples, but the main exceptions to this trend seem to be where BI is part of a more business focused area, maybe Strategic Change, or the Change Management Office. Here it would be surprising if change management skills were not stressed. When BI is part of IT it seems that the list of requirements tends to be very technology focussed.

In an earlier article, BI implementations are like icebergs I argued that, in BI projects, the technology – at least in the shape of front-end slice-and-dice tools – is not nearly as important as understanding the key business questions that need to be answered and the data available to answer them with. In “All that glisters is not gold” – some thoughts on dashboards, I made similar points about this aspect of BI technology.

I am not alone in holding these opinions, many of the BI consultants and experienced BI managers that I speak to feel the same way. Given this, why is there the disconnect that I refer to above? It is a reasonable assumption that when a company is looking to set up a new BI department within IT, it is the CIO who sets the tone. Does this lead us inescapably to the the conclusion that many CIOs just don’t get BI?

I hope that this is not the case, but I see increasing evidence that there may be a problem. I suppose the sliver lining to this cloud is that, while such attitudes exist, they will lead to opportunities for more enlightened outfits, such as the one fronted by Maureen Clarry. However it would be even better to see the ideas that Maureen espouses moving into the mainstream thinking of corporate IT.
 


 
Maureen Clarry is the Founder and President/CEO of CONNECT: The Knowledge Network, a consulting firm that specializes in helping IT people and organizations to achieve their strategic potential in business. CONNECT was recognized as the 2000 South Metro Denver Small Business of the Year and has been listed in the Top 25 Women-Owned Businesses and the Top 150 Privately Owned Businesses in Colorado. Maureen also participates on the Data Warehousing Advisory Board for The Daniels College of Business at the University of Denver and was recognized by the Denver Business Journal as one of Denver’s Top Women Business Leaders in 2004. She has been on the faculty of The Data Warehousing Institute since 1997, has spoken at numerous other seminars, and has published several articles and white papers. Maureen regularly consults and teaches on organizational and leadership issues related to information technology, business intelligence and business.
 

Business Intelligence Competency Centres

Introduction

The subject of this article ought to be reasonably evident from its title. However there is perhaps some room for misinterpretation around even this. Despite the recent furore about definitions, most reasonable people should be comfortable with a definition of business intelligence. My take on this is that BI is simply using information to drive better business decisions. In this definition, the active verb “drive” and the subject “business decisions” are the key elements; something that is often forgotten in a rush for technological fripperies.
 
 
The central issue

Having hopefully addressed of the “BI” piece of the BICC acronym, let’s focus on the “CC” part. I’ll do this in reverse order, first of all considering what is meant by “centre”. As ever I will first refer to my trusted Oxford English Dictionary for help. In a discipline, such as IT, which is often accused of mangling language and even occasionally using it to obscure more than to clarify, a back-to-basics approach to words can sometimes yield unexpected insights.

  centre / séntər / n. & v. (US center) 3 a a place or group of buildings forming a central point in a district, city, etc., or a main area for an activity (shopping centre, town centre).
(O.E.D.)
 

Ignoring the rather inexcusable use of the derived adjective “central” in the definition of the noun “centre”, then it is probably the “main area for an activity” sense that is meant to be conveyed in the final “C” of BICC. However, there is also perhaps some illumination to be had in considering another meaning of the word:

Centre of a Sphere

  n. 1 a the middle point, esp. of a line, circle or sphere, equidistant from the ends, or from any point on the circumference or surface.
(O.E.D.)
 

As well as appealing to the mathematician in me, this meaning gives the sense that a BICC is physically central geographically, or metaphorically central with respect to business units. Of course this doesn’t meant than a BICC needs to be at the precise centre of gravity of an organisation, with each branch contributing a “weight” calculated by its number of staff, or revenue; but it does suggest that the competency centre is located at a specific point, not dispersed through the organisation.

Of course, not all organisations have multiple locations. The simplest may not have multiple business units either. However, there is a sense by which “centre” means that a BICC should straddle whatever diversity there is an organisation. If it is in multiple countries, then the BICC will be located in one of these, but serve the needs of the others. If a company has several different divisions, or business units, or product streams; then again the BICC should be a discrete area that supports all of them. Often what will make most sense is for the BICC to be located within an organisation’s Head Office function. There are a number of reasons for this:

  1. Head Office similarly straddles geographies and business units and so is presumably located in a place that makes sense to do this from (maybe in an organisation’s major market, certainly close to a transport hub if the organisation is multinational, and so on).
  2. If a BICC is to properly fulfil the first two letters of its abbreviation, then it will help if it is collocated with business decision-makers. Head Office is one place than many of these are found, including generally the CEO, the CFO, the Head of Marketing and Business Unit Managers. Of course key decision makers will also be spread throughout the organisation (think of Regional and Country Managers), but it is not possible to physically collocate with all of these.
  3. Another key manager who is hopefully located in Head Office is the CIO (though this is dispiritingly not always the case, with some CIOs confined to IT ghettos, far from the rest of the executive team and with a corresponding level of influence). Whilst business issues are pre-eminent in BI, of course there is a major technological dimension and a need to collaborate closely with those charged with running the organisation’s IT infrastructure and those responsible for care and feeding of source data systems.
  4. If a BI system is to truly achieve its potential, then it must become all pervasive; including a wide range of information from profitability, to sales, to human resources statistics, to expense numbers. This means that it needs to sit at the centre of a web of different systems: ERP, CRM, line of business systems, HR systems etc. Often the most convenient place to do this from will be Head Office.

Thusfar, I haven’t commented on the business benefits of a BICC. Instead I have confined myself to explaining what people mean by the second “C” in the name and why this might be convenient. Rather than making this an even longer piece, I am going to cover both the benefits and disadvantages of a BICC in a follow-on article. Instead let’s now move on to considering the first “C”: Competency.
 
 
Compos centris

Returning to our initial theme of generating insights via an examination of the meaning of words in a non-IT context, let’s start with another dictionary definition:

Motar board

  competence /kómpit’nss/ n. (also competency /kómpitənsi/) 1 (often foll. by for, or to + infin.) ability; the state of being competent.

and given the recursive reliance of the above on the definition of competent…
  competent /kómpit’nt/ adj. 1 a (usu. foll. by to + infin.) properly qualified or skilled (not competent to drive); adequately capable, satisfactory. b effective (a competent bastman*).
(O.E.D.)
 

* People who are not fully conversant with the mysteries of cricket may substitute “batter” here.

To me the important thing to highlight here is that, while it is to be hoped that a BICC will continue to become more competent once it is up and running, in order to successfully establish such a centre, a high degree of existing competence is a prerequisite. It is not enough to simply designate some floor space and allocate a number of people to your BICC, what you need is at least a core of seasoned professionals who have experience of delivering transformational information and know how to set about doing it.

There are many skills that will be necessary in such a group. These match the four main pillars of a BI implementation (I cover these in more depth in several places on the blog, including BI implementations are like icebergs and the middle section of Is outsourcing business intelligence a good idea?):

  1. Understand the important business decisions and what figures are necessary to support these.
  2. Understand the data available in the organisation, how it relates to other data and to business decisions.
  3. Transform the data to provide information answering business questions.
  4. Focus on embedding the use of information in the corporate DNA.

So a successful BICC must include: people with strong analytical skills and an understanding of general business practices; high-calibre designers; reliable and conscientious ETL and general programmers; experts in the care, feeding and design of databases; excellent quality assurance professionals; resource conversant with both whatever front-end tools you are using to deliver information and general web programming; staff with skills in technical project management; people who can both design and deliver training programmes; help desk personnel; and last, but by no means least, change managers.

Of course if your BI project is big enough, then you may be able to afford to have people dedicated to each of these roles. If resources are tighter (and where is this not the case nowadays?) then it is better to have people who can wear more than one hat: business analysts who can also design; BI programmers who will also take support calls; project managers who will also run training classes; and so on. This approach saves money and also helps to deal with the inevitable peaks and troughs of resource requirements at different stages in a project. I would recommend setting things up this way (or looking to stretch your people’s abilities into new areas) even if you have the luxury of a budget that would allow a more discrete approach. The challenge of course is going to be finding and retaining such multi-faceted staff.

Also, it hopefully goes without saying that BI is a very business-focussed area and some BICCs will explicitly include business people in them. Even if you do not go this far, then the BICC will have to form a strong partnership with key business stakeholders, often spread across multiple territories. The skill to manage this effectively is in itself a major requirement of the leading personnel of the centre.

Given all of the above, the best way to staff a BICC is with members of a team who have already been successful with a BI project within your organisation; maybe one that was confined to a given geographic region or business unit. If you have no such team, then starting with a BICC is probably a bridge too far. Instead my recommendation would be to build up some competency via a smaller BI project. Alternatively, if you have more than one successful BI team (and, despite the manifold difficulties in getting BI right, such things are not entirely unheard of) then maybe blending these together makes sense. This is unless there is some overriding reason not to (e.g. vastly different team cultures or methodologies. In this case, picking a “winner” may be a better course of action.

Such a team will already have the skills outlined above in abundance (else they could never have been successful). It is also likely that whatever information was needed in their region or business unit will be at least part of what is needed at the broader level of a BICC. Given that there are many examples of BI projects not delivering or consuming vastly more resource than anticipated, then leveraging those exceptional people who have managed to swim against this tide is eminently sensible. Such battle-hardened professionals will know what pitfalls to avoid, which areas are most important to concentrate on and can use their existing products to advertise the benefits of a wider system. If you have such people at the core of your BICC, then it will be easier to integrate new joiners and quickly shepherd them up the learning curve (something that can be particularly long in BI due to the many different aspects of the work).

Of course having been successful in one business unit or region is not enough to guarantee success on a larger scale. I spoke about some of the challenges of doing this in an earlier article, Developing an international BI strategy. Another issue that is likely to raise its head is the political dimension, in particular where different business units or regions already have a management information strategy at some stage of development. This is another area that I will also cover in more detail in a forthcoming piece.
 
 
Conclusions

It seems that simply musing on the normal meanings of the words “competency” and “centre” has led us into some useful discussions. As mentioned above, at least two other blog postings will expand upon areas that have been highlighted in this piece. For now what I believe we have learned so far is:

  • BICCs should (by definition) straddle multiple geographies and/or business units.
  • There are sound reasons for collocating the BICC with Head Office.
  • There is need for a wide range of skills in your BICC, both business-focussed and technical.
  • At least the core of your BICC should be made up of competent (and experienced) BI professionals .

More thoughts on the benefits and disadvantages of business intelligence competency centres and also the politcs that they have to negotiate will appear on this blog in future weeks.
 

The importance of feasibility studies in business intelligence

Introduction

Feasibility Study

In a previous article, A more appropriate metaphor for business intelligence projects, I explained one complication of business intelligence projects. This is that the frequently applied IT metaphor of building is not very applicable to BI. Instead I suggested that BI projects had more in common with archaeological digs. I’m not going to revisit the reasons for the suitability of looking at BI this way here, take a look at the earlier piece if you need convincing, instead I’ll focus on what this means for project estimation.

When you are building up, estimation is easier because each new tier is dependent mostly on completion of the one below, something that the construction team has control over (note: for the sake of simplicity I’m going to ignore the general need to dig foundations for buildings). In this scenario, the initial design will take into account of facts such as the first tier needing to support all of the rest of the floors and that central shafts will be needed to provide access and deliver essential services such as water, electricity and of course network cables. A reductionist approach can be taken, with work broken into discrete tasks, each of which can be estimated with a certain degree of accuracy. The sum of each of these, plus some contingency, hopefully gives you a good feel for the overall project. It is however perhaps salutary to note that even when building up (both in construction and in IT) estimation can still sometimes go spectacularly awry.

When you are digging down, your speed is dependent on what you find. Your progress is dictated by things that are essentially hidden before work starts. If your path ahead (or downwards) is obscured until your have cleared enough earth to uncover the next layer, then each section may hold unexpected surprises and lead to unanticipated delays. While it may be possible to say things like, “well we need to dig down 20m and each metre should take us 10 days”, any given metre might actually take 20 days, or more. There are two issues here; first it is difficult to reduce the overall work into tasks, second it is harder to estimate each task accurately. The further below ground a phase of the dig is, the harder it will be to predict what will happen before ground is broken. Even with exploratory digs, or the use of scanning equipment, this can be very difficult to assess in advance. However it is to the concept of exploratory digs that this article is devoted.
 
 
Why a feasibility study is invaluable

At any point in the economic cycle, even more so in today’s circumstances, it is not ideal to tell your executive team that you have no idea how long a project will take, nor how much it might cost. Even with the most attractive of benefits to be potentially seized (and it is my firm belief that BI projects have a greater payback than many other types of IT projects), unless there is some overriding reason that work must commence, then your project is unlikely to gain a lot of support if it is thus characterised. So how to square the circle of providing estimates for BI projects that are accurate enough to present to project sponsors and will not subsequently leave you embarrassed by massive overruns?

It is in addressing this issue that BI feasibility studies have their greatest value. These can be thought of as analogous to the exploratory digs referred to above. Of course there are some questions to be answered here. By definition, a feasibility study cannot cover all of the ground that the real project needs to cover, choices will need to be made. For example, if there are likely to be 10 different data sources for your eventual warehouse, then should you pick one and look at it in some depth, or should you fleetingly examine all 10 areas? Extending our archaeological metaphor, should your exploratory dig be shallow and wide, or a deep and narrow borehole?
 
 
A centre-centric approach

In answering this question, it is probably worth considering the fact that not all data sources are alike. There is probably a hierarchy to them, both in terms of importance and in terms of architecture. No two organisations will be the same, but the following diagram may capture some of what I mean here:

Two ways of looking at a systems' hierarchy
Two ways of looking at a systems' hierarchy

The figure shows a couple of ways of looking at your data sources / systems. The one of the left is rather ERP-centric, the one on the right gives greater prominence to front-end systems supporting different lines of business, but wrapped by a common CRM system. There are many different diagrams that could be drawn in many different ways of course. My reason for using concentric circles is to stress that there is often a sense in which information flows from the outside systems (ones primarily focussed on customer interactions and capturing business transactions) to internal systems (focussed on either external or internal reporting, monitoring the effectiveness of processes, or delivering controls).

There may be several layers through which information percolates to the centre; indeed the bands of systems and databases might be as numerous as rings in an onion. The point is that there generally is such a logical centre. Data is often lost on its journey to this centre by either aggregation, or by elements simply not being transferred (e.g. the name of a salesperson is not often recorded on revenue entries in a General Ledger). Nevertheless the innermost segment of the onion is often the most complex, with sometimes arcane rules governing how data is consolidated and transformed on its way to its final destination.

The centre in both of the above diagrams is financial and this is not atypical if what we are considering is an all-pervasive BI system aimed at measuring most, if not all, elements of an organisation’s activity (the most valuable type of BI system in my opinion). Even if your BI project is not all-pervasive (or at least the first phase is more specific), then the argument that there is a centre will probably still hold, however the centre may not be financial in this case.

My suggestion is that this central source of data (of course there may be more than one) is what should be the greatest focus of your feasibility study. There are several reasons for this, some technical, some project marketing-related:

  1. As mentioned above, the centre is often the toughest nut to crack. If you can gain at least some appreciation of how it works and how it may be related to other, more peripheral systems, then this is a big advance for the project. Many of the archaeological uncertainties referred to above will be located in the central data store. Other data sources are likely to be simpler and thus you can be more confident about approaching these and estimating the work required.
  2. A partial understanding of the centre is often going to be totally insufficient. This is because your central analyses will often have to reconcile precisely to other reports, such as those generated by your ERP system. As managers are often measured by these financial scorecards, if you BI system does not give the same total, it will have no credibility and will not be used by these people.
  3. Because of its very nature, an understanding of the centre will require at least passing acquaintance with the other systems that feed data to it. While you will not want to spend as much time on analysing these other systems during the feasibility study, working out some elements of how they interact will be helpful for the main project.
  4. One output from your feasibility study should be a prototype. While this will not be very close to the finished article and may contain data that is both unreconciled and partial (e.g. for just one country or line of business), it should give project sponsors some idea of what they can expect from the eventual system. If this prototype deals with data from the centre then it is likely to be of pertinence to a wide range of managers.
  5. Strongly related to the last point, and in particular if the centre consists of financial data, then providing tools to analyse this is likely to be something that you will want to do early on in the main project. This is both because this is likely to offer a lot of business value and because, if done well, this will be a great advert for the rest of your project. If this is a key project deliverable, then learning as much as possible about the centre during the feasibility study is very important.
  6. Finally what you are looking to build with your BI system is an information architecture. If you are doing this, then it makes sense to start in the middle and work your way outwards. This will offer a framework off of which other elements of your BI system can be hung. The danger with starting on the outside and working inwards is that you can end up with the situation illustrated below.
A possible result of building from the outside in to the center
A possible result of building from the outside in to the centre
 
Recommendations

So my recommendation is that your feasibility study is mostly a narrow, deep dig, focussed on the central data source. If time allows it would be beneficial to supplement this with a more cursory examination of some of the data sources that feed the centre, particularly as this may be necessary to better understand the centre and because it will help you to get a better idea about your overall information architecture. You do not need to figure out every single thing about the central data source, but whatever you can find out will improve the accuracy of your estimate and save you time later. If you include other data sources in a deep / wide hybrid, then these can initially be studied in much less detail as they are often simpler and the assumption is that they will support later deliveries.

The idea of a prototype was mentioned above. This is something that is very important to produce in a feasibility study. Even if we take to one side the undeniable PR value of a prototype, producing one will allow you to go through the entire build process. Even if you do this with hand-crafted transformation of data (rather than ETL) and only a simplistic and incomplete approach to the measures and dimensions you support, you will at least have gone through each of the technical stages required in the eventual live system. This will help to shake out any issues, highlight areas that will require further attention and assist in sizing databases. A prototype can also be used to begin to investigate system and network performance, things that will influence your system topology and thereby project costs. A better appreciation of all of these areas will help you greatly when it comes to making good estimates.

Having understood quite a lot about your most complex data source and a little about other ones and produced a prototype both as a sales tool and to get experience of the whole build process, you should have all the main ingredients for making a credible presentation to your project sponsors. In this it is very important to stress the uncertainties inherent in BI and manage expectations around these. However you should also be very confident in stating that you have done all that can be done to mitigate the impact of these. This approach, of course supported by a compelling business case, will position you very well to pitch your overall BI project.
 

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.
 

A review of “The History of Business Intelligence” by Nic Smith

Introduction

I had been aware of a short film about the history of Business Intelligence flitting its way around the Twitterverse, but had not made the time to take a look myself. That changed when the author, Nic Smith from Microsoft BI Solutions Marketing, contacted me asking my opinion about it.
 
 

 
 
Back in the day I was a regular Internet Movie Database reviewer, coming out of “retirement” recently to post some thoughts about Indiana Jones and the Kingdom of the Crystal Skull (see also A more appropriate metaphor for business intelligence projects). More recently, I have reviewed rock climbing DVDs, filmed rock-climbing shorts with my partner and have even written a piece aiming to apply Hollywood techniques to Marketing Change. Given this background, I thought that I would treat Nic’s work as art and review it accordingly. This article is the result.
 
 
The review

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:

  1. Where have we been?
  2. Where are we now?
  3. Where are we going? and
  4. 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.
 

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.
 

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.
 

 

The specific benefits of Business Intelligence in Insurance

Introduction

Insurance

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.

I have described various aspects of this work elsewhere, for example in The EMIR Project and my collection of articles on Cultural Transformation. I have also written about the general benefits of good Business Intelligence for any organisation. This article focuses on the business benefits of BI that are specific to the Insurance industry.
 
 
Of pigs and men

  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

Pricing

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.
 
 
Distribution strategy

Insurance Broker

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.
 
 
Changing risks

The changing face of risk
The changing face of risk

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.

An example of The Insurance Cycle
An example of The Insurance Cycle

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.
 
 
Summary

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:

  1. Using historical data to justify BI investments – Part I
  2. Using historical data to justify BI investments – Part II
  3. Using historical data to justify BI investments – Part III


 

Short-term “Trouble for Big Business Intelligence Vendors” may lead to longer-term advantage

linkedin Chief Information Officer (CIO) Network

This post is another that highlights responses I have made on various LinkedIn.com forums. In this case, a news article was posted on the Chief Information Officer (CIO) Network group (as ever you need to be a member of LinkedIn.com and the group to view the original thread).

The news article itself linked to a piece / podcast on The IT-Finance Connection entitled: Big BI Vendors Facing Big Challenges. In this Nigel Pendse, author of the anual BI Survey, was interviewed by IT-Finance Connection about his latest publication and his thoughts about the BI market in general.

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.

See also: BI implementations are like icebergs

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.