Enterprise Decision Management - a Weblog

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November 30, 2006

Book Review: The Only Sustainable Edge

I recently heard John Hagel (who has a blog) present (at the webMethods conference) on the book he wrote with John Seely Brown, "The only sustainable edge". In the book John and John discuss how recent changes in the world will force, indeed are forcing, companies to change how they think about offshoring and outsourcing, innovation and even their core business processes. They describe how a combination of "Converging forces generate margin squeeze" where those forces are digital technology (driving down interaction costs) and public policy (deregulation, trade and market liberalization and globalization). These trends are certainly real and visibly changing our world as we watch. Not only can "Customers can access more information about more vendors, negotiate more effectively with still more vendors, and switch from one vendor to another whenever they find greater value" but companies have more options for how to piece together the resources they need to do business. These new conditions and options, though, require companies to change the way they plan, operate and turn a profit and it is these changes that the book mostly discusses. The authors argue that these trends and opportunities are actually changing what it means to be a company. Redefining the role of the firm from economizing on market transactions, the original raison d'etre of most companies, to one of accelerating knowledge and capability growth.

In terms of practical steps the authors suggest measuring the actual and planned behavior of your organization (by considering budget dollars and management time invested) and mapping them to four areas:

• Efficiency to eliminate costs and waste
• Specialization to focus on a smaller number of areas where the company can excel
• Coordination of third party resources to fulfill the rest of the company's needs
• Accelerating capability building to get and keep ahead

They identify a number of techniques for this. Firstly loose coupling of which they say "Loose coupling represents a more modular approach to process management". This means creating independent activities with clear owners and interfaces and performance guidelines. Thee activities can then be assembled and disassembled more easily to meet changing needs. Such an approach implied very tight business relationships (particularly strong in terms of trust) so that these process components can be loosely coupled. Decision management plays a role here for two reasons. Firstly companies assembling these loosely coupled processes for a variety of customers will want to allow those customers to change how decisions are made within the process - many decisions will have to be automated to meet the real-time nature of most of these processes. Thus some of the activities will be decision-centric and controlled by the customer of the process while still remaining tightly integrated. Secondly decision management helps generate the kind of audit trails and decision outcome logs that build trust both between companies and between companies and their regulators.

Secondly they discuss process outsourcing and offshoring and talk about the new outsourcers who "address the core operating processes of the firm". Indeed the move from looking for commodity services to reduce costs to looking for world class capabilities elsewhere in the world. In this new world, specialization trumps savings. I have blogged before about how offshoring and outsourcing can and should drive decision automation and on how complimentary they are as well as on the outsourcer's dilemma.

Thirdly they talk about productive friction and how to turn it into innovation. For example, handling exceptions can be costly and inefficient and often people are brought together once, solve the problem and then all record is lost. They suggest the use of social software to capture this ad-hoc behavior and learn from it. As they say "Exceptions are a rich seedbed for business innovation. They force employees to address unexpected challenges and opportunities and to push their practices into new directions" and ultimately they change processes. While this may seem unrelated to decision automation, think about what the people who are needed for this exception handling are doing in your company. Well they are probably grinding through large numbers of repeatable decisions. If you automated those decisions you could free up this expertise to handle exceptions, look for broad patterns, help third parties work with you better and so on. Decision Automation may not create "productive friction" but it can free up the resources you need to generate it. They also note that "In particular, effective resolution of these exceptions requires a rich understanding of the context of the exception by all the stakeholders" and one of the attractions of decision automation is that when there is an exception the decision service can explain exactly why there is one - which rules fired or did not fire, which models return which results etc. This kind of context can help greatly in developing a resolution. Lastly, of course, decision automation gives you another way to learn from the exceptions as you process them - you can go and add more rules to the decision. Indeed in a recent post John Hagel discusses Automation vs. amplification and argues that "Too many companies have concentrated their IT investment on initiatives to automate processes †removing people wherever possible †rather than exploring how IT might be better used to amplify the talent of the people left." While decision automation can remove people from a process it can also "amplify" the value of your staff, for instance by allowing underwriters to focus on agency management and geographic trends not rubber stamping policies.

Two final notes:

• I think that companies doing the kind of diagnostics they suggest could consider the use of decision yield to see how they are doing in improving all aspects of their critical decisions
• The authors talks a lot about building shared understanding for people but it is also true for systems. Decision management technology and an Enterprise Decision Management approach can make it possible to build "shared understanding" for all your information systems and for information systems outside the enterprise.

The book does a good job of showing how some companies are competing in ways that would be unimaginable just a few years ago and the authors lay out a compelling case that companies who do not respond to these new threats and opportunities are taking an enormous risk. Whether or not you believe the change will be as widespread as the book implies, the changes are real and will impact your business to at least some degree and this makes the book worth reading. You can buy it here.

Technorati Tags: business agility, business intelligence, business process management, business rules, decision automation, EDM, Enterprise Decision Management, offshoring, outsourcing, predictive analytics, strategy, Trends

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Posted by James Taylor at 1:47 PM | Comments (0)

The problem with programmers

Firstly let me say that not only are some of my best friends programmers but that I have been a programmer, development manager, product manager, architect and methodology author in my career so please don't consider this some marketing guy whining about programmers!

Anyway, I was reading EDS' Next Big Thing blog and saw this post "Is Programming The Problem?" discussing an interview with Bjarne Stroustrup called "The problem with programming". Like Randy Mears at EDS I found the interview interesting. Randy summarized it by saying "some blame goes to programming language complexity, while most goes to our development methods" with which I agree but not, perhaps, for the reasons you might think.

I think the problem is not with programming languages per se but with the idea that programmers should be responsible for coding the behavior of the business, it's business logic or decisions, in the first place.

When Bjarne says that bad programs sometimes show signs that "programmers clearly didn't think deeply about correctness, algorithms, data structures or maintainability" he may be right but I think his comment that "a system just 'sort of evolved' into something minimally acceptable" is closer to the truth. How can we expect a programmer to be both an expert in programming (with all the architectural, design, language and technology skills that implies) and an expert in the business? Clearly we cannot. If we are to build systems that work the way the business needs them to then those who understand the business will have to take a real role in the development of information systems. Otherwise programmers will build what they think is needed and then "sort of evolve" it into something that works. But programmers and those who understand the business have a fundamental difference in perspectives. Unlike Bjarne I don't think that allowing programmers to express "real-world ideas succinctly and affordably" is what makes a language useful, at least not when those real-world ideas are things like "follow the state regulations when approving loans".

Bjarne's brief definition of a good system is "correct, maintainable, and adequately fast" and it's a good definition. It must be noted, though, that "correct" and "maintainable" go together in the sense that code that starts off correct but is not maintainable will rapidly be incorrect. As this kind of change is inevitable systems should be designed, and languages selected, to focus on this maintainability. This focus on maintainability is one of the reasons why business rules can be better than other coding styles. When correctness must be described in business terms, rather than technical ones, then you need languages that enabled what Forrester calls"Collaborative Business Engineering" - an approach where the business and the programmers are working jointly on solving a problem and keeping that solution current rather than the business throwing it over the wall to the programmers. Now there are those that say that requirements are the problem - if we could just get the users to get them right we could build the right system. Personally I call this the requirements tarpit and have blogged before as to why requirements show the problem but aren't the problem.

Anyway, one of Bjarne's solutions is to "use more appropriate design methods, and design for flexibility and for the long haul". I could not agree more and think the evidence that business rules and business rules management systems can address this is compelling. Don't let your programmers write business logic, make your business users do it!

Technorati Tags: BRE, BRMS, business agility, business rules, decision automation, decision service, programmer, programming, requirements, Bjarne Stroustrup

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Posted by James Taylor at 11:51 AM | Comments (0)

New article on enterprise policy hubs today

I have a new article on BPM Institute today - Why you need an enterprise policy hub. It's an extract of my chapter from The Business Rules Revolution (which I reviewed here) so if you like it, buy the book. Enjoy.

Technorati Tags: BRMS, business agility, business rules, decision service, enterprise policy hub, policy hub, SOA, BRE

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Posted by James Taylor at 10:14 AM | Comments (0)

November 29, 2006

Where has all the risk gone?

A couple of the blogs I read mentioned this LA Times article today - Insurers learn to pinpoint risks -- and avoid them. In particular RiskProf and Workers Comp Insider covered the article. RiskProf felt that the article missed the point and that better risk assessment means better precision in pooling risks and less subsidies for those who chose riskier behavior. RiskProf also pointed out that new, and hard to estimate, risks tend to put off insurers but this is temporary. As a friend of mine put it "there's no such thing as a bad risk, only a bad price" so once the risk is known there will be a price. Finally RiskProf wanred against over-regulation as a solution, preferring competition, arguing that capping rates causes insurers to stop writing policies when the rate no longer covers the risk. Meanwhile Jon over at "Workers Comp Insider" feels that the article makes some good points - that micro-segmentation defeats the pooling that makes insurance work and that losers (people identified as bad risks and made to pay a higher premium) will outnumber winners (good risks who get discounts) and that this will cause potentially severe repercussions for Insurers. Interestingly the students at UC Berkeley's Services Science Management and Engineering class asked exactly this question when I talked about micro-segmentation in insurance.

I'm summarizing both - read the posts for details - but I found both the article and the responses interesting so I thought I would add my points.

• Why should losers outnumber winners?
  There seems to be no particular reason why those whose rates rise because they are bad risks should outnumber those whose rates fall because they are better ones.
• Why should those who take good decisions subsidize those who don't?
  Should non-smokers pay more for life insurance so that smokers do not? I think most people would say no. So why should people who buy safer houses, live in safer places, drive safer cars pay more for insurance so that those who take more risks do not?
• I think regulators should focus on those things someone cannot change like genetics and on ensuring that anything used to assess price can be justified and supported mathematically.
  Clearly not everything that impacts risks can be controlled by an individual and I think regulators could make a case for preventing the use of aspects outside your control from impacting your risk, forcing insurers to manage that kind of risk in a pooled way. Regulators should also push for causal relationships (smoking causes lung cancer therefore smoking increases risks and can be used to raise rates) while recognizing that very strong correlations that some logical "root cause" should also be allowed (the level of responsibility people take over meeting their credit obligations, for instance, can be inferred from their credit data and a lack of responsibility might tend to cause more claims on auto insurance - poor credit behavior does not cause accidents but the correlation is very strong). Regulators should clamp down hard on anything that cannot be shown to have a real mathematical relationship - they should aim to replace bad judgment with good math.
• I think the industry, and regulators, can encourage price transparency so customers can understand what they get
  Hiding behind fine print is not OK - insurers need to explain what the pricing options are, how they are calculated and what customers can do about it. This need to build defensible and explicable models (see this discussion of legal issues with analytics in the predictive analytics FAQ) is well established in credit and in some areas of insurance and allows people to find out why they are not getting the best rate (some states require this explanation and I think that's a good thing).
• Some insurers will rush to low risks only, others will specialize in high risk.
  Someone will figure out how to tell the difference in risk between drivers in a particular category and price accordingly. This will cause others to be adversely-selected against and competition will cause a change in behavior. New products, like Pay as you drive insurance, will target segments for whom the standard risk models cause high prices.
• It seems to me that insurers are pooling risks, ideally "like risks", but also spreading risk evenly over time.
  Even if I have a high risk of loss (say I own a sea-level property on the coast) I am not going to get a loss every year. The insurance company is spreading my losses evenly across time as well as across similar policy holders. This element of pooling is not going away.
• No matter how fine grained insurance companies try and get they still need a statistically significant base in each segment so there is a natural limit to their size.

The insurance industry is busily adopting Enterprise Decision Management or EDM to improve the precision, consistency and agility of their underwriting decisions. Nothing in this article, or the responses to it, makes me think this will (or should) change - especially the growing use of predictive analytics. There's lots more in the Insurance section of this blog.

Technorati Tags: analytic application, business rules, compliance, EDM, Enterprise Decision Management, insurance, predictive analytics, risk management, segmentation, adverse selection

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Posted by James Taylor at 9:47 AM | Comments (0)

November 28, 2006

One complaint about the Long Tail

Having reviewed "The Long Tail" and written a long piece about how decision management can help build the systems the Long Tail requires, now I have to register a complaint. In the book Chris talks about the limitations of physical stores and uses as his example Best Buy. He discussed how they have to distribute supply across stores "hoping to guess roughly at where the demand will be". In fact, this is far from the truth.

In fact retailers like Best Buy are treating these decisions also as opportunities for analytic improvements by thinking about store layout not as a single decision but as a store by store decision. While it is true that stores, unlike websites, must obey physical laws - they cannot reconfigure store around each customer - it does not mean they can do nothing. As it says in "The secrets of capitalizing on customer insights", Best Buy is using analytics to reconfigure each individual store based on local demographics. As Fair Isaac noted in a piece on Best Buy:

"the realignment and reconfiguration of stores to local neighborhood demographics, leading to an 8.4% increase in same-store sales (compared to 2.3% for traditional stores)"

Indeed in a recent interview Best Buy talked about using analytics to gain a complete understanding of which customer transactions trigger the purchase of additional items and at what point in time, enabling them to generate targeted, customer-centric marketing, merchandising and in-store decisions. While, as Chris says in the book, "As a store manager you have to guess as to where most people would expect to find a windbreaker" you don't have to guess where most of the people in a specific store might expect to find it or what they might want to buy with it. That you can calculate.

The moral of the story? Always try and break down decisions into more granular ones. Don't send a letter to a group of customers where you could tailor the letter for each of them and don't make all your stores the same when you could segment them based on the kinds of customers they attract.

Technorati Tags: analytic application, best buy, customer experience, customer segment, decision automation, long tail, niche marketing, predictive analytics, retail, segmentation, customer insight

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Posted by James Taylor at 1:53 PM | Comments (0)

November 27, 2006

Hits and Niches

"The era of one-size-fits-all is ending, and in its place is something new, a market of multitudes", so says Chris Anderson (who blogs at www.longtail.com) in his recent book "The Long Tail". The phrase "The Long Tail" comes from a classic Pareto distribution or power curve that has a "head" consisting of "hits" and a long tail consisting of "niche" products (as shown on the right). He begins with three observations:

1. The "tail" of available variety is far longer than we realize
2. This tail is now within reach economically thanks to the Internet
3. All the niches aggregated makes for a significant market

He believes that where the 20th century was about hits, the 21st will be about niches. He illustrates this with what he calls "The 98 percent rule" that 98% of online products will be sold often enough to notice. For instance, 95% of netflix movies are rented in a quarter, 98% of amazon's books sell at least once a quarter and so on. Indeed if an online business has 20, 30 or 40 times as many products as an offline retailer (and they do), then the products that are only available online amount to 20-40% of sales. "In an era without the constraints of physical shelf space and other bottlenecks of distribution, narrowly targeted goods and services can be as economically attractive as mainstream fare". So far, so good.

He then outlines a number of themes, the first three of which seem particularly relevant from a decisioning point of view

1. There are far more niche products than hits
2. The costs of reaching these niches is falling fast
3. Consumers will only buy from these niches if they have ways to find the niches they value quickly.

Automating decisions using an Enterprise Decision Management (EDM) approach, especially those around cross-sell/up-sell, recommendation, precision marketing and so on is clearly going to be key for #3. The value of automated decisions in self-service also matters in this world as the number of choices, and variety of channels, will mean customers helping themselves more and valuing those companies that help them help themselves and that those that can recreate the feel of a corner store across a huge range of products and customers.

Chris identifies three forces that are driving this - moves to democratize production and distribution and the power to connect supply and (potentially thinly spread) demand. This last force is about lowering the "search costs" or reducing the economic cost of finding what you want. In a world where finding something is expensive unless it is very popular (the old model), hits matter and niches do not. In a world where the Internet and related technologies reduce this cost, niches are more viable. Decisioning technologies can reduce the "search cost" for something in several ways:

• Make it easier for customers to specify their own rules
• Make it easier to handle many more customer segments
• Analytically derive segments based on customer behavior
• Predict niche interests for customers using information about other customers
• And so on

In the book he quotes Frog Design (a consultancy) "Information gathering is no longer the issue - making smart decisions based on the information is now the trick". This is the essence of my regular comparisons with BI/DW (information gathering) and the predictive analytics in Enterprise Decision Management (improving the quality of decision made using this information). In particular the kinds of analytic models that take your behavior - both implicit (what you buy) and explicit (what you recommend) - and use predictive analytics to make better decisions. Predictive analytics take the past behavior of all customers and use it to infer the likely future behavior of a specific customer. This is akin to what is sometimes called the "wisdom of crowds".

Now one often hears discussion of these kinds of analytics only in the context of using customer recommendations or explicit preferences to make targeted recommendations to a customer. However, what a customer looks at and, even more, what a customer buys are also elements of behavior. This kind of behavior also does not require a customer to invest any time in writing a recommendation (something only a minority will do). Using customer behavior and comparing it to others to make predictions about likely future behavior is the basis for everything from fraud detection to credit scoring to retention risk to, yes, product recommendation. Don't think you need customer recommendations to leverage your customers' collective wisdom - you know a lot about what they think from what they do. Further Chris points out that if I can match customer behavior to specific individual characteristics (like location, gender, age) then I can segment my customers very precisely (though there are obvious privacy issues to be considered). This segmentation is crucial to success as "In a world of infinite choice, context - not content - is king" (Rob Reid, Listen.com founder). Not only does explicit segmentation of products and customers into like groups allow for targeting, it also improves the value of recommendations and makes it easier to infer from previous customer activity.

One of the issues in the long tail is the signal to noise ratio. As Theodore Sturgeon, a Science Fiction writer, said "ninety percent of everything is crud". What makes niches different is that one person's noise is another person's signal. In general Chris suggests that this means we must replace the kind of pre-filters (editors, buyers, marketers) that try and promote the most likely to succeed products with post-filters (blogs, playlists, reviews, customers and their recommendations) having made the widest set of products available. If you have this option, if your products/services lend themselves to mass customization and niche-targeting, then you had better be good at turning lots of data into useful, predictive insight that let's you connect customers with the products they want. Otherwise you risk having customers picked off, niche by niche. His discussions of how niche products, such as narrowly focused blogs, pick off customers from broader and less differentiated products one at a time reminded me of Clayton Christiansen's "Innovators Dilemma" where new competitors take your least profitable customers initially and then work up the value chain. All that's different is that these niche competitors never become a direct competitor except as a swarm. As a swarm, though, they target niche after niche and drive you out of the market.

The last section of the book lays out the 9 steps Chris suggests:

1. Increase the inventory you make available
2. Make customers do work
   I am not sure this is essential. You can gather an enormous amount of information from what they do without making them work. Clearly the more they tell you, the more useful you can make everything. Regardless, the lesson for EDM is to capture this information and use it to drive recommendations, cross-sell, pricing etc. This involves precision and agility.
3. One distribution method does not fit all
   This means supporting lots of channels. One of the challenges with multiple channels is ensuring consistency across them. Focusing on decisions as a single point of automation and sharing those operational decisions across channels is important for the customer experience and to make multiple channels work.
4. One product does not fit all
   The era of mass customization and off micro-variations between products is upon us. Not only does this mean thinking about the information content of your products (the easiest part to vary), it also means thinking about automating decisions relating to products to handle the increased complexity.
5. One price does not fit all
   Move to variable and dynamic pricing as quickly as you can and automate the way you price products so you can show regulators and auditors that you have a repeatable, reliable process for generating prices.
6. Share information
   Such as how you made a recommendation for example or why your credit score is what it is. Check out the way myfico does this for credit scores, for instance.
7. "And" not "Or"
8. Trust the market
   Use the data you gather to respond with post-filtering - don't try and pre-filter. To do this you must be able to respond quickly - you must be agile.
9. The power of free

Chris boils the whole thing down to this:

1. Make everything available
2. Help me find it

Decision automation and management can't help with the first one but it's going to important for you to succeed in the second one. I'll close with my favorite comment from the book - one from Raymond Williams, Marxist sociologist, who said

"There are no masses; there are only ways of seeing people as masses."

With EDM there are no masses, only finely targeted micro-segments!

Technorati Tags: business rules, channels, customer experience, customer segment, decision automation, EDM, Enterprise Decision Management, long tail, personalization, predictive analytics, segmentation, niche marketing

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Posted by James Taylor at 1:52 PM | Comments (0)

Book Review: The Long Tail

I have just finished reading "The Long Tail" in which Chris Anderson does a nice job of introducing some key concepts that are redefining business in the Internet era. As he says "The era of one-size-fits-all is ending, and in its place is something new, a market of multitudes". In this world the ability of the Internet to give customers access to a vast (and rapidly growing) array of choices is changing not just how they buy but what they buy. The book has some solid research on how companies, both pure Internet retailers and mixed offline/online retailers are adapting to this world. The book discusses everything from Sturgeon's Law ("ninety percent of everything is crud") to the "98 percent rule" (98% of anything sold online will have at least occasional sales even if the online catalog is 40 times the offline one).

The book covers how hits have dominated in the past century and how niches will dominate in this one. It also gives some general suggestions as to what you can do about it although it does somewhat leave you hanging in terms of specific advice for how to do marketing, build information systems etc in this brave new world. There are lots of clear linkages between this new reality and the drive to automate more decisions. So many, in fact, that I will wrote a whole post on them. This book is worth reading no matter what kind of business you work for and you can buy it here.

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Posted by James Taylor at 11:29 AM | Comments (0)

November 22, 2006

Gathering requirements, and rules

I saw this post on gathering requirements by Scott Sehlhorst today. Now I don't think requirements are the same as business rules (particularly given the nature of business rules is to change) and that one should keep them separate and linked to use cases, for example. The techniques were well summarized though so here's the list, with some comments about applying the techniques to rules.

1. Brainstorming
   An effect way to gather rules that have not been documented or automated before such as rules of thumb or judgmental rules.
2. Document Analysis
   Normally used on policy and procedure manuals and legislation to derive the regulatory rules.
3. Focus Group
   Useful for gathering feedback on rule templates, designed to allow a specific group of people to manage some rules themselves e.g. a group of marketing folks and the templates for cross-promotional rules.
4. Interface Analysis
   Not typically a major issue but could be useful for designing a rule maintenance interface that fits seamlessly with, for example, reporting and dashboards.
5. Interview
   A way to gather expert rules and to find out what the objectives of the decision automation should be.
6. Observation
   Not only can observation give insight into how decisions are made (what data is consulted, what questions are asked of a customer etc) it can also be used to find decision automation opportunities when applied to the process that includes the decision. When does someone executing the process have to refer a customer to someone else? Why? and so on.
7. Prototyping
   Not generally used in rules development other than for designing rule maintenance interfaces.
8. Requirements Workshop
   Obviously one would hold rules workshops to gather rules in a very similar way.
9. Reverse Engineering
   As Scott notes, an exercise almost of last resort. Analyzing code for rules will almost always result in over-technical rules, rather than business rules. Mining code for rules is something best avoided unless there is code that implements something for which no policies, procedures, regulations or experts exist.
10. Survey
    Not generally useful.

Anyway, a nice list and very helpful.

Technorati Tags: business rules, requirements

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Posted by James Taylor at 10:36 AM | Comments (0)

New Podcast with Donald Light of Celent

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Posted by James Taylor at 9:39 AM | Comments (0)

November 21, 2006

Vote on the blog design

I thought it would be fun to get a quick vote on the new blog design. Let me know what you think.

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Posted by James Taylor at 4:58 PM | Comments (0)

Enterprise Decision Management and important IT trends

I saw this article in Baseline - The 30 Most Important IT Trends for 2007 - and it struck me how many of them pointed out the need for an enterprise decision management approach. Highlights included:

• Needing to focus on improving customer-facing process and the customer experience
• Making better use of information - moving beyond Business Intelligence to predictive analytics and data mining
• A more holistic approach to compliance and risk management
• A need for agility and innovation, especially at the intersection of IT and the business
• SOA is coming to dominate architectural decisions

Enjoy.

Here's the complete list with comments and links

1. Process improvement will be job No. 1
   The survey showed lots of work around process change and a vision for much more automation across the board. Decision automation is going to be important to drive the levels of automation being targeted.
2. IT works on closing the sale
   There seems to be lots of focus on customer acquisition and exploitation. Interestingly there was broad agreement (65%) that using the Internet allowed companies to reach niche markets, pointing to the value of segmentation.
3. Companies make their Web sites more engaging
4. Customer service gets a tune-up
   Analyzing behavior to build predictive models, providing self service, being consistent across channels, personalization and segmentation all up year over eyar, as is collecting customer feedback
5. Companies put their mounds of data to work
   Making better use of data is considered key (#3) and most IT folks think they are both effective at using data (75%) and that they have users who complain can't get data they need (55%). I think this mismatch comes from a failure to focus on actions/recommendations rather than data. In addition, the need to move to more event-driven, Business Activity Monitoring solutions means you need decision management as routing rules and event rules are not the same as decision rules.
6. Information governance gains momentum
7. CIOs strive to be strategic
   I think decision technology can be a crucial driver for operationalizing strategy.
8. The division between IT and business will diminish
   Only, I suspect, if the right technologies are adopted to close the gap.
9. CIO compensation keeps climbing
10. IT organizations will keep growing
11. CIOs struggle to find business-savvy technologists
    This points up the need for "Purple People"
12. Outsourcing changes IT management
13. Outsourcing growth slows
    I think companies underestimate their ability to outsource business processes and still control their business and ignore the fact that they can resist the commoditization of processes using business rules.
14. Offshoring shifts from India
15. Companies invest in IT leadership
16. Demonstrating ROI will remain a struggle
    I think showing an ROI from Business Intelligence is particularly difficult.
17. No abatement of IT security threats
18. Security concerns turn users away from Windows
19. Security morphs into risk management
    One of the examples was the risk to data when employees leave but the data they have is not the only issue.
20. Compliance achieves what government intended
    A holistic approach to compliance has better results, for instance a combination of rules and process automation works well for compliance
21. Compliance spurs financial process improvement
22. The move to a new architecture marches on
    SOA is in and business rules and decision services complement it.
23. Enterprise applications start losing their luster
    Will SOA kill the big application vendor suites or not?
24. Data quality demands attention
25. IT reluctantly embraces Web 2.0
26. IT innovation loses traction
    I think there is more innovation opportunity if IT departments can eliminate their maintenance backlog and focus on developing new, agile systems.
27. Business process management services and software will frustrate users
    Remember Business Rules Management and Business Process Management are complementary but not the same.
28. For business intelligence, the best is yet to come
    But only if they move from collecting data to using it
29. IT organizations start going green
30. Dissatisfaction with vendors is on the rise

Technorati Tags: Baseline, BPMS, business agility, business intelligence, business process management, business rules, CIO, compliance, customer experience, decision service, predictive analytics, risk management, ROI, SOA, Tag 16, Trends

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Posted by James Taylor at 11:02 AM | Comments (0)

Don’t Sell Insurance Like Dollar Meals: CEO

The National Underwriter News Service yesterday reported statements made by Boston-based Liberty Mutual at 18th Annual Executive Conference for the Property-Casualty Industry:

Insurers to be competitive need to focus on service and underwriting rather than price, the head of the ninth largest U.S. property-casualty insurer told industry counterparts at a meeting here yesterday.

Ted Kelly, chairman and chief executive officer of Boston-based Liberty Mutual, told his audience that one of their biggest challenges is breaking from a commodity-based business modelâ€akin to one used by fast food giant, McDonalds.

Mr. Kelly ranked "differentiation based on service rather than price" as the second greatest challenge for insurers, during a keynote speech at the 18th Annual Executive Conference for the Property-Casualty Industry.

He ranked this challenge directly behind dealing with the federal government on issues like the extension of a terror insurance backstop, and keeping the government out of natural catastrophe insurance.

I couldn't agree more, and I believe I've said as much on this blog, and elsewhere, particularly in my recent ViewPoints article: The 21st century insurer: Beyond priceâ€Successful responses to shrinking opportunities

My suggestions for how insurers can avoid selling insurance "like dollar meals" are all laid out there, so I shall not belabor the point in this post.

Technorati Tags: Annual Executive Conference for Property-Casualty Industry, Insurance, Liberty Mutual, National Underwriter, Ted Kelly, ViewPoints

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Posted by James Taylor at 8:56 AM | Comments (0)

November 20, 2006

Shameless promotion

I just wanted to say how highly I think of InScope Solutions - an utterly wonderful group of people. Not only are they promoting and successfully implementing business rules, they are also helping spread the word by participating in The Business Rules Revolution book and with Brian Stucky's wonderful phrase "purple people".

On a completely unrelated note, I am now the proud owner of a Garmin Nuvi 350 Portable GPS! I won the Inscope draw at the recent Business Rules Forum when I was in DC. Is that cool or what?

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Posted by James Taylor at 12:17 PM | Comments (0)

New Blog design

Well I hope you like the new blog design. If you do, or don't, please let me know!

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Posted by James Taylor at 11:35 AM | Comments (0)

November 17, 2006

Customer-led Services at UC Berkeley

Last night I gave a presentation to Bob Glushko's class on Services Sciences, Management and Engineering. It was a lot of fun and I got some great questions from the students. In particular the discussion around privacy and around who owns information about your behavior was fascinating. It seems like a very interesting class.

Anyway, here are the slides and the links I suggested the students read as a prequel to the slides:

• Here's a way to have 1:1 communication and scale too
• Using decisioning to build the bank of the future
• Pay-As-You-Drive Insurance is Here!
• The customer's influence on decisioning

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Posted by James Taylor at 8:48 AM | Comments (0)

November 16, 2006

Insurers Focus On Attracting And Retaining Customers

(Posted by Guest Blogger, Ian Turvill.)

An article in today's Insurance Networking News focuses on the outcomes of a conference this week that has been exploring "approaches to attracting and retaining customers in various market segments". There were several points raised in the article that made me think about the value of applying Enterprise Decision Management to address this business challenge.

First:

"Panelists at the conference noted that insurance marketing programs must appeal to three distinct generational groups: Generation Y (ages 18-29), Generation X (ages 30-40) and baby boomers (ages 41-59). Each group has distinct demands for service; therefore, insurers must offer different Web-based services that address their consumers' varying levels of comfort with technology."

This sounds distinctly like a problem that EDM is able to solve. By controlling processes through centralized rules and analytics , and then controlling customer interactions through customer-facing operational systems, EDM allows organizations to readily treat their consumers in a segmented manner.

Second:

"Consumers are more comfortable with technology than ever before, but if potential customers on your Web site can't get a quote in seven to eight minutes, if it's too complicated or there are too many fields to fill out, they're going to bail. We need to make sure we have the internal talent or a third-party supplier that can continually streamline those processes," said Lori Lehmann, director of Legal Initiatives at Columbus, Ohio-based Nationwide Insurance Co

EDM can provide the ability to make decisions "on-the-fly" by adapting the questions that are presented to customers based on the responses given earlier. Where GEICO did this using Enterprise Decision Management, they were able to reduce the number of people dropping out of the online quotation process from 9 out of 10 to just 4 out of 10. In other words, six times more consumers were getting quotes from GEICO!

Third:

"As an industry we need to take a lesson from the fast-food restaurants by adopting more of a pilot approach," says Andrew MacDonald, vice president and CIO at The Hartford Financial Services Group, Inc., Hartford, Conn. "Before a fast-food restaurant rolls out a new product, it is tested in at least one market to determine customer acceptance. To enable this, it is very important to have flexibility in our systems so we can quickly and easily pilot new products."

Yep, good idea. But what about going one step further. The predictive analytics that EDM offers through "Pre-Market Offer Testing" allow insurers to test many different new value propositions and get robust data even before they take it the test market phase. The outcome is even faster "speed-to-market" and much broader sense of consumer preferences and behaviors. See my recent article in Fair Isaac's ViewPoints online magazine to see how this can be achieved.

Technorati Tags: Andrew MacDonald, Baby Boomers, GEICO, Generation X, Generation Y, Insurance Networking News, Lori Lehmann, Nationwide Insurance, Pre-Market Offer Testing, The Hartford Financial Services Group

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