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December 21, 2007
Happy Holidays, see you in 2008
(Posted by guest blogger, James Taylor)
Well, 2007 comes to an end and this is my last post of the year. This year we had more than 80,000 visits, 144,000 page views.I thought I would highlight some of my (and your) favorite posts. Starting with the 10 posts that got the most hits:
- Business Intelligence 2.0 and Enterprise Decision Management
- Why are business rules better than traditional code? (from 2005)
- Windows Workflow Foundation - rule engines and rule management (another oldie)
- The secret of business user rule maintenance (not a big surprise as I reference it a lot)
- Customer Segmentation to increase profits
- Business Rules, Business Decisions, Intelligent Processes, Enterprise Decision Management (a personal favorite)
- Book Review: Competing on Analytics (the only book review to make the top 10, though Execution. The Discipline of Getting Things Done came close)
- What's the difference between Business Rules Management Systems and Business Rules Engines?
- Different Perspectives (one of my favorites)
- Using decisioning to build the bank of the future (I was happy to see this one as I really like it)
The business rules FAQ index and the predictive analytics FAQ index were big draws and the case study index came in close behind.
Besides these I really like:
- Why do you need Adaptive Control? and the series that followed it as I think adaptive control is one of those areas that deserves more attention.
- What you need to know about Decision Services
- Why business rules? A curious reader asks.
- Think Micro not Macro - decisions, that is
Have a wonderful holiday season, see you in 2008.
Visit my Smart (Enough) Systems Blog(RSS) or my ebizQ blog (RSS). Buy the book or visit the companion wiki.
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Posted by James Taylor at 1:04 PM | Comments (0)
December 18, 2007
Using EDM to build loyalty to your organization
(Posted by guest blogger, James Taylor)
1:1 Magazine had an interesting post this week titled "Loyal to what" that discussed a challenge for companies trying to build customer loyalty. As the article says,
But what happens when those employees succeed a little too well -- when the customer ends up being loyal more to the salesperson than to the products and services offered by the company itself?
Clearly this is not what most companies have in mind. They are not trying to build loyalty to a specific individual, instead they are trying to create loyalty to the store or bank or brand. How to do this, how to manage the ongoing process of building and sustaining loyalty without unnecessarily transferring it to a specific employee is a challenge. One of those quoted in the article suggests
that companies institutionalize behaviors. "Starbucks has a huge employee training program, where they invest heavily to make sure it's a very positive experience. And yes you may love your barista, but if she leaves, the next one is going to be equally warm and friendly."
But what else can you do? Specifically, how can adopting enterprise decision management (EDM) help you increase loyalty and keep it associated with the company, not an individual.
The first thing to consider is why it is that customers become loyal to a particular person. Perhaps that person got something done for them or made them a really compelling offer. Perhaps that person took the time to read the customer's file and so dealt with them appropriately. Perhaps they just have a knack for making a customer feel loved. While there is not much EDM can do about the last one, there is plenty of opportunity for EDM in the overall management of customer loyalty.
If you focus on the decisions that customers want made about them (pricing, refunds, shipping prices and times, offers, loyalty programs) as well as those you want to make about them (cross-sell, up-sell, retention offers) and automate and improve those decisions using EDM you can do a lot:
- By automating decisions you can ensure that customers are not referred around the organization unnecessarily. The first person they speak to can act immediately because the system can deliver the answer without having to get a supervisor on the line. They won't become loyal to the individual who can "work the system" if the system empowers everyone.
- By embedding best practices as rules in a decision, every use of the CRM system can respond like the most successful, at least at some level. So the difference between the best and the worst customer service representative will be smaller making it less likely that one particular representative will become a customer's favorite.
- Using predictive analytics and statistically significant segmentation rules, customers can be treated more appropriately and in a more targeted fashion. Their wants and likes can be more accurately included in decisions made about how to treat them. Automation of this means that everyone can see the trends in the behavior and value of a customer and know what action to take. Trying to use dashboards and reports means that only the most analytically sophisticated representatives can do this, risking the transference of loyalty to them not the company.
- Automation means that the same decision can be delivered through any channel - at the store, on the web, in the call center. Consistency of treatment builds loyalty (assuming the treatment is not obnoxious) and knowing that you can get the same treatment from anyone keeps that loyalty linked to the company not to an individual. Instead of always coming to the branch to see a particular person they will know that they can use any channel and still get great service.
Great service and customer loyalty take more than EDM for sure, but EDM is a key component in a customer loyalty strategy that delivers loyalty to the company not to individuals. Other posts on this topic include:
- What's old is new - personalization is back
- Using EDM to make call centers work better
- Automating decisions for better customer service
- Using EDM to meet CRM challenges and use what you know about your customers
- New White Paper on Smart-Enough Customer Decisions
- Using EDM in the loyalty economy
- A refund story
- Customer Loyalty, EDM and "the corner store"
Visit my Smart (Enough) Systems Blog(RSS) or my ebizQ blog (RSS). Buy the book or visit the companion wiki.
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Posted by James Taylor at 8:07 AM | Comments (0)
December 17, 2007
SOA Patterns - your chance to contribute
(Posted by guest blogger, James Taylor)
Thomas Erl is working on a new book of SOA patterns and is looking for suggestions and input. You can read about the project on www.soapatterns.com. In particular, those of you interested in decision management might want to provide comments on the Rules Centralization pattern using the form here. If you have additional pattern suggestions, you can submit those here and if you have ideas for patterns you can submit those here.
I am going to cross-post this to all three blogs, apologies if you get it more than once as a result.
Visit my Smart (Enough) Systems Blog(RSS) or my ebizQ blog (RSS). Buy the book or visit the companion wiki.
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Posted by James Taylor at 12:54 PM | Comments (0)
December 14, 2007
Standardizing Decision-based Approaches
(Posted by guest blogger, James Taylor)
I attended a meeting of the OMG this week and we had some good discussions that both showed the value of externalizing decisions and pointed the way for decisions to become a solid part of the OMG standards stack.
One of the first presentations was on rules-driven process modeling. There was some interesting discussion of structural rules that define state (e.g. gold customers are…) and operative rules about activity (such as when to make a customer gold or when to allow gold customers access to the warehouse). However the subsequent discussion tried, in my view, to distort how rules and processes should be managed and defined to try and unify them in some way. In fact the addition of a decision to the process and the mapping of decisions to rules would work much more simply. In the example, for instance, there are other rules like "customers can only have access to the warehouse during business hours" and a fairly complex way of managing these was proposed whereas the inclusion of a decision "can customer have access now" - would have simplified the process and made it easier to manage the rules. This presentation made it pretty clear to me, and others I think, that rules and process can be hard to bring together if the reason the rules are in the process is not to control the process but to make decisions.
Later in the day there was a presentation on case management as a special class of processes. The point was made that case management processes are different and it is not clear how a standard like BPMN can support semi-structured and case management processes. Case management is different because it is less structured, less repeatable, data-centric and rules-based. It is not really flow based and so flow modeling is very unhelpful. Instead a more state/event and rules-based approach was proposed with lots of case-centric events taking place over time including follow-up and milestone events. These events were linked to activities using rules to control the allowed states - to define the applicability rules of activities. In this scenario decisions also work well as the decisions would be “what is expected next” and “what is allowed next” and “is case complete” defined for each kind of case. The point that both a flow-centric and a case-management-centric approach pointed to the need for decisions was not lost on anyone.
The session in which I participated was to discuss the inclusion of decisions as a formal concept in some of OMG's standards. Paul Vincent of TIBCO went first and talked about decision models and the potential value of having a formal decision model to intersect between some of the existing standards such as SBVR (Semantics of Business Vocabularies and Rules - policy rules), BMM (Business Motivation Model - strategy and tactics design) and PRR (Production Rule Representation - business rules at the execution level). I would add use cases / requirements too as the intersection between rules and requirements is best served with decisions. Larry Goldberg of KPI presented next on some of the practices and real-world experiences of KPI around decision models.
KPI's definition of a business decision is a judgment, based upon business criteria, about a business concept or about a business concept attribute that the business is interested in managing. Determine, assess, compute are all good candidate names. Larry said, and I agree, that it is critical not to just gather rules and then try and see what can be done but to focus on decisions. The KPI model of decision is purely declarative but I think that is limiting even though they are considering the platform/computational independent model. While Larry feels that any procedural stuff should be considered part of the process, I think non-simple business decision can easily have multiple steps each of which is declarative.
To the audience’s relief, I then spoke about the challenges and opportunities in using decisions, both to link process and rules and to bring data/analytics (via formats such as PMML or the Predictive Model Markup Language) into the IT mainstream. The whole group had a spirited discussion but it went very well and we ended up with broad agreement as well as some action items and suggestions.
- Should business
decisions be defined in BMM alongside, or instead of, business
rules?
Me I think that some decisions are important enough to be modeled at a strategy level. For instance, the way a bank decides to underwrite a loan is likely to be critical to its strategy and so should be modeled along side goals etc. - The BPMN/BPDM standards are merging and should include the concept of a decision activity.
This may be simply an implementation of a certain kind of behavior but I think it deserves some kind of explicit modeling within a process. - The need to discuss decisions in the context of Use Cases was identified
It is increasingly a best practice to keep decision rules out of use cases and mapping both the use case and the rules to a decision allows for maximum clarity and reuse. - Decisions have certain characteristics in a system sense
We discussed many such as being stateless, not updating the state of the core business objects and so on.
Anyway, it was an interesting discussion and hopefully there will be a role for explicit decision definitions and/or models within the OMG business model stack. The discussion will no doubt continue in future OMG meetings before going to an RFI/RFP stage, or maybe being stealthily adapted by the existing standards. Or maybe Larry and KPI will propose the OMG adopt their model, just like the Business Rules Group did with BMM. Regardless those of us in the decision management community will keep pushing to get broader adoption of the key principles involved and so make it easier for everyone to adopt EDM.
Some other posts you might find interesting include:
- Bridging strategy and technology with enterprise decision management
- Please don’t just “unify” rules and process
- Use Cases, Business Rules and Decisions
- Live from Business Rules Forum (almost) - Getting It Right. Rules and Requirements in Software
- Live from Business Rules Forum - From Business Rules to Enterprise Decisioning
- Production Rule Representation at OMG - a sneak peak
Visit my Smart (Enough) Systems Blog(RSS) or my ebizQ blog (RSS). Buy the book or visit the companion wiki.
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Posted by James Taylor at 8:12 AM | Comments (0)
December 12, 2007
EDM and interesting business technology trends
(Posted by guest blogger, James Taylor)
Visit my Smart (Enough) Systems Blog(RSS) or my ebizQ blog (RSS). Buy the book or visit the companion wiki.
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Posted by James Taylor at 7:13 PM | Comments (0)
December 7, 2007
The future of BI had better include EDM
(Posted by guest blogger, James Taylor)
A number of posts on what the future of BI is likely to bring caught my eye this morning and I thought I should add my thoughts on how I see BI evolving into EDM. Several trends, I believe, are going to force people to move beyond thinking about BI to thinking about EDM.
- If BI is about actionable insight, EDM is about insightful action
BI practitioners are focused on delivering "actionable insight". Not data, not information, but information that adds new knowledge to the person consuming it and does so in a way they can take action. This is all fine and good but there is no guarantee that the action will actually be taken. The person seeing the insight may not understand or feel authorized to take the action. The action might need to be taken in an automated way to support a web or other real-time transaction. EDM recognizes this need to ACT not just to know. - Operational BI is (almost) an oxymoron
Operational BI has several problems. Firstly, BI tools are mostly focused on analyzing the past and so are insufficiently real-time to deliver something "operational". Secondly, operational systems are often used by people who do not have the analytic skills to use BI. Thirdly, operations are increasingly 100% automated or "straight through" and so there is no-one there to see the results of BI at all. EDM, in contrast, is tightly focused on the kinds of high-volume, rapid-response, operational decisions involved in operational systems. - BI tends to focus on the past
The use of BI to understand what has happened in the past and to be able to see both what happened and why is necessary to a business improving itself. But it is not sufficient. Effective decision-making requires predictions about the future not just understanding of the past. EDM revolved around the use of predictive analytics to help make decisions that take into account not just what is known about a customer or a transaction, but what can reasonably be expected of that customer in the future. - Business process management needs analytics but not BI
The functionality common in BI systems is not very helpful once an organization starts automating business processes using a BPMS. The ability to take process information (state, history) and data and help a process complete more effectively requires EDM not BI. BI might help you understand what happened in all the various instances of a process but it is not going to help you improve each instance, at least not if you have very many instances! - Traditional BI does not add enough to Enterprise Applications
With many of the big traditional BI vendors now bought by enterprise applications vendors we are going to see an interesting issue arise. While companies have been frustrated that they cannot use the information resources created by their enterprise applications, they are also frustrated by the inability of the enterprise application to learn from this data. The growing support for BI from the EA vendors will help companies understand the data in those enterprise applications but it won't make the enterprise applications any smarter. EDM, in contrast, is an effective way to inject more "intelligence" into those enterprise applications and so turn that data into better operations.
However hot BI is as a market, and it's pretty hot, it is not going to solve the operational issues that companies have. EDM builds on BI to take it to the next level and that's where companies need to be going.
Here are the links that prompted the post.
If you want more on BI v EDM there are many posts on this blog and my others (ebizQ and Smart (Enough) Systems). So many, in fact, I am not going to list them all!
Visit my Smart (Enough) Systems Blog(RSS) or my ebizQ blog (RSS). Buy the bookor visit the companion wiki.
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Posted by James Taylor at 2:06 PM | Comments (0)
December 4, 2007
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.
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Posted by James Taylor at 2:11 PM | Comments (0)
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.
Share:
Posted by James Taylor at 2:11 PM | Comments (0)
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.
Share:
Posted by James Taylor at 2:11 PM | Comments (0)
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.
Share:
Posted by James Taylor at 2:11 PM | Comments (0)
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.
Share:
Posted by James Taylor at 2:11 PM | Comments (0)
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.
Share:
Posted by James Taylor at 2:11 PM | Comments (0)
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.
Share:
Posted by James Taylor at 2:11 PM | Comments (0)
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.
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Posted by James Taylor at 2:11 PM | Comments (0)
Progressive Financial Services Firms Demonstrate the Value of Predictive Analytics
(Posted by guest Blogger, Gib Bassett)
Yesterday an article I wrote about Decision Services appeared in the online edition of DM Review’s December 2007 issue (Decision Services: Pragmatic Real-Time Analytics). In it, I describe how Decision Services, by providing a framework for managing both business rules and analytics, can pave the way toward adopting predictive analytics.
One of the points I make is that many firms rushed to adopt predictive analytics for a single business function or customer channel, and this focus bit many an organization by overlooking the broader application of predictive analytics to the entire enterprise. Decision Services offer a solution to this problem by serving as the basis for rules automation today while providing a growth path to including predictive models in a deployment. This point is driven home in a Nov. 12, 2007 article brought to my attention today in Insurance amp; Technology Magazine, titled “Predictive Analytics and Complex Event Processing Technology Move to Cutting Edge of Financial Services Industry.” The article raises several examples of how leading financial services firms have successfully deployed predictive analytics by taking an enterprise view. Consider:
“A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide, he (Bravo) says.”
Another example:
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
While any financial services firm would like to implement predictive analytics and reap similar benefits, the first steps are not always clear, a problem raised by the Data Warehousing Institute's Wayne Eckerson mentioned in this recent blog posting. Also in that posting, I refer to Decision Yield as one way many organizations are determining their initial foray into predictive analytics as part of an Enterprise Decision Management (EDM) strategy. Financial Services firms of any sophistication owe it to themselves to explore how predictive analytics could positively affect their business and a Decision Yield assessment can point the way.