The first webinar from my ongoing research into Decision Management Systems Platforms is coming up on February 16th at 10am PT – register here  for Four platform capabilities for Decision Management Systems.

This webinar kicks off what will be a continuing series of webinars as our definitive report on Decision Management Systems Technology Platforms comes to fruition in 2012. The report will describes the wide range of technology available – Business Rules Management Systems, Predictive Analytic Workbenches and Optimization Suites, as well as recent advances around in-database analytics.

Based on the initial version of the report, this webinar will describe the four key capabilities of a Decision Management Systems Platform and show how these components fit together into a coherent architecture.

Look forward to seeing you there.

I have recently begun writing a column for Information Management magazine and a new one just published. The magazine is now completely online at www.information-management.com/ and you can follow their twitter feed @infomgmt. Here are my columns so far:

• Requirements for Advanced Analytics
  How to use decision management techniques to effectively gather the requirements for data mining and predictive analytic projects
• Four Principles of Decision Management Systems
  Highlights of the critical principles described in my new book, Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.
• Where to begin with Predictive Analytics
  Some suggestions on how to find the kind of decisions that will repay an investment in predictive analytics.

A customer of mine is looking for a Drools developer to be based in Ann Arbor, MI:

HealthMedia, a member of Johnson & Johnson’s Family of Companies, is recruiting for a dynamic Java/Drools Developer to join our top-notch software development team, located in Ann Arbor, MI. This talented individual will be building applications using Drools/Java/J2EE (or equivalent) technologies. HealthMedia (HMI) is the global leader in online delivery of behavior change interventions.  Our behavioral support programs create unique health improvement plans for each individual.  This plan and the subsequent continued care plans teach the participant how to achieve his or her individual goals.
This individual will be responsible for the design and implementation of various aspects of industry leading software applications, focused on leveraging a Rules engine.

Responsibilities include rules-based development, design recommendations, analysis and design, testing and QA etc. They are looking for someone with a degree in Computer Science or equivalent experience.  5+ years experience with JAVA and 2+ years of experience using DROOLS.

Apply here

2011 has been a great year for market awareness of Decision Management as an approach and of the value of Decision Management Systems. Product, partnership, acquisition and funding announcements have enhanced the available technology. As vendors continue to improve and enhance their product offerings to fully support Decision Management this is only going to reinforce and further grow the market.

There is a wide range of technology available for building Decision Management Systems. Business Rules Management Systems, Predictive Analytic Workbenches and Optimization technologies can be used alone or in combination to build custom Decision Services. In-database analytics and other analytic infrastructure can be used to maximize the effectiveness of predictive analytics.

In each Decision Management Systems platform product category, products will be reviewed and key elements for successful Decision Management Systems (such as for business user analytic modeling, in-context rule management, decision simulation etc) identified and highlighted with “badges”. Customer case studies and use cases will be used throughout. An index of vendors and product categories will be provided. The list of vendors identified to date are listed below (if you have suggestions for additional vendors, please drop me a line at info@decisionmanagementsolutions.com).

The report will be available free to download as a white paper and as a web page at decisionmanagementsolutions.com/decision-management-technology where we currently have links to our existing First Look product reviews.

Obviously one of the challenges with a report like this is deciding who to include. With that in mind we have identified a few criteria:

1. While there are many innovative companies developing complete solutions – pre-configured Decision Management Systems – we will be focusing in this report on tools for building custom systems. To be included it must be possible to build a custom Decision Management System with the product.
2. The technologies must be productized, released, for sale, and must have at least one production customer who can be contacted.
3. The product should be sold generally, not only to companies that have also bought a pre-configured application – it must have customers and a market presence as a genuine platform technology.
4. The business rules, analytic or optimization technology involved cannot only be an OEMed product that is included separately in the report.

There is no fee for vendors to participate and everyone will be covered, sponsor or not.

We have begun work on this report and will be releasing it over the first two quarters of 2012. The first installment of this report will include the business case for Decision Management Systems, the overall technology architecture and a list of product categories and vendors. Over the following months we will publish First Looks on all the technologies involved and several updates to the report:

• Initial report early Q1 2012
• Report update with “badges” late Q1 2012
• Report update with customer stories and use cases early Q2 2012
• First complete report late Q2 2012
• Report update for new/revised products Q4 2012

We will announce each new release and all the related activities in our newsletter, which you can subscribe to here.

Candidate Vendors

• 11Ants
• Angoss
• Attensity
• Be Informed
• Bosch Software Innovations
• Clario Analytics
• Experian
• FICO
• FuzzyLogix
• GDS Link/Modellica
• Gurobi
• IBM
• IDIOM
• Infocentricity
• InRule
• Jboss
• KNIME
• KXEN
• MathWorks
• Microsoft
• OpenRules
• Oracle
• Pega/Chordiant
• Predixion
• Progress/Corticon
• Revolution/R
• Salford Systems
• SAP
• Sapiens
• SAS
• Sparkling Logic
• Statsoft
• Teradata
• Tibco
• Transunion
• usoft
• Yottamine
• Zementis
• Zoot

Don’t forget, if you have suggestions or comments on the vendor list please email us at info@decisionmanagementsolutions.com.

We have announced the full results of our Predictive Analytics in the Cloud survey. The results are available as a white paper and as a recorded webinar – go to smartdatacollective.com/predictive-analytics-cloud to register for all the deliverables. There were some interesting results and I thought I would share a few.

• The core focus for predictive analytics, and for predictive analytics in the cloud, is improved targeting and development of customers. It dominated the top outcomes from predictive analytics as well as the top areas of focus in both predictive analytic sand cloud.
• All five of the scenarios – cloud-based predictive analytic solutions (decisions as a service), cloud-based deployment of predictive analytics into SaaS applications, cloud based deployment of predictive analytics to on-premise applications, using cloud-based data in modeling and pushing modeling to the cloud – were seen as powerful with no obvious winner. None of them are that widely adopted yet but, as you would probably expect, pre-packaged analytic applications did best. The runner up was the use of cloud to embed predictive analytics into on premise applications – an interesting result that shows the importance of deploying predictive analytics not just building the models.
• Decision Management was clearly an important element for successful analytic adopters. We asked companies how they used predictive analytics and overall people were split between predictive analytics providing occasional insight and predictive analytics being tightly integrated in operational systems (the basis of Decision Management). But when you focus in on those who have already seen significant positive results from predictive analytics the percentage tightly integrating predictive analytics into operations rose while occasional use dropped. Among those transformed by predictive analytics a whopping 2/3 tightly integrate their predictive analytics with day to day operations! The power of decision management.
• These more successful companies also valued different types of data for building models. Near real-time and real-time data were seen as more important by the respondents overall but among those with more experience both batch and static data scored much higher – experience clearly shows that less volatile data can be valuable too.
• Finally a couple of surprising negative results. I really thought that more experience with predictive analytics would make people more tolerant of “black box” models but in fact the percentage who really wanted transparency in their models started high (well over half) and climbed to 80% among those with the most positive results so far.
• Even success does not make people comfortable with black box models it seems. On the cloud front I really thought that transaction based pricing – pay as you go – would be a big driver but it did poorly across the board. Reducing the demands on IT and empowering the business were what people were looking for from cloud. I think transaction pricing has a lot to offer folks with decisions as a service cloud-based solutions in particular but it’s not apparent that the survey takers agree with me.

Register at SmartData Collective for more. Thanks to Clario Analytics, FICO, Opera Solutions, Predixion Software, SAS, Teradata and Toovio for sponsoring.

I listened in to IBM’s call about their recent analytics study conducted with MIT Sloan – The Widening Divide (available from www.ibm.com/thewideningdivide). This is the second year for the analytics study and surveyed 4,500 people from 30 industries and 120 countries – a very broad view. Three key results:

• The competitive advantage created by analytics is widening
• Competitive analytics involves three competencies
• There are two distinct paths to analytic sophistication

The survey results divide companies into three buckets – Aspirational companies just thinking about analytics, Experienced companies with some solid progress on analytics and Transformed companies, those with advanced capabilities and significant results. This year showed a slight increase in transformed companies but not much change over 2010 despite a lot more survey respondents. While these segments were self-assessed, the team did validate them with some clustering analysis and found that there was strong correlation between those in the segments (though Experienced companies split into two groups as we shall see).

The ability to create competitive advantage from analytics is way up from last year – from 37% who felt they were creating a competitive advantage from analytics in 2010 to 58%. And those that reported this competitive advantage were 2.2x more likely to be outperformers (up from 2.0x last year). More competitive advantage seen and more impact from it.

Transformed organizations were particularly likely to integrate analytics into their operations (over 70%) while Experienced organizations also said this (55%). Decision Management clearly being more important among Transformed organizations and these organizations were 3.4x more likely to be outperforming their peers.

Transformed organizations got their advantage by focusing on three areas:

• Making decisions faster (72% say this is an area of intense focus v 49% for Experienced)
• Enterprise risk (86% v 6%)
• Customers (62% v 49%)

This is another pointer towards Decision Management in my mind – automating decisions (or partially automating them) with Decision Management Systems is key to making them faster. Decision Management Systems also have two key areas of focus – managing risk (rather than simply monitoring it) at the point it is acquired and improving customer treatment.

The study asked a lot of questions about analytic usage in different areas. High percentages used analytics in finance (budget, forecasts) as well as in monitoring operational processes. These dashboard-and reporting-oriented analytics were very widespread, even among laggards, and there was not much of gap between Experienced and Transformed organizations. In strategy and HR topics the Transformed organizations were getting close to 50% usage of analytics while everyone else was lagging behind. In terms of focus on customers Transformed don’t make 50% for anything except identifying prospects while Aspirational were down around 20% and Experienced around 30-35%. Clearly there is room to compete using analytics to target and manage customers. Personally I would like to have seen a very different set of areas of use as this list was far too focused on reporting/dashboard analytics for my taste.

Moving on, Transformed organizations have mastered three areas:

• Manage the data
  Solid information foundation, standard data management practices and make insights accessible and available. For instances nearly 5x more likely to integrate data effectively and 4x more likely to make insights available
• Understand the data
  Analytic skills are valued and developed, enabled by a robust set of tools and delivering action-oriented insights embedded in processes – a clear Decision Management reference
• Act on the data
  Fact-driven leadership, analytics as a strategic asset and strategy and operations guided by insight. Need a data-oriented culture.

The most distinctive characteristics of Transformed organizations (those with the biggest difference in how much focus they give them relative to others) are:

• Ability to analyze data
• Ability to capture and aggregate data
• Culture open to new ideas
• Analytics as a core part of strategy and operations
• Embed predictive analytics into processes (my emphasis)
• Insight available to those who need them

This list was fascinating from a Decision Management standpoint. Embedding predictive analytics into processes means Decision Management Systems and these kinds of systems make insight available to those who need them even when those people are not themselves analytic (store staff, call center staff) or are not even people (websites, self-service applications). Decision Management Systems make analytics a core part of operation and a culture open to new ideas is critical for the kind of test and learn experimentation that is important for good Decision Management Systems. All good stuff.

Finally the study tried to see how people get from Aspirational to Transformed and found that Experienced companies, those on the journey, showed a strong bifurcation into two paths. One of these is focused on information management, on collaboration and on a top-down strategic approach to push analytics fairly evenly across the organization. A platform strategy if you like. The second path is more focused on developing analytic skills and tools in specific lines of business. Both can lead to transformation, they are just different. The collaborative organizations were trying to get broad improvement in customer engagement for instance while the specialized organizations were more focused on specific key performance indicators.

Interestingly the specialized group included many folks who found organizational change in their organizations difficult but technology change was easier. In contrast collaborative organizations struggled to deliver more advanced analytic capabilities, like predictive analytics, because they are spending too much time on a broad-based platform.

IBM recommends three steps:

1. Assess your current analytic sophistication – know where you are
2. Focus on improving your competencies – information foundation, analysis skills and tools, create a culture that ACTS on analytics
3. Have an overall information agenda to tie things together

A good list and I especially liked the point about building a culture not of analysis but of acting on analysis. Those who know first don’t win, those who act first do!

We are announcing the full results of our Predictive Analytics in the Cloud survey next week (Thursday, 10am Pacific) and will be making the results available also as a white paper at the same time. You can register on SmartData Collective for access to the white paper when it is published and for other deliverables including a new podcast on sneak peak results. I thought I would give everyone a sneak peek of some interesting results.

• One of the questions we asked was about the top outcomes of using predictive analytics. Improved targeting/allocation of resources was widely cited and frankly that was a bit of a surprise.
• All five of the scenarios – cloud-based predictive analytic solutions (decisions as a service), cloud-based deployment of predictive analytics into SaaS applications, cloud based deployment of predictive analytics to on-premise applications, using cloud-based data in modeling and pushing modeling to the cloud – were seen as powerful.
• Decision Management was clearly an important element for successful analytic adopters. Among those transformed by predictive analytics a whopping 2/3 tightly integrate their predictive analytics with day to day operations!
• Companies with more history of analytic success also placed a higher value on less volatile data than those with less experience.
• More experience with predictive analytics does not make people more tolerant of “black box” models
• Transaction based pricing – pay as you go – unexpectedly did not score highly as a motivation for cloud solutions.

See you on Thursday for more.

Our Predictive Analytics in the  Cloud survey closes today so this is your last chance to tell us what you think. We’ve had hundreds of replies but we want yours too.Go to smartdatacollective.com/predictive-analytics-cloud-survey to take the survey – should only take you 10 minutes tops. If you started it and never finished, now would be a good time to go back and complete it!

Don’t forget also to register for the results as well as all the other lovely deliverables from the study at smartdatacollective.com/predictive-analytics-cloud.

My new book - Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics – is officially shipping – you can now order it (rather than pre-order it) at IBMPressbooks.com, at amazon.com or at InformIT.

Build Systems That Work Actively to Help You Maximize Growth and Profits

Most companies rely on operational systems that are largely passive. But what if you could make your systems active participants in optimizing your business? What if your systems could act intelligently on their own? Learn, not just report? Empower users to take action instead of simply escalating their problems? Evolve without massive IT investments?

Decision Management Systems can do all that and more. In this book, the field’s leading expert demonstrates how to use them to drive unprecedented levels of business value. James Taylor shows how to integrate operational and analytic technologies to create systems that are more agile, more analytic, and more adaptive. Through actual case studies, you’ll learn how to combine technologies such as predictive analytics, optimization, and business rules—improving customer service, reducing fraud, managing risk, increasing agility, and driving growth.

“A very rich book sprinkled with real-life examples as well as battle-tested advice.”
Pierre Haren, VP ILOG, IBM

“James does a thorough job of explaining Decision Management Systems as enablers of a formidable business transformation.”
Deepak Advani, Vice President, Business Analytics Products and SPSS, IBM

You can buy at IBMPressbooks.com, at amazon.com or at InformIT.

We have had great response to our Predictive Analytics in the Cloud survey – thanks to KD Nuggets for their support in particular this last week – and I have recorded a podcast with some of the initial highlights. We are leaving the survey open through the end of Predictive Analytics World so you can still tell us what you think.

Here’s a couple of the highlights:

• There seems to be more interest from those working in predictive analytics than from those working in cloud with 40% having established predictive analytics as critical to decision-making in their organizations (much higher than overall averages).
• Cloud has had less impact so far on our survey takers but most have specific plans with a focus on reducing internal IT demand. 40% have some standardized approach to cloud software which is interesting and shows how quickly cloud has become a standard deployment option.
• Interest is pretty even across the 5 areas we identified in the position paper with embedding predictive analytics in SaaS applications being the most widely seen use of predictive analytics in the cloud. The potential for using predictive analytics in the cloud to push analytics into legacy applications scores high marks for the future though.
• Reduced time to market is clearly the most important driver for adopting predictive analytics in the cloud.
• Data security and privacy concerns as well as regulatory issues are barriers.
• As I noted before there is a curious absence of IT management and lots of marketing people.

So, tell us what you think and take the survey. Don’t forget to also register so you can get access to the results as we publish them.