I am giving a webinar with Gagan Saxena (@Gagan_S), CIO of Apple Vacations on A New Approach to Business Design at Apple Vacations on May 22, 2012 at 10:00 am PT, 1:00 pm ET.

Gagan and I will present how how Apple Vacations applied Decision Management to redesign the consumer experience and create a “smart system” that would bring the new capabilities of business rules management, analytics, big data, predictive modeling, complex event processing and natural language processing into the organization.

I hope you can join us. Register here.

I am giving a webinar with Gagan Saxena (@Gagan_S), CIO of Apple Vacations on A New Approach to Business Design at Apple Vacations on May 22, 2012 at 10:00 am PT, 1:00 pm ET.

Gagan and I will present how how Apple Vacations applied Decision Management to redesign the consumer experience and create a “smart system” that would bring the new capabilities of business rules management, analytics, big data, predictive modeling, complex event processing and natural language processing into the organization.

I hope you can join us. Register here.

Johan Gerber of MasterCard kicked things off on day 2. MasterCard does not actually issue the cards with their logo on, they are a technology company that provides a network to link consumers, 32,000,000 businesses and 22,000 card issuers. The network is very high performance, handling 100 pieces of information and processing each transaction in less than 130 milliseconds and can handle 14B instructions in a second.  Security, back up, data management and volumes are all extreme. To build this network, MasterCard partner with IBM to provide Decision Management and message handling. They can now deploy new rules for a bank issuer in just a few hours using hot deployment of rules directly into the IBM platform within their network. Rules for regulatory requirements, to manage a fraud attack or to deliver new products and services deployed directly to the network more or less immediately. This immediacy drives opportunity for new product innovation, product differentiation and more.

After a very cool demo of some future thinking, Steve Mills came on to follow up. Steve’s focus is on transaction processing – how to manage transactions with certainty, with control and with suitable response to problems. Obviously IBM has a long history in managing transactions, helping manage the structure and execution of transactions in an ever more complex environment. With 800M+ payment card transactions a day, projections of 10B proximity transactions in 2016 and other scale and mobility challenges must intersect with transaction management capabilities to ensure that the infrastructure can support these transactions. Steve pointed at MasterCard’s network as well as Marriott’s that handles 1,500 transactions/second with sub-second response times, China Mobile’s 148M transactions/day from 600M customers. Support for these kinds of networks means handling process integrity and ensuring that a high percentage of transactions get through without errors or problems. This was hard enough with a single, coherent, integrated system. With the kind of distributed, heterogeneous environments common today this problem has just become more challenging.

This is what IBM is trying to deliver with its platform. IBM continues to invest in its transaction processing capabilities, making sure that companies can use SOA and get the kind of scalability and integrity they need while taking advantage of the modularity and agility of SOA. Steve sees a particular focus going forward on integrating mobile, handling big data and in-line analytics as well as cloud deployment and social media.

Katie Linendoll introduced the next customer story, The Ottawa Hospital. The critical challenge with hospitals and medical delivery generally is that the technology cannot disrupt the interactions between medical professionals and patients. Part of the solution to this is the use of mobile devices, so that medical professionals can use technology at the bedside. This builds on the general availability of an electronic medical record, a foundation or “step 0” capability. But you also need to manage the processes and decisions involved too. Ottawa Hospital is trying to manage the processes for consistency, completeness and to keep patients and medical professionals engaged in the care process. In particular they focus on the end to end process from check-in to check-out.

The use of mobile devices allows them to replace paging with a more interactive communication, allowing the “paged” doctor to access the record of the patient, confirm they are coming etc. The devices allow the doctor to show patients information and images particularly. The hospital is also managing the large numbers of people involved in care, both at the hospital and after discharge. The system will allow everyone involved to see and contact the rest of the people involved. Of course if you can add decision management to this you can do things like automatically manage discharge regulations and policies, identify dangerous drug interactions, check dosages and more.

Phil Gilbert, VP of BPM and Decision Management, came up next to show off his new release of Business Process Manager. Last year they announced release 7.5, the first integrated set of process capabilities post the Lombardi acquisition. Since then they have also merged the Business Events and Business Rules products into the new Operational Decision Management product. Both these products deliver visibility and governance at scale for businesses and Phil is clearly proud of how integrated and flexible these products are. Beyond this the team is continuing to integrate these products into z/OS, into Tivoli Security Management, the MDM product, the Advanced Case Management/ECM product and SAP. In each case the integration is beyond the execution level offered by SOA and includes design-level integration. The new platform, release 8, is in Phil’s view the first of a new class of platform that lets you truly integrate existing applications into a flexible process and decision platform.

With version 8 of BPM and Decision Management the focus is on real-time collaboration across the functionality. New features being demonstrated today include a new decision console, a global rules search, a new more social process portal, a designer for coaches, real-time task collaboration and in-line task completion. The existing decision console is web-based and less technical than the Eclipse editor for business rules but it is still pretty technical. The new interface is much more Blueworks-like with a nice look and feel with a social tone showing comment streams, supporting the following of rules, recent edits, collaboration etc. Users are taken directly to the right editor with a re-written editor as well as a new Facebook-like timeline and support for global search. A radically redesigned interface that is clearly aimed at business users working on business rules.

Similarly the BPM portal has been re-written to focus on a more social, task-oriented interface. This is also supported by a completely documented set of APIs so that customers can integrate their own elements. Redesigned coaches allow some nice forms for working the tasks and Phil was very bullish about the new designer for these, claiming it is dramatically easier to develop these interfaces. The task interface displays an information stream about the specific process instance including all those who have worked on it. The interface also uses analytics to predict who might be useful to add to the process instance based on their previous work. Real-time collaboration is possible with people in different locations working on the same task at the same time. Both task editing environments show what the other is doing in real-time. Finally Phil picked up his iPad and showed how you can access BlueWorksLive and Business Process Manager from the app complete with nice UI, photo attaching and more.

BodyMedia came up next to talk about their new tracking device and the associated decision management system. BodyMedia has a body sensing device that collects information about your activity, health and well being. They have conducted studies showing weight loss and health management being more successful when the body sensing technology is used. Whether it is managing weight, sleep or diabetes, the senor provides information both to the person being monitored and their care givers.

The solution involves an arm band with four embedded sensors reading 5000 data points a minute. This information is fed through machine learning algorithms and other decision management systems and the results are fed back to laptops, mobile devices etc. The armbands are getting smaller, cheaper and more capable. The software processes the data and displays usable results like quality of sleep, calories burned, amount of activity and more. The use of business rules to build a decision management system allows them to personalize and direct the advice and suggestions being made. In the future this could be combined with a smart home to make your home environment an active participant in managing your health.

Bob Suter came on to talk about mobile. 10 Bn mobile devices by 2020, 60% of CIOs put mobile as a priority and mobile apps can generate 45% improvements in productivity. Clearly he says mobile matters and is affecting every industry. To bring mobile to bear on problems you need to build the systems and connect the devices, you have to manage the devices and handle security and only then can you extend and transform your business using these mobile devices. To support all this IBM has released a new Mobile Foundation including an IBM version of Worklight (a recent acquisition), WebSphere Cast Iron and an endpoint manager for mobile devices. Plus IBM has launched some new services to help, including some quick start consulting packages.

And with that, I have to go get ready for my session.

After a great introductory session from Walter Isaacson (biographer of Einstein, Franklin and Jobs), we kicked off the main session at IBM’s largest IMPACT conference with over 8,500 attendees with a focus on re-thinking IT.

Marie Wieck started by discussing how core applications are changing. IBM claims that $5Trillion run on IBM hardware. These systems must be increasingly integrated with Billions of mobile devices – everything from smart phones to sensors like the Bodymedia armbands. This drives a need for cloud, for open APIs, for automated interactions (powered by decision management systems presumably). More of the transactions handled by IBM systems are being initiated using multiple channels, mobile devices and new forms of communication. Meanwhile more people have an impact on your business both business contacts and consumers, increasingly using social media of various forms. Cloud, mobile, social and more are creating an interconnected economy. To support this IBM believes you need an open, robust SOA that can handle the different kinds of workloads, transactions and interactions being driven by this change- new development patterns, new cloud deployments, new APIs and new Apps.

Whirlpool came up next – another 100 year old company with $19B in revenue across 170 countries. This drives them to create an IT platform focused on globally scalable value chain solutions. Consumer engagement is changing as the web channel moves from simply seeking information on a web page to a more interactive, social environment that involves e-commerce and can be mobile, global. Global transactions in the value chain are scaling in volume and the need to support a global supply chain makes for ever more complex transactions. These trends push Whirlpool to aim for a platform that is geared for real-time global engagement. Scale, reliability are critical – you can’t just chase the latest cool technology, you must deliver reliable real-time transactions.

Marie came back to talk about some of the requirements that customers have asked for that are driving IBM products:

• Faster release cycles (weeks or days not months or years) driving better visual tooling, smaller footprint and improved support for virtualization in the application server.
• Demand for mobile devices and a rich user experience on those devices is driving a new mobile foundation that supports Android, iOS and Windows with a single development platform and management server.
• The need to integrate BPM, decisions and analytics to support a complex logistics platform, for instance, has driven a new release of the BPM product with a new portal for collaborative work as well as support for cloud deployment and mobile integration.
• New releases in the SOA platform include updates to MQ and Message Broker focused on performance and broad integration with common industry standards. A Cast Iron Live makes creating and releasing APIs quicker and easier while providing stronger tracking of API usage for ongoing analysis.

Huntington Bank came up next, founded in 1866 with total assets of $50B (just big enough to be Fed regulated) and 11,000 employees (small enough to be nimble) with a very mid-west focus. To make progress in the current financial climate, Huntington has been somewhat contrarian with a focus on eliminating fees and making it easier for customers to avoid penalties. While this did hit their fee revenue per account, it has been more than offset with new business. To support the changing business, the IT department has refocused on skills-based ITIL shared services organization rather than silos. A focus on agile, on shared services and on information integration have been key. They have moved from a highly customized aging legacy platform to a new WebSphere platform. This is great but it’s more important as a platform for processes and decisions with the BPM and business rules products that will allow faster time to market and more business empowerment.

Marie returned to talk about challenges. With 25% of IT projects being over budget and 34% still being deployed behind schedule IBM is offering its new IBM PureSystems that combine built in expertise, integration and cloud readiness. At IBM IMPACT they are launching PureApplication and Jason McGee showed a high speed video of the 4 hours they claim it takes to install. All the software is pre-installed and power and network cables can be plugged in to connect it. A wizard links the system to the network and then the system initializes itself. A web configuration tool is then available to push new applications on to the server. Over 150 applications from IBM and partners are available for rapid install to the system, which handles all the configuration, workload management, monitoring and more for these applications. Failure management is built in (and was demonstrated by Jason pulling out a node live on stage). A new development kit and a cloud sandbox are being released to make it easy for companies to develop deployable patterns for PureSystems. These will allow a single code base to be deployed on to PureSystems in a data center or to a cloud deployment.

The session wrapped up with a nice shout out to partners (Decision Management Solutions is an IBM partner) and to IBM Champions (including yours truly).

Steve Mills kicked off the IBM Smarter Analytics Leadership Summit. Business Analytics matter, he says, as shown by the focus of CEOs (8 out of 10 expect complexity to increase, enterprises applying analytics are more successful etc). The need for analytics is pervasive, with every industry seeing a massive expansion in the volume of data as well as an array of missed opportunities – such as the estimate $90B in missed sales because retailers don’t have the right products – and poorly managed risk – such as the hundreds of millions of dollars in healthcare fraud every day.

The key drivers of business analytics, he says, are the emergence of Big Data (price performance of hardware makes handling more data practical), the shift of power to the consumer (a focus on personalization and the increasing levels of social interaction between consumers) and continued pressure to do more with less (especially as the price of computing continues to fall). In response IBM is making massive investments – $16B for 30 acquisitions since 2005, 8 analytics solution centers, 10,000 technical professionals and 9,000 consultants and so on. Three example customers:

• Best Buy with their focus on registered customers and analyzing their behavior to drive an 8-10x improvement in advertising effectiveness to these customers while spending 5-7% less (opportunity creation)
• Alameda County Social Services using analytics to manage entitlements for citizens and identifying and eliminating fraud while better managing cases for a saving of $25M annually (fraud reduction)
• A major telco analyzing system logs to improve overall system reliability with real-time root cause analysis (risk management)

These three – opportunity maximization, fraud reduction and risk management – are the three classic use cases for analytics and for Decision Management.

Next up Mike Rhodin and Bridget van Kralingen to drill into some details of the new Smarter Analytics initiative. Mike kicked off talking about the need for analytics to be paired with Big Data and the general trend towards more intelligent, cognitive systems (the purpose of Decision Management, of course). Analytics and Big Data, he says, must be at the core of all the smarter planet solutions being developed across every industry. Big Data and analytics are enabling a new wave of front-office transformation. Solutions have moved from enterprise data to big data, from a business initiative to a business imperative and from something that transforms a single organization to something that transforms entire industries.

From a client perspective, IBM sees a new agenda among many C-suite executives who see analytics as both the biggest threat and opportunity on the horizon. In addition there is a move from scarcity of data and insight to an environment where there is an abundance of data and increasingly of insight. This means that driving to take action on this insight, Decision Management, will become critical. In addition professions in the front office will change as it becomes increasingly digitized – CMOs, for instance, must learn how to market analytically to succeed. This change is reflected in IBM’s survey data where the number of companies seeing analytics as a competitive advantage increased by 57% between 2010 and 2011 while those who identify themselves as using analytics to compete are more likely to be outperforming their peers.

Companies are moving from seeing analytics as a potential opportunity to something that is increasingly essential – something that is core to the company’s strategy and operations. CIOs in successful analytic adopters are taking the position that data and analytics are abundant resources and seeing how they can drive those into new solutions. In particular they are seeing big opportunities to grow retain and satisfy customers and increase operational efficiency.

To do this companies need to:

• Align their organization around data
  Create an organization-wide trusted platform for big data
• Anticipate
  Focus on predictive analytics at both the transactional and portfolio levels. Predict fraud, risk and opportunity
• Act
  Making real-time decisions where it matters – A Smarter Analytics Decision Management platform across text analytics, predictive analytics, optimization, business rules and entity analytics supported  by IBM’s Big Data platform
• Learn
  Then you need to learn so you can transform over time.

IBM feels it is in a unique position with its platform technologies, 20,000+ analytics projects and 9,000+ consultants, and a research program that includes Watson as well as many other analytic research projects. Their experience has also resulted in three focused Signature Solutions:

• Customer Next Best Action – a Decision Management solution
• CFO Performance Insight
• Anti Fraud Waste and Abuse – another Decision Management solution

Robert LeBlanc, head of the Middleware software group, came up to talk about the technology at the heart of these solutions. This technology has to deal with much large volumes of data from everywhere, data that changes more often – having higher velocity – and data that is of many new types an variety. This leads you to a platform that supports this kind of data environment – a Big Data platform – that handles both new and traditional forms of data and supports advanced analytics against it. This new environment will drive a need for new capabilities in much the same way that e-commerce and the web drove the creation of the WebSphere application server platform combined with a set of open standards and an ability to leverage previous generations of technology. Any Big Data platform must integrate with the current generation of data systems, must build on the open standards that exist and must be part of a robust ecosystem.

IBM sees this environment opening up new data sources (entity resolution from unstructured data for instance or network analysis) and new methods (adaptive analytics or optimization under uncertainty). New data sources and the ability to manage this data create new opportunities – 1.7B daily events at T-mobile or 1,000 buses being monitored in real-time across 150 routes in Dublin or sub-millisecond interventions to detect security intrusion in Brocade products. Of course one interesting example of this is in IT – the potential for using Big Data to improve the way IT operates across cloud provisioning, mobile enterprise and security.

IBM’s Big Data platform has several elements therefore:

• Information integration and governance
• Data Management:
• Hadoop for new data sources
• Stream computing (InfoSphere Streams) for data in motion
• Data Warehouse for traditional data at rest
• Accelerators layered on top of this data
• Capabilities for
• Visualization and discovery
• Application Development
• Systems Management
• Analytic Applications to make this easy to consume

They are also working to bring new partners into this space with over 100 new Big Data partners and are determined to create the best possible Big Data platform.

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.

Cross-posted from International Institute for Analytics

There was a great article recently in the McKinsey Quarterly – The Second Economy. In it W Brian Arthur discusses the fact that

Digitization is creating a second economy that’s vast, automatic, and invisible—thereby bringing the biggest change since the Industrial Revolution.

It’s a good article and worth a read. Brian talks about the systems that drive this economy – that automate decisions so that systems can communicate and collaborate without human intervention. These systems for automating decisions have three characteristics it seems to me:

• They are very agile or “constantly changing” as Brian says – easy to change as needs and circumstances change.
  The digital economy moves faster than the physical one as it has fewer constraints on change – it’s much easier to reconfigure something electronic than it is to reconfigure something physical
• They are analytic – using the data available in the network to decide what will work best
  We have more data than ever before both inside our organizations and in the network as a whole. The systems for the digital economy consume this data analytically, using it to behave in more effective, more profitable ways
• They are adaptive or self-configuring as Brian puts it – testing and learning to see what might work better over time
  Whether people conduct the experiments or the systems conduct experiments automatically, the systems for the digital economy learn and evolve constantly to maximize their value.

These, of course, are the characteristics of Decision Management Systems. To participate in the digital economy your organization needs these kinds of systems so learn to build them.

Decision Management Systems are the next generation of information systems. Agile, analytic and adaptive, Decision Management Systems put business rules management systems, predictive analytics and optimization to work improving the effectiveness and efficiency of your operations. Decision Management Systems are agile – easy to change, easy to keep compliant and transparent in their behavior. They are analytic, using historical data to predict risk, fraud and opportunity. And they are adaptive, responding to changing market needs and supporting experimentation and continuous improvement. This series of webinars will show you how to build this exciting class of system.

How to Build Decision Management Systems: Part 1 Decision Discovery
Wednesday September 21, 10 am PT

This webinar will introduce Decision Management Solutions proven approach to decision discovery. Based on multiple client engagements, this approach identifies the decisions that matter in your business. Mapping decisions to your organization, objectives and existing systems and processes puts the decisions in context. Modeling decision dependency and capturing essential analysis information ensures you will focus on the right decisions and the right technologies. Register here.

Sometimes people talk about the value of data mining or predictive analytic modeling coming from “aha moments”, where the analytics deliver some piece of dramatic insight that enables a company to see some fantastic new market opportunity or fundamentally change the way it does something. This is only a small part of the story.

The companies I work with or know of get tremendous value from analytics by applying analytics to improving operational decisions. They use analytics:

• To better predict the fraud risk of a claim so they can pay fewer fraudulent claims.
• To better predict credit risk so they can manage credit lines more effectively.
• To predict which cross-sell offer will be most likely to succeed.
• To predict which price with convert a visitor.

These companies have an implementation process to make sure analytically derived predictions are turned into useful actions, often by applying business rules technology.

So when you think about analytics, don’t think only about aha moments, think about the operational, transactional, micro-decisions that drive your business. Think about how analytics could make each of those decisions a little better, adding up to a whole lot of value. It’s not about “aha” moments - It’s about making better operational decisions.

Decisions are where the business, analytics and IT all come together.

[Article first published in the Decision Management Solutions December 2010 newsletter]

Analytics can transform your business. I shared with you how to transform customer retention and marketing with analytics. This time I’d like to share with you how to use the power of analytics and Decision Management to allocate constrained resources and to give you a competitive edge.

How to maximize resource effectiveness with analytics

Organizations are under constant pressure to improve the efficiency and effectiveness of their operations. Budget and headcount restrictions are common place. In this environment you must focus on maximizing the resources you have by rethinking traditional process and workflows to be more efficient. What are currently complicated and resource-heavy processes are often in actuality simple processes that have been over complicated by poor decision management. You can streamline processes by finding the decisions where many manual steps are required to process data.

There are three steps to transforming resource allocation.

Step One: Decision-driven Data Integration

The backbone of analytically based decision making is integrating the data required to make a particular decision. Many organizations have data scattered across multiple sources and implement processes to manually integrate and aggregate this data. Much of this data has only ever been considered from a financial perspective. Resources are directly wasted on these processes and indirectly wasted because decisions based on assumptions and habit rather than data.

Decision Management Edge: Begin with the decision instead of the data. Many data integration efforts flounder because they attempt to integrated data without a purpose.

Step Two: Focus on High Value Decisions, automate the rest

With the data integrated, the opportunities for cost reduction abound. Business rule management systems can streamline basics like alerts that align to business objectives. But moreover, they can act automatically on some or all of their transactions using this information. Business rules can easily express the regulations and policies that need to be applied, allowing 80% or more, sometimes up to 95%, of transactions to be automated. Resources can now focus on high value decisions and have the time they need to use their data to make better ones. The combination of business rules-based automation of day-to-day transactions and analytics to minimize the exceptions to those rules, streamlines processes and the resources needed. Business rules automation makes it easier to apply resources and analytics where they can be most effective.

Decision Management Edge:  Organizations that consider business rules and analytics as a pair automate more routine manual decision making and focus human decision-makers where they make the most difference.

Step Three: Proactive value creation

Instead of waiting for something to go wrong and then spending money and resources to fix it, organizations can identify much cheaper preventative steps. They can apply resources to the prevention of problems not their cure. Predictions can be of fraud, of natural disasters or other problems, of equipment failure and much more. A rich, integrated history of what has happened in the past can provide the data needed to produce these kinds of models. Predictive modeling techniques use this data to turn uncertainty about the future into usable probabilities. Instead of being taken by surprise, because they are unable to predict when a problem will occur, organizations can estimate how likely each kind of problem is to occur in time to act. By analytically deriving where risk truly exists, organizations can focus resources where they will create the most value.

Decision Management Edge:  Using predictive analytics to detect problematic trends and estimate future risk avoids pay-and-chase fraud investigations and allows resources to be assigned to the next best case, maximizing the value of your resources.

Each step builds value – making data-driven decisions improves efficiency, streamlining processes by automating decisions frees up resources for higher value work and using predictive analytics lets you assign resources where they will make the most difference. Companies and government agencies are already using these approaches to transform resource allocation and drive value to their bottom line. This is not a future vision but a practical way to make improvement today. And Decision Management gives you the edge in quicker realized value of analytics implementations.

These insights come from the collective wisdom of over 50 organizations that have transformed their business using analytics. These organizations tackled core business issues like customer retention, marketing, service delivery, operations and customer centricity, in industries as varied as Telecommunications, Retail, Healthcare, Education, Government and Banking.