Syndicated from ebizQ

In a press release just over a month ago, Gartner said that Context-Aware Computing will provide significant competitive advantage. As the press releases says:

Gartner defines context-aware computing as the concept of leveraging information about the end user to improve the quality of the interaction. Emerging context-enriched services will use location, presence, social attributes, and other environmental information to anticipate an end user’s immediate needs, offering more-sophisticated, situation-aware and usable functions.

Why is this interesting? Well I think that context-aware computing will have to be decision-centric. Think about it – if you are not able to make decisions with this context, what good will it be? You need to have a decision in your system or process into which you can feed this context. A decision, for instance, about what coupon or offer to make that uses the consumers current location and environmental factors to target them more effectively. A retention decision that takes their social interactions into account when deciding how valuable they are as a customer (like SingTel Optus do, for instance).

Gartner go on to say:

new business opportunities will emerge, by virtue of knowing the customer more intimately; and productivity will improve as systems eliminate complexity for the user.

Knowing more about your customer won’t help if you cannot act on that knowledge – if you can’t make decisions using it – and system that don’t make decisions, don’t act cannot eliminate complexity for the user. Decisions and decision management are necessary for context-aware computing.

I gave a keynote at the Business Rules Forum today on Smarter systems for uncertain times.  I gave the presentation without slides and had planned to use my notes as a post but, as the notes ran to 5,000 words, I have decided to write a white paper based on them instead!

To keep you going until I get around to that, here are the high level points I made in the presentation:

• The business climate requires smarter systems
  • Change is constant and increasing with most executives, for instance, saying they face more competition than they did 5 years ago
  • The competitive landscape is changing with lower barriers to entry and the flattening effect of the Internet making for uncertainty and new competitors in unexpected places
  • The volume of transactions involved in running a modern business means you need systems that can deal with this climate not just an organization that can
  • More and more actions are required in real time and cannot be delayed until tomorrow, next week
  • Businesses are evolving to use complex business webs with outsourcing, partners and more replacing centrally managed corporations
• Smarter systems have four key characteristics
  • They are action-oriented
    They make decisions so they can take appropriate actions on your behalf rather than waiting
  • They are flexible
    In business terms, not IT terms, because these decisions are managed using business rules that bring business people in and put them in control
  • They are forward looking
    Because they embed predictive analytics that turn uncertainty about the future into usable probabilities
  • They learn
    Because they support adaptive control/champion-challenger testing and because they use analytic models that learn and adapt
• Getting there requires Decision Management
  • A management discipline focused on decisions not a technology stack
  • First step is to discover the decisions that matter to your business, understand them and separate them from the processes and systems that hide them
  • Second step is to build decision services or decision agents, components that can make decisions for other components
  • Third step is to implement decision analysis so you can monitor, improve and learn about decision making

You can also check out these posts from Sandy Kemsley and Eric Charpentier who were in the audience:

• Smarter Systems for Uncertain Times #brf
• #BRF Keynote: Smarter Systems for Uncertain Times

The second full day of the Business Rules Forum/Enterprise Decision Management Summit is over and once again I have been taking notes rather than blogging live. Once again there were some great sessions – today I heard Steve Hendrick of IDC, Sandeep Gupta of Equifax, Chaitan Sharma of DAASL, Zach Springborn of OneData and Mo Masud of Deloitte. I have to confess to skipping Eric Siegel’s session as I have heard it recently (check out 5 ways to reduce cost with predictive analytics or watch the webinar Eric and I did on Optimizing business decisions with predictive analytics).

Keynote: BRMS at a Cross Roads: The Next Five Years

Steve Hendrick thinks that the business rules market is at a crossroad thanks to increased visibility by virtue of the big players joining the market, the growing acceptance of decision services as an implementation approach, the availability of open source alternatives and more. Nevertheless the market is currently small – just $285M for the BRMS market itself out of a total software market of more than $280B. The market is continuing to grow but from a fairly small base. Steve sees no reason for this growth to stop and identified market momentum, growing awareness and companies’ desire to improve Governance Risk and Compliance or GRC as reasons for this growth to continue.

Steve, like me, sees the future not in terms of BRMS products as a standalone market, however. Instead he is talking about a Decision Management Platform and his description of it would resonate with any regular reader of this blog – business rules, predictive analytics and event processing. BRMS is good for categorical or data-driven decisioning while analytics can improve rule relevance and quality, make the data more readily consumable and refine the outcomes of rules-based decisioning to find optimal solutions to decisioning problems. The combination is thus particularly powerful.

Steve adds event processing and the handling of state because he believes that an always-on decisioning platform that listens for events, analyzes the information available when the event happens and then decides what actions/processes are appropriate is the best way to handle decisioning in an increasingly real-time world. He sees a decision management platform as enabling a move away from process-centric to information-centric decision making.

Steve advised vendors to focus on analytics as a complement to business rules, to think about how to migrate to the decision management platform he outlined and to track developments in CEP, BRMS, Analytics and event-driven architecture more generally. For customers he again advised a focus on analytics, especially segmentation (like me he thinks this is the best way for companies using rules to get started with analytics), and urged them to think about real-time decisioning going forward.

Maturing with Business Rules and Business Intelligence – The Combined Power

Sandeep gave an interesting presentation on the use of rules with business intelligence. He was talking not about using BI in the context of a transactional decision, the major use of business rules, but the possibility of using business rules as part of the strategic or management decisioning that relies on BI. Equifax is a user of business rules in a number of its solutions, but these use rules to handle decisions at a micro level – is this transaction fraudulent, is this customer a good risk etc. The work he was discussing in this presentation was attempting to bring the power of rules to increase transparency and business control as well as the action-orientation, to a BI environment.

Clearly the two environments have a significantly different focus – transactions on one hand, summaries and analysis on the other. Yet both are built on a business object model and in both cases there is a move to empower business people to write their own rules or build their own dashboards. If the business object model could be fused between the two environments, perhaps the rules approach could be used to make the dashboards and reports more actionable. This would require that the rules could have conditions that took advantage of the statistical analysis and summary capabilities of the BI environment as trying to handle this kind of data crunching in a rules engine would perform poorly.

Equifax has been conducting some research work to allow a user to specify the query, the WHERE clause if you like, in the condition of a rule using the business-friendly and English-like syntax of their rule engine (they use IBM ILOG). The rule can then be executed in the rule engine and can reach into the BI environment to get the data analysis it needs. Actions are taken using the rules engine’s usual ability to execute the THEN side of a rule. While this research is interesting, there are challenges with the impedance mismatch of the two environments as well as the difficulty of turning intuition (based on this graph I think I should do this) into an explicit rule.

An interesting topic – almost the reverse of the usual rules+analytics story in that it is bring rules to an analytic summary rather than analytics to a rules-based transactional decision.

Decision Automation – Implementation Challenges and Productivity Improvements

Chaitan presented on his experience working with ICICI Lombard, India’s largest general insurer, on a legacy modernization effort using FICO Blaze Advisor. The driver to modernize these applications was a change from fixed to variable pricing in the insurance market along with an influx of new customers about which very little is known – the information business is less developed in India so many of these customers must be taken on with an expectation that a different approach will be required once their behavior becomes known. It was a classic legacy modernization challenge in that SMEs were in short supply, the code was not documented and the system was in use while it was being reengineered.

To manage the process, DAASL focused on a couple of things. They classified business logic so they could prioritize the use of business rules based on the likely degree of change and the degree of reuse. They built a pilot by observing the current system so they could show business users how it would look. They did code walkthroughs to draw out rules and then matched these up with the output of business user interviews to see where the inconsistencies were. And they delayed integration/governance functionality until the business had experience with several decisions and with several iterations so that the requirements for these elements would be grounded in actual experience with the BRMS. All good advice for anyone approaching rules for the first time.

Besides the general improvement in agility and accuracy from using business rules, the system also gave the business the power to generate explanations and reason codes for the decisions as they were made and to log exactly how each decision was made. These were important as it enabled some real analysis on the part of the business into what works and what does not.

One final piece of advice from the team was to keep on top of the technology/platform deployment issues. Incompatible product versions, sudden changes in the platform and other technology-centric problems could have derailed the project even though the rules piece was going well. Keeping the project on track meant dealing with these issues and not allowing them to derail the main effort to extract, document and implement the business rules.

Decisioning: Managing Counterparty Risk for Financial Markets

Zach gave an interesting presentation on the use of business rules and extract-transform-load tools in delivering better counterparty risk management. OneData helps companies manage risk by providing information to them and, in this case, the focus was on counterparties. In particular the challenge of managing the final counterparty – who owns the company that owns the company that owns the company that you are considering taking a position in? And what, therefore, is your exposure to a risk that the parent company gets into trouble. He gave a great illustration of how complex companies like ABN AMRO or AIG get with a wonderful snowflake diagram with ABN AMRO at the center and all its layers of subsidiaries fanning out.

Anyway, the project combined TALEND (open source ETL tool) with IDIOM’s Decision Manager to bring multiple data sources together and largely automate the process of matching securities that were being bought and sold with the ultimate counterparty. About 90% of the securities could be matched using the decisioning engine and the remainder had between 2 and 15 options that had to be manually considered. This is complicated by the fact that different customers might have different rules for who counts as a counterparty – some consider 25% ownership enough, for instance, while others consider 51%.

The end result was a counterparty hierarchy matched to the individual securities that was fed into the Algorithmics risk management platform so that companies could consider the risk of their portfolio in terms of the ultimate counterparties involved in their positions. Traders and desk managers could see what their total exposure was to a particular parent company or what the impact of a particular trade would be on their risk exposure.

A somewhat unusual use of rules but a fun example of how two technologies, ETL and business rules, can be used together to deliver real business value.

Integrating Predictive Analytics and Business Rules Management to Enhance Insurance Marketing Strategies

Last session for me today was Mo Masud talking about the role of predictive analytics and business rules in commercial insurance. Deloitte has been doing a lot of work in this area, focusing on expanding the use of predictive analytics beyond its usual home in underwriting and pricing to marketing, agent selection, customer service and claims. This is important as the insurance industry has been losing money, as an industry, for a most of the last 10 years. Combined ratios over 100 (meaning they spend more on claims and admin than they take in in premiums), poor investment returns and some bad years for catastrophes have all contributed. But the net effect is that insurance companies must get better at their core business so they can drive down their combined ratio – and rules/analytics/decisioning has been shown to do just that.

The use of rules and analytics to automate decisions and so drive out subjectivity, identify profitable opportunities and improve efficiency is critical. In particular, an enterprise view of the use of these approaches so they are not applied only in underwriting, say, but broadly and systematically works wonders for the bottom line.

Deloitte has found that companies need to focus on data and data sources because these drive the kinds of models they can develop and on business rules to make these models actionable. Mo gave a great example of using a predictive analytic model to rank-order those applying to be agents in terms of how likely they were to become profitable for the carrier. This model was combined with rules to allow many applicants to be accepted or rejected automatically while those in the middle were referred to marketing managers for further analysis. Rules considered things like geography and coverage as well as the likely profitability of the agent. Using this approach, and focusing on signing up the agents in the top 7 deciles of the model, one carrier shaved 2 points off its loss ratio and saved 10-15% of its marketing/administration fees for agency management.

A good illustration of the power of analytics and rules in areas of the business not traditionally associated with them.

Here are other posts on today’s sessions:

• Sandy Kemsley
  • Business Rules Management: The Misunderstood Partner to Process #brf
  • BRMS at a Crossroads #brf
  • Business Rules Governance and Management #brf
  • Comprehensive Decision Management: Leveraging Business Process, Case Management and CEP #brf
  • Business Rules and Business Events: Where CEP Helps Decisions #brf
  • Rapid Change: The New Decision Dilemma
• Jim Sinur
  • Business Rules Forum 2009
• Eric Charpentier
  • #BRF Difference between CEP and Business Rules
  • #BRF Keynote BRMS at Cross Roads
  • #BRF Introducing a rules methodology part 2 Presentation
  • #BRF First hundred days of a BPM effort
  • #BRF Practioners Panel
  • #BRF Business Rules and Business Events

It’s the end of day 1 of the Business Rules Forum/Enterprise Decision Management Summit and time to write a wrap up post for the day – no live blogging today as I have too much on as track chair to sit behind my keyboard!

Today I got to attend Jim Sinur’s keynote and sessions from Roger Ahern of Experian, David Wilson of John Deere, Stephanie Alsbrooks and Kartheek Veeravalli of ThinkCash Financial and Sundar Vallianayagam of Jarus Technologies. Here goes with the summary:

The Decision Dilemma: Making Better Decisions in the Face of Uncertainty

Jim Sinur started the day off with a presentation on Gartner’s perspective around pattern-based strategies. Arguing that things will never be the same after the recent meltdown and that change is the new constant for companies, Jim made a pitch for pattern-based strategies. Companies must find a way both to be proactive to change that might reasonably be expected as well as rapidly reactive in identifying and responding to unexpected change. This requires a focus on leading indicators, especially those outside the organization, as well as ways to make people, process and technology change happen quickly. A pattern-based strategy that seeks, models and adapts suitable patterns is essential. Business rules, business process, event processing, streaming data are all key to this approach.

Organizations must become pattern seeking, performance-driven and transparent. They must also seek what Jim called the Optempo advantage – an ability to improve their competitive rhythm and increase their rate of operational adaption. He identified 4 key disciplines:

1. Seek patterns that will matter to your organization, especially strategic opportunities for innovation
2. Become a performance-driven culture, one that measures and improves
3. Match the pace of change, your ability to change, to a business purpose
4. Become transparent about why and how you do things

This requires organizations that use predictive analytics to look into the future, social media to understand what people are saying, complex event processing/decision platforms to handle real-time response, business planning tools to be specific about the kind of organization you are running and BPM/business rules/BAM to ensure systems are adaptive to change.

As usual Jim put a lot of material out there and asked a lot of interesting questions. With the keynote done it was time to go into the decisioning sessions.

Aligning Decision-Making Improvement Initiatives with Business Goals and Objectives

Experian has been working on helping companies automate the decisions in the credit lifecycle for a while and has identified 19 different decisions that can be usefully automated. The value of this automation comes in  terms of improved accuracy, agility, speed, efficiency and consistency. Roger pointed out that a bank with just 500,000 customers probably makes more than 20,000 credit lifecycle decisions every day. With the difference between a good credit decision, that might make a $150 in profit, and a bad one, that might lose $1,000 or much more in fraud, this becomes very significant quickly – little decisions add up, as I have said before.  When a company like HSBC makes 50Bn decisions a year, it becomes even clearer…

Experian works with a lot of banks and, like me, really tries to focus customers on mapping decisions to the metrics that those decisions influence (after all metrics are what most executives are accountable for). Given that rules and analytics, decisioning technology, can improve most decisions by 10-15% in their experience it is not normally hard to see the likely benefit in terms of business measures where decisioning can move the dial. Nevertheless, companies must prioritize decisions y their impact.

Experian sees decisioning having several pieces – a data and attribute engine that manages data and calculated attributes; a decision engine that handles scoring, policy and decisioning; and an optimization tool to create optimal approaches. Decisioning can and should be used to enhance existing applications and processes, enhancing them with advanced decisioning.

A solid presentaiton on the value of decision management and on the adoption of decisioning,

From Raw Data to Proactive Decisions: Using Business Rules for Condition Based Maintenance

From financial services credit decisions to proactive maintenance of earth moving equipment was quite a shift but it showed the value of the rules approach in a broad range of problem domains. David talked about a new product being rolled out  by John Deere to take all their maintenance, testing and telemetrics data and turn it into actual recommendations – suggested work orders – for their distributors. The idea is to take the know-how inside John Deere and put it to work in the field by making sure that all machines sold by John Deere are given the preventative maintenance their need.

The decision engine John Deere has built pulls data from multiple sources and uses it not to make pretty pictures but to generate concrete recommendations for dealers – things they can and should do right now. The previous approach of just making the data available failed what I call the “so what” test – no-one knew what to do with the information. With the new system the data that is available is used to drive specific, actionable recommendations for dealers. Instead of having to wade through lots of data, and lots of paper to understand the data, the dealer gets concrete next steps for each piece of equipment.  Building the engine that recommends the right next step should mean that more preventative maintenance is done, making machines do more for less for customers and reducing warranty work for John Deere – a win win.

The original approach was to try and capture the knowledge in Word and then Excel but the project only really got underway when they adopted a business rules management system to manage the logic. With this system the business users are able to design the rules that are needed while the IT folks manage the detailed implementation. The initial development is in pilot now and the ability to rapidly revise and redeploy rules has been as critical as the original improvement in development. The changes to the rules that “real life” requires are significant, even when experts are writing the rules, and ongoing maintenance and updates will be critical.

John Deere expects to gain real competitive advantage out of this system – it will help ensure that their dealers do the best possible job helping companies keep their equipment running effectively reducing down time for the customer and warranty costs for John Deere. The release is soon and personally i think it is going to be wildly successful – it smells like one of those great rules ideas.

Addressing the Challenges of Developing a Universal Decision Management Platform in an Emerging Company

This presentation was about a previous project, nothing to do with the presenters’ current employer, but was a great discussion of how to develop a coherent decisioning strategy and platform. The project came about when a new auto lender was formed that wanted to target all possible auto borrowers (prime to sub prime) while managing risk in an innovative way and delivering a better dealer experience.  With a plan to develop a cost-effective, customer-centric consumer platform with flexible processes and a rapid time to market, the team made some interesting choices.

First and foremost they decided to buy the common processes – those where little differentiation was possible – and build a unique and differentiating decisioning approach. This would give them a competitive edge without requiring a completely unique platform. They also outsourced the development of IT infrastructure and workflow while retaining critical decisioning know-how in house. This let them develop a platform for processing loans quickly while still providing differentiation. The decisioning originally focused on origination before expanding into servicing and funding. The initial releases involved manual review but rapidly evolved to fully automated decisioning with random manual audits.

Segmentation of prospects/customers to manage risk effectively and champion/challenger adaptive control were both critical to rapidly and effectively evolving a unique risk management approach. Simulation was widely used to understand the impact of a potential change and this was combined with the champion/challenger approach to ensure the business understood the impact of any change being considered. The combination of predictive analytics for risk assessment, optimization for pricing and business rules for policies and decisioning resulted in a very sophisticated decisioning platform. Most decisions were real-time and 100% automated and the business and IT groups were able to collaborate on the definition of these decisions.

Automating Commercial Underwriting Using Business Rules

Last session of the day was on commercial underwriting using business rules and predictive analytics. The platform that was built in this way handled all the key decisions – eligibility, underwriting, quoting and binding and issuing the policy. A business rules management system allowed business analysts to manage the business rules while also providing a mechanism for quick and accurate deployment of predictive analytic models developed to predict risk. Critical to the project were effective rule harvesting (what rules are involved) and rule architecture. The team had IT work on the actions that rules could take and this allowed the business analysts to specify the conditions for each rule while picking one of the allowed action. This simplification allowed less technical users to participate in writing and maintaining rules.

The combination of predictive analytics with this rules-based environment used PMML and took advantage of the flexibility of a rules-based engine to allow rapid model deployment. This combination allowed for what the customer called “surgical  pricing” and the retention of the risks the company wanted at a price that made sense. The project won an Innovate award from Insurance Networking News.

A classic rules and analytics story in insurance.

Here are some posts from the conference – Sandy Kemsley, Paul Vincent and Eric Charpentier all posted great information:

• The Decision Dilemma #brf
• BPM, Collaboration and Social Networking #brf
• Process Notations #brf
• Collecting, Connecting and Correcting the BPM Dots #brf
• BRF09: Jim Sinur on Business / Event Patterns
• #BRF Making Better Decisions in the Face of Uncertainty Keynote
• #BRF BPM, Collaboration and Social Networking Presentation
• #BRF Business Rules enhance agility in BPM presentation
• #BRF Enabling Effective Business – IT Collaboration Presentation
• #BRF Enabling Effective Business – IT Collaboration Presentation
• #BRF An Evolutionary Perspective of BRMS Presentation
• #BRF Vendor Panel: BRMS at a crossroad?

If you are following along on twitter – check out #brf as a tag.

Syndicated from ebizQ

The new book from Barb von Halle and Larry Goldberg, The Decision Model: A Business Logic Framework Linking Business and Technology, is now available. This book outlines an approach for capturing and managing the business rules you need to understand in order to implement a Decision Service. It complements the use of a business rules management system as it is a modeling approach for rule analysis and design. It has some nice features, not least a set of rules for checking your decision model for completeness and validity.

I wrote a chapter for the book on how the Decision Model complements a more general Decision Management approach. While not every decision for which the Decision Model is relevant is also relevant to Decision Management, many will be. The use of the Decision Model with Decision Management is very similar to use of Entity Modeling with Information Management – the Decision Model will help ensure you write the correct rules as you implement your decisions. The power of the Decision Model to help business users understand how decisions are made, what rules are required and to check those rules for completeness and consistency adds tremendous value. Modeling rules during the requirements gathering approach, as I have noted before, requires that they be considered separately from the rest of your requirements and the Decision Model provides a way to do that. Decision Management also provides a well defined approach for taking the Decision Model and putting it into production using production business rules management systems and sophisticated analytics in combination, something essential for risk-based and opportunity-based decisions like fraud detection, credit, marketing, retention and more.

The Decision Model page on KPI’s website has some nice resources including a primer on the approach. I highly recommend both the book and the approach.

I am at the Business Rules Forum this week. I will be acting as co-chair so there won’t be live blog posts from sessions but I will write a wrap up for each day.

The new book from Barb von Halle and Larry Goldberg, The Decision Model: A Business Logic Framework Linking Business and Technology, is now available. This book outlines an approach for capturing and managing the business rules you need to understand in order to implement a Decision Service. It complements the use of a business rules management system as it is a modeling approach for rule analysis and design. It has some nice features, not least a set of rules for checking your decision model for completeness and validity.

I wrote a chapter for the book on how the Decision Model complements a more general Decision Management approach. While not every decision for which the Decision Model is relevant is also relevant to Decision Management, many will be. The use of the Decision Model with Decision Management is very similar to use of Entity Modeling with Information Management – the Decision Model will help ensure you write the correct rules as you implement your decisions. The power of the Decision Model to help business users understand how decisions are made, what rules are required and to check those rules for completeness and consistency adds tremendous value. Modeling rules during the requirements gathering approach, as I have noted before, requires that they be considered separately from the rest of your requirements and the Decision Model provides a way to do that. Decision Management also provides a well defined approach for taking the Decision Model and putting it into production using production business rules management systems and sophisticated analytics in combination, something essential for risk-based and opportunity-based decisions like fraud detection, credit, marketing, retention and more.

The Decision Model page on KPI’s website has some nice resources including a primer on the approach. I highly recommend both the book and the approach.

I am at the Business Rules Forum this week. I will be acting as co-chair so there won’t be live blog posts from sessions but I will write a wrap up for each day.

Last session at the SAS/BetterManagement.com event this week on IT and personalized healthcare. Yan Chow of Kaiser Permanente presented on their vision for IT-enabled healthcare. Kaiser is an integrated system with 8.6M patients and 180,000 employees and physicians. Integrated means that insurance/delivery are all aligned and Kaiser is very focused on prevention. It is also a not for profit so it spends heavily on research and community investments as well as providing funds for 100,000 uninsured patients. Lastly it is a big researcher and a big investor in medical technology.

One of the biggest patients have is whether their healthcare provider knows them – their drugs, their allergies, their lifestyle etc. Beginning in 2003 they started to invest in technology to answer this question – they selected EPIC and have deployed it for more or less everyone. This is a very large implementation of electronic medical records, probably the largest outside the government/military. 7 Petabytes of data, 380M views per week and 80,000 concurrent users! Only about 1.5% of healthcare providers have a comprehensive electronic medical record so this is huge.

The system is designed to be cross-channel (web, call center, visit) or connected and consistent in that the same care, and evidence-based care, is delivered all the time. This focus on evidence-based medicine is a big shift for doctors who are basically trained using an apprentice approach. The system is also personalized and this is where predictive analytics comes in.

The system has what they call Smart Tools to identify at-risk populations so they can intervene earlier and more effectively. People in Kaiser are 30% less likely to die from heart disease and they have reduced by 88% the risk of post cardiac arrest mortality! The system already brings patients in using a portal and in the future they see virtual care teams all being connected through the system.

To personalize care it is necessary for patients to see the effects of treatments and health decisions in the context of their unique physiology. Kaiser uses Archimedes a tool designed to modeling disease and impact at a molecular level. This allows people to see the impact of a disease or a lifestyle decision on them personally.

From a predictive analytics perspective they are focused on improving their information management and strategy. They have to consolidate and standardize data across systems as well as develop a common analytic infrastructure so they can develop a Center of Excellence around BI and analytics. Kaiser has a lot of data so this is an area with lots of opportunity.

To support innovation Kaiser has developed the Garfield Innovation Center – 37,000 sf facility that has a ward, operating rooms and more for full-scale simulations, workflow design and space design. Colors, equipment, wiring and connectivity approaches and more are all investigated and the best outcome selected for future use as Kaiser builds out new facilities. It also has a tele-health simulator representing a home to see how technology could be used to provide remote consulting on health issues and monitor them remotely. This involves a lot of rules-based automation to provide 24×7 support with a virtual team of people engaged through this model. Making the patient the center of the treatment network. The 21st Century house call will be remote, by satellite say.

An interesting presentation on Kaiser, its business model and its technology.

Malcolm Gladwell, Thornton May (author of The New Know: Innovation Powered by Analytics)and Tom Davenport (author of Competing on Analytics, reviewed here) made up a high powered panel for this. Various random comments follow:

• Healthcare is being used as an example by the panel as an obvious point where analytics and expertise intersect. There is a challenge to creating in experts because they need so many hours – 10,000 as Malcolm mentioned – and time pressure is making harder and harder to get this. When the number of facts you need to know to be an expert (medical specialists are often said to need to known 2,000,000 or more facts) there is a compelling need to have systems be involved. However, the different criteria for success – patients often have different, less precise criteria – mean that people are unlikely to be replaced completely by computers. The empathy of doctors, their "bedside manner" is not going to be replaced by a machine but it should be supported and given context by one.
• There is a critical difference between experience-based intuition and unaided intuition or "gut feel". Businesses must value the former but what about the latter? When does one trust intuition rather than analytics. The first criteria is how often we have done this before – if we have done it before often then we should use analytics. If it is a new problem, an attempt to be radically different, then intuition is critical. The second is related – how much data do we have about the problem.
• Financial crises is a crucial test case for analytics – all these bad decisions were made by people who had sophisticated analytics. The analytics led these folks to believe they could manage all elements of risk and the analytics got into the hands of people who did not understand the limitations of the models. This is a critical issue – the executives have to be able to understand the analytics, they must be analytically informed. Similarly there is a limit to how much can be modeled, some risks for instance are just out of the ordinary (Black Swans, as they are known). Modelers must be clear these risks are not in the model. And they must make sure that everyone downstream from them understands these limitations.
• The tools you can create with analytics, the very useful tools, you must also have an appropriate context for these tools. A regulatory framework, for instance, provides context for a model. There is a skills gap between executives and analytics folks but a larger one between the analytics capabilities of the regulators and those they regulate. Why, for instance, are regulators all lawyers rather than analytics people? No simulations or models were done, for instance, on the impact of the stimulus money. Not enough analytic depth in the regulatory framework.
• We must understand intuition as the fruit, the outcome, of many years of study and experience. To create people with good intuition we must be willing to have them spend time on decision making. And those who are good decision makers must learn from their mistakes, they must be more proactive in analyzing how their intuition let them down as they can improve it. A willingness to engage in introspection is essential.
• Human beings are not good decision makers about their finances and it is time to have regulators enforce some ethics on the ability of companies to use analytics to manipulate consumer behavior – companies are getting way smarter than consumers and are using it to manipulate them.
• It is just as important to be able to tell a good story with data, as an analyst, as it is to be able to do the analytics.

Closing thoughts:

• Tom: Lots of opportunities, lots of new tools, more understanding of how we make decisions. Time to systematically look at how we make decisions.
• Thornton: World is digitizing so the cost of experimentation is falling and the opportunities are greater than ever
• Malcolm: We must remember that failure is critical in experimentation and make it cheap and easy to fail and to minimize the impact of failure.

These guys were fun, but hard to blog. Hope the post is helpful anyway.

Malcolm Gladwell took the stage to present next. Malcolm is the author of some great books (such as Blink, reviewed here, and Outliers). Gladwell began by noting that Vegas seems an odd place to hold a conference on analytics!

His first story was about art – a dealer brings a new statue to the Getty, a Kouros, and they were excited but wanted to check its authenticity. The lawyers go first and find no problem with the paperwork. Next they check the stone to see if it comes from the right quarries and see if it has been out of the ground long enough. So far so good. They decide to buy it, pay a huge amount of money ($10M) for it and invite an expert to see it. She takes one look and says its a fake but can’t say why. Another expert, same immediate response but not able to say why. They go to Greece and unveil it to a huge audience of experts and have the same response. When they get back, the lawyers call to say there is now in fact a problem with the paperwork and the geologists call to say there is a problem with the age test. In the end, then, the data shows it is a fake just like the experts thought it was.

So, in a business, how do you create conditions so your decision makers can act based on their expertise, their judgment when it matters?

In the statue example the experts were doing a kind of complex pattern matching – taking a pattern they had in their head about real Kouros statues and matching it to the actual example in front of them. This pattern recognition – rapidly seeing the pattern in the real world and knowing what to do. In military circles they talk about coup d’oeil – at a glance – the ability to see immediately what was needed. Building this requires massive amounts of experience with seeing the pattern. And this is backed by research that says you need 10,000 hours of experience – say 4 hours a day for 10 years. It is very hard to find people who are top of their profession without this 10 years of experience. So what is going on to let these folks become experts in this 10 year, 10,000 hours? Lots of feedback is what happens – try, fail, get feedback, learn, repeat.

Lesson #1, then, is that tools that help improve the quality of feedback we can increase the value of experience in building judgment.

Judgment, though, has some drawbacks. In particular it is mysterious. When Gladwell spoke to the various experts who said the statue was fake they had no idea why they thought it was fake. They did not make their judgments lightly – this was a big deal, reputations were riding on their statements. Even so, they still did not know why they made this critical judgment. This is typical of judgmental decisions – lots of use of our unconscious. This reliance on the unconscious makes it hard to explain why they were made. And this is true of everyone from a tennis coach who could spot double faults before they happen every time to tennis players who cannot explain how they hit topspin forehands. Their knowledge has been transferred to their unconscious so they can’t really explain, only give a plausible explanation (that may well be wrong).

Lesson #2 is that decision making tools can help by exposing and making transparent decisions so that the knowledge can be taught and shared.

What about the Getty? Why did they buy the statue. They had staff members who were experts in statues of this kind, including one of the world’s best. So why didn’t they call the fake what it was? Part of the problem is that the Getty can only buy art from before 1900. This, of course, is hard so there is a desire to find art to buy and in this case there was an emotional desire to believe the statue was real. This kind of emotional investment can seriously undermine judgment. On a similar vein he looked at a group of appointed CEOs. Overwhelmingly these CEOs, who didn’t found the companies, were tall, white men. And really tall, much taller than average. Why are boards picking these tall men as CEOs well because tall used to be an advantage for savanna hunters and this desire for tall leaders lurks in our subconscious and distorts judgment. In legal issues, black criminals are punished more severely than white criminals who commit the same crime. Bias exists and distorts judgment in many areas. Judgment is very fragile.

Lesson #3 is that judgment is weak and needs tools that protect judgment from corruption and bias.

Last section. Think about the Getty who spent 11 months to gather tremendous amounts of data but the experts just show up and overturn this, get a better result. Unaided human judgment tends to work best where there is just a little bit of data – a glance at the statue, for instance. Getty knew too much and it swamped their ability to apply their judgment. For instance, doctor’s do a poor job of diagnosing heart attacks in patients coming to the Emergency Room. Analysis of results shows that insisting on doctors asking just a few, pertinent questions (4 in fact) then their decision making improves dramatically. The analytics have clarified the field of decision making. All to often the problem with decision making failures, in military intelligence for example, can be traced to too much information. For instance, before Pearl Harbor or 9/11 there was so much information available to analysts that they could not see the pattern.

Lesson #4 is that systems must clarify and focus on the information that will matter so that decision makers can act on a reasonable number of data elements.

He finished up with a story of a female trombonist who was mistakenly invited to audition (they thought she was a man) and who then auditioned behind a screen. The conductor is sure she is the best and is shocked when he sees that she is a woman. Like most conductors at the time he was convinced that men made better players than women and he had a 100% male orchestra. He had been picking less good players because of this bias. And as screens go up for other auditions, in the early 90s, more and more women are taken into orchestras changing the percentage from 2% to 50%. Until screens went up these people had been unable to make a good decision because of their bias. With a screen, all you get as input is their playing. Without a screen the visual data, that does not matter in selecting a musician, overwhelms the process.

To get the best decisions from your decision makers you must build tools to nurture and protect those decision makers.

Gladwell spoke without notes, without slides and was very engaging.

Jim Goodnight, CEO of SAS, and Geoffrey Moore, author of Crossing the Chasm among other books, had a discussion on the use of analytics in innovation. Several areas were touched on from the global economy to innovation approaches to education.

The growth of low cost competitors in the global economy has commoditized that which can be commoditized, said Geoffrey, so companies in developed nations have to find ways to differentiate to sustain their profit margins. How to get these higher returns is getting more complex as the old advantages, of simply having more technical skills, degrade. Instead things like an ability to try and fail, and a balance between innovation and optimization (effectiveness and efficiency, say) are essential. For instance, grid computing might reduce a job from many hours to a few minutes and that’s optimization but it also allows innovation by making it possible to try things more rapidly, cycle more rapidly.

Geoffrey made some great points about the global economy. There are clear positives in the growth of new economies and the general rise of the standard of living in those companies. This does represent a challenge though. Not, perhaps to companies that are increasingly global and able to take advantage of the world economy, but to the middle of the economy. The top of the US economy is still generating innovation and talent that is competitive worldwide. But below that is a layer that no longer has a social contract with employers and that is struggling to be competitive in a more global economy.

Innovation must have a purpose – it must create differentiation from your competitors. You need to innovate consistently enough in a particular area to create a compelling differentiation from your competitors – be different from your competitors. Ongoing investment and steady improvement may be more useful than some great leap – SAS, for instance, prides itself on having made consistent investment in analytic and software R&D over many years. One of the hardest things in innovation is picking the right place to make the ongoing investment – finding the "crown jewels" of an organization, the key strengths of competitors and the needs of customers is critical to drive this decision.

There was some discussion of the education system and the challenges of education in a global economy. Most kids who drop out of high school, and 30%+ do, report that they drop out because they are bored. The fact that educational style (teacher at the front talking about a subject) has not changed in the last 50+ years despite the huge change in the interactivity and technology usage of the new generation is contributing to this. SAS has developed curriculum material and is focused on creating a more interactive, computer-based, technology-enabled environment. Curiously neither speaker spoke about the drag on the entrepreneurial economy caused by the fragmented and inefficient US healthcare system.

The general impact of the internet in terms of disintermediating everything from IT departments to schools and its ability to support self-organizing groups is that massive change to the way organization work. Building on this, getting ahead of it, is going to be critical as it cannot be resisted, only leveraged.

Finally the scale of the economy and the fact that it means systems have to be used to manage the data, manage the customer interactions is driving analytics. If interactions are so numerous or products are so varied that computers must be used then optimizing your business, making this work for you involves analytics. Over the last 30 years the shift has been from presenting analytics to people so they could make decisions to systems where no human being can be in the loop and the analytics drive decisions. These systems require a second loop, outside the transactional environment, to manage and improve the analytics in the system. This is essential because it is only the people that have a strategic intent that can drive the system in the right direction.

The panelists were asked their key philosophies for success. Geoffrey said know what your core is and then declare it, make it public. Find ways to optimize non-differentiating processes to free up resources for your core. Jim said to treat people as though you believe they can make a difference and supply challenging work to keep people engaged.

Syndicated from BeyeNetwork

I am at the Premier Business Leadership Series, SAS/BetterManagement.com’s event, and I got to attend a great panel on Analytics in the Executive Suite. Barbara Pindar of Aeropostale, Eric Webster of State Farm Insurance, Cameron Davies of Disney and Keith Collins of SAS made up the panel. Each panelist gave a quick introduction:

• Eric is in State Farm’s marketing arm and is focused on using analytics for demand generation – where is the next customer going to come from. They have noticed that while you can create more and more analytics you have to think about who will consume them. Their have had great success by burying analytics into a transactional system – embed the intelligence  in systems that everyone touches. Decision Management in other words. Great example of giving agents a system that lets them specify their marketing budget and get an analytically driven marketing plan back.
• Barbara talked about Aeropostale where it is about having the right product. They have focused on marrying the art of designing products with the science of predicting what will work. Their “artists” value the science, which is critical. A lot of upfront analytics to make the initial purchases right – make sure that customers can come into the store to find what you want, the right size and color.
• Cameron from Disney got the attention of his management team when they showed how analytics, applied to just 30-40% of the business, impact the earnings per share of Disney. Now they are working with non-theme park elements of the company. Advertising is an interesting area because there are agencies, multiple companies involved but you need to target consumers. Partnering with Nielsen to predict ratings so can manage the advertising inventory and then use analytics to segment advertising opportunities so can sell them to agencies.

Moving on to trends:

• The first was the ability to handle and process quickly much larger volumes of data. A customer can drop TBs of data and an analytic proof of concept only takes a few weeks making it easy to prove the value.
• Eric sees a trend of executives asking about the numbers first, see what the numbers imply and then build a business plan rather than just using numbers to validate a plan. This shift is recent but making a big difference to analytic decision making at the executive level.
• Barbara is from an industry, retail, that has been a lot less analytic than insurance for example. But this industry is also moving and is focused on the value of the inventory they purchase. Even in a fast moving fashion business much of the inventory has to be purchased months in advance. Analytics can really help mitigate the inventory risk by having lower inventories, turn it faster and makes sure the colors and sizes are right.
• Broad adoption is creating centralized analytic teams. Cameron talked about having experts managed centrally but ensuring that there was a layer of business-focused folks who could take requirements from business units and interface to the experts.
• Social media is obviously a hot topic but the analytic impact of this was obviously zero as none of the panelists had anything to say about analytics and social media. Social media is another channel for communication, and direct to consumer communication but not something of analytic import yet. Cameron talked about some uses of social media data but it all seems very disconnected and not yet of real impact.

Next, the organizational issues:

• Is analytics a centralized function, a central department? Eric made a great point that there is an advantage in the ability to hire real experts and give them a place to work but a downside that this can become too separate from the business groups who need to integrate the analytics into what they do every day. Need a balance between centralization for expertise and decentralization for business impact, to embed it in the fabric of daily activities. Get people to the point where they want to check in on the analytics as they move along.
• Barbara too is part of a centralized group but very matrixed into cross-functional teams across the company. Trust, especially at the executive level, is essential. Executives must be sure that the analytic team understand what decisions are going to be made with the data/analytics being presented
• Cameron emphasized measurement and delivering proof that the work being done is useful. Taking the predicted value and measuring how well the results match to that predicted value. Team members must be closely linked to their clients and must understand their business so that they trust his advice – back to validating that the analytics team understand the decisions being made. This need to be “part of the business team” is also something Barbara sees as critical

Begin with the decision in mind – all the panelist emphasized the need to understand the decisions being made with the analytics before gathering data and doing analytics. Executive sponsorship, of course, came up repeatedly.

When it comes to finding talent a number of good points were made:

• Analytics is a word that means too many things to different people and this makes hiring a challenge.
• Finding people who can play in both worlds – analytics and business – is critical. Easier to find expertise on the business side and on the analytics/statistics side is doable but the cross-overs are hard to hire.
• Business people can learn the numbers, as long as they have numbers-people to back them up, but having the numbers people learn business skills is much harder. Create people who understand the numbers and can make business recommendation as a result.

Last topic – how to get started:

• Establish a long range plan – understand your end goal for analytics
• Partner with the technology team because technology will be critical
• Invest in the right people, don’t be cheap
• Invest in the right tools, don’t be cheap there either
• Get some low hanging fruit – something you can do quickly and show a great return – and put together “brag boards” to show how well you did to build support
• Make sure analytics people get some time to pursue ideas not driven by the business – let them see what could be done not just do what the business thinks it wants.

Fabulous panel!