I have just opened ITWeb’s BI 2010 in Johannesburg talking about decisions and importance of decision making in making BI matter (I will post my slides later). Great audience, nearly 200 people with a strong showing from end user customers (75%) and, very interestingly, nearly half considered themselves business / IT straddlers which is a great trend.

The next session, although focused on BI, was very relevant to decisioning and decision management. Martin Rennhackkamp spoke on strategic BI and focused on a number of critical things that help you make BI more strategic:

• Understand the strategic business objectives, make sure you understand them and focus on how BI can support them
  This is absolutely critical in my opinion. If you don’t understand your business and how your BI investments can support it then you cannot possibly make good investments in BI. I particularly liked how he mapped business objectives to BI initiatives, showing how much of what the objective needs is delivered by each. This allows you to see which ones matter and where you have holes.
• Focus on crucial business processes
  Again, good advice and I liked his focus on operational processes. Not only should people identify the data that flows through those processes but they should also ask if the processes getting the information they need to make the right choices, the right decisions. Especially, are the people executing these operational processes getting what they need.
• BI to the masses
  It is critical for information to flow down to everyone on the floor – pervasive and operational. And important not to buy the tools without thinking about how this works.
• Master measure management
  Liked this phrase. Bring KPIs into the picture, linking KPIs to operational behavior and data. Understand how different roles need to consume information and analytics and how all this maps to company objectives and measures. Focus most of your effort on measures that the consumer of a measure has control over and to which they contribute. As I said once perforce, a dashboard should do more than just raise your blood pressure!

Syndicated from Smart Data Collective

I often tell companies and other organizations that they should treat decisions and decision making as assets. In Smart (Enough) Systems, the book I wrote with Neil Raden, we said

Operational Decision Making as a Corporate Asset

If operational decisions must be made well for your organization to deliver on its strategy, they can’t be made randomly. They have to be made systematically. You have to turn operational decision making into a corporate asset you can measure, control, and improve. After all, when [customers] interact with you, they consider every decision you make to be a “corporate” one—that is, a deliberate one.

But is it reasonable to consider decisions, decision making, as an asset? After all an asset provides future value to an organization – tangible or intangible (goodwill and trademarks for example add intangible value while a factory adds more tangible value). Fundamentally an asset “contributes to future cash flow”. How does this work for operational decision making?

Operational decisions, those taken in a transactional context, include decisions like next best offer, pricing or discounts, product eligibility, claims approval, credit or fraud risk. Clearly each such decision has an impact on cash flow and profitability – good decisions having a more positive impact, bad ones a more negative one. The thing about operational decisions, though, is how often very similar decisions are made.

Consider claims – even a relatively small insurer (like Infinity Insurance discussed here) might receive 10,000 claims or more a month. Each claim must be considered and approved or rejected and making the right decision in each case adds to the bottom line. As a result the insurer needs a defined decision making process for claims – each one cannot be considered as a special case if 500 or more are to be handled efficiently every day. If the insurer has a good decision making process then each decision will be more likely to be a good one. If they don’t, less likely.

If we apply our definition then an effective operational decision making process is a form of asset – it contributes to future value by ensuring that better operational decisions are made. If we define the business rules, the analytics that make up this decision making process then we are investing in an asset. If we embed those rules, those analytics, into our operational systems and processes then we can ensure this asset is fully exploited.

In the book we went on to identify some characteristics typical of other corporate assets. Each of these can be applied to decisions and decision making:

• They are strategic
  Planning exercises and budgets should consider decisions – ensuring that plans that rely on changed to decision making, for instance, include the definitions of the changes needed. Executives don’t care about individual decisions but they should care about the decision process.
• They are managed
  The company invests in decision management and governance so that the quality of decision making doesn’t degrade over time
• They are visible
  The cumulative value of an operational decision should be known (multiply the difference between a good and a bad decision by the number of times such a decision is made) and the investment made in improving decision making should show up on balance sheets and be visible to management
• They are reusable
  Companies, well run ones at least, don’t duplicate assets or leave them idle. So with decision making.
• They are improved constantly
  Companies should invest in analytics and experimentation to constantly improve decision making – this is the equivalent of preventative maintenance and upgrades for machine tools.

High volume operational decisions drive your business every day, playing a role in every transaction. Investing in operational decision making will ensure that these decisions add, rather than destroy, value.

For more on decision making check out Thinking different with decision analysis by Ted Cuzzillo,  Tom Davenport’s article Make Better Decisions, this piece on Micro decisions for macro impact (references another Tom Davenport article), Prepare for Impact (Teradata magazine) and of course Smart (Enough) Systems.

I received a pre-release copy of Tom Davenport’s new book Analytics at Work: Smarter Decisions, Better Results. The book is a follow-on to Competing on Analytics (reviewed here) and is a shorter, pithier book than its predecessor. Once again Tom collaborates with Jeanne Harris and this time Robert Morison of the Concours group. Where the previous book focused on so-called analytic competitors, this is about “analytics for the rest of us”. It is a very readable book with some good practical advice that does not require the remaking of your company in a new image. It is also a quick read, it is only 180 pages or so, which should help get more people to read it.

And I hope people do read it. As Tom says “The unexamined decision isn’t worth making” and too many companies and organizations are making unexamined decisions, failing to apply data they have about what works and what does not, making the same mistakes over and making dumb decisions. Like Tom I think it is time for this to stop and this book will tell you how.

In the initial chapter, the book outlines the difference between areas with a history of analytic decision making and those where it is new – performance metrics may be progress in the latter but something like customer segmentation and treatment requires more advanced analytics to score and segment them. It’s important to remember this, to find the right degree of analytic sophistication to make a difference. The book’s focus is broad, covering how analytics can address key questions of information and insight in each of the past, present, future – reporting, alerts and forecasting give information in the past, present and future while modeling, recommendations and predictions/optimization do the same for insight.

For me the most useful part of the book is part one – a set of chapters describing The Analytic DELTA – Data, Enterprise, Leadership, Targets and Analysts – what Tom regards as the 5 critical elements of successful analytic adoption:

• D – accessible, high quality data
  I particularly like the focus on uniqueness as a criteria and on using the business need (decision) to drive quality and integration needs – being with the decision in mind. Focusing BI/analytics people on the quality of decisions they enable not on the data they manage like Humana’s “advocate of all matters quantitative” who relentlessly improves “corporate decision making efforts”.
• E – enterprise orientation
  The point here is not to focus on fractured analytic projects but on coherent ones across the enterprise. Enterprise-serving projects not self-serving ones. The authors make the great point that getting value from your enterprise applications means anticipating how to use the information they provide to improve performance.
• L – analytical leadership
  An organization’s leaders must care about analytical decision making, especially where it is multiplicative and delivers leverage (in highly repeatable operational decisions, for instance, where the improvement in decisions is multiplied across all your transactions).
• T – strategic targets
  A crucial element, that of focusing on using analytics to develop distinctive capabilities. This chapter has a great list of processes that lend themselves to analytics because they are data rich, asset or labor intensive, dependent on speed or consistency and more. The focus on decisions that are complex or ca be optimized, where consistency is required and those done poorly today is spot on. The “ladder of analytic applications” is a great tool for seeing how to develop from simple to more complex analytic solutions working from getting your data in order to segmentation and differentiation, becoming predictive, institutionalizing and finally optimizing. Interestingly this sequence matches exactly the pattern I have seen in research I have been doing for IBM on analytic journeys.
• A – analysts
  A nice chapter with good thoughts on how to manage analysts as a strategic resource.

Part two addresses how to stay analytical through embedding analytics in business processes, building an analytic culture, reviewing your business comprehensively and embarking on an analytical journey towards “more analytical decisions and better results”. I really like the focus on embedding analytics in business processes – this is a topic close to my heart – and like the authors agree that the use of analytics is especially valuable in workhorse or operational processes. The authors do a nice job of explaining why organizations need to adopt a test and learn mindset, to be always unsatisfied and mindful of change and to focus on an “industrial” analytic process.

While Tom and I disagree over the extent to which analytics can be used to drive fully or mostly automated decisions, we are in synch on his definition of nirvana – an organization that knows its decision points, relies on analytics, integrates them into its operations and monitors performance to close the loop. And one that MAKES DECISIONS AND TAKES ACTIONS using analytics – one that realizes it is not enough to just analyze its data.

The authors end by pointing out that becoming analytic is not a one-time activity but must be ongoing – it is a journey which organizations must begin, where they must build momentum and where they must go from thinking of analytics to thinking about decisions and decision making, from analytic management to decision management.

It’s a great book and you should buy it.

Syndicated from ebizQ

This week I thought I would write about decision-centric organizations. Organizations face many challenges in today’s business climate. Organizations whose success or failure is determined by the decisions they make (which claims to pay, which customers to target, which transactions to investigate for fraud) are handicapped by systems that are centered on processes or functions. As a result, these organizations struggle to improve business productivity while managing costs and find it hard to make changes in their systems quickly, despite a pressing need to do so. To succeed, these organizations need to move their thinking from processes and functions to decisions. They need to become a decision-centric organization as only a decision-centric organization is going to be able to deliver agility, control, compliance, personalization and decision support in a coherent, integrated way.

Decision-centric organizations deliver agility because they can make rapid changes to the way they conduct business. Decisions are the changeable elements of most operations and rapidly changing policy or regulation and competitive pressures affect these decisions, not the processes or functions within which they are made. Decision-centric organizations deliver the business control executives want over operations by giving them control over the decisions that drive day to day operations and implement business strategy. These decisions are compliant, and demonstrably so, because those who understand the regulations are driving the decision with no IT/business disconnect.

A decision-centric organization maximizes straight through processing, delivers consumer- and information-driven processes that are infinitely customizable and that flow easily from automation to case management and back again. Decision-centric organizations gain operational advantages and a competitive edge through a systematic focus on decision making throughout the organization. Decision-centric organizations deliver increased agility by decoupling the IT and business lifecycles and it dramatically reduces the complexity of IT and hence its cost.

Decisions have always been at the core of an organization’s behavior but for too long they have been buried, considered only as part of an organizational function or a business process. Such buried decisions are rarely automated effectively, are hard to improve and the lack of explicit management of these decisions leaves organizations at a loss to know how to maximize their effectiveness.

Tomorrow, the focus on decisions.

Syndicated from ebizQ

I was reading an old SPSS presentation the other day and found a great definition of a Predictive Enterprise:

A predictive enterprise:

• Derives maximum value from its data assets
• Understands its business by gaining deep insight
• Leverages advanced analytics to predict outcomes
• Turns this knowledge into action to optimize decision making across all areas of its operations

I like this definition for several reasons. First, I like the focus on using analytics to maximize the value of data assets. Secondly I like the way it focuses on analytics to predict outcomes. And finally I like the focus on operationalizing this knowledge. Decision Management, with its focus on getting more value from data using analytics and its focus on putting those analytics to work using business rules is the best way to create a predictive enterprise. Using business rules in this way also means that the optimized decisions you are making are transparent and easy to change, agile, thanks to the engagement of business users that business rules make possible.

For 2010, you should be striving to become a predictive enterprise. Are you?

Happy Holidays

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

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.

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 ebizQ

I got an interesting comment on my recent post about the top 4 concerns of CIOs.

Joanne makes a number of points in her comment that I thought should be addressed:

a business rules engine is not nearly enough. What is needed instead is a means to model manage and measure the impact of a decision on desired business outcomes, and to do so not in a silo of a process but in the context of a business activities, a variety of changing
market conditions and business scenarios.

Absolutely. This is why I talk about decisions, not just about rules, and why I think it is critical to link decision management and performance management (something I will be discussing at the Business Rules Forum during the alignment symposium).

The notion of making decisions based on either historical data or using predictive analytics or rules engines is contrary to becoming agile.

Nope, disagree completely. The reality of a modern organization is that it makes thousands, millions of decisions that simply cannot be made by a person – there is not the time or money to do so. These must be automated and a rules engine is the only way to ensure some kind of agility. Yes business leaders need to learn and improve decision making, but a rules-based decision management approach gives them the power to actually change the decision making in their systems and so does deliver agility.

Agile implies proactive so these days decision making is not about playing “what if”, it’s about playing “then what”. Enabling a decision maker to measure the value of a decision criterion against other criteria and make the appropriate trade-offs in advance of a market condition creates the still elusive agility companies seek.

Completely agree. This is another benefit of formally modeling and understanding operational decisions. Only then can simulations, scenarios be developed that use the actual transactional behavior of the company and its systems rather than some roll-up or aggregation. Seeking tradeoffs using traditional tools that roll up transactions and make assumptions about how decisions will affect the group is not enough – you need to be able to apply tradeoffs and new approaches customer by customer, transaction by transaction to truly understand their implications.

Automating a decision using a rules engine does not allow an organization to make more informed decisions because the rules are not conditional upon non-included variables – and even if they are, then the exception becomes the norm and norm the exception.

Using a rule engine does not enable more informed decisions, true. But it allows an organization that figures out the best way to make decisions to ensure that this is, in fact, how decisions are made. Too many companies think they make decisions one way but have no way to check/ensure this is what happens at the front line or in their systems. Rules engines can be used to implement bad decision making, of course, but they allow you to see how bad it is, change it rapidly and engage those who understand the decision directly so they are way better than the alternative.

too often its the squeaky wheel that gets the oil. In many cases decisions are based on satisfying one stakeholder group when in fact the agenda touted by that group may be a quick win with long term ramifications that do more to hinder than to help drive the revenue growth or improved effectiveness the organization desires.

Too true. But how will you do this if you don’t have some explicit record of how decisions are being made (and if you are not automating a decision with a rules engine then you don’t have such a record: people never record their decision making process clearly and traditional IT systems are completely opaque). By automating and managing the decision you create the record of decision making and its implication that would allow you to address this.

Great comment Joanne – appreciate it.

I got a chance to sit down with Nancy Pearson, Vice President BPM, SOA, WebSphere and Industry Marketing at IBM last week to talk about Smart Work and other related topics. Smart Work is one of the four major themes that are part of IBM’s overall Smarter Planet initiative – New Intelligence, Green and Beyond, Dynamic Infrastructure and Smart Work. Like all these themes, Smart Work is a cross IBM initiative designed to create an agenda-setting conversation. Nancy has worked on the New Intelligence piece of Smarter Planet and is now leading Smart Work and there’s a great video with Nancy talking about Smart Work on youtube.

Nancy’s focus with Smart Work is on how to help organizations work smarter and on what’s new about this challenge in today’s environment. Both business optimization and business agility are critical to the Smart Work vision.  In her own words

“We are hearing more and more of our customers ask us to help make their work environment that is more agile, collaborative, and connected. They can’t handle today’s disruptive marketplace shifts just by working harder. And they can’t just dedicate more and more resources to their work. Instead, they need to work smarter.” From IBM’s perspective this means that the SOA, Business Process Management, Business Event Processing, Information on Demand and advanced analytic products capabilities all have a role to play along with their collaboration tools and their various service lines. Working smarter really draws from capabilities across IBM.

Nancy has been working with clients, business partners and internal folks to answer three questions:

1. How to help business evolve to adapt and respond dynamically
2. How can people collaborate better to maximize their effectiveness
3. How does technology enable flexibility to meet those changing business needs quickly?

In particular Nancy feels this leads to a focus on managing and modeling dynamic business processes – using SOA, BPM and business rules – but also collaboration tools. Businesses can automate some (most) of the process and collaborate on improving it as well as on handling exceptions or problems. From my perspective I see that collaboration is hot right now, with all the focus on social media, but it is important not to implement collaboration tools without a focus on a real business problem. Nancy was really clear on this – you can’t just bolt on collaboration – you must be trying to fix the process too. For instance, one IBM insurance customer is using collaboration tools to help its independent agents, underwriters and others work together on insurance processes like policy origination and claims processing. But automating and streamlining these processes is critical as well. Process optimization and collaboration must work together.

This need for a holistic focus was clear in some of the survey work IBM has done. For instance their most recent CIO study had 70% of CIOs identifying a focus on optimizing business processes as one of in their top 5 priorities. About the same identified collaboration with customers and partners as important as well. In addition, the increased use of analytics score highly and the expectations of what analytics can do is growing too.

“We’re seeing organizations address work-related challenges as an inter-related whole rather than as a disconnected set of piece-parts”, said Nancy.

IBM sees its clients asking for more sophistication, not just BI but analytics that let them micro optimize operational decisions and integrate analytics and rules/process/event management. Clearly these priorities are all getting applied to the same set of business problems and all of them have to tie to an ROI. Hence the broad nature of the Smart Work effort with multiple product and service lines involved.

IBM clients, of course, are focusing on specific problems. Nancy gave the example of Atlas Air who is using SOA and BPM to make a more dynamic business. Atlas Air is a freight airplane outsourcing company that could not handle new client requests, find airplane sin real time, change flight or crew schedules due to silos and rigid systems. Using SOA and BPM they have built a more flexible infrastructure but also seen big reductions in costs and increases in productivity. They have built real-time decision support (analytics) on top of this to help make more informed decisions in collaboration with business partners and customers.

The acquisition of ILOG and the integration of its product lines into IBM is an important element of the overall Smart Work initiative. Nancy is excited about the ILOG products’ role, for instance, in allowing business users to create rules that carry out an automated response when predefined conditions are met in their business. Deciding what to act on, what to do, what patterns matter, how to automate or modify process are all critical in handling these events and the combination of IBM’s Business Events product and ILOG rules and optimization is exciting.

I took away a key message from this discussion on working smarter – the integration points between the various information, analytics, process and collaboration technologies are critical as product combinations are the order of the day. The richness of IBM’s product portfolio is clearly an advantage here.

I think another of IBM’s strengths is its Industry Frameworks. I saw that the banking one, for instance, now has a link to rules so I asked Nancy if IBM had a systematic process for assessing the potential for rules, analytics and optimization within these frameworks. How, I wondered, is IBM going to bring these newer approaches into a data- and code-centric set of frameworks?

Nancy has been involved in 14 of IBM’s acquisitions in the last few years. With each acquisition IBM always goes back to look for opportunities in existing frameworks for the new technology. They try and find the quick wins – the obvious places to apply the technology. Each change in the technology available causes a systematic effort to re-assess the existing frameworks and see how to use the technology. From a practical point of view this should mean that companies can watch the changing nature of the relevant industry framework for their industry and see what opportunities exist to adopt new technologies. Personally I am looking forward to seeing rules and analytic adoption increase as more industries understand the potential thanks to more decision-centric industry templates.

I went on to ask Nancy about another announcement I saw recently, the new business performance center of excellence. Does this center have business rules, optimization and analytics as part of its core mission? How about performance management software and approaches?

The Business Performance Center of Excellence was recently announced as part of Smart Work. Within IBM such centers try to provide a set of capabilities to support clients who want to look at a project and see what is possible – learn how the capabilities can be applied. This new center takes skills from consultants in SOA and BPM as well as some collaboration experts and ILOG skills. The center is intended to show how IBM can help customers and to be customer driven. Customers can bring business question to the CoE and get a coherent answer across multiple technologies, product lines and skills. At the center leverages the industry frameworks too, often pointing customers to so-called hot spots or entry points. Customers, Nancy says, need help pulling together a number of things. The danger for all of us is falling into presenting one component as the answer. The Center of Excellence should help customers avoid this trap. The center will advocate a focus on BPM, flexible SOA, collaboration tools etc but where to start depends on where the client is and what they need.

I enjoyed the conversation with Nancy and particularly liked the broad-based, non-product specific approach she is taking to Smart Work. I look forward to seeing and hearing more about it. If you want to see some interesting discussions on elements of Smart Work you can check out the archive for the recent Smart Work Jam (in which I participated). You can see the results of the conversation linked from ibm.com/smartwork/virtual until early October.