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December 31, 2006

Book Reviews: Good to Great, Creative Destruction

Two more book reviews, "Good to Great" and "Creative Destruction"

Good to Great first. This book will (probably) terrify you if you work. Clearly outlining critical characteristics of super-successful companies it show over and over again why most companies are not. Think about where you work, and how you invest, in the light of what the authors discuss. Like Execution, another book I really liked (and reviewed here), it analyzes how companies that sustain a high level of effectiveness might do that and gives some practical advice you can follow, whether or not you are the CEO. In addition this book is highly recommended for anyone thinking about becoming a CEO or engaged in a CEO search.

Unfortunately the same cannot be said of Foster and Kaplan's book "Creative Destruction: Why Companies That Are Built to Last Underperform the Market - and How to Successfully Transform Them". The book starts of reasonably well. Its general themes explaining why large companies tend to behave in ways that make them less effective at responding to change than the market are well described. As the book tries to show examples of companies that did or did not respond well to the forces of change in business they lose their way. Not only do they extol a number of companies seemingly purely because they were founded by friends from McKinsey, they also use Enron as a successful example! Too many of their examples have not done well since the book was published and that undermines their message. The book also lacks concrete advice, though I must confess to skimming towards the end. My takeaway? The market as a whole will ALWAYS innovate more effectively than any company so get over it and be prepared for companies to come and go and change constantly. There's not much, if anything, you can do about it. The book should have been subtitled "Why Companies Underperform the Market in the Long Run" as that's really what it comes down to.

You can buy "Good to Great" here and "Creative Destruction" here.

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December 29, 2006

New "Retail" Category Started in the EDM Blog

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Predictive Analytics: Aisle 3

(Posted by Guest Blogger, and ardent shopaholic, Ian Turvill.)

It's that time of year again, when we're supposed to make predictions about what the New Year will bring us. And apparently, I'm not alone in that belief, since Susan Reda, Executive Editor of The National Retail Federation's "Stores" magazine has compiled a list of seven things she predicts 2007 has "in store" for us.

Alongside greater use of mobile marketing technology, "fast fashion" (you'll have to read the article to see what that is - it's just too much to explain), greater emphasis on green issues, a difficult economy, and pressure on supermarket formats, she highlights two specific trends that appear to have direct relevance to our EDM blog.

First, Susan Reda says that demographics will assume greater power

"Immigration, aging Boomers and a host of other demographic shifts will be shaping and reshaping the retail landscape for years to come. Retailers who set their sights on micro-merchandising and micro-marketing will triumph; those who run with the herd may find themselves getting trampled."

Another win for Enterprise Decision Management, then! EDM's capability to find trends and to apply them at a highly granular level makes it ideally suited to address this particular challenge. If you know of any retailers who are not regular readers of this blog, I recommend pointing them here so that they are best equipped to deal with the challenges of 2007.

Separately, Susan emphasizes the impact of numerous new technologies on the retail industry, including business intelligence, RFID, contactless payment, and biometrics, as well as predictive analytics. I'm very surprised that Susan did not make more of an explicit link between between the issue of the demographics and the application of predictive analytics. However, she does quite aptly state:

"Retail success is about finding the sweet spot. Doing so requires business intelligence -- predictive analytics that allow you to distill key customer information from a sea of data, capture missed opportunities and smooth out sales anomalies."

Of course, I think James and I would probably extend that further and say that what is required is "actionable business intelligence" and the capacity to apply those insights as a core part of all transaction streams and customer interactions. Susan seems to conflate the ideas of BI and predictive analytics, and as regular readers of this blog all know, there are many reasons why BI and EDM are not the same thing. But we'll certainly accept Susan's point, nevertheless.

I predict that James and I will be blogging a whole lot more about the applications of Enterprise Decision Management in the retail industry the New Year. It seems to be an area that is ripe for some good ol' decision automation!

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Posted by James Taylor at 1:10 PM | Comments (0)

The art of the decision

Well hopefully you all saw me on the front page of DM Review - The Art of the Decision. If this is the first time you have seen the blog, welcome! To coincide with the profile I am going to write on some of the key topics that come up talking with Jim. I have posted on some already:

• What is agility and why would you care?
• Managing decisions as a corporate asset

Next up is something on transaction-centric processing. Look for it after the July 4th holiday.

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December 28, 2006

Using business rules to write maintainable code

Reddit pointed me to this post on "Writing Maintainable Code" by Jeremy D. Miller over on CodeBetter.com. It's a nice article and I look forward to reading the rest of his posts on this topic.There's lots of good stuff but a number of his comments led me to dig up some of my posts on maintainability and agility:

"Enable Change or Else!" because "Change is a constant in an enterprise software system"

Regular readers will know how much I agree with this. I have written before about how rules can help you love change and construct dynamic applications built with change in mind. Using business rules for core business logic can also help you respond to changing requirements. This is sometimes called business agility and I wrote some notes on agility based on some Gartner research on this topic.

"single most important quality for an enterprise software system is maintainability"

I recently posted on using rules to avoid "write only" code and on using rules to improve the application maintenance process.

Jeremy has "a strong preference for creating maintainable code throughout the codebase" rather than building in points of extensibility.

While I think he is right, I also think that the reality is that some parts of an application will have change driven by business users and some by more technical requirements.. Jeremy asks the question "where should this code go" and I think there should be a follow-up question of "what kind of code should this be". Assuming that everything in an application should be code is a risk as there is a problem with (our expectations of) programmers. After all you have to remember the different perspectives of programmers and business people.

Jeremy then lists some great questions along with proposed approaches to address them. Some of them seem to me to relate very strongly to business rules:

• Can I find the code related to the problem or the requested change?
  And once I find it can someone who understands the change also read the code?
• Can I understand the code?
  And who am "I"? A programmer or someone who runs the business?
• Is it easy to change the code?
  Can I change the "rules" and put new rules into production without downtime?
• Can I quickly verify my changes in isolation?
• Can I make the change with a low risk of breaking existing features?
• Will I know if and why something is broken?

All these points lead to me repeat some of my thoughts on why business rules can be better than code and on business user rule maintenance secrets.

Lastly he has a very strong focus on the value of layering and separation of concerns. He identifies one layer as "Business logic, rules, domain model". Could not agree more.

As an aside he also talks about agile methods. I have posted a virtual conversation with Scott Ambler on rules and agile approaches and an article on the same topic if you are interested.

Technorati Tags: application maintenance, BRE, BRMS, business agility, business rules, change time, maintenance, programmer, programming, agile

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Posted by James Taylor at 8:34 AM | Comments (0)

December 27, 2006

Book Review: Data Mining Techniques

I am getting caught up on book reviews over the break. Today's is Data Mining Techniques by Berry and Linoff. This is one of the classic works on data mining and well worth the read.I really liked the book both because it is well written and because, although it drilled into a fair amount of detail about some of the techniques, it started each new section off at a high level. This allows someone without a statistical background, such as me, to read as far as I can in each section and then skip ahead to the next technique. This is a nice change from books that simply get more and more detailed as page follows page, preventing you from gaining an overview of the subject. The book introduces data mining and a methodology for applying it, talks about some of the applications in "Marketing, Sales, and Customer Relationship Management" (as the subtitle puts it), walks through some statistical techniques and then spends the bulk of the book on various data mining techniques. It wraps up with a nice summary of how data mining plays with other technologies and with some practical advice on getting started.

One of the best summaries of where data mining, and indeed EDM, fits is given early in the book where an enterprise is encouraged to:

• Notice what its customers are doing
• Remember what it and its customers have done over time
• Learn from what it has remembered
• Act on what if has learned to make customers more profitable

The authors point out that Data Mining is focused on the "Learn" stage or, as they put it data mining suggests but businesses decide. EDM, of course, is concerned not only with learning but also with acting, most particularly acting by automating decisions in front-line systems. Merely finding patterns is not enough - you must respond to the patterns and act on them, ultimately turning data into information, information into action and action into value.

The methodology section, and the subsequent notes that relate to applying these techniques in real life, talked about the feedback loops between steps in data mining - there is not a linear "waterfall" sequence of steps but constant iteration and learning. They also emphasized the importance of finding the right business problem at the beginning - start as someone once said, with the end in mind. This was reiterated when they quote Voltaire who said "Le mieux est l'ennemi du bien" ("The best is the enemy of good"). In other words, don't get hung up on trying to find the perfect algorithm, perfect answer. Instead build something that is good, that works, and learn and improve over time.

The authors made a big point out of the value of data mining for "mass intimacy", where you want to treat customers differently and there is a business reason to do so but where customers are too numerous to be assigned to staff. One of the issues they pointed out was that staff must be trained in customer interaction skills while also using all the data you have. This can be a real challenge and is one of the reasons I prefer an EDM approach, where the decisions those staff need to make are automated, to other approaches. By giving them the decisions they need you free them to work on the relationship (as I have discussed before). The value of data mining, and EDM, in building a customer-centric organization cannot be overestimated.

Some random snippets of useful stuff from the book:

• A model "can result in insight" and "produce scores". The first kind is used in EDM largely to product rules while the second is often embedded directly in the decision services being built
• Analysis can be directed (find the value of something) and undirected (find structure)
• Data visualization is very useful during the initial exploration of information.
• There is some discussion of the difficulty in deploying models when the step involves"a programmer takes a printed description of the model and recodes it in another programming language so it can be run on the scoring platform". EDM's focus on automating the deployment of models into a rules-based decision service is designed to address this issues.
• Besides coding the actual model, data transformations are also a big issue and remain one even in EDM.
• Decision trees are "powerful and popular" for classification and prediction because they can be represented by, and represent, rules. Indeed decision trees are a cross-over artifact between rules and models that are critical in EDM also. One of the things that makes trees particularly useful is because they need less data preparation as they can handle all kinds of variables well.
• The authors emphasize repeatedly the importance of time series data e.g. detecting early signs of attrition by tracking all actions of checking account customers in the time up to when they leave a bank. The time-based signatures thus created are great predictors. They note also that this is one of the weaknesses of data warehouses when using them for analytics - they tend to arrange data by absolute time/date when the analytics are more useful relative to an action or event.
• The value of neural nets is noted but the problems neural nets have with respect to traceability and explicability are also noted. This makes neural nets great for things like fraud detection, where results matter and reasons matter less, and poor for things like credit assessment where regulators expect to see compliance with rules.
• The section on market basket analysis and association rules is very good and describes these forms of undirected analysis well. They point out that these can, if you are not careful, describe the history of marketing promotions rather than genuine decisions to purchase products together. They also give some good examples of using product hierarchies to generalize where some products are much lower volume than others.
• They describe a pyramid with operational data on the bottom, summary data next, the database schema on top of that followed by metadata and finally busienss rules - what's been learned from the data.
• They worry that"rules" are not actionable but I think this is because they focus on rules that describe the data not on rules that describe the actions to be taken

You can buy the book here and it should definitely be on your bookshelf.

Technorati Tags: algorithms, analytic application, business intelligence, business rules, CRM, customer insight, customer segment, decision automation, EDM, Enterprise Decision Management, marketing, predictive analytics, segmentation, statistical analysis, data mining

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December 22, 2006

A Short History of Bad Decisions

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It's Christmas: Time to Talk about Wars

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December 21, 2006

Here's a way to take advantage of mobile devices

I saw this post by David Raab over on his blog - Business Intelligence on Smart Phones: Not Just Humbug. Like the author of the original article to which David refers (Power Of A Data Warehouse In The Palm Of Your Hand by Elena Malykhina) I am cynical. Her comment that "It remains to be seen how many mobile professionals actually need to slice and dice data from handheld devices" really struck a chord with me. I don't see even weary road warriors wanting to do "traditional" BI on a smartphone. But as David correctly points out the follow-up question is interesting:

The more intriguing question is what new business intelligence functions a smart phone platform would make possible

Now substitute the works "decision management" for "business intelligence" and I think you are on to something. One of the the differences between BI and EDM is the focus on taking action using insight gained from data rather than showing someone the data and helping them gain some insight. I would say that David's examples are all, in fact, EDM examples. They use the information the phone has (position), insight from the data the company has (fraud likelihood, wait times) to take an action (dispatch the person with the phone to a particular place, tell them to do or not do something). I don't see traditional BI vendors having much to offer here - the whole reporting/OLAP infrastructure they have developed is predicated on knowledge workers doing analysis. If you want to take advantage of mobile devices you need to think about automating decisions for the person holding the device. For instance:

• Use mobile phones held by maintenance engineers to track their location and then use analytics to predict which pieces of equipment are most likely to fail soon and rules to assign the nearest, qualified engineer before sending the directions on where to go to the engineers phone.
• Don't show them reliability graphs or travel times, tell them where to go to make best use of their time
• Use the mobile phone of a real estate appraiser to find out which risk zones a property is in and what the predicted difference is between a house inside and outside that risk zone
• Don't show them a picture of the risk zones
• Use a doctor's mobile phone to route them to the most useful hospital during an emergency based on predictions of patient load, the hospitals they know and their specialties
• Don't show them graphs of wait times and pie charts of specialties needed
• Use a customer's mobile phone to make them an offer at a store that is nearby having predicted that they are likely to buy it, checked that is in stock there and estimated that they are more likely to respond in person than to an email promotion to the website

And so on. Automate decisions and use mobile devices to provide context for those decisions and to deliver decisions to people out and about. Don't send them reports. Please.

I have blogged before about the value of location information in automation of decisions and on location intelligence with EDM and wrote an article in BI Journal (subscription required) with Ed Gandorf of MapInfo "Driving Decision Automation with Location Intelligence".

P.S. An extreme example of this might be something like pay-as-you-drive insurance as described by my colleague Ian.

Technorati Tags: analytic application, analytics, business intelligence, business rules, customer experience, customer insight, decision automation, EDM, Enterprise Decision Management, personalization, predictive analytics, mobile device

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Posted by James Taylor at 12:43 PM | Comments (0)

December 20, 2006

Book Review: Execution. The Discipline of Getting Things Done

Over the weekend I finished "Execution. The Discipline of Getting Things Done" by Larry Bossidy and Ram Charan. This book is a succinct summary of all that is wrong in many companies. Larry and Ram analyze many of the most dysfunctional behaviors seen in large corporations and lay out some steps to address them. While many of their stories focus on senior management and execution failures, their suggestions and guidelines work just as well for all levels of management. If you are responsible for planning and getting things done, this book will give you some tips and ideas as well as codifying your "gut feel" for why some people just don't get things done. My only complaint with the book was that it did not address the problems of getting your information systems to "get things done". As businesses are increasingly embodied in their information systems I think this is going to become more and more important. Clearly this is my bias but to give you a sense of what I mean, here are some of my favorite quotes from the book with commentary.

• "when a company executes well, its people are not brought to their knees by changes in the business environment"
  But if that company has information systems that do not change easily then it will lack the agility it needs to respond to these changes. In reality most businesses now have information systems that must be changed to cope with a new business environment. If these systems are hard to change, they will be brought to their knees.
• "leaders placed too much emphasis on what some call high-level strategy,...,and not enough on implementation" and "unless you translate big thoughts into concrete steps for action, they're pointless"
  Ram and Larry are talking here mostly about the implementation in terms of making sure successive layers in the organization can deliver on the strategy - that all the pieces add up. Again, if the lowest levels of your organization are driven by information systems, or if your customers interact directly with your information systems, you need to also be concerned with the implementation of your strategy in those systems. But most information systems are impenetrable to most business people and so it can be hard to tell, let alone ensure this.
• "If your business has to survive difficult times, it if has to make an important shift in response to change - and these days just about every business does - it's far, far more likely to succeed if it's executing well"
  I have written a lot about the need to have agility in your information systems to cope with change but I thought this quote brought home how essential this is.
• "when decision-making is decentralized or highly fragmented, ..., people at many levels have to make endless trade-offs"
  In reality people at every level are making trade-offs and you need to decide how to make sure that the right trade-offs are being made even when the trade-off is being made by someone with limited business know-how or by an automated system. Using analytics to embed effective risk management and risk/reward trade-offs will help make the information systems at the bottom of your organization manage this.
• "Behaviors are beliefs turned into actions...They're where the rubber meets the road"
  The business rules embedded in your information system are where the rubber hits the road. They decide how your website treats customers, how your IVR system works and so on. Controlling them is essential for turning your beliefs into behaviors.

There were some other interesting sections from an enterprise decision management or EDM perspective. One of the building blocks identified was "insist on realism" and it struck me that this is part of what makes the use of analytics in EDM so powerful. Analytics are, because they are derived from actual data, steeped in realism. Using them to drive decisions can really improve the amount of realism in your decisions. Similarly the use of rules to define how customers are treated allows for a realistic assessment of how they were, in fact, treated in a way that interviewing people and asking them how they treat customers will never be.

Finally I thought the quote about execution below was lovely and very relevant to EDM. EDM is not tactical, it is fundamental to your strategy. If your systems don't follow the rules your strategy implies or use the data on which you based it, how likely are they to do it right?

"People think of execution as the tactical side of business. That's the first big mistake. Tactics are central to execution, but execution is not tactics. Execution is fundamental to strategy and has to shape it"

You can buy the book here.

Technorati Tags: analytics, business agility, business rules, decision automation, EDM, Enterprise Decision Management, risk management, strategy, management

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December 15, 2006

A media story

Some time ago I posted a comparison of how a bank operated with how an EDM process might work (A banking story). Thanks to a friend I have another example is this genre - this time from the news media.

Old Way

A former subscriber to the Wall Street Journal (WSJ) she received an offer in the mail designed to entice her back - the offer included both the print edition and the online wsj.com. The offer came in a personalized letter and had various codes identifying it. The offer came from "The Wall Street Journal. Print amp; Online".

• Deciding to pay online she found a different set of offers with no apparent way to enter any of the codes from the offer letter. Nevertheless one seemed to match and so she signed up for it.
• The first print issue arrived promptly but no information was forthcoming on the online subscription
• A short email conversation ensured which, in the end, yielded an acknowledgment of the deal and instructions for signing up online
• She tried to follow the email instructions but they didn’t work. When it asked for the print account number she got an error message “account already in use” on page 1 of the create account pages
• She called the WSJ online and got a long menu of options which kept repeating what she could do online but otherwise was not helpful. After a long wait she got a Customer Service Rep (CSR). The CSR was confused by the offer but then the CSR said that she had an old account (my friend's a lapsed subscriber remember) and the CSR said it was fixed it right then, My friend tried it and sure enough the error “account already in use” went away
• Having got past page 1 of the process (there are 4), she got to Page 3 which asked which billing period she would prefer. Of course she had already paid so she called back (same long menu, same repeated instructions, some long wait). This time she got another CSR who was also confused by the offer. After reading the notes this CSR said that “print didn’t set up a combo account” and told my friend she had to call them. The CSR transferred her but then she went back in the queue (same long menu, same repeated instructions, another long wait).
• She then got a print CSR who said the system showed that my friend had been a print customer since 2004 and the offer was only available to new subscribers.
• Through gritted teeth my friend explained that the offer was explicitly sent to lapsed subscribers and that it was good for anyone who had not been a subscriber in the last 180 days and furthermore that sheI had been doing this now for 30’ or more and was going to get very cranky. The new CSR read all the (now very long) notes and emails, put her on hold twice, then eventually popped on and said you now have a combo account, wait 24 hours then call online or try again.
• 9 hours later she received an email from the online part of WSJ with instructions to set up the account and it worked.

A long-winded, customer-hostile process that damaged their brand, annoyed a customer and cost them a ton of money in CSR time (while also increasing the delays for everyone else trying to get a CSR with a knock-on customer service impact). How could this have been done better?

EDM way

• The online environment would have had the ability to ask for offer codes or address so as to identify the customer initially
• The online form would have shown the same offer as the offline and created the account the right way the first time
• Even if the customer had lost the code and signed up for the wrong offer online, the CSRs would all have seen the same offers available to the customer as the system would run the eligibility rules and display those for which the customer was eligible. The systems the CSR used would have allowed them all to trigger the allowed decisions, there would be no transferring to different CSRs.
• When signing up for the online service the re-use of the account number would have caused a sensible decision (like questions to see if this person is the same as the one who has the account in the system) not an error message.
• Similarly when it got to the billing part if would have checked the account and seen that a fee had been paid that made the customer eligible for both online and print editions (it would know she was a returning customer who had been gone more than 180 days and who was therefore eligible for the combined service offer and that she had paid the amount associated with that offer). It would then have displayed the existing billing data, confirming the included online subscription and carried on with the rest of the process

Why is this a better process?

Well it's way more consistent - the online and offline experiences are coordinated and the decisions that need to be taken to sign customers up whether they mail in orders, use the web or talk to a CSR are all automated correctly. CSRs are empowered to focus on the customer because the system knows what decisions are allowed for each and tells the CSRs so that they can execute them quickly and easily and without the risk that they will approve something they should not. The online environment would have run rules at each step to see if additional data was required so that it did not repeat questions or get confused. Some kind of smart form would have been displayed that responded intelligently at each in the process.

Technorati Tags: business rules, customer experience, decision automation, marketing, media, personalization

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Posted by James Taylor at 4:27 PM | Comments (0)

Marketing to (and with) algorithms with EDM

Ian, my fellow blogger here at edmblog.com, pointed me to this article You Must Market To Algorithms, Not Just People. The article nicely summarized the growing role of algorithms in marketing and, as predictive analytics and even rules can be considered a form of algorithm, it made me want to talk about enterprise decision management or EDM in this context. Let's start with the key concept:

As more human behaviors emit trails of digital residue, the more opportunities reside for algorithms to harness those human-induced data and become information intermediaries

Absolutely right. And these data are available in such volume that reporting on them is not going to help anyone - you have to build insight from the data so that you can use it, not just report on it with BI. This means building predictive analytic models based on the data that can be embedded into your operations - algorithms in other words. In reality you must also combine rules - about the user's preferences so as to maximize the customer's influence on decisions and about policy and regulation to ensure compliance. This is particularly true for any business subject to the Long Tail. Automating decisions in this way can let you improve the customer experience and scale 1:1, personalized communication to thousands or millions of customers.

As Max says, "all behaviors .. create halos of metadata, which algorithms process, mediate and disperse to others" and you need to account for all this information in your decisioning. Max gave some examples of algorithms and I thought it would be fun to show how EDM-like some of them were:

• Restaurant recommendation
  Well you could use predictive algorithms to segment people by the kind of restaurants they like/use and to build recommendations by comparing to other people. The customer can set rules for price, location etc as well and the combination comes up with restaurants.
• Real-estate
  Looks like this one was just rules - rules from the customer about properties in which they are interested
• Travel
  More preference rules plus models predicting occupancy and handling dynamic pricing as a result
• Music playlists
  Rules and predictions for like and dislike
• Fraud
  Rules and analytics, a classic EDM one.

Technorati Tags: analytic application, analytics, business rules, customer experience, customer insight, customer segment, decision automation, EDM, Enterprise Decision Management, fraud, long tail, marketing, predictive analytics, algorithms

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December 14, 2006

Book Review: The World Is Flat

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Posted by James Taylor at 3:46 PM | Comments (0)

Using EDM to deliver competitive productivity

A friend (Hi Ken) referred me to this post about search by David Berlind. Now while I think unstructured text in decision automation and Enterprise Decision Management or EDM is going to be more and more important and while I realize I have not blogged much about unstructured text analysis (note to self, write post on this), I actually wanted to drill down in David's phrase "competitive productivity". Here's how he introduced it:

To businesses with a lot of information workers, any technological advancements that can whittle that 25 percent [time spent searching for information] down to 20, 15, 10 or even 5 percent means that respectively, those workers can be spending 5, 10, 15 or event 20 percent more of their time on tasks that contribute more directly (more directly than searching) to competitive advantage. In fact, freeing up time to focus on those activities that contribute to competitive advantage � long-hand for what I'm going to start calling "competitive productivity" (versus plain ole' "productivity")

This is a great concept and highly relevant to anyone thinking about EDM. EDM is about automating and improving decisions, particularly those that require some expert judgment or for which some data exists that could be used to make or improve the decision. Clearly these decisions are often those taken by information workers - underwriters in insurance, loan officers, ad pricing managers, materials master designers, supply chain managers, staffing schedulers, diagnostic engineers, eligibility managers and so on. If I can automate these decisions, at least the most common 85-90-95%, then I likewise free up their time to work on more complex, high-value tasks.

Now some of this is going to overlap - some of the 25% of time spent searching for information is going to be included in the time saved by not having to make the decision manually because it is information being sought to make a decision. I suspect, however, that most of the benefit is additive. The time spent searching for information is probably in support of the most complex decisions and those therefore most likely to be in the small percentage still referred for manual review. The process of automation will free up some of this time though because you will get a lot more context for the referred decision and this will make it easier to search for information. For instance, a referred policy that needs manual underwriting will say why it is being referred and that will focus the need to search for information down to just the information that will resolve that issue.

Each of the case studies above shows how different decisions can be automated and how they free up information worker time for "competitive productivity".

Technorati Tags: BRE, BRMS, business rules, decision automation, decision service, EDM, Enterprise Decision Management, information worker, search, knowledge worker

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