Enterprise Decision Management - a Weblog

Another interesting McKinsey Quarterly today on Bank branches that meet customer needs. Part of it's summary is that"banks should put utility before appearances, favor lower-budget tactical adjustments, and use branch formats to manage customer visits actively". This last point caught my attention - "manage customer visits actively". Hmmm sounds like a job for Enterprise Decision Management (EDM). If you think about the things youcan do better in a branch than anywhere- improve cross sell, build loyalty, deepen customer relationships - you can quickly see that decisions are at the core of these things. Which product to cross-sell, what kind of loyalty award will make you do more or stay a customer longer, what's the best next action for a customer to built their value and so on. While the branch context means having a person deliver these decisions, automating and managing these decisions so as to constantly improve them is still called for. Using automation, instead of just trying to train staff more, also allows the same (better) decisions to be delivered using in-branch interactive devices and through self-service channels such as ATMs and the web. Not only that, it also means that you start to capture your experience with customers in an information system not in the heads of employees.

Besides the basics of automating these decisions, you could consider dynamic marketing messaging inside and outside the branch - regarding each display of a new message or promotion at a specific location as a decision.These decisions can then be based on local demographics (what kind of neighborhood is the branch in), branch type (based on analytic segmentation of the people who use a specific branch - this is analogous to Best Buy's redesign of stores based on customer patterns), time of day and so on. Thus a branch mightpromote one offer during the morning commute, another between then and lunch time and a third when workers are getting lunch.Automation allows ATMs to make, say, cross-sell offers too but the cross-sell offers at an ATM that is part of a branch should be different when the branch is open as it may be more effective to drive customers into the branch than to offer to have someone call or send email after the fact. More sophisticated analytics would also add value. Again using a retail analogy, take the kinds of tools retailers are using to spot purchase patterns over time and apply them to the purpose of visits by a customer over time to see if there are patterns that can be identified and used.

All of this is part of why I think EDM is so important to banks as they move into the future. I have blogged before about Why Banks need to focus on "Growth Decisions", how tobuild the bank of the futureand about a banking story.

BTW I find a lot of the McKinsey Quarterly pieces interesting and subscribing to it might well be worth your while.

Technorati Tags: analytics, banking, Best Buy, best next action, customer experience, Customer Service, customer-centric, EDM, enterprise decision management, financial services, McKinsey, predictive analytics, ATM

Thanks to Rolando I linked to this article Era of the Super Cruncherin which Ian Ayres discusses his new book - Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart. I have not read the book yet, though I will, butit seems to me that to do what Ian suggests, that is replace intuition with data-driven decisioning on an industrial scale, you need an approach to automate, improve and connect the decisions that drive your business. You need, in other words, EDM or Enterprise Decision Management.

Technorati Tags: analytics, data mining, decision management, EDM, enterprise decision management, Ian Ayres, predictive analytics, Super Crunchers

Roeland Loggen had a nice post over on his blog today - BPM Suite as a component in a logical architecture. I liked his architecture except that I think the Decision Platform he identifies also needs to be able to support both the CEP and BAM components. After all, determining which process to trigger, which action to take or when to inform someone may be a non-trivial business decision and so require decision management. I also think that a decision platform needs both rules and predictiveanalytics, as you might expect, and that it needs to be available to all the various components that might need decisions (typically implemented through decision services perhaps as a decision service hub as Neil and I discussed in Smart (Enough) Systems).

I blogged around this topic a couple of times. I liked thisthe IDC graphic I referenced in this oneand a couple of reports I blogged about in Business Rules, Business Decisions, Intelligent Processes, Enterprise Decision Management. You also might want tocheck out this post on how CEP/BAM and decision management are all complementary, this one on the alphabet soup of acronyms and how they relate to decisioning

Technorati Tags: analytics, BPM, BRMS, business process, business rules, CEP, decision management, decision service, decision service hub, EDM, enterprise decision management, event processing, predictive analytics, Smart (Enough) Systems, smartenoughsystems, Roeland Loggen

(By guest Blogger, Gib Bassett)

Acronyms are way of life it seems for both crime drama television shows, as well as business rules technology. Just as you have CSI, SVU and probably others I can’t recall in TV land, you have numerous terms such as BRE, BRM, and BRMS to describe variations on rules management concepts. What spurred me to think about this was the August 3 Gartner research paper titled “Hype Cycle for Business Process Management (BPM), 2007.” This very detailed report describes the adoption of different technologies used to support BPM, including business rules (in case you are curious, they rate Business Rules Engines (BRE) as offering a high benefit relative to other technologies and a mainstream adoption horizon of less than 2 years). The report also dwells quite a bit on distinctions between technology approaches to BPM and organizational issues related to becoming a process oriented enterprise (isn't that the same story we've heard about CRM?). The manner in which this logic played out in the discussion of BRE particularly caught my eye.

Gartner’s David McCoy relates ““By way of analogy, BRM (business rules management) is equivalent to “law and order,” while a BRE is equivalent of the local police department (that is, an engine of law and order).”” What he’s saying is that a BRE does not guarantee excellence in managing business rules on an enteprisewide basis, just as the presence of local police cannot guarantee that law and order will prevail everywhere. In a very simple way, David has described a key value behind Enterprise Decision Management (EDM); that it is as much a methodology or approach as it implies a specific set of technologies. He goes on to say “Although the engines are mature, the concept of BRM remains a poorly penetrated topic in most enterprises. With the advent of SOA (services oriented architecture) and BPM (business process management), greater emphasis will be placed on leveraging explicit business rules as defined as reusable assets.” BREs that have evolved into more functionally rich EDM-compliant Business Rules Management Systems (BRMS) perform this role exactly.

So although Gartner says “BRE has lost its emerging technology glow,” that does not imply a lack of innovation in the business rules technology market � quite the contrary. To read more about how BRMS that ascribe to an EDM approach to managing decisions can help your organization realize the true value of business rules management, you don’t have to wait till the Fall TV season. Just visit here.

(By guest Blogger, Gib Bassett)

As a guest Blogger on the EDMBlog, I am obviously a proponent of Enterprise Decision Management and the impact it can have on a company’s operational decisions; you could say I think EDM is “outta sight.” By that, I mean really effective, specifically how EDM can optimize the many, often hidden, decisions underlying an organization’s day to day operations. “For example, decisions may be hidden, because they are not automated, but instead made on an almost unconscious basis by employees at the front line of interactions with clients: think about tellers at banks or ticket agents at airport gates.”

This is the essence of a July 2007 McKinsey report titled “Improving field service productivity: Real-time information can help companies manage “invisible” employees” (registration required). Just as hidden decisions have little management oversight, “invisible employees” are those whom management has little influence over as they go about their jobs. Instead, oversight happens after the fact, which the report points out results in “detailed productivity metrics and reports that tell executives only what happened in the past, thereby missing the opportunity to make improvements.”

This problem sounds exactly like traditional backward looking business intelligence and is in part what operational analytics is intended to address. However, whereas operational analytics is intended to provide insights to front line staff, what the McKinsey report is talking about is the use of sophisticated, real time, and analytically aware systems that keep employee downtime to a minimum by optimizing the use of their time. EDM-capable systems are ideally suited to managing the complexity inherent in highly fluid field service operations as well as providing integrated intelligence to anticipate disruptive events and suggest alternatives, all in real time.

As an approach, not a specific set of technologies or products, EDM also shares adoption challenges with McKinsey’s recommendations. The separation of hard coded rules and policies from operational systems � such that they can be centrally managed and shared � is at the heart of EDM and can be a barrier to adoption for less progressive IT organizations. Similarly, McKinsey points out that “Field technicians too will have to adapt, learning to be more flexible in their daily schedules and accepting limits on the autonomy they value. In a recent survey of field technicians at one security company, 92 percent said that independence was their favorite aspect of the job.” No one ever said change was easy, but fortunately there are logical and proven approaches to implementing EDM.

This leads me to what one Business Rules Management System (BRMS) vendor refers to as “Transparent Decision Services”. Skeptical IT buyers could scoff at this label as another term for vaporware or a “decision service that does nothing,” but it refers to what we call simply Decision Services, or “services in your Service Oriented Architecture that automate and manage highly targeted decisions that are part of your organization’s day-to-day operations.” This BRMS vendor confuses things by referring to hard-coded business rules as decision services while those built with business rules are transparent decision services. In fact, transparency is inherent to Decision Services, and stems from both the fact they are easily implemented alongside legacy systems and from their use of business rules to ensure the logic is declarative and developed collaboratively with the business.

So remember, hidden decisions -- even those involving the scheduling of invisible employee time -- can be optimized via tangible (not transparent) Decision Services that are not hard coded business rules, but rather an SOA construct for wrapping rules and analytics into coherent, reusable services.

Gib, who writes for the blog once in a while, wrote a nice piece on Making the Most of Operational Analytics with Enterprise Decision Managementfor DM Review.

Enjoy.

Technorati Tags: analytic application, analytics, BI, BI 2.0, business intelligence, DM Review, EDM, enterprise decision management, operational BI, predictive analytics, Smart (Enough) Systems, smartenoughsystems, operational analytics

I was lucky enough to get a pre-release copy of IT Risk: Turning Business Threats into Competitive Advantageby George Westerman and Richard Hunter. The book approaches IT risknot as a technical issue but as a business and management one with potentially serious consequences.As businesses increasingly are there information systems, this point of view is both necessary and valuable. The book introduces IT risk and its consequences, discusses the authors'4A framework and outlines3 core disciplines for IT risk management. Itthen drills into actual steps to take to fix the foundation, develop risk governance processes and establishing a risk aware culture. It wraps up with some forward looking thoughts and a list of ways in which executives can improve IT risk management.

Their basic premise is that effective IT governance essential in times of high change and increasing complexity (of systems as well as of business/problems). They discuss 4As - availability of systems, access to systems and data, accuracy or data and results, and agility in terms of ease of change - as being the framework for risk management. These 4As are supported by a foundation, a risk management process and a risk aware culture. The framework and the disciplines mostly work well for the authors, only occasionally becoming confusing to the reader. From an enterprise decision management perspective I found the focus on agility very interesting as possible changes to systems should be considered along with general IT effectiveness when managing risk. Also, while the foundation is lower level than EDM, I think the objectives for the foundation can all be met more easily by and organization that has adopted EDM. EDM can make iteasier to assess risk, easier to maintain systems, easier to change and fix them. It can also make it easier to apply risk assessments in operational systems by calling out the decisions that must be made, which is where risk assessment matters.

Fixing the foundation is described as a journey and I really liked the focus on incremental improvement. The foundation is a problem as most companies developed their IT infrastructure in stages. However, a poor foundation undermines agility by degrading the business/IT relationship and by making change to existing systems, to meet changing business needs, hard. While I think there are other ways to add agility into existing systems,I do agree with their assertion that you need to change and replace foundation to some extent. They make some fairly good suggestions for broad steps you can take and show the kinds of payoffs that come from the capabilities you enable with a better infrastructure. The authors make a critical point when they show how change in infrastructure is IT change while change in applications is business change but most IT departments don't see the difference - they see it all as "system"change making it harder to manage than necessary. Again, a focus on separate automation and management of decisions can help clarify this difference. There is a fair amount of useful discussion in the book about the need for both local and central management to which I would add one more category - where do decisions live in your organization? Should they be managed locally or centrally? The book outlines both incremental and "big bang" approaches to fixing the foundation and notes that incremental change is slower but surer - EDM is very much in this space despite the "E" word, with its focus on adding better decision-making to existing systems. I also think you could simplify applications by externalizing decisions. The discussion of how legacy application modernization might be business value based or risk based (human resourcesor technology risk for instance) or both (such as a need to change to support a new business strategy) was well done. I also really liked their idea of a renewal and reinvestment budget to keep legacy modernization ongoing and they had some great stories about human resources risk coming from retirements and the need to get knowledge out of people's heads and into systems (something about whichwe have blogged before, e.g. in this post on insurance).

The section on a risk governance process was thorough, although I think you need to be careful not to implement all of it blindly, and I liked the focus on broad risk awareness - not "risk-averse" or "risk-pro" just "risk-aware". Think about this in terms of decisions - what is the risk of the wrong person making a decision (a delivery person making a decision about service levels for your best customer) or of a decision being hard to change? To support this idea, IT needs to build systems in a risk-aware way - they need to drive their use of technologies and languages, consider the consequences of a failure to update documentation or code and so on. It occurred to me while reading these sections that organizations considering a policy manual for this stuff should also consider the value of rules and decision management as a basis for a "policy engine" (see this post).They had a particularly nice example of a mid-sized company finding its legacy applications, and the lack of agility in them, to be a key risk and investing in replacing and upgrading systems to make maintenance and evolution easier and less risky. This kind of agility improvement is something enhanced by a parallel focus on decision management.

The book was a fairly quick read, had lots of useful suggestions and some good ways to think about the problem. You can buy the bookhereand if you think IT risk matters, you should do so.

Technorati Tags: agility, business agility, compliance, EDM, enterprise decision management, governance, IT Risk, risk aware, risk management, George Westerman

Nice little post on What do we mean by tacit knowledge?over on the anecdote site. This reminded me of a couple of points I made when I reviewed Carl Frappaolo's book on Knowledge Management. Firstly, as technology and our understanding of it has advanced, more and more decisions can be automated. Some of this comes from better understanding of how to use business rules and some comes from understanding how to build predictive models, such as neural nets, for predicting likelihood in complex situations. The second point was one Carl made in the book:

"In some cases, knowledge believed to be tacit is only so labeled because no-one has ever taken the time or energy to codify the knowledge"

Which meshes nicely with the anecdote post.

I am also reminded of a great post by Rolando Hernandez about Knowledge Management is Key to Preventing Brain Drain. BP found out the hard way -he uses some nice contrasting stories about BP's problems with its oil pipeline, caused in part by a failure to manage expert knowledge when someone left, and another oil company's use of knowledge management to avoid a similar problem in another area. This is a common problem, especially as the baby boom retire, and a survey by Barb von Halle's KPI found it to be one of the top reasons for adopting a business rules management approach. Not only would a business rules approach be a great way to capture the knowledge, the use of a business rules management system to manage and deliver that knowledge would let you automate, or at least support, those expert decisions so that customers, front-line staff, other information systems could leverage it. You can buy Carl's book here.

Technorati Tags: business rules, knowledge management

Some time ago I suggested that readers might like to complete a survey for Karel Rexer of Rexer Analytics and a number of you did. The results of the survey are now available here(you need to email Karl and ask for a copy, instructions on the page), and there are some useful nuggets of information in the report. A couple of things struck me when I saw my copy:

• When asked to identify the fields in which they employ data mining the most frequently identified fields were CRM/marketing, financial, academic, telecommunications, and retail.
  Interestingly all categories on the blog with the exception of academia.
• Data miners working with financial data strongly valued a tool's ability to automate repetitive tasks
  I wonder if this relates to the tendency of financial institutions to constantly update their models and develop multiple alternatives for use in adaptive control
• There was nothing on deployment
  I found this curious as deployment of models seems really important to me. Not clear if Karl did not ask any questions or did not find the answers interesting (Karl?)
• Top 4 challenges found by respondents included dirty data, difficult access to data, explaining data mining to others, and finding qualified data miners
  No big surprises here, apart from the explaining one - while I always find that a problem I was surprised to see it come up so high
• Other problems included that data mining results were not used by business decision makers and difficulties in deployment/scoring
  I think using business rules as a platform for deploying analytics can help with both of these - it makes actual deployment easier and allows you to engage business people in the automation of the decision (through editing rules) in a way that might make them feel more comfortable.

Karl references some polls on KDNuggets about which I blogged before - one on what people do with analytics (here, CRM andbanking came top), one on data mining deployment (here)and this one about making better decisions with computers (here).

Technorati Tags: analytic application, analytics, data mining, EDM, enterprise decision management, predictive analytics, KDNuggets

Wayne Eckerson published a nice report for TDWI recently called "Best Practices in Operational BI: Converging Analytical and Operational Processes", which you can download from the TDWI site here (free registration required). It's a very interesting report and the summary goes like this (emphasis mine)

"Operational business intelligence (BI) represents a turning point in the evolution of BI. Traditionally, BI has been the province of technically savvy business analysts who spend many hours with sophisticated tools analyzing trends and patterns in large volumes of historical data to improve the effectiveness of strategic and tactical decisions. But operational BI changes this equation: it moves BI out of the back room and embeds it into the fabric of the business, intertwining it with operational processes and applications that drive thousands of daily decisions. This report describes the promise of operational BI and provides suggestions about how to surmount the challenges involved in converging operational and analytical processes."

This is a great summary of the difference between what you might call "traditional" BI and enterprise decision management or EDM. Where BI is somewhat separate, reporting on the business, EDM is woven into the fabric of your business. As Wayne goes on to say

"Using predictive analytics and rules engines embedded within a real-time data delivery environment, companies can streamline processes and automate responses to various types of business events."

Wayne has a nice model of maturity with multiple levels and, in the section on level 4 (Execute Processes), he says that this is "perhaps the most exciting stage of operational BI".This isthe top level of his framework (which is a nice way to view growing sophistication in BI) and he talks about such a platform needing business rules andscoring code (I would say deployed predictive analytics, perhaps using PMML) and being action oriented. While he has a slight focus on event-centric decisionsat the expense of process-centric ones - me I think there is a need for both - he uses decision services very much the way I would. Perhaps because of this event-focus he does not include the business rules management system vendors (Fair Isaac et al) as platforms for operational BI but obviously I would - I wrote an article on rules as a platform for operational BI here.

Wayneends by giving some great advice on the kinds of processes/decisions that lend themselves to decision management (I call these micro decisions) and makes a strong recommendation not to maintain a rigid boundary between analytic and operational processes. If you enjoy the report, check out these posts on a webinar he gave on Decision Automation from TDWIand this post about a previous report From BI to Predictive Reporting to Predictive Analytics. You might also enjoy the book Neil Raden and I recently published, Smart (Enough) Systems.

One last thing. Wayne makes the point that operational BI implies "more users, more decisions, and lower latency data"and I think the latency issue was described very succinctly by Richard Hackathorn in an article I referenced in a post on my other blog - Decision Technologies and Active Data Warehousing.

Technorati Tags: analytic application, analytics, automated decision making, BI, BRMS, business intelligence, business rules, data mining, decision management, decision service, decision-centric, event processing, event-driven architecture, micro decisions, predictive analytics, Smart (Enough) Systems, smartenoughsystems, TDWI, Wayne Eckerson