Big Sky Thinking

SOA: The Cure for Accidental Architecture

noreply@blogger.com (Hanno Ekdahl) — Mon, 02 Jun 2008 03:08:00 +0000

The term Accidental Architecture was coined in a paper published by the University of Kent that discusses the evolution of IT architectures over time and the complex issues surrounding legacy systems. The term has come to represent how narrowly focused technology investments result in an enterprise IT infrastructure that is both complex and challenging to manage. In most organizations, functional units drive IT investments in areas such as procurement, operations, and service delivery. Oftentimes, these IT systems are built to meet ‘local’ requirements and are not thought of as an enterprise-class solution that will interact with other systems and platforms. The net result of these siloed IT investments is an “Accidental Architecture”.

This is not to say that each individual solution is not well-architected, nor that if fails to meet the defined business need, but rather that at an enterprise level the architecture becomes ‘accidental’ as it is dictated by the provincial IT and business decisions that created it. In essence, IT decisions are made without evaluating their full impact on the enterprise and without an eye to the bigger picture, thus creating information gaps in the value chain. This is a missed opportunity to improve information quality, drive new efficiencies, and enable collaboration to provide additional value to the customer or end user. The end game here is to eliminate the information gaps to improve decision-making about operations, investments, and the execution of the organization’s mission.

So how do we avoid the pitfalls of an accidental architecture? We take a move out of Steven Covey’s playbook and ‘begin with the end in mind’. If our goal is to eliminate islands of information and barriers to collaboration, then we require a solution that promotes interoperability. The way to achieve this goal is to adopt enterprise standards that specify how different applications will interact and that also bridges the gaps between different platforms. We recommend adopting the IT principles commonly known as Services Oriented Architecture (SOA). SOA is an architectural style that is based on a set of evolving standards (some of which are mature, e.g. SOAP) that provide a framework for the development of Web services.

SOA provides the tools to manage Web services and specifies the means for service integration and interoperability. This allows developers to create the applications they need to solve business problems without having to worry about application integration issues down the road. This effectively separates the infrastructure from the application, which provides flexibility, reduces development costs, increases re-use, and allows the organization to deploy solutions that work across the enterprise.

As organizations move toward a Services Oriented Architecture, they often take an incremental approach and work to meet a discreet need before implementing web services on a large scale across the entire enterprise. A case study of ING Bank’s first SOA implementation is discussed in “The SOA Magazine” and gives a good overview of the process and lessons learned.

Using Visuals to Make Decisions: Excel 2007

noreply@blogger.com (John Dillard) — Sat, 17 May 2008 13:51:00 +0000

We've previously posted about the importance of using visuals to make decisions. I ran across an excellent post by Jon Peltier at PTS blog that provides a very useful overview of the new charting functions in excel. For those of us who use excel frequently to manage and present data in support of decision-making, a solid understanding of the new 2007 features are a must-have. Thanks, Jon.

Decision-Making Traps Part 6: The Estimating and Forecasting Trap

noreply@blogger.com (Vishal Khushalani) — Mon, 07 Jan 2008 16:58:00 +0000

In our previous post in this series, we introduced “The Hidden Traps in Decision Making” by John S. Hammond, Ralph L. Keeney, and Howard Raiffa, in which they describe six traps in organizational decision-making that can adversely affect performance. This week’s post covers the final trap, the “The Estimating and Forecasting Trap”. Even though most of us are not very good at making estimates, we tend to be overconfident about our accuracy – which can lead to bad decisions. There are three different traps that can have a particularly distorting effect in uncertain situations because they cloud our ability to assess probabilities.

The Overconfidence Trap – Tend to be overconfident about our accuracy

The Prudence Trap – Over-cautiousness or prudence

The Recallability Trap – Base predictions of future events on the memory of past events.

Big Sky sees this trap in our clients especially when executives have strong domain or market experience. In addition, because very experienced people have excellent instincts, they can overlook trends that change the implicit assumptions in their mental decision-making process. For example, Clayton Christensen’s work on Innovation shows how the excellent customer-focused instincts of executives can actually crush the development of new, market-changing products. You can read more about Christensen’s “Innovator’s Dilemma” here.

Techniques to overcome:

Start by considering the extremes, the low and high ends of the possible range of values

Challenge estimates of your subordinates and advisers (overconfidence trap)

Always state your estimates honestly and explain to anyone if or not the estimates have been adjusted (prudence trap)

Carefully examine all your assumptions to ensure they’re not unduly influenced by your memory (recallability trap)

Closing thoughts on Decision-Making:

When it comes to business decisions, there’s rarely such a thing as a no-brainer. Our brains are always at work, sometimes, unfortunately, in ways that hinder rather than help us. At every stage of the decision-making process, misperceptions, biases, and other tricks of the mind can influence the choices we make.

The best protection against all psychological traps – in isolation or in combination – is awareness. Forewarned is forearmed. Even if you can’t eradicate the distortions ingrained into the way your mind works, you can build tests and disciplines into your decision-making process that can uncover errors in thinking before they become errors in judgment.

Decision-Making Traps Part 5: The Framing Trap

noreply@blogger.com (Vishal Khushalani) — Mon, 07 Jan 2008 16:42:00 +0000

In our previous post in this series, we introduced “The Hidden Traps in Decision Making” by John S. Hammond, Ralph L. Keeney, and Howard Raiffa, in which they describe six traps in organizational decision-making that can adversely affect performance. This week’s post covers the fifth trap, the “The Framing Trap” which states that the way a problem is framed can profoundly influence the choices one makes. Research proves that people are risk averse when a problem is posed in terms of gains, but risk seeking when a problem is posed in terms of avoiding losses.

Big Sky sees this trap in our clients especially when executives consciously or unconsciously frame the problem such that their proposed solution seems to be the best answer. Also, executives might focus on highlighting their pain areas which might not be a pain area for somebody else.

Techniques to overcome:

Don’t automatically accept the initial frame, try posing problems in a neutral redundant way that combines the gains and losses or embraces different reference points
Think hard throughout your decision-making process about the framing of the problem, and when others recommend decisions, examine the way they framed the problem.

Our next post in this series will discuss the “Estimating and Forecasting Trap” outlined in the article.

Decision-Making Traps Part 4: The Confirming-Evidence Trap

noreply@blogger.com (Vishal Khushalani) — Wed, 28 Nov 2007 00:55:00 +0000

In our previous post in this series, we introduced “The Hidden Traps in Decision Making” by John S. Hammond, Ralph L. Keeney, and Howard Raiffa, in which they describe six traps in organizational decision-making that can adversely affect performance. This week’s post covers the fourth trap, the “The Confirming-Evidence Trap” that leads us to seek out information that supports our existing instinct or point of view while avoiding information that contradicts it. This not only affects where we go to look for evidence, but also how we interpret the information that we receive.

Big Sky sees this trap in our clients especially when executives are biased based on their past experience and knowledge, or if they are pushing for their pet projects. For example, when we help client prioritize investments, we find that some executives are very surprised with how an objective process is at odds with their “gut.” For a great read on this phenomenon, check out Moneyball by Michael Lewis. Clients who are in “The Status-Quo” and “The Sunk-Cost” traps also succumb to “The Confirming-Evidence” trap.

Techniques to overcome:

Confirm that you are examining all the evidence with equal rigor
Find someone you respect to play the devil’s advocate
Be honest with yourself about your motives (are you gathering information to confirm what you already think?)
While seeking advice of others, don’t ask leading questions that invite confirming evidence

Our next post in this series will discuss the “The Framing Trap” outlined in the article and will be posted next week.

Decision-Making Traps Part 3: The Sunk-Cost Trap

noreply@blogger.com (Vishal Khushalani) — Mon, 05 Nov 2007 22:06:00 +0000

In our previous posts in this series, we introduced “The Hidden Traps in Decision Making” by John S. Hammond, Ralph L. Keeney, and Howard Raiffa, in which they describe six traps in organizational decision-making that can adversely affect performance. This week’s post covers the third trap, the “Sunk-Cost Trap”, that talks about our deep-seated biases to make choices in a way that justifies past choices, even when the past choices no longer seem valid. These past decisions often involve considering sunk costs in assessing the viability of a project – old investments of time or money that are no longer recoverable. One might also call this “throwing good money after bad.”

Big Sky sees this trap in our clients especially when…switching costs are high or if there are budget constraints. When huge investments have already been made, the team that made that decision tries to justify that decision. In many of Big Sky’s government clients’ large complex IT projects continue to command major investment well after it’s clear to all that the projects have failed. Rather than redirecting investment to better options, some organizations will continue to try to salvage huge IT investment by bolting on functionality or placing band-aids on system issues.

Techniques to overcome:

Always be mindful of long-term objectives and examine how they would be served by the status quo, if at all
While considering other options, evaluate the status-quo alternative if it was just another option, rather than the front-runner
Avoid exaggerating switching costs
And finally, always evaluate alternatives in terms of future as well as present context

Our next post in this series will discuss the “The Confirming-Evidence Trap” outlined in the article and will be posted next week.

Decision-Making Traps Part 2: The Status Quo Trap

noreply@blogger.com (Vishal Khushalani) — Tue, 30 Oct 2007 18:46:00 +0000

In our previous post in this series, we introduced “The Hidden Traps in Decision Making” by John S. Hammond, Ralph L. Keeney, and Howard Raiffa, in which they describe six traps in organizational decision-making that can adversely affect performance. This week’s post covers the second trap, the “Status Quo Trap,” that we also observe in our clients on a regular basis. In organizations that display the Status Quo trap, decision makers display a strong bias toward alternatives that perpetuate the status quo. Breaking the status quo means taking action, and when we take action, we take responsibility, thus opening ourselves to criticism and to regret.

Big Sky sees this trap in our clients especially when…organizational cultures do not encourage change. When employees are not rewarded to take risks but are penalized for unfavorable outcomes, they choose to stay with the tested-and-tried way of doing business. For example, we have observed in some R&D clients a tendency to reward large projects that transition to operational use, but stigma is applied to failed attempts at innovation. This encourages R&D planners to over-weight project with a high probability of success and projects that are already fully funded, rather than creating a portfolio that includes some high-risk projects.

Techniques to overcome: A few ideas for avoiding the status quo trap include:

Remind yourself of your objectives and examine how they would be served by the status quo
Identify other options – don’t assume that there aren’t any
Ask yourself if you would choose the status-quo alternative if it was not the status quo
Avoid exaggerating switching costs
Evaluate alternatives in terms of future as well as present context

Our next post in this series will discuss the “Sunk-Cost Trap” outlined in the article and will be posted next week.

Decision-Making Traps Part 1: The Anchoring Trap

noreply@blogger.com (Vishal Khushalani) — Fri, 12 Oct 2007 21:14:00 +0000

John S. Hammond, Ralph L. Keeney, and Howard Raiffa wrote an article in Harvard Business Review “The Hidden Traps in Decision Making” which discusses different traps of the mind and different ways in which we can overcome these traps. All of us are susceptible to making bad decisions and judgments unless we can learn to recognize and avoid them. Even though the article was written nearly a decade ago in 1998, it is still highly relevant and important in the context of decision making. Hence, despite its age, we decided to devote a series of posts highlighting each of these six “traps,” and the techniques that these authors recommend to overcome them. This first post discusses “The Anchoring Trap” and will be followed by one post for each of the five remaining traps identified by the authors.

#1: The Anchoring Trap: When considering a decision, the mind gives disproportionate weight to the first information it receives. Initial impressions, estimates, or data anchor subsequent thoughts and judgments. This pernicious mental phenomenon is known as anchoring.

Big Sky Sees this trap in our clients when. . . time is of the essence. When organizations are under particular pressure to make a decision fast, the anchoring trap becomes particularly acute as there is little patience to wait for alternative information.
Techniques to overcome: View a problem from different perspectives (use a different starting point to avoid the anchor), be open minded, be careful to avoid anchoring your advisor (tell them as little as possible and ask for their opinion), and be particularly wary of anchors in negotiations.

Our next post in this series will discuss the “Status Quo Trap” outlined in the article and will be posted next week.

Interview with James Taylor, Author of "Smart (Enough) Systems"

noreply@blogger.com (John Dillard) — Mon, 30 Jul 2007 14:29:00 +0000

James Taylor, a frequent commenter on this blog and the author of one of the best decision analytics blogs out there, recently published a guide to Enterprise Decision Management called Smart (Enough) Systems. We recently had the chance to speak to James about the book and about the relationship between technology and effective decision-making.

BST: Thank you for taking the time to talk to us about your new book, Smart (Enough) Systems. What motivated you to write a book on this subject?

JT: It seemed to me that there was a very limited awareness of how existing technologies were being used to make point systems smarter – both in terms of companies who had used the technology in one area but not others and in terms of companies who did not seem to realize what was possible. Companies were putting up with really “dumb” systems because they thought the alternative was something that only existed in the lab. Neil’s experience was very similar in that he too saw companies getting far too little value out of their data. A book offers both a platform for explaining something thoroughly and a “proof point” that this is a serious and “real” approach.

BST: You place a lot of emphasis on Operational Decisions in the book. Could you talk about why they are particularly important?

JT: It is not so much that operational decisions are more or less important than, say, strategic decisions. It is more that they are totally neglected in most organizations. When I talk to companies about their operations and identify the decisions that drive the high volume systems and processes in their business, they have typically not even considered them. The operational decisions that make or break the profitability of a customer or the cost of a transaction are delegated to programmers, front-line staff or generic software packages almost without thought. That’s why the book focuses so much on them – they are sadly neglected.

BST: We found your discussion of strategic alignment and choosing the right decisions particularly important in the book. How critical is it for organizations to find the right decisions to improve?

JT: Well the approach we outline in the book is clearly aimed at a certain class of decisions, operational decisions as you note, and so focusing on that kind of decision is very important. It won’t work well to try and use enterprise decision management, the approach outlined in the book, to manage and improve strategic decisions for instance. That said I think companies can use the techniques and technologies we discuss on a very wide range of decisions and that it is not critical to pick the right one out of them to get started. One of the hardest problems can be getting people to realize that decisions are being made in a certain point in a process and then getting them to focus on it as a distinct opportunity for improvement. Given this difficulty it may well be that you have to start with the decisions people can “see”.

BST: We have written about how many organizations that could be applying more sophisticated decision analytics – or any type of more rigorous quantitative analysis – haven’t yet done so. How do you convince these more reluctant organizations that there is real value in this approach?

JT: Well I think the biggest issue is understanding how they can apply these things to their operations and the focus on decisions, not on reports or analysis, is critical. I think the process we lay out in the book that takes a series of baby steps to get more and more sophisticated about decision-making having called out and focused on the decision.

BST: The book emphasizes the importance of business rules, and of having an integrated development environment to hold those rules to provide services. This is a very similar concept to Identity Management, which provides a central database to manage the user lifecycle across multiple connected systems based on business rules and policies. Given the similarity of these approaches, do you see a convergence of these technologies, and if so, what do you see as the end game?

JT: That’s an interesting question and one I have not really thought about. In general, I tend to differentiate between using rules as part of solving a problem (like identity management) and focusing explicitly on decisions and decision management, using rules. In this case, then, I would expect to see rules-based identity management (using rules to make identity management decisions) and a separate use of rules as part of taking control of business decision making.

BST: In several examples in the book, you discuss not only the technical challenges to EDM, but also the cultural challenges. What are the most common cultural barriers/challenges that organizations must overcome to adopt better decision making methodologies? Are these challenges restricted to “traditional” businesses (like banking)?

JT: I think there are a number. There is a lack of management focus on operations – execs see their role as making strategic decisions not improving execution. There is a lack of trust and collaboration between IT and business units that make collaborating on decision management hard. Data is often poorly understood and what understanding there is tends to be backward facing not forward. Lastly there is a change issue – can companies really change the way they regard operational decisions. Each varies in companies but all are an issue that need to be considered as part of an adoption plan.

BST: Another interesting point you make is the need to enable IT capabilities so that IT can be where the business needs it. This can be a bit of a chicken and egg problem, because in many organizations the IT department is seen as a cost center and as such, it is often starved for funds. Who typically drives these infrastructure changes? In your experience, are there particular governance models that enable the type of adaptability and collaboration necessary to make EDM efforts successful?

JT: Well there are a couple of perspectives. A risk-based approach where the risk of not being able to change systems drives a willingness to invest in modernization (calculate the risk to the company of not being able to change systems quickly enough or where their accuracy is too poor). An opportunity-based one where the approach is applied only to new systems and the more effective development and ongoing evolution of those applications gradually frees up dollars to revisit old ones. Lastly there is a growth-oriented one where a company allows a proposal to spend money for a return that drives a new decision automation project that drives additional revenue. The additional revenue is then allocated to clean up old ones. There’s no magic bullet.

BST: If your readers want to learn more about this discipline, what would you recommend that they read next (after they read “Smart Enough Systems”)?

After they read it and recommend it to all their friends you mean? Well, there are some good blogs out there (yours, mine and others) on analytics (both technical and organizationally focused), rules, and customer-experience. There are some great books too – Competing on Analytics, Linnoff and Berry on Data Mining, The Business Rules Revolution and many others – on the specifics of various parts of the approach (all of these are listed on the companion site, www.smartenoughsystems.com) . Mostly, though, I would suggest starting! The hardest thing is having experience so getting started is key.

Why Optimizing Decisions is the Most Important Thing You Can Do, Part III

noreply@blogger.com (John Dillard) — Mon, 09 Jul 2007 16:36:00 +0000

In our last post in this series, we introduced a simple, four-step approach to optimize decisions that we call Decision-Centric Business Improvement. The critical decisions identified through that four-step process represent the resource-intensive turning points of every organization’s growth. These decisions are diverse; some are large: corporate acquisitions, multi-billion dollar procurements, and 5-year strategic goals. Some are smaller: choosing a commodity supplier, making a hiring decision, or choosing the functionality of a software solution. Some decisions are manual, while some are automated. Some require one person; some require groups or even multiple organizations.

The last step in Decision-Centric Business Improvement is optimizing decisions along three angles: strategic relevance, technique, and technology. When optimizing decisions, it is critical that an organization work through each of these angles to build a coherent, balanced approach to the decision in question. The figure below illustrates these three “angles” of decision making.

The Decision Strategy Angle
The first angle of effective decision making is how the decision influences advancement of the organizational strategy. To clearly understand this angle, an organization should isolate the most important strategic metrics of the organization and describe the decision in terms of those metrics. If a decision cannot be shown to have a measurable impact on strategic goals, there is little chance that the decision can be successful.

The right approach in this angle is not to develop a new strategy, but rather to understand the strategy (whether implicit or explicit) and to define a particular decision in the context of the strategy. Traditional strategic planning tools—such as SWOT analysis, multiple forces analysis, or Value Chain Analysis—may be useful in this angle but should be focused on the decision.

The Decision Technique Angle
The second angle of effective decision making is the selection and application of the right tool for the job. A carpenter wouldn’t use a sledgehammer to drive carpet tack; similarly, a good decision maker chooses the tool that is just complex enough—but no more complex—to do the job. In this angle, an organization must understand both the soft and hard aspects of the decision. Hard aspects include the required speed and frequency of a decision, as well as the number of variables involved and whether the decision requires descriptive (backward-looking) or predictive (forward-looking) results. Soft aspects invlude the level of organizational buy-in required, political consequences, human factors, and transparency requirements.

For an automated supply chain decision, an organization might choose to develop a sophisticated algorithm that completes on the fly multivariate analysis. For a one-time strategic decision at a board meeting, it might use a decision tree or a consensus building method. Hypothesis testing, analytic network process, analytic hierarchy process, real options are other approaches that might be used to aid decision making.

The Decision Technology Angle
The third angle of effective decision making is the application of appropriate technology to enable the decision. Most organizational decisions will benefit from better management and distribution of information aided by technology, but not all. Knowing if, when, and how to apply technology is the component of decision optimization least understood and most prone to error.
Good decisions result from a qualified decision maker armed with the right information, delivered at the right time in the right context.

Rather than selecting one-off technology solutions, an organization should understand their “Decision Architecture” – an architecture optimized for effective decision-making. In many cases, this architecture may be comprised of existing systems rather than expensive new ones. Effective organization and adaptation of organizational IT can transform decision-making capabilities in many organizations.

While “hard” decisions—those with many variables or high speed requirements—are the most obvious candidates for the application of technology, technology can be a critical enabler of softer decisions too. Collaboration tools, role-based access control, and innovative application of existing technology (like wikis) can be critical enablers of infrequent, collaborative decision-making. In every analysis of a critical decision, whether “hard” or “soft,” technology should be considered as a important enabler of long-term success.

Seven Steps for Defining Decision-Making Projects

noreply@blogger.com (Manoj Malhotra) — Thu, 21 Jun 2007 16:48:00 +0000

I recently visited a client site to participate in the definition of a consulting project that had been broadly scoped, but that required decisions to be fleshed out in terms of specific project objectives, scope, and key performance metrics.

Most of the management group that would be affected by the project was in attendance, including representatives from logistics, operations, and marketing. They were knowledgeable individuals, well motivated, and responsible for many key decisions in their functional areas. Though eventually successful, it was a long meeting, and we had some trouble honing in to the key elements of scoping the project. To some extent, this should be expected, especially for a complex decision making problem.

Yet, as I reflected on the meeting, I realized that there were certain steps we could have followed that would have been helpful for the task at hand, improved decision quality, and lowered the complexity and time involved in concluding our discussions

Define decision making boundaries for the project at the very outset. What is excluded is as important if not more important than what is included inside the decision making framework.
Reduce the fuzziness in the project definition to the extent as possible through a precise definition of the roles of each constituent group involved.
Reduce the decision to a well defined process at the right level in the organization. I have written on this issue in an earlier blog entry.
Create project timelines with periodic milestones consisting of broad work structures, with specific attention paid to how the project scope agreed upon would mesh with those milestones.
Define key performance indicators that would be used to measure project success, and directly link them with final project scope and definition agreed upon by everyone.
Create consensus by explicitly seeking inputs for each of these steps from all the key stakeholders present at the meeting.
Revisit and re-iterate the steps in the order listed if stuck in discussions that seem to be stalling. Long discussions on performance indicators for instance may be driven more by not having followed the earlier steps (such as defining project boundaries or process level of analysis) rather than by a lack of understanding on what is important for measurement purposes.

While individual situations vary, following these steps will in all likelihood accelerate the project scoping process, reduce fuzziness associated with multiple constituencies focusing their attention on different levels of analysis, and create defined goals and timelines. It will also result in a decision making charter for the project that will have a greater chance for success and goal attainment.

Why Optimizing Decisions is the Most Important Thing You Can Do, Part II

noreply@blogger.com (John Dillard) — Sun, 03 Jun 2007 14:28:00 +0000

In our previous post in this series, "Why optimizing decisions is the most important thing you can do," we discussed the reasons why organizations should think hard about focusing on the critical decisions in their organizations, and why the speed and quality of those decisions will determine which organizations remain competitive. Today's discussion outlines our approach to optimizing decision-making in your organization.

Organizations that want to make better decisions, faster should adopt a decision-centric approach to improving business processes and their core capabilities. This approach, which we call Decision-Centric Business Improvement, is a four-step technique that requires the identification of critical capabilities, the description of those capabilities in terms of a process, the isolation of the critical decisions in that process, and the optimization of those decisions. At a high level it looks like this:

Identify core capabilities. First and foremost, every organization must understand those very few things at which it must be great. Not just good or very good, but great. Many organizations know what those things are; it might be research and development, it might be sourcing, it might be hiring, or it might be project management. In the diagram above, the circle represents just such a capability.

Describe it as a process. Second, every capability should be expressed in terms of a process. Whether your process is simple or complex, all have key elements in common, including: tasks, inputs, outputs, and decisions. After you know which capabilities are most important, they should be detailed in a business process that can be decomposed and analyzed.
Identify the important decisions. Third, the organization should highlight and understand the decisions in the processes. Most processes--and all important processes--contain decisions. Those decisions vary in complexity, importance, and frequency, but they are the turning points of every process. In the diagram above, the decisions are indicated as diamonds.
Optimize the critical decisions. Once it's clear where the decisions are in your most important processes, proceed with optimizing the most important decisions (the ones with the biggest impact on the process outcomes). Big Sky advocates evaluating each of these decisions from three angles: strategic relevance, technique, and technology. In other words, "why make the decision," "how to make the decision," and "what do we need to do to enable the decision."

Although the scope and duration of these engagements will vary with the size and complexity of the organization, we suggest starting small with a rapid diagnostic using this approach, and then layering on more challenging improvements over multiple project generations.

In our next post in this series, we will outline each of the three "decision angles" mentioned in #4 above.

The Three C’s of Governance

noreply@blogger.com (Hanno Ekdahl) — Thu, 10 May 2007 17:42:00 +0000

Most companies struggle on some level with decision-making. Let’s face it; keeping a large company working in a coordinated manner is no simple task. In the case of IT, this problem tends to be exacerbated by the fact that there is a lack of common language between groups. The business speaks in terms of finance and marketing, while IT talks about projects in terms of features and technical feasibility. The result is a lack of alignment, which hurts both groups. The solution to this dilemma is governance. The goal of governance is to fix broken processes and bring focus back to the strategic priorities of the business. In this entry, I suggest that we can look at governance through three lenses: control, communication, and change.

Effective governance exerts control either through formal authority or influence, depending on the degree of (management) centralization and the authority granted by executive leadership. In either case, the governance board works to establish standards and policies for the organization to guide development efforts. In addition, establishing a project approval process is essential to managing priorities and keeping IT departments focused on key initiatives instead of working in reactive mode to respond to the request of the day. The goal here is to balance the needs of the business with technical requirements to develop solutions that deliver business value and are secure and efficient to maintain.

The benefits we hope to achieve through control begin with communication. Project selection, policies, and standards should all be established through conversations between the business and IT within the context of strategic objectives and desired outcomes. These decision points hinge on a common understanding of the value delivered, the risks mitigated, and the cost to implement. Alignment between the business and IT is essential to set the right policies and to ensure that organizational compliance is high. Furthermore, a formal project selection process allows the IT and the business to set priorities together, stop projects that aren’t delivering value, and redeploy resources where they are most needed.

The last dimension of governance is change; how do we remove political obstacles and also get users to adopt the solution? Governance offers the opportunity to establish a structured, organized change management process to get us from our current state to a desired future state. Politics represent one barrier that can be challenging to overcome. It is the role of business stakeholders on the governance council to help IT overcome political barriers by identifying and addressing issues early on. User resistance to change is another matter entirely. Resistance to change is rooted in a desire to avoid disruption to the user’s established routine and to stay with the systems they know well. The best way to counteract this resistance is to improve communication and involve end users early in the development cycle. The project lifecycle should take care to generate user buy-in by asking for feedback, evaluating and incorporating changes from the user population, and supporting the transition to the new system.

Another look at the three C’s of Governance:

Control
· Formalize project selection/prioritization
· Employ IT portfolio management
· Establish standards
· Define policies
· Balance formal authority against influence
· Deliver consistent solutions

Communication
· Communicate business value – How will we benefit from this project?
· Gather business requirements – Does the solution meet our needs?
· Create transparency and better understanding of IT activities and performance
· Improve understanding of objectives and expectations
· Improve visibility of project issues and priorities
· Change perception of IT as a ‘cost center’ to strategic partner

Change
· Involve users earlier in the development cycle
· Capture enhancement requests
· Improve solution adoption
· Establish communication plan
· Identify and plan for objections/resistance to change
· Overcome organizational politics
· Provide end-user training

Building Support for Decisions: Effective Facilitation

noreply@blogger.com (John Dillard) — Wed, 11 Apr 2007 11:57:00 +0000

At Big Sky Thinking we've written quite a bit on using data in decisions and how to structure a decision-making process to increase speed and quality. For many decisions, getting the decision-makers to commit to a course of action is just as important as finding the right course of action.

Dan over at Decision Making Methodology Discussion Forum highlights how important effective facilitation can be in building support. I've seen Dan's advice work even when data is abundant; sometimes, cultural and political forces--which have surprising resilience in the face of objective data--can only be managed by effective group facilitation and well-designed change management.

You can read Dan's post here.

DARPA Director: Automated Reasoning Within Reach

noreply@blogger.com (John Dillard) — Fri, 23 Feb 2007 18:42:00 +0000

We've had a great response to the post below on why optimizing decision-making is the most important thing an organization can do. In particular, we've had a number of folks ask about decision automation. I read this interview with Tony Tether, Chief of the Defense Advanced Research Projects Agency (DARPA) on Wired's blog today that touches on advanced research related to decision automation. It's pretty far-out science--more thought-provoking than practical, but nonetheless demonstrates the imperative for organizations to focus on this issue. Here's an excerpt:

TT: Since the '90s to now, our ability to create algorithms that can reason -- can more abstractly reason -- about a problem and come up with answers, and also remember what they did using Bayesian techniques and changing values, has really advanced. I mean, it tremendously advanced in the past -- from the '90s to, say, the early 2000s. At the same time, computers became more powerful. We're on the verge of having computers with densities approaching a monkey's brain, and it won't be long before we'll have a computer with the density of transistors, or equivalent to neurons and almost human. What we're missing is the architecture. So it seemed like it was time. We had great advances in algorithms for reasoning and in algorithms that learned in general. At the same time, the computers, the actual intrinsic hardware, was really approaching the density of a human brain. And so it seemed like it was time to try again. We've had some great success. This cognitive program I told you about is actually showing that it is learning, and it is learning in a very difficult environment. This is the program Stanford Research runs for us.

NS: Which program is this?

TT: It's PAL [Perceptive Assistant that Learns].

Interesting stuff. We'll post Part II of our thought on the importance of optimizing decisions soon.

Why optimizing decisions is the most important thing you can do

noreply@blogger.com (John Dillard) — Sat, 17 Feb 2007 22:38:00 +0000

The most important piece of advice we can give to organizations and their leadership for the next 30 years is this:

Optimizing decisions is the single most important factor in long-term organizational success. It's more important than strategy, organization design, quality, customer relationship management, innovation, or any other business model, technique, or practice.

That statement is provocative, but it's the reason why we started a company. It also begs the question, "What has changed to make optimized decisions so important?" This post outlines some of the reasons why; the next post will discuss ideas on what to do about it. The reasons are far too many to list here, but below are my views of the key interdependent factors.

1) Business model innovation. Innovations in business models--the underlying mechanisms that define the way organizations operate to provide goods and services--have been changing at breakneck speed in the last 15 years, and there is no reason to expect a coming period of stabilization. Organizations that are successful don't adopt a model and stick with it; they are hyper-adaptive to new business model opportunities when they emerge. The number of choices in business models and the resulting consequences are rapidly multiplying.

2) Intensifying expectations for regulatory compliance. Companies and governments entered a new era after 9/11 and the Enron scandal marked by a dramatic intensification of oversight by shareholders, regulatory authorities, Congress, OMB, and others. Not only is there pressure to make critical decisions quickly and accurately, but organizations must explain to overseers why the decisions were made. This new emphasis on transparency of decision-making is not supported by 20th century decision-making processes.

3) Compressing decision cycles. As business models shift and information becomes more accessible and available, organizations are faced with compressing decision cycles, particularly in critical capability processes. They have less time to choose options, and more options to choose from. In any decision, data must be aggregated, criteria established, options considered, and decisions made. Organizations have less and less time to pass each gate.

4) Advancing decision automation. Advancements in artificial intelligence compound the severity of the decision making problem. More and more decisions may be automated every year, placing additional pressure on manual decisions to either be expedited or automated themselves. Critical decisions will either be severe time traps in critical processes, or the source of substantial competitive advantage. Ignore this technology at your peril--over the next ten years the ability of software to solve complex, unstructured problems will revolutionize what organizations define as their core capabilities. James Taylor writes the best blog out there on decision automation.

5) Accelerating acceleration. As everyone knows, the innovation in technology, business, and life is accelerating. This is well documented---Moore's Law and studies of technology adoption curves are just two good pieces of evidence. However, what places so much more pressure on decision cycles is that the rate of change is also accelerating. Why? Because enablers of innovation are themselves undergoing rapid, logarithmic change. Ray Kurzweil and Alvin and Heidi Toffler have done some great writing on this phenomenon.

These are just five thoughts on my list. . . it's certainly not exhaustive. Our next post will focus on how we view solutions to the challenge--specifically, decision-centric capability development.

First Steps with Identity Management: White Pages

noreply@blogger.com (Hanno Ekdahl) — Fri, 02 Feb 2007 19:34:00 +0000

Identity Management solutions are very complex and require collaboration across organizational boundaries, IT systems, and networks. Often times, identity data is assumed to be of sufficient quality (see our Blog entry “The Five Elements of Data Quality”) to provision users automatically, define roles, and develop workflows. Unfortunately, this assumption is often false and has severe consequences on any value that you hoped to get from your IdM solution.

One recommendation that we typically make to organizations that are just starting to build their IdM infrastructure is to release a white pages application as their first step. While the value to the organization is often low, so too is the risk if the application fails. This “low value, low risk” approach does offer significant insight, and value, to a small group of concerned individuals: the project team.

One of the nice things about the white pages application is that you can open it up to your end user community for self-service updates. User attributes like Hiring Manager, Department, phone number(s), and job title can all be opened up for editing. End users can update their own information using self-service tools and publish that information to the directory.

From this simple application we get the following benefits:

Visibility into all data stored in the directory
Ability to troubleshoot the Identity Management infrastructure (connectors, schema, namespace, self-service tools) in a production environment and develop a data remediation plan
Centralized tool to collect updates for user data
Validation of user data against standards and business rules before publication to the directory and synchronization with other systems
Improved data quality across all connected systems

Identity Management: Begin with the Process

noreply@blogger.com (Hanno Ekdahl) — Fri, 19 Jan 2007 19:46:00 +0000

In one of our earlier entries, "Identity Management: Observations from the Trenches", we discussed some of the pitfalls of rushing to implement an Identity Management solution without looking at data quality, data requirements, and starting with a point solution instead of an enterprise approach. I wanted to add to that discussion and emphasize the importance of knowing your processes before you implement identity management projects.

Many times a project can seem straightforward both technically and organizationally, but often there is something you don’t know that will send it to the scrap heap. Let’s look at the following example: a company wants to improve the time to provision new hires into their non-connected systems by sending an automated email as soon as the user’s identity is created in the directory. Based on the way the process runs today, the client believes they can save two weeks in new hire provisioning, and they are eager to cash in on this golden opportunity. Fair enough.

At this point, the development team is ready to start building something immediately. The core infrastructure is already in place, we just need to slap in some workflow with emails and we are golden, right? Not so fast. It turns out upon further evaluation, that the organization did not clearly understand their onboarding processes. The assumed bottleneck, that staff were not receiving notifications of a new hire promptly, was not really the problem. Instead, the delay in the process was caused by the manual assignment of the user’s id by another department. This id was required for all provisioning activities, including creation of the user’s identity in the IdM system, which means that system-generated emails buy us nothing; they simply automate a part of the process that is already working fine.

So what are our lessons learned?

a)Bring key stakeholders (business owners, corporate staff, etc.) into the early planning meetings and touch base with them regularly

b) Start by mapping out your processes from recruiting through creation of the user identity in the directory (work with your stakeholders to complete this analysis)

c) Identify key data that are needed (user id, etc.) to support the project, where the data reside, and when they become available along the process

d) Validate your process maps with your stakeholders. Step everyone through the process and discuss:

a. the process steps (Are they correct? In the right sequence?)

b. process timing (How long does each step take?)

c. data flow (What data are available when?)

d. process bottlenecks (What are the slow points in the process? Rank them in terms of time consumed.)

e. process dependencies, and identify any process changes that will be required (Can we get people into the ERP system sooner? Can key data fields be entered earlier? Do we need to change the way approvals occur in the process?)

Decision Services: A Definition by James Taylor

noreply@blogger.com (John Dillard) — Fri, 19 Jan 2007 13:40:00 +0000

James Taylor, who writes one of the best blogs out there on automated decisions, posted a good definition and explanation of "Decision Services" that is worth a read. James's definition of "decision service" is "a self-contained, callable service with a view of all the conditions and actions that need to be considered to make an operational business decision."

He goes on to explain some of the conditions that a decision service must meet. My favorite addition is: "not only should [a decision service] respond sensibly when it cannot decide, it should ensure that enough context is returned as to why it could not decide to assist a manual process."

That's an excellent point that gets at a key concern that some of my clients have about automated decisions--a perceived lack of control or transparency about how decisions are made. This is particularly important to clients that have compliance responsibilities such as government regulation, Sarbanes-Oxley, or HIPAA. It's critical that those organizations have the ability dramatically increase decision speed and quality through automation, while maintaining the ability to provide explanation and justification to external authorities, and/or fairness to their stakeholders or customers.

Thanks to James for a great post.

Choosing among many decision making visuals

noreply@blogger.com (John Dillard) — Wed, 10 Jan 2007 12:50:00 +0000

A few weeks ago we posted on using visuals to make decisions, and how it can help in both conducting manual decision-making processes and understanding automated decisions.

Today I ran across this post at BoingBoing by way of Seth's Blog. It's a periodic table of visualization methods that depicts 50+ techniques and categorizes them by type and appropriate usage. It's a pretty slick graphic by itself, but the authors have really dialed up the cool factor by providing examples of each item on the table when you roll over it with your mouse.

I'd highly recommend passing this link around the office, even for those of you who tremor at the mere mention of high school chemistry.

A Holiday Resolution for Decision Makers

noreply@blogger.com (John Dillard) — Mon, 25 Dec 2006 17:12:00 +0000

This holiday season, I've been giving some thought to the kinds of conflicts we see in decision-making and how they might be resolved with some Christmas cheer.

There are two distinct camps of decision-makers out there: dataheads and those who work from the gut. While there are many who can blend these two styles, there is inevitably tension between the two. The tension manifests itself as conflict between decision makers, internal conflicts within ourselves, or (in the worst case) indecision. In the very worst case, suspicion and resentment can keep these two styles from interacting and achieving great results.

These conflicts arise because decision makers often don't know how to reconcile the need to make snap decisions based on experience and intuition (a la Blink) and the need to make decisions that are defensible, traceable, and airtight.

Camps advocating decision making based solely on one or the other is by nature flawed. One camp--the dataheads--underestimates both the human ability to make quick judgments and the human need to make an emotional commitment to a decision that goes beyond the math. The other camp is too quick to trust the gut when data is readily available, or (at worst) rejects data that contradicts our intuition (Freakonomics is my favorite reference work on how our gut can be very wrong).

The best decisions blend good data and the experience of decision makers. There are great models that can make this work; Analytic Hierarchy Process and Conjoint Analysis can apply modeling to the preferences of human beings. Decision trees and probability analysis are rooted in many cases in the gut instincts of experts on the likelihood of certain outcomes.

The moral of the story: these 2 camps can not only get along, but they can act as "force multipliers" for one another. This holiday, dataheads and gut decision makers should resolve to get along in the new year. Better yet, keep the eggnog coming until you're fast friends.

A Systemic View of Decision Making

noreply@blogger.com (Manoj Malhotra) — Fri, 01 Dec 2006 14:22:00 +0000

In earlier posts we have stressed the importance of choosing process as the correct unit of analysis, and selecting the process level of detail commensurate with the decision making problem at hand. It is also important to identify the core processes that represent the building block for any organization, and then to evaluate each process through appropriate performance metrics that directly relate to that process. Doing so allows an organization to have precise visibility into how process-level decisions advance organizational objectives.

In spite of doing some of these things well, organizations end up with sub-optimal decisions and failed outcomes. Why?

Decision Interactions Aren't Explicitly Recognized.

One major reason is that interactions between decisions made and underlying relationships they affect are not explicitly recognized. The systems viewpoint suggests that exactly the reverse should be done. A whole field of study on systems analysis exists to provide insights on how and why such outcomes occur.

Swimming in a Vortex

One of the best expositions on identifying the patterns that control events and how to obtain leverage from them in the business context is contained in Peter Senge's seminal text The Fifth Discipline (Doubleday Currency, New York 1990). This book is a must-read for most managers and decision makers, because the author succinctly provides alternate explanations for why doing the most intuitive thing at first glance would not lead to desired consequences. One particularly telling example is that of a swimmer caught in the vortex of a whirlpool. While the instinct would be to fight against going down, the only chance of survival depends on allowing oneself to go down the vortex and then swimming laterally to escape the drowning. Only a systemic view of the situation would yield such a perspective.

Like the swimmer in Senge's example, managers and decision makers need to take the holistic view point on how their decisions affect other parts of the organization. Inter-relationships and interactions among and between decisions may often determine the quality of outcomes, rather than any single decision by itself.

Bullwhip Decisionmaking

Instances of lack of systemic thinking abound in real world. The bullwhip effect in supply chains that costs retailers, manufacturers, and distributors large losses due to excessive investment in inventories which do not yield appropriate customer service and returns on investment, is a well-known example of lack of systemic thinking and decision making.

Real-World Example: Systems-Based Decision-making

I have evidenced it myself in some different contexts. For one of the product lines, a second tier supplier to a large firm (say Ajax for convenience) had a high cost structure, and was thus charging higher prices to Ajax. Negotiations only yielded minor improvements in price over time. A more systemic analysis revealed that a key input to the second tier supplier was being procured from a third tier firm, which also had pre-negotiated lower price point contracts with Ajax on some other items. By extending this lower price point to the second tier supplier, the cost structure of the second tier supplier was reduced to the extent that it could now supply the same input material to Ajax at a much lower price than before. These improvements would not have come about without looking at the entire set of supply chain entities in a more holistic fashion.

So What: Systemic decision making reduces decision error and increases the return on decisions made.

It is clear that the level of analysis should be broad enough and high enough to reveal all the interactions and relationships in detail. Drilling down very deep into specific processes and their nested sub-processes is entirely appropriate for several kinds of decisions, and it also makes the analysis easier. However, caution must be exercised that the decision maker does not go so deep so as to lose sight of the benefits that can only come with systemic decision making and bigger picture analysis.

Three factors of effective decision analysis

noreply@blogger.com (John Dillard) — Sun, 08 Oct 2006 14:20:00 +0000

Decisions come in a wide variety of sizes and shapes, any of which can mean success or failure for an organization. At one end of the spectrum are transactional, high-volume decisions like choosing among many suppliers of a particular commodity; at the other end are extremely infrequent, high-stakes decisions made by executives in the strategic planning process.

When we look at decisions and how to get them right, there are three factors to consider regardless of the size and type of decision.

1) Strategy. Ok, we will admit that this is a grossly overused and misused word. Here's the point: if you don't know why you are making a decision it's pretty hard to get it right. When analyzing a decision, learn first why the decision is being made and how it is intended to advance the strategy of your organization. No decision is small enough for strategy to become irrelevant.

2) Decision method. Once you know why you're making a decision, you need to know how to do it. Having a deep understanding of the nature of the decision is critical here. Is it a high frequency decision or is it an annual decision? Is it inherently a high human interaction decision or could it be automated? Is it a subjective or objective decision? Once you know the character of the decision, you can choose a method. There are many choices, from group consensus to executive directive, to more sophisticated and predictive approaches like real options, ANP, AHP, conjoint analysis, and the like.

3) Decision technology. We don't advocate particular technology approaches to decisionmaking, but technology can be your best friend in the decisionmaking process. Once you know how you're going to make the decision (see #2), you can then identify who makes it, what information they need to make it, and when they need that information. Application of technology can be as simple as using a solid prioritization tool, or as sophisticated as using data mining, ERP systems, and identity management to increase the precision and depth appropriately with what's required for the decision.

Obviously, there are many other factors to consider, but covering these three bases when you're determining whether you're making a decision effectively will get you a long way toward success.

Core Processes and Value Chain Linkages

noreply@blogger.com (Manoj Malhotra) — Sat, 02 Sep 2006 13:52:00 +0000

In the blog entry entitled “What is the Right Unit of Analysis for Decision Making," I had argued that identification of key underlying process is critical in making sure that managerial attention is indeed directed where it will have maximum impact and chance of success. But what are the core processes in any business—manufacturing or service, and how can they serve as the right unit of analysis for decision making? In a book published recently, my co-authors and I proposed a process oriented framework for analysis in which internal value-linkages and external value-linkages are defined in terms of core and support processes. That framework is shown below.

Source: Krajewski, Lee J., Larry P. Ritzman, and Manoj K. Malhotra, Operations Management - Processes and Value Chains, Eighth Edition, Pearson Prentice Hall Publishers, 2007, ISBN 0-13-169739-0.

The four core processes found in any organization are:

Customer relationship processes

Identify, attract, and build relationships with external customers and facilitate the placement of orders. It is also sometimes referred to as customer relationship management (CRM).

New service/product development processes

Design and develop new services or products from inputs received from external customer specifications.

Order fulfillment processes

The activities required to produce and deliver the service or product to the external customers. Most of the operations management activities fall within this domain.

Supplier relationship processes

Select suppliers of services, materials and information and facilitate the timely and efficient flow of these items into the firm. Such inter-firm activities aimed at working effectively with suppliers can add significant value to the services and products of the firm.

Organizations also have many support processes that provide key resources, capabilities, or other inputs that allow the core processes to function. An example of such support processes may be human resource development, regulatory compliance, budgeting, capital acquisition, or information systems, to name a few. When performance failure may be related to these support processes, they should be examined closely for their impact on the proper functioning of core processes.

The value chain linkages between the four core and support processes illustrate an important point of consideration - that both external customers as well as external suppliers play a strong role in influencing the business outcomes. In addition, each of these core processes is usually sufficiently complex, and may contain several nested processes within them. So the decision analyst must drill down through these nested levels until the right level of process that matches the decision-making problem at hand is reached. Cross-functional linkages, as well as the role of external and internal customers, should be explicitly recognized when charting related process maps and improvement trajectories. Such a comprehensive approach is more likely to lead to higher quality decisions and problem resolutions that meet or exceed the intended performance improvement targets.

Decision Analysis Meets Identity Management

noreply@blogger.com (John Dillard) — Sat, 19 Aug 2006 13:21:00 +0000

Those reading this blog have noticed a couple of areas of emphasis: decision analysis and identity management. At first blush, these seem quite unrelated. . . . what does an organization's ability to make decisions at all levels have to do with how it manages identities of people and assets?

Just about everything.

Organizational decision making, regardless of scope, includes a few essential ingredients:

1) A decision making process,
2) Data and tools required to make the decision, and
3) Decision makers.

The Burton Group coined a definition of Identity Management that is fairly widely used and includes three pieces:

1) Business processes,
2) Supporting infrastructure, and
3) digital identities.

Let's line these up and see what it looks like:

1) Process. The decision process is one of many processes influenced by effective identity management, and it is probably the most important one.
2) Identity-Based Decision Architecture. An infrastructure and architecture purpose built for effective decision making must include a coherent identity management component.
3) Decision Maker Identities. The identities of decision makers will drive which decisions they are permitted to make, how they make them, and what information to which they must have access.

Organizations may certainly approach identity management and decision analysis separately, but we think that the real power of each of these two important disciplines lies in their confluence.