Decision categorization

Decision Categories

Decision Types and the Technology that Supports Them

Neil Raden and James Taylor February 2010

© 2009 Neil Raden and James Taylor. Duplication only as authorized in writing

Decision Categories : Decision Types and the Technology that Supports Them 2

© Neil Raden and James Taylor, All Rights Reserved, No Duplication without permission

1 Executive Summary The primary focus of IT for the last 50 years has been the gradual automation of business processes, a quest that is more or less complete (though innovations will continue). These technologies, including Decision Support systems of the 80’s to ERP of the 90’s to Business Intelligence of today, can provide more than adequate information to make better decisions, but they lack the framework and the methodologies to make the leap from merely informing people to actually making decisions. That gap needs to be closed by making software tools, and the companies who use them, more decision-centric, not data-centric.

Being decision-centric means elevating the art/science of making better decisions to an enterprise level. But what does it really mean to make better decisions? How do you do it? In fact, if you were to evaluate how well decisions were being made in your organization, where would you start? Can you find the decisions and label the different types? In most cases, the answer is no.

For all of the automation in today’s business systems, there is little discussion about how, exactly, systems facilitate decision making. Not so long ago, decision-making was a carefully controlled business, with clear lines of authority delegated through chains of command. However, organizations ceased being isolated enterprises about a decade ago and the universal connections of the Internet and worldwide competition have changed the game. Things happen too quickly. Relationships change continuously. We can expect the pace of these changes to accelerate. Only by understanding the types of decisions you organization makes, where they are made and how they are made, will you be able to stay ahead.

In this paper, we start by creating a model of the essential “dimensions” of decisions, then match those with decision “categories” and line them up to evaluate which technologies are most suited for addressing each combination. For example, operational risk category decisions

score high in the dimensions of repeatability, measurability and upside/downside, while a strategic category decision scores low in the first two but very high in uncertainty. By examining different types of decisions in this manner, we can more easily identify what technology approaches are most appropriate for making, tracking and optimizing decisions.

The research and writing of this paper was supported by SPSS.



Table of Contents

1 Executive Summary ........................................................................................................................ 2 2 Decision Making in Organizations ................................................................................................ 4

2.1 The Business Process Approach ............................................................................................ 4

2.2 The Business Intelligence Approach ..................................................................................... 5 2.3 The “Decision Gap” ................................................................................................................ 6

3 The Dimensions of Decisions ......................................................................................................... 7 3.1 Repeatability ............................................................................................................................ 7 3.2 Measurability ........................................................................................................................... 7

3.3 Time to outcome ...................................................................................................................... 8 3.4 Approach ................................................................................................................................. 8

3.5 Upside vs. Downside .............................................................................................................. 8 3.6 Regulation ................................................................................................................................ 9 3.7 Available Data ......................................................................................................................... 9

3.8 Historical Data ......................................................................................................................... 9 3.9 Uncertainty .............................................................................................................................. 9

4 Core decision categories ............................................................................................................... 11 4.1 Strategic .................................................................................................................................. 11 4.2 Planning ................................................................................................................................. 12

4.3 Knowledge Worker ............................................................................................................... 12 4.4 Research ................................................................................................................................. 13

4.5 Formulaic ............................................................................................................................... 14 4.6 Consensus .............................................................................................................................. 15 4.7 Operational Risk .................................................................................................................... 15

4.8 Operational Value ................................................................................................................. 16 4.9 Case Management ................................................................................................................. 17

5 Conclusion ..................................................................................................................................... 18 6 Appendix – Decision Making Technologies............................................................................... 18

6.1 Database and Content Management Systems .................................................................... 19

6.2 Data Collection ...................................................................................................................... 19 6.3 Reporting ............................................................................................................................... 19

6.4 OLAP ...................................................................................................................................... 19 6.5 Visualization .......................................................................................................................... 19 6.6 Data Mining ........................................................................................................................... 19

6.7 Predictive Analytics .............................................................................................................. 19 6.8 Text Mining ............................................................................................................................ 20

6.9 Web Analytics ........................................................................................................................ 20 6.10 Execution framework ............................................................................................................ 20

7 References ...................................................................................................................................... 21

8 Parking Lot ................................................................................... Error! Bookmark not defined. 8.1 Data that can be collected: ................................................... Error! Bookmark not defined.



2 Decision Making in Organizations There are many different kinds of decisions and decision-making processes. The “big” decisions are usually made by consensus and collaboratively, typically by those entrusted with the overall direction and well-being of the organization. These decisions are not always limited to just the executive suite, however. Boards, investors, advisors, consultants and even those in a more tactical role are often part of these decisions. This is a common misconception in devising informational processes – that they type of decision is determined by the role of the individual making it. There is no clean “pyramid” in the process. Decisions about where to do business, what kind of company to be and what markets to enter or exit are usually composite – pieces of the decisions are made by different players, not just the CEO.

In general, managers decide how to incent their staff, manage their budgets and set policy. Knowledge workers assess situations and make recommendations for actions while front-line workers must decide how to treat a customer, when to refund a fee or how to fix a problem. Keep in mind, though, that these job titles can be a little murky, A more accurate description would be, “an individual working in a managerial capacity,” or “an individual performing knowledge work.” Regardless, any of these decisions, if made poorly, can undermine the success of a company. It does not matter how well a company executes its processes - if it executes the wrong ones at the wrong time for the wrong reasons or at the wrong price, outcomes will suffer. Decisions matter.

To set priorities effectively, identify alternatives and select the best from among many options involves skillful decision making. One could easily argue that making the right decisions is the single most important skill in any organization because it cuts across every other activity. It fact, it is so central to everything organizations do that it is often overlooked as a subject of its own. For example:

Organizations invested heavily over the past decade to break down the operational silos to achieve a more process-oriented way of doing business, but most business process software leaves the decision to the person using the system or makes provision for only limited decision logic. Decisions and decision making are not sufficiently addressed in business process improvement methods.

Business Intelligence, now a mature discipline that can provide decision-makers almost

limitless amounts of information in a wide range of presentation and visualization types, still lacks the mechanisms to “close the loop” by putting that information in motion as decisions. A common justification for investing in Business Intelligence is to “get the right information to the right person at the right time so they can make better decisions,” but there is no discernible connection between BI and decisions, except the manual one.

2.1 The Business Process Approach

The focus on business process solutions tends to ease the flow of information within companies through systems implemented to cut across disparate departments, functions and locations. By eliminating or streamlining hand-offs and enabling more transparent operations, business process management approaches and technologies can lay the foundation for more agile, effective day-to-day activities. But this foundational framework is only a template for conducting business. It does not address the core element of conducting business effectively – making the right decisions.



In many ways the current trend towards standardized enterprise application suite “best practices,” outsourced applications or Software-as-a-Service (SaaS) packaged offerings minimizes the differentiation between businesses. All of this standardization and codification of practice serves to increase the importance of making better decisions. No competitive advantage can be realized when competitors conduct business with the same set of rules. Consider the scenario where several competing companies use the same CRM system to capture customer information and use the same logistics vendor to ship products sourced from the same factory in China. In this case, only the way they manage their decisions really differentiate them. Decisions about pricing, offers, loyalty bonuses or warranty handling affect how they are perceived by customers, for example. These decisions differentiate them from their competitors.

2.2 The Business Intelligence Approach The whole point of Business Intelligence is not only to report on the past with facility and efficiency, but also to inform the decision-making of the people in your organization. This is an area that has been neglected for much of the history of Business Intelligence, but has seen a burst of activity in the past few years and the emergence of some excellent technology to support it, such as dashboards and collaborative features.

For someone to be effective in making the right decisions (or to participate in decision-making, since it is often a collaborative process), the first step is establishing priorities – what needs attention. Routine reporting is useful for identifying trends and results, but without benchmarks or targets, if is difficult to sift through to spot potential problems and opportunities. Performance Management is a loosely defined term that includes the methodology for setting Key Performance Indicators (KPIs) and tracking them. The reporting and analysis tools of Business Intelligence are the perfect vehicle for putting Performance Management to work. In fact, no other enterprise system is as uniquely qualified to manage and analyze KPIs as a data warehouse because it is the single source of time-based, integrated information.

Where the data warehouse standardizes the data needed for reporting and analysis, Performance Management standardizes the formulas for KPIs such as Revenue, Market Share and Return on Investment in commercial firms (or Student Credit Hours, Faculty FTE, Instructional Spending) and, more importantly, the lower-level KPIs that roll-up to them. This is

the key point – the clear and unambiguous linkage of KPI’s in a hierarchical fashion cannot be delivered in the operational systems or downstream departmental applications like spreadsheets, with the integrity and maintainability of a Performance Management capability linked to the data warehouse.



But as the diagram above depicts, informed decisions are usually the last step in the maturation process of a Business Intelligence system. Most implementations never get there, and even if they do, the extended industry of vendors, analysts and or influencers offers very little guidance.

2.3 The “Decision Gap” Decisions are particularly tricky for companies for a number of reasons. Decisions are often constrained by company policy and by regulations. Decisions are taken very personally by customers, who react to decisions as though they are intentional and deliberate. Ideally, decisions should be informed by experience and by history. Those impacted by a decision may have preferences that should be considered while the impact itself may take some time to play out or become clear. Decisions must often be made while data is still unavailable or at least uncertain and it may not be clear what decision will be the best. Decisions are made at the confluence of policies, data, know-how, regulations, expectations and much more.

Despite this, most companies have systematically under-invested in technology to help them make better decisions. As noted before, lots of time, money and technology has been invested in executing the actions decided upon quickly, accurately and efficiently. Similarly, investments in the collection, storage and protection of data and in tools to report on this data, in theory to

support better decision making, have been significant. The technology that is required to help ensure decisions are made well, legally and by the numbers is available and proven yet underutilized. In part this is due to a lack of understanding of the types of decisions, their characteristics and of the relative value of different kinds of technology in supporting these different types of decision.

Understanding what kinds of decisions there are, how they differ, who makes them and how to make them better presents a substantial opportunity to improve the performance of an organization.

Trend Forecasting Trend Analytics

Data Modeling Data

Warehouse

Informed Decisions

Reporting

• Grants

• Trend Forecast Model

• Retention • HR

• Cost & Utilization

• Benchmarking •

• Budgeting

• Recruiting

• Development

• Staffing • Financial Aid

• Compliance •

• Finance



3 The Dimensions of Decisions Because there are so many decisions that matter in a typical company it is no simple thing to determine what skills and technology are required to execute them effectively. Aligning different kinds of decisions with the right tools, information and environments to execute them effectively is critical and this requires categorization of decisions into different types. A first step is to define the essential characteristics or dimensions of a decision. In this way, each decision type can be evaluated against a matrix of these dimensions and, therefore, understood more clearly. The various decision types identified can be assigned to and serviced by a particular decision-making approach. Based on our research the essential dimensions describe business decisions are:

Repeatability

Measurability

Time to Outcome

Approach

Upside v Downside

Regulation

Available Data

Historical Data

Uncertainty

3.1 Repeatability Repeatability is an extremely important dimension because a high degree of repeatability suggests a process ot methodology as opposed to judgment or probability. Repeatability ranges from low repeatability such as one-off decisions to those that are completely repeatable or mechanistic. A completely one-off decision is one that is only taken once and for which any decision making process is created specifically for that decision. An example might be a decision to merge with or acquire another company or to enter a major market such as China. A repeatable decision is one for which a decision process can and should be defined such that an identical process is carried out each time a decision of that type is made. An example of this might be underwriting an automobile insurance , approving a vacation request or making a retention offer to a cell phone client. In theory, at least, an identical process is followed and identical circumstances (data) should result in identical action. There is a sliding scale between one-off and continuous decision making with each decision having some point on that scale.

The same decision may be more or less repeatable in different companies. Cisco, for instance, has made a successful business out of a very repeatable company acquisition decision process while most companies still regard these decisions as one-off, custom decisions. Companies may also change the repeatability of a decision over time, gradually tightening or loosening the strictness of the decision process in response to changing market needs or company policies.

3.2 Measurability

To improve a decision, even to assess how good a decision was made, it must be possible to measure it. It must be possible to tell the difference between a good decision and a poor one, between a decision with a positive outcome and a decision with a negative outcome. Some decisions are extremely easy to measure directly – the outcome of the decision is something that is either positive or negative. For instance, a cross-sell decision either results in an additional



sale or it does not. Some decisions are measurable but only through a subsequent set of activities. For instance an underwriting decision cannot be assessed until some time has passed and claims made or not made against the policy. Some decisions can be measured only in terms of what is not true, such as a merger decision that can only be compared with the estimated behavior of the company without the merger. Some decisions are extremely hard to measure at all.

3.3 Time to outcome

It has already been noted that a decision must be measured if it is to be managed and improved. The time it takes to see how well or poorly a particular decision performs can vary enormously. The success of some decisions is almost immediately apparent – a customer retention decision,

for instance. Others cannot be evaluated for some time – the value of offering a 2 year subscription at a discount cannot be assessed until the first year is up as only then will the renewal rate of 1-year subscriptions be known, for instance. Understanding the time horizon for the outcome of a decision will drive how it is assessed and how often it makes sense to re-evaluate the decision process.

Even when a decision appears to have a fairly immediate outcome it is often worth considering the long term implications to see if they are different. As an example it might be tempting to consider the outcome of an underwriting decision only in terms of which customers took the policy but it is probably more useful to consider the claims and costs that result before deciding if the underwriting decision was a good one or not.

3.4 Approach Some decisions are fundamentally judgmental – there is no right or wrong answer. Others are

well defined and a “right” answer can be identified. Decisions that are judgmental - that are harder to define as right or wrong - are also more likely to be collaborative. Those that are more directive and constrained may be done by a group but that group cannot really be said to be collaborating as their approach is highly constrained. A decision driven entirely by policy requires no collaboration and offers little room for judgment. Others require multiple people with their own perspectives to collaborate before an effective decision can be made.

3.5 Upside vs. Downside The potential value of positive outcomes from a decision could be very large, as in the case of entering a new market or very small as in the case of a specific cross-sell offer. Bad decisions have negative consequences, similarly ranging from large to small. There is a clear difference between those decisions where the difference between downside risk and upside value is very small (a cross-sell offer might cause a customer to abandon a shopping cart for instance but this risk is very low) and those where it is high (a poorly judged credit line offer might allow an identity thief to make off with tens of thousands of dollars). Understanding the potential loss or negative result from a decision is critical in deciding how much impact analysis and testing will be worthwhile. The time and energy invested in a decision needs to reflect both this upside value and the downside risk.



3.6 Regulation

The extent to which a decision is regulated, by internal or external bodies, will affect how a decision is made, what is recorded about the decision making process and more. In general regulated decisions are more constrained and the documentation required to support the decision is more extensive. However, it is important to distinguish those decisions that are driven by statute or regulation, from those where the regulation is directional. For example, a regulation may be that a flight crew becomes “invalid” after a certain number of hours and must be replaced with a fresh crew. Many decisions though are subject to regulatory review after the fact where the exact action to take is subject to some judgment and/or circumstances. For example, providing a mortgage in a neighborhood where the internal risk models show unacceptable risk, but government regulations forbid discrimination.

3.7 Available Data How much data is available when a decision must be made and what that data is makes a material difference to the approaches that can be used when making a decision. Some decisions are made in a data and information-vacuum while others can become bogged down in the huge amount of information that might be useful. For any decision the data available, and potentially relevant, should be considered and managed. This can also be considered from the other perspective – what data is needed to make a decision. Decisions can be analyzed to see what data would enable an effective decision to be made and that data can then be sought out for or by the decision maker.

Of course, a common trap is to assume that getting the right information to a decision maker insures the right decision. Data alone rarely guarantees a good decision.

3.8 Historical Data

Besides the data available when a decision must be made, there may be additional data about the history of similar decisions. If a decision is made repeatedly, such as the decision to underwrite a loan or extend credit, then historical data about the impact of past decisions can be used to predict what is likely to work or at least what the impact of specific actions might be.

In general historical data tends to be available for more repeatable decisions – they are likely to have been made many times in the past – but not for more one-off ones. That said, some one-off decisions can be mapped to historical trends and information by considering the potential results of the decision. If a one-off decision is measured by change in a particular dimension (say the profitability of a product line) then it can be useful to consider other decisions that had an impact on the same dimension. While these decisions may not be very similar, it may be possible to gain some insight into what kinds of things have positive or negative impacts on the dimension.

3.9 Uncertainty Uncertainty affects all decisions, some more than others. Some decisions must be made when the available data is uncertain – it may not be possible to be certain about the values of the data. It may not be possible to know exactly what happened or there may be missing data that makes it impossible to draw a definitive conclusion from the available data. Similarly many decisions have a degree of uncertainty in outcome. This may be as simple as uncertainty as to the exact profitability impact of a decision, say, to an inability to define exactly what the consequences of



a decision will be, perhaps due to interacting and overlapping decisions. Some decisions have more uncertainty than others. Where data is uncertain the decision making process must take this into account. Where outcomes are uncertain it may be harder to choose between alternatives.



4 Core decision categories In theory there are an infinite number of ways in which these characteristics can be combined. Any particular decision in any particular organization could have any given combination of values for these characteristics. In practice, however, there are some common categories of decisions. These core decision categories have specific values, or ranges of values, for each characteristic. This allows them to be described and the portfolio of technologies that might be useful in making each category of decision can be assembled. What follows is not a complete list but the most commonly identified. Each is described in general terms and in terms of the characteristics noted above and suitable technology for each is discussed. The categories are:

Strategic Decisions

Planning Decisions

Knowledge Worker Decisions

Research Decisions

Formulaic Decisions

Consensus Decisions

Operational Risk Decisions

Operational Value Decisions

Case Management Decisions

4.1 Strategic

Strategic decisions are the ones executives, and their staff, are paid to

make. These complex, decisions are generally performed for a one-time, specific purpose. They require lots of analysis, hypothesis testing and exploration and are generally collaborative. Even if one person is going to be the final decision maker, it is

Repeatability

One-off Continuous

Measurability

Poor Specific

Time to outcome

Immediate Delayed

Approach

Judgmental Definitive

Upside v Downside

Narrow Gap Wide Gap

Regulation

None Complete

Available Data

None Lots

Historical Data

None Lots

Uncertainty

None Lots

Merrild Kaffe

Every year, Danes spend DKK 1.5 billion (approximately US $243.6 million) on coffee. Market leader Merrild Kaffe holds approximately one-third of the market and sells a variety of coffee brands.

To stay competitive, Merrild Kaffe relies on SPSS products to predict trends and anticipate market response to given influences, such as price changes and marketing campaigns. For example, Merrild uses the software to determine if a new price strategy will result in increased sales, increased market share, temporary market expansion, or first-mover benefits.

“We assess the effect it will have on our own sales and on the market in general if, for instance, we reduce the price on one of our coffee brands. We are able, with a reasonable degree of certainty, to assess the effect of various sales and marketing campaigns.”



unlikely that they will be the only participant.

It is often not clear what makes for success and the decisions generally have an extended time before the outcome will be known. Although lots of data is available for the decision makers there is unlikely to be much history of making this particular decision, limiting the ability to derive trends or predictions. There is a lot of uncertainty in these decisions and the scope of data to be considered and the range of measures are typically both broad.

Visualization, interactive drill-down and free-form query tools will be the primary technology used. It is hard to pre-define reports or models that will help in this kind of decision so the ability to rapidly develop and iterate useful views of the information will be critical. Collaboration environments, allowing sharing of views, visualizations, conclusions and hypotheses will be important as these complex decisions require many people to work together. Supporting information presented to show how a strategic decision is made is likely to be highly custom and built by hand with the manual insertion of graphics, tables etc.

4.2 Planning

Planning decisions are those taken as part of a regular planning cycle. This might be an annual planning cycle, common in many companies, a quarterly update to such a plan or even, in the most dynamic companies, a continuous planning exercise. The decisions taken in this are typically relatively complex yet repeatable and for which a fair amount of historical data exists.

Because planning is a repetitive process it is likely that standard reports will be a mainstay of this kind of decision. The reports will show current state and historical variances relevant to the planning decision. Some interactivity may be useful, to explore what-if scenarios for instance, but most planning decisions are conducted in a highly repetitive framework.

4.3 Knowledge Worker

Knowledge worker decisions are typically relatively complex decisions made by a single person – a knowledge worker or manager to deal with a repeating but highly variable circumstance. These decisions require a significant amount of analysis but it is more repeatable – the same kind of questions must be answered in the analysis. There is not much testing or exploration but a focus on drilling into and understanding data. Some historical data is available for these decisions but it is not generally possible to relate it directly as there is significant variation between decisions. Outcomes of these decisions can usually be measured accurately and relatively quickly and there is little uncertainty.

Repeatability

One-off Continuous

Measurability

Poor Specific

Time to outcome

Immediate Delayed

Approach

Judgmental Definitive

Upside v Downside

Narrow Gap Wide Gap

Regulation

None Complete

Available Data

None Lots

Historical Data

None Lots

Uncertainty

None Lots



While these decisions will use many of the same interactive, free-form tools that Strategic decisions use, they will generally be more driven by standard reports or dashboards, repetitive queries etc. Templates that are useful may be defined at an organizational level or at an individual one and will be used regularly for decisions of a given type. Visualization and drill-down will be particularly important while collaboration will be less so. An ability to rapidly generate summaries of the information used as supporting documents for a decision is very valuable.

4.4 Research Einstein once said “If we knew what it was we were doing, it would not be called research, would it?” Research is, by its nature, unstructured and uncertain as decision-makers are searching for new insights. It is typically some time before the success or failure of a research decision can be judged and a wide gap between a successful and an unsuccessful research decision. There is usually some indirect link to some business need but the decisions being made are very freeform and unplanned. There is potentially a great deal of data available but it is often not clear what matters. Researchers often find they need to collect additional data before they can make a decision.

Flexibility is key in technology to support research decisions. The ability for researchers to pull data from multiple sources, integrate it and analyze it in a freeform and highly interactive way is essential. Research often involves teams so some collaboration tools and the ability to share models and other artifacts can be important also. When research decisions need data that does not yet exist then easy access to outside data services and rapid collection of new information from research subjects will be required.

Queensland Fire and Rescue Authority

The Queensland Fire and Rescue Authority in Australia oversees the fire and safety practices that cover the state‟s 3.5 million people. The Authority tracks details such as the time, date, and address of fires; the amount of property lost; the cause and cost of the fire; and the number of casualties—all with the goal of improving the firefighting and emergency services provided to the public.

The Authority‟s statistician turned to SPSS statistical analysis software to consolidate their data files, increase the usability of the system, and reduce the time needed to complete data analyses.

“We now have people walk in and say, „I need an analysis of how many fires there were in a certain region in a certain time period,‟ and it takes longer to print the analysis than it does to run it. With SPSS, I can focus on areas of analysis that have been ignored in the past. We‟re now actually using the data we‟re collecting.”



4.5 Formulaic Formulaic decisions are those that follow a very fixed approach with little room for maneuver. Often heavily regulated or very constrained by company policy, these decisions tend to be repeatable and repeated in a very consistent way. Results are normally fairly clear and uncertainty, in both data and outcome, low. For instance, a decision about eligibility for a particular product is formulaic, with little or no judgment being applied.

Little analysis typically takes place in formulaic decisions so few tools are required. Reporting, especially standard reports about a customer or product, might be used to present the relevant information so that the decision can be made quickly and effectively. Many formulaic decisions are completely automated based on rules and policies.

Yamaha Motor Europe Keeps Pace with Customers

Yamaha Motor Europe N.V. (YME) is the European headquarters of Yamaha Motor Corporation and sells Yamaha motorcycles in 24 European countries.

In 2001, the company developed an online portal that delivers interesting stories geared toward sports bike enthusiasts. Accompanying the stories are links to surveys powered by SPSS‟ mrInterview, which provide a cost-effective means of collecting consumer data.

YME saves time and reduces costs since the new product development team can now conduct focus groups with more specific information gathered beforehand; Web results also make some travel unnecessary. YME also uses SPSS‟ data collection solution to conduct internal research, asking dealers questions on a range of issues.

“It‟s extremely important to stay in touch with how our customers feel,” said Hennes Fischer, YME‟s product planning consultant. “Thanks to mrInterview, we never feel out of the loop, which gives us confidence that we‟re always delivering a product that truly meets their expectations.”



4.6 Consensus

A consensus decision involves multiple decision makers collaborating or a single decision, typically not a very repeatable one, to develop a consensus answer. This is generally because no definitive answer is possible – there is no right or wrong outcome. Measurability of these decisions can be poor as a consequence and/or there can be a significant delay before the outcome can be determined. Historical data and regulation are not generally important for these decisions and the degree of uncertainty is often high – that is why consensus is being demanded.

Collaboration tools are essential for consensus decisions. Unless the group of decision makers can collaborate effectively, share models and proposed solutions, discuss the effect of alternatives then a poor decision is likely. As decision makers are increasingly geographically dispersed, these tools must allow web-based and asynchronous collaboration.

4.7 Operational Risk Operational Risk decisions are those, such as insurance underwriting or credit management, where decisions about risk must be made repeatedly in operational processes and systems. In general the downside risk is significantly greater than the upside value, so risk assessment and management is critical. Many of these decisions are also policy or regulation-heavy and most are made in relatively high volume so automation or straight through processing is desirable. Complex decisions will be referred to manual decision making (see Case Management below) and analytics for propensities as well as risk assessment will be used to ensure that most such decisions can be made systematically. Generally there are very clear measures of success, there is often quite a long lead time and the impact of risk on the decision is quite uncertain.

Creating accurate models of operational risk and of customer propensities is critical in these kinds of decisions. These models must use historical data, of which there is generally a great deal, to turn uncertainty about the future into probabilities – probability of claim, probability of default and so on. These models must be built, verified and deployed quickly so the automation of data cleansing, integration and analysis is very helpful. Before these models can be built and deployed the data must be understood so strong analysis and visualization tools are also useful – they allow the analyst to see what kind of model might work. Many different techniques are generally applied to each decision so a broad range of analysis tools and algorithms is called for.



4.8 Operational Value Operational value decisions, like cross sell or retention decisions, are high volume, low individual value decisions. There is typically little or no downside risk and a modest potential gain. Regulation is less likely though many companies will still have policies that apply. Complete automation of these decisions is the norm as there is no particular reason to refer to a person given the lack of a downside risk. Analytics are used only for propensities – who is likely to buy for instance - not risk assessment and the decisions usually have very clear measures of success that can be rapidly assessed.

Data mining and analytic tools that can create simple to execute formulas or equations to calculate propensities are essential. Whether an automated decision is being made or a manual one, the decision maker can only use those pieces of insight that can be delivered quickly, typically as scores or simple flags.

Telecommunications Fraud Detected in Real-time

There are many ways to avoid paying for telecommunications services, from stealing phone card numbers to bypassing phone circuitry. These types of fraudulent activities cost the telecommunications industry billions per year. ECtel Ltd. created FraudView®, a comprehensive fraud management solution that uses SPSS predictive analytics software.

FraudView gathers information about calls in real-time and compares the calls against fraudulent scenarios produced by its MineView™ component. MineView uses SPSS‟ data mining solution to generate the scenarios that most accurately predicts fraud. MineView calculates a suspicion score—the probability that a fraudulent subscriber is using a suspect number—based on data collected in real-time. Whenever this score exceeds the preset threshold, MineView sends an alert. This real-time data collection is a significant advantage over other data mining systems.

“Upon seeing a single alert from MineView, the investigator is ready when a second call appears from that subscriber and can block that call as it‟s happening,” explains Eric Kaplan, FraudView group manager, ECtel Ltd. Thus, money is saved because fraud is detected and can be acted upon immediately.

More than 150 blue-chip telecom service providers worldwide, including national carriers in Germany, France, China, and the United States, have turned to ECtel‟s FraudView to manage fraud.



4.9 Case Management The final category is that of making decisions about complex cases. These decisions are often relatively high volume but not terribly repeatable. A good outcome is generally defined but a fair amount of judgment is called for. Case management decisions often result from operational risk decisions that cannot be made using the standard approach – the exceptions – so the gap between a good and a bad decision can be significant. Lots of data is typically available, though uncertainty can be high and historical examples while numerous will not be a perfect match lessening their value.

Ad-hoc query and drill-down tools will be called for so that the case manager can rapidly and effectively explore the data available for the decision.

World‟s Largest Mail-Order Wine Company Personalizes Customer Offers

Direct Wines, the world‟s largest mail order wine company, is an experienced database user. Direct Wines worked with SPSS to reach customers with specialized offers. The use of SPSS software is helping Direct Wines to build on its existing database and personalize customer offers more efficiently.

Direct Wines has a server running SPSS‟ server-based software and three workstations running SPSS‟ client version. SPSS‟ statistical and data management package is also used as the data-retrieval system, taking data from an Oracle® database. Direct Wines was confident that SPSS‟ solution met all the necessary criteria. Jon White, Direct Wines customer database analyst stated, “We could not have grown as a company without SPSS.”

White adds, “At first sight, SPSS has an interface with many different statistics options and transformation tools. However, the speed of its transformations and manipulation is staggering, and its ability to store syntax and scripts allows analysis to be repeated many times with ease.”



5 Conclusion There are a few opportunities in optimizing decision making that are readily accessible right away. The first is searching for decisions that are out of sight because they are small and they are part of a cluster of decisions that appear to be one larger decision. It stands to reason that making these “hidden” decisions more uniform, more consistent, more complete and more unified, the larger decisions will be better. The other area is striving to extend business intelligence capabilities from informing people to actually putting things in motion. The technology to do this is already in the market and there is considerable interest in “Operational BI” and “Pervasive BI,” initiatives that may not be directly related to decision making, but which could provide the impetus and the funding to start a decision audit as a first step.

Whichever approach you take, it would be wise to seek external expertise to guide you through the initial efforts.



6 Appendix – Decision Making Technologies Throughout this paper various technologies for decision making have been discussed. This appendix lists the various technologies discussed and gives a baseline definition for each.

6.1 Database and Content Management Systems

Data must be available for decision makers and decision making systems. Databases and Content Management Systems store structured and unstructured data so that it can be retained, updated and used for decision making.

6.2 Data Collection Some decisions require that additional data be gathered as part of the decision making process. Web-based survey tools and interactive data entry environments allow additional information to be collected and used in the decision. For example SPSS Dimensions.

6.3 Reporting

One of the most widely used technologies in decision making is that of basic reporting. Reporting tools give access to raw data in a repeatable, consumable form so that decision makers can review it without having to access a database directly. For example, SPSS Statistics includes reporting capabilities.

6.4 OLAP Online Analytical Processing – OLAP – is designed to allow decision-makers drill-down access to information so they can move from aggregated results to increasingly granular details and potentially to related data.

6.5 Visualization

When large amounts of information must be analyzed and understood, the visualization of that data as images can greatly aid understanding. Often combined with reporting, OLAP and more, visualization tools help decision makers understand their data more rapidly. For example, SPSS Statistics provides visualization tools and SPSS Viz Designer allows companies to develop advanced visualization components for use in their information systems.

6.6 Data Mining

Data mining tools take raw data and use mathematical techniques to understand that data, developing patterns and drawing conclusions. These might be descriptive, drawing conclusions about the past, or predictive, drawing them about the future. For example, SPSS Clementine is one of the world’s leading data mining tools.

6.7 Predictive Analytics

As data mining moves into drawing conclusions about the future it increasingly involves building predictive models. A predictive model is a mathematically sound equation or function that turns uncertainty about what will happen in the future into a probability that something specific will happen. These predictive models can be embedded in reports or in operational



systems. For example, SPSS Clementine supports the creation of a variety of predictive analytic models.

6.8 Text Mining

Most data mining tools work only on structured data so an additional class of products exists for drawing conclusions and making extrapolations from unstructured data. These text mining tools allow a decision maker to analyze large amounts of unstructured text to see what conclusions can be drawn. For example, SPSS Text Mining for Clementine and SPSS Text Analysis for Surveys support extensive text mining on both stored and newly captured information.

6.9 Web Analytics

Because web traffic information is so voluminous and has a particular structure, a separate class of tools exist for analyzing web traffic and developing insights about web usage. For example, SPSS Web Mining for Clementine is an extension specifically designed to support this kind of analysis.

6.10 Execution framework Many decisions are taken in sufficiently high volume that a framework is needed to execute these repeatable decisions consistently and accurately. Such frameworks include business rules management systems, business process management systems and enterprise applications and platforms. Having an analytic platform, such as SPSS Predictive Enterprise Services to support the execution framework can ease deployment and improve the effectiveness of an execution framework.



7 References In a knowledge based economy, we propose that a knowledge worker’s primary deliverable is a good decision. (Chugh, Milkman, & Bazerman, 2008)

Ayres, I. (2007). Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart . New York: Bantam.

Chugh, D., Milkman, K. L., & Bazerman, M. H. (2008, August). How Can Decision Making Be Improved? Harvard Business School Working Knowledge .

Davenport, T., & Harris, J. (2007). Competing on Analytics: The New Science of Winning. Boston: Harvard Business School Press.

Taylor, J., & Raden, N. (2007). Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions. New York: Prentice Hall.

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