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Planning for the Unplanned: From Scheduled to Event-Based Business Intelligence

  Article published in DM Review Magazine
September 2003 Issue
  By Robert Blumstein and Henry Morris

The pace of events can easily outrun the ability of people in a business to react, understand and decide on an appropriate response. People can plan for many of these events, but unplanned events can stop a business in its tracks. How should an organization's decision cycle be designed with unplanned events in mind?

Unplanned Events

First, it's necessary to define an unplanned event. In the context of this article, an unplanned event may or may not be in the lexicon of predicted possibilities. If it has been named as a possibility, then it has been assigned a low probability and few or no contingency resources. In particular, people haven't set aside time to interpret and respond to it.

With some thought, it is possible to list and classify a wide variety of unplanned events that can affect an organization. (Try it as an exercise!) Once a list has been created, analytics can be used to help create response templates for unplanned events. When a business is fully analyzed, most sudden threats can be classified even if they can't be predicted within narrow windows of time. Most organizations have contingency plans for unscheduled events such as fire alarm evacuation procedures and IT crash procedures; some corporations have poison pills in place for unplanned acquisition attempts.

When an unplanned event occurs, there is a contest for reaction time between the new event and known events that are already accounted for in people's schedules. The very structure of business requires scheduling. To conduct business properly, people need the continuity that scheduling provides. People also need interaction with other people and processes for receipt of work, evaluation and hand-off of completed work. They also need shielding from interruption to complete tasks that require sustained attention. They even need to plan for future processes with new job structures and justifiable economic structures. The success of all of these activities hangs on the time lines that we call "scheduling."

A Brief History of Scheduling

Scheduling was originally governed by diurnal and seasonal cycles. Farmers, who were largely their own producers and marketers, mutually agreed on a market day during which to collectively sell and trade. The industrial era introduced the need and capability for more atomized timing of events, processes and procedures because companies couldn't efficiently coordinate complex manufacturing and inventory processes without careful timing. Professor Todd Rakoff, Harvard Law School, traces the standardization of time zones and hourly/weekly labor laws in the 19th century as key enablers of the exact schedules required for the growth of rail service (A Time for Every Purpose: Law and the Balance of Life, Harvard University Press, 2002).

Schedule-driven business processes have been traditionally dominant in nearly every business function. Consider the marketing function as an example. Throughout most of the 20th century, the marketing experience was still governed by diurnal and seasonal cycles. However, electronic communications introduced live marketing outside of normal business hours via mass media, telephone and, most recently, e-marketing. Now the diurnal cycle holds less sway as companies go global (it's always normal business hours somewhere), and people shop online outside of normal business hours. Every online store is really a convenience store.

Planning for the Unplanned: An Event-Driven Decision Cycle

However, into all of these schedules comes the unplanned event. Prior to the occurrence of an unplanned event, an organization must establish strategic goals, operational goals and the analytics to determine whether the organization is on track in its decisions concerning an unplanned event. The organization must also create a classification system based on prior experience. This must be a multilevel system to encompass ever more general classes of events and more generic reactions to the events based on information. The system also needs to have an "other" category for previously unclassified events.

Figure 1 shows a decision cycle that enables rapid responses to events, augmenting the scheduled flow of information. The event- driven aspects of the decision cycle are shown in red, emphasizing the requirements for event-based business intelligence.

At the center of the decision cycle is the policy hub. The policy hub represents the decision-making process, the critical link between analytics and operations in a closed-loop system. The results of the analysis and modeling work are considered along with business judgment and knowledge to decide on changes or adjustments to business policies or rules. Software automating the policy hub should enable decision-makers to compare the impact of alternative policies, track the decisions made and review how effective they were.

Let's follow the event-driven decision cycle shown in Figure 1 in a clockwise direction, following the sequence of the tasks and activities that comprise the closed-loop process. We begin with the occurrence of an unplanned event, shown at the upper left.

Event Monitoring (Track/Detect)

When an unplanned event occurs, the normal business activity monitoring process may not even detect the event. Detection may occur serendipitously in any part of the organization, and this means that the organization must have robust and flexible internal communications to speed entry of the event into the tracking portion of the decision cycle.

Event Analysis and Classification (Analyze, Model Improvement)

Next, the organization must classify the unplanned event and decide whether it really matters enough to incorporate into decisions. To accomplish this, it is necessary to access the policy hub library to find procedures linked to the classification.

These procedures may or may not be available or may be too general to act on for the event at hand, or it may be necessary to decide on a new action. The policy hub isn't capable of instantaneous action that is finely tuned to a new type of event; therefore, there will either be an emergency meeting of people involved in this type of decision, a delay in taking any action (that may cost business or may fail to prevent a loss) or a default generic action that is automatically taken with subsequent review by people. Note that a decision concerning an unplanned event may require new and unfamiliar types of data that have not been accessed or collected. Existing analytics and modeling may be inadequate or irrelevant for the task of interpretation.

The data available for analysis falls into two major categories. First, some data leads to scheduled contacts based on a prediction of likelihood. Scheduled events can be generalized such as holiday seasons in which many individuals spend more money, or they can be individualized such as birthdays, anniversaries and corporate acquisitions. Second, data can be based on an unplanned event, such as a new prospect entering a real or virtual store. The prospect may walk around a showroom, configure a new product or initiate a search for specific information. The prospect may also make an initial purchase (regarded as an event) that can benefit from accessory sales (such as batteries to power portable electronics). Sometimes it is valuable to combine information on both scheduled and unscheduled events.

Collaborative Decision Making (Policy Hub)

The fact is that people cannot always meet periodically due to other obligations, and this fact forces a statistical (non- individualized) response to events. An alternative to periodically available decision-makers is to have extra internal or outside contracted personnel with decision authority who are dedicated to unplanned events (structured like a high-level outside overflow telemarketing facility).

If the organization automatically takes a generic action, the action will be triggered and bounded by measures such as dollar potential, a change in key competition characteristics, a change in key supplier characteristics or a major political event that changes the landscape. Inside the policy hub, people must also review the accuracy and completeness of the classification system and grapple with its implications for organizational strategy.

Decision Communication and Implementation (Adjust/Act)

Once the decision leaves the policy hub and is implemented, it follows the same route as planned events - with results being tracked, analyzed and modeled to optimize future response. The results are incorporated into the policy hub library and reviewed for changes in future implementation. The revised policy is then translated into rules understandable by each of the relevant operational systems, governing future transactions. The uniqueness of the unplanned event will initially require an above-average priority in the policy hub until the appropriateness of the organization's response has been clearly demonstrated.

Supporting the Event-Driven Decision Cycle: The Need for Event-Based BI

Much has been written about real-time business intelligence (BI). However, the discrete steps in the decision cycle do not happen all at once. What is needed for business to be more responsible to fast-moving events is an accelerated flow of information, analysis and decision making across the cycle. This requires organizational agility most of all, but it also requires the augmentation of schedule or batch-based BI with event-based capabilities. As noted in the figure, these include:

  • Real-time business activity monitoring (BAM), augmenting scheduled operational reporting tools.
  • Event-triggered extract, transform and load (ETL) and analysis, augmenting batch-based ETL tools.
  • Event classification according to a previously established taxonomy.
  • Event-responsive, collaborative decision making via the policy hub.
  • Implementation of the decision via event-triggered enterprise application integration (EAI). The policy arrived at must be translated into rules in the form required by each relevant transactional system.

Structuring the decision cycle to support a strategic goal - improving CRM effectiveness, for example - requires coordinating the data with the steps in the decision cycle and the supporting technology to provide a synchronized process. The supporting technology and decision cycle must be brought into line with each other in terms of time and sequence. Those designing the process need to consider what should be changed organizationally to take advantage of the data and technology in the face of unplanned events. Is the organizational reporting structure expertise-based or information source-based? Is the organization capable of "morphing" temporarily to handle unplanned events so that the appropriate experts and gatekeepers come to the fore, or is the organization frozen until the unplanned event cracks it?

Planning for the unplanned must go beyond broad statements of "empowerment" and involve actual testing. Initial tests will probably bring a mass of confused information to the policy hub, but the information flows can be refined. It is better to wade through this refinement as part of a test than to sweat through it during a live event.

In addition to creating alternate reporting maps for different circumstances, organizations need to identify gaps in data and analytics. What do current data sources illuminate and what do they leave dark? Similarly, what key performance indicators (KPIs) do current analytic techniques feed, and what types of unplanned events fall completely outside of current analytic capabilities? Considering these questions now will benefit an organization when the unplanned happens.


For more information on related topics visit the following related portals...
Business Intelligence (BI).

Robert Blumstein is research director, CRM Analytics, Sales and Marketing Applications, for IDC.

Henry Morris is the group vice president and general manager for for IDC's Integration, Development and Applications Strategies (IDeAS) solution research group. Dr. Morris started the Analytics and Data Warehousing research service at ICD and coined the term "analytic applications" in 1997. Currently, he is exploring the relationship between business intelligence and business process automation. Morris may be contacted at hmorris@idc.com.

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