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Enterprise Decision Management and Decision Intelligence

Originally published November 11, 2008

In a recent article – Full Circle: Decision Intelligence (DSS 2.0) – Colin White and Claudia Imhoff introduced the idea of decision intelligence as an overarching term that includes business process intelligence, business data intelligence and business content intelligence. Their focus on decisions and thus on better decision making is welcome, and they identify a pair of trends as the primary driver for this approach:

The need, however, to use business intelligence to make more timely decisions, monitor and optimize daily business processes, and to deploy business intelligence to a broader user audience (my emphasis).

The second of these two trends, broader use of business intelligence (BI), has been thoroughly and usefully discussed here on the BeyeNETWORK. The first is the focus of this article, and it has two elements as shown with my emphasis above: a need for more rapid decision making and a need for this decision making to optimize operational business processes.

Too many people and articles assume that operational decision making uses standard business intelligence functionality with real-time data. Colin and Claudia, in contrast, recognize that:

“The consumers of decision intelligence may be operational processes (application-centric decision intelligence) or business users (user-centric decision intelligence).”

This focus on application-centric decision intelligence is missing from far too many discussions of operational BI. If the only change for operational BI was one of speed – if you could simply make the data closer to real time and still use the same reporting and presentation tools – then it would be simply a performance challenge. But much more of a change is required if you want more intelligent applications. If you want to make your operational processes smart (enough), then you need to embed intelligence in those processes in new ways. Applications, after all, cannot read reports or look at visualizations to understand the patterns in data. They cannot apply their own experience or the organization’s policies to ensure they make a legal and appropriate decision. A new approach is needed if you are to deliver application-centric decision intelligence – an approach known as enterprise decision management (EDM).

Neil Raden and I recently introduced an updated definition of enterprise decision management:

ENTERPRISE DECISION MANAGEMENT is an approach for managing and improving the decisions that drive your business. It involves:

  • Making your decisions explicit

  • Tracking the effectiveness of these decisions to improve them

  • Learning from the past to increase their precision

  • Defining and managing these decisions for consistency

  • Ensuring they can be changed as and when necessary for maximum agility

  • Knowing how fast they must be made to ensure they are up to speed

  • Minimizing their cost for the required effectiveness

As you can see from the definition, adopting EDM does not mean buying a particular package or using a particular methodology. It is a business approach that involves finding the decisions that create (or destroy) value in your organization and making them explicit so you can track and improve them. It recognizes that decisions involve intelligence, for sure, but also that decisions are not static; they change. Being able to adapt to changing circumstances by changing how decisions are made and ensuring that these changes are applied consistently are also important.

Adopting EDM means moving beyond traditional business intelligence tools and adopting some new technologies:

  • Data mining and predictive analytics technologies to create executable analytic models rather than visualizations.

  • Business rules technologies to provide a policy and regulatory framework for executing decisions in operational systems and processes while allowing rapid and consistent change to be applied.

  • Optimization and simulation technologies to enable decision approaches to be worked on “in the lab” before putting them into systems.

It also means taking technologies well established in most companies and applying them to decisions. For instance, performance management technologies must be employed for decision analysis so that the effectiveness of decisions can be monitored and managed. Data warehouse technologies too must be used differently. They must store and manage transaction-level data, not aggregated data. Not only do operational systems need this transaction-level information, but also only the management of it will allow those doing data mining and predictive analytics to share information resources with those developing reports and dashboards. All too often today the analytic teams are extracting, cleaning and analyzing data extracted directly from operational systems and ignoring the data warehouse because it contains aggregated data that is no use to them. It is also essential that the data warehouse technology supports the very mixed workloads a combination of traditional BI and EDM will place on it.

The benefits of applying EDM to deliver application-centric decision intelligence fall into 5 broad categories.

Precision of decision making.
Precision can be improved by using the analysis of historical data to create more fine-grained segmentation, of customers for example, so that they can be targeted more carefully. It can also be improved by developing models that predict useful information, such as retention risk so that decisions can be made more accurately by taking those predictions into account.

Consistency of decision making.
By focusing on decisions, externalizing them from processes and systems and embodying them in decision services, organizations can ensure that all the channels, processes and systems that need a decision can get it and get it consistently.

Agility in changing decision making.
The way a decision is made changes constantly. Because competitors change, markets change and regulations change, the “best” way to make a decision must also change. Being able to rapidly and accurately change a decision-making component delivers the business agility you need.

Speed of decision making.
In this day and age, operational systems and processes need decisions taken in real-time.

Cost of decision making.
No one can afford to spend more money making a decision than absolutely necessary.

Organizations that adopt decision intelligence and enterprise decision management can become what Neil and I call “decision-centric organizations.” Neil and I define a decision-centric organization like this:

A decision-centric organization is focused on the effectiveness of its decisions rather than aggregated, after-the-fact metrics.

A decision-centric organization recognizes decision making as a competency and devotes resources to elevate, understand and continuously improve its decisions.

Decision-centric organizations will be better able to weather the storms caused by the current global financial crisis and will be more able to use their customer data to compete effectively.

So, are you using your data to become decision-centric and compete on decisions or are you just generating reports?

  • James TaylorJames Taylor

    James is the CEO of Decision Management Solutions and works with clients to automate and improve the decisions underpinning their business. James is the leading expert in decision management and a passionate advocate of decisioning technologies – business rules, predictive analytics and data mining. James helps companies develop smarter and more agile processes and systems and has more than 20 years of experience developing software and solutions for clients. He has led decision management efforts for leading companies in insurance, banking, health management and telecommunications. James is a regular keynote speaker and trainer and he wrote Smart (Enough) Systems (Prentice Hall, 2007) with Neil Raden. James is a faculty member of the International Institute for Analytics.

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