The predictive enterprise is an IT infrastructure that supports business operations by delivering informed predictions about what is likely to happen in a business process. Implementing a predictive enterprise presents both cultural and technological challenges. However, many of the technologies required to build such an infrastructure are now mature, including enterprise data management (EDM) and predictive analytics. Also, new middleware methodologies such as service-oriented architecture (SOA) can be used to interface with the various technology components. Moving to a predictive enterprise and delivering results to business decision-makers across an organization, from the call center to the boardroom, is now achievable.
The central objective of implementing a predictive enterprise is to use the company’s knowledge about its past business activities, stored in data warehouses, data marts, log files, and other sources, and the current state of business operations to make predictions about future events. These predictions can be used to support the decision making of all individuals in an organization. Applications include the intelligent promotion of offers to specific users on your Web site, providing differential services to your best customers and detecting fraudulent transactions.
Many organizations have developed a business intelligence (BI) capability where raw data is cleansed, transformed and stored in a data warehouse using a format suitable for delivering commonly requested management reports. The traditional implementation maintains separate technology stacks, one for the operational (online transaction processing or OLTP) style of applications and another for BI and reporting applications. This method uses extract, transform and load (ETL) tools to move data in a single direction, from the operational system to the data warehouse. In a predictive enterprise, the goal is to close the loop in the flow of information, so that predictions that are derived from the historical data can be fed back into the OLTP applications.
Building a predictive enterprise solution that closes the loop between the OLTP and the BI domains can prove challenging, as there are many elements required to make it successful. The following steps illustrate the basic roadmap to the predictive enterprise.
Figure 1: Closed Loop Predictive Enterprise Model and Key Technologies
Selection of Business Process
A predictive enterprise starts with understanding the business processes that are at the core of most OLTP systems. To deliver the most value, a predictive enterprise solution needs to be used at an operational level by making predictions about individual transactions, irrespective of whether this is a customer interaction, sales transaction or supplier transaction. The first step in developing a predictive enterprise is to identify business processes that can benefit from adding predictive elements. The best candidates are those processes that have the potential to improve the bottom line by achieving more with less. The process should also have a predictive element where patterns or behavior can be predicted such as those related to an individual customer’s needs, desires and behaviors or items related to specific financial transactions.
Once selected, the business process that will be implemented as part of the predictive enterprise must be redesigned to deliver variants of business processes, taking into account predictions and decisions inherent in the process. Without variations of the process, it is impossible to provide differentiated routes based on the nature of the decision. For example, an interaction with a customer who is predicted as likely to defect should be processed with a different variation of the call center process than a customer who is likely to remain loyal.
Definition of Predictive Services
The second phase of developing a predictive enterprise is to develop predictive services. The construction of a predictive service starts with defining the different decision points in the business process and determining what information can be predicted that can support those decisions. These predictions might include, for example, the likelihood of a customer defection to a competitor, the likelihood of a transaction being fraudulent, which offer a customer is likely to respond to, the best time to discount prices of perishable stock and the likely demand for a product.
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