Traditional BI Systems A re N ot Des igned For A gility

The traditional approach to business intelligence (BI) has reached its limits. Over the past 20 years we have developed a set of procedures and technologies that allow us to aggregate,
cleanse, prepare, and report on enterprise data. While there have been incremental improvements in efficiency and speed over that time, the fundamental approach to BI has not changed much. During that same period, however, businesses and their information needs have changed dramatically. Enterprises have transformed themselves from rather isolated entities, often with an inward focus on operational efficiency, to players in geographically dispersed, multi-partied ecosystems that need to have as much understanding of their customer’s interests and partners’ operations as they do their own operational efficiency. The information they collect, transport, and analyze has changed accordingly and, more importantly, so has the speed with which they need to act.

Today agility is the goal of most organizations regardless of their industry and it is a top goal of business leaders whether they are in sales, marketing, manufacturing, engineering, customer care, or service delivery. Businesses are competing not just on efficiency, but on their ability to sense market conditions and quickly respond. That is exactly where BI and the business needs have diverged.

In many organizations, BI systems are used almost exclusively to generate standard monthly or quarterly reports. These reports deliver great value – certainly most organizations could not survive without them. But traditional BI systems require an expensive and time-consuming process to identify all the answers the business users ultimately want, build a data model that captures that information and unify data from disparate sources into that data model. Often, multiple cycles of this process are required before the needs of the business users are fully addressed. More than anything, a traditional BI approach is designed to structure data, ensure consistency across systems, and efficiently handle large amounts of structured data – goals that seemingly run counter to the need for agility.