Previously we introduced the concept of a life-cycle for business intelligence data. Data are captured into transaction processing systems like the general ledger accounting system. At some interval these data are moved into a BI database and processed to make them suitable for consumption. At some point older data are archived or even removed when they are no longer useful.
At first the BI or decision support life-cycle was typically built around financial reporting, and thus depended on the quarterly or monthly accounting close. Once the books were closed for the accounting period, data were pulled from the accounting system, loaded into the BI or decision support database, reports were run, and data analyzed.
As BI moved beyond the financial realm, managers in business areas like Sales and Operations found that they could achieve competitive advantage by getting (and acting upon) more frequent updates of data as opposed to organizations that remained tied to the monthly accounting cycle. This spurred the increase in the frequency of data updates to weekly, daily, and so on. The result is an evolution toward a "24-hour BI cycle" that parallels the evolution toward a 24-hour news cycle in the news media. In some applications BI data are now updated almost continuously. Rather than being the exception, this high-frequency updating of data will increasingly become the rule.
The impetus toward real-time business intelligence and analytics has implications for both technology and business. Vendors are challenged to devise new solutions to the problems introduced by requirements for more frequent updates. With the increase in the amount and frequency of data updates, businesses need to rethink how they consume data. We'll address these issues in more detail in the next article.
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