Transforming Banks for a Digital Future: The Winners, The Losers, and the Strategies to Beat the Odds
Can the technologies we have today cope? At the basic level, we certainly have more connectivity, computational capacity and data storage available than we did 20 years ago. We can also write much smarter and more flexible rules and rule processors. We know we don't have to automate everything to make the automation effort worthwhile, and we understand how to build in the capability to adapt going forwardsometimes automatically, more often through periodic human intervention via performance review and technology redesign.
Creating the real-time enterprise is still a major challenge, but there's a compelling core value proposition here. A functional real-time enterprise gives you a number of significant advantages:
Greater return on assets through dynamic control models. It's possible to make the case for an additional 30 percent to 40 percent use of assets where dynamic models replace static models.
Richer and more consistent customer interactions. This is possible because the same set of information is being used in every customer contact, and single point-of-contact constraints and costs can often be avoided.
Steady improvement in decision quality. The system can "remember" its decisions, and we can analyze the outcomes to improve both the rules and how they are applied.
Greater productivity from people at all levels. They can work with better information and focus their attention on critical processes. (However, this is a mixed blessing on a macroeconomic scale. Our own and others' models, and data from early adopters, indicate that extensive deployment of the real-time model could reduce demand for human resources in business by perhaps as much as 10 percent overall and much higher in some processes. In an economy growing only in the single digits, that has serious social implications.)
At the same time, dynamic systems and fast responses can cause instability if the decision analysis and feedback isn't handled correctly. And automatic control systems have to be able to smooth out data flows when anomalies are detected but aren't significant to the underlying process. Without the ability to do this smoothing, automatic systems tend to overcontrol. These are new design considerations that business systems designers will have to learn pretty much from scratch. Also, software and systems quality is critical when full automation is the goal. You can't have systems that fail in unexpected ways or that contain the number of defects that a lot of business software has today.
This might seem like a lot to askand it is. Yet the embedded systems and safety-critical software developers get very close to defect-free designs and implementationsat least 99.999 percent (the magic "five nines") correct most of the time. And they do it at productivity rates 10 to 20 times that achieved by business software developers. There is a lot to learn from their processes and practices.
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