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Smart Systems in Business

K. Mani Chandy

Drivers for Smart Systems in Business: Part II

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In my last post I looked at factors driving the use of smart
systems in business. The major factors are represented by the acronym PC-cubed
for Price, Performance, Pervasiveness,
Celerity, Connectedness, and Complexity
. The last post analyzed the P-cubed,
producer "push" factors of price, performance and pervasiveness. Now lets look
at the C-cubed, consumer "pull" factors of celerity, connectedness and

Celerity (Speed)

Nuclear radiation material must be detected and contained in seconds; minutes can be too late, and the consequences can be disastrous. Airport checks of passengers must take only seconds; checks that require minutes per person result in unacceptably long waits. Society is confronted with security problems today that are more widespread than they were a decade ago, and these problems require rapid detection and containment.

Pressures for increased speed appear in many applications in addition to security. The smart grid uses renewable energy sources, such as solar and wind, by relying on "smart" information technology. As clouds move across solar farms the power generated fluctuates, and smart IT is required to ensure that these fluctuations don't destabilize the circuit. Utilities need greater IT speed to harness renewable energy.

CRM systems that respond to each customer's specific and changing needs,
for each interaction between the customer and the enterprise, for all of the
enterprise's touch points, must be fast. A customer's interaction lasts seconds
or a few minutes, and in that short period the application must determine the
information from which the customer will acquire the most benefit.
Event-driven marketing systems are smart systems that sense and respond rapidly to customer activity.


Information from an individual in Nigeria is relevant to the safety of an airplane landing in Detroit. Melamine contamination in animal food in a plant in China of food in China hurts dogs in New York. Phasor measurement units (PMUs) send data collected at different points in the power grid to command centers within a utility and also to other utilities and independent system operators (ISOs). The world is more interconnected today than it was a decade ago. Smart systems acquire and correlate data from points around the globe enabling enterprises to detect and respond to significant events and trends.

Interconnectedness means more than connecting geographically dispersed sources of information. Just as important, it means connecting organizationally dispersed sources. Systems that fuse information from different divisions of the same organization have to be "smart" in the sense that they have to map semantics from one group to another, and correlate what may appear to be inconsequential information from different groups to detect significant events.

Enterprise application integration is difficult, and integrating data from sources outside the enterprise is even more difficult since external sources have little incentive to use terminology and communication protocols used within the enterprise. Pressure for IT systems that support greater interconnectedness is also pressure for IT systems that translate across multiple ontologies; in other words pressure for smarter systems.


Business processes are becoming more complex. For example, ensuring compliance across multiple state and country jurisdictions is much more complex now than it was a decade ago. A consequence of this increase in complexity is demand for IT solutions that help monitor compliance. For example, Nenshad Bardoliwala, says, "Performance, risk, and compliance management will continue to become unified in a process-based framework and make the leap out of the CFO's office." 

Fraud detection, intrusion detection, and detection of money laundering have become more difficult as adversaries employ more subtle strategies. The volume of data that must be analyzed to detect improper activity continues to increase and so does the complexity of analysis strategies.

Consider the problem of determining who should and who should not be permitted to fly as a passenger on a commercial airplane. This problem was much simpler a decade ago than it is today. The difficulty is not only that data has to be acquired from multiple geographic sites such as Nigeria, Amsterdam and London, and at different points in time, but also that data items are inaccurate, noisy and fuzzy, and data correlation is complex. Moreover, an increasing volume of information must be analyzed rapidly to extract actionable nuggets of information. Complexity, volume and speed demand smarter systems.

Responding to each individual customer as a "market of one" requires up-to-date analysis of millions of markets, one per customer. This too has similar problems of high volume, rapid response and increasing complexity. Supply chains exhibit similar trends. Many aspects of enterprise operations are getting more complex. And this will drive demand for systems that are smarter, detect important situations faster, and respond quicker.


Advances in technology push and demand from enterprises, represented by the PC-cubed trends, ensure that IT systems will get smarter over the next few years. In following posts I'll describe architectures, costs/benefits, designs and applications of smart systems.

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I think the growing complexity of regulation and the need to demonstrate compliance with such regulations proactively is also having an effect. Systems must increasingly be demonstrably compliant and this adds a layer of complexity to their development. I also think the volume of data is forcing the issue but I suspect that is covered in your general complexity.

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Smart systems use historical data and data acquired continuously from multiple sources; they correlate this data and identify patterns that indicate important events; they predict probable futures; determine appropriate responses; and then respond. Smart systems help businesses by identifying and responding to changing situations rapidly and appropriately. This blog describes smart systems, its key foundational ideas, and their applications.

K. Mani Chandy

K. Mani Chandy is the Simon Ramo Professor at the California Institute of Technology in Pasadena, California. He received his B.Tech from IIT Madras in 1965, MS from the Polytechnic Institute of Brooklyn in 1966, and PhD at MIT in 1969. He worked at Honeywell and IBM, was a professor at the University of Texas at Austin from 1970 to 1987, and has been at Caltech since then. View more


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