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Smart Systems in Business

K. Mani Chandy

Drivers for Smart Systems in Business

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Let's look 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.

Technology Push and Business Pull

Improvements in technology provide better price, performance and pervasiveness of IT devices. Business demands improvements in celerity (speed), connectedness (monitoring activities within and outside the enterprise across the globe), and complexity (dealing with complex factors such as monitoring compliance across multiple jurisdictions). Technology "pushes" the P-cubed features while business "pulls" the C-cubed characteristics. This note discusses the P-cubed features of improving price, performance and pervasiveness of IT. A later note discusses C-cubed and other features.


Look at how rapidly the costs of infrastructure - such as bridges, electric power networks, and water-supply systems - have increased over the last forty years. Similarly, look at the massive rates of increase of costs of critical services in the same period; for example, compare the cost of a day in hospital or the cost of a 4-year college degree in 1970 and 2010. Then look at how rapidly the costs of IT devices - including sensors, communication bandwidth and computational power - have decreased in the same interval.

Moore's Law is common knowledge; however, the key metric that drives smart systems is the ratio of cost of IT devices to the costs of goods and services. This ratio has been dropping exponentially, over this period, at a phenomenal rate. Prices dictate greater use of sensors, communication, computation and actuators in bridges, power grids, food and water supply chains, health care delivery, security, education and most aspects of life and business.

IT devices offer performance that wasn't available a decade ago.

Sensors: For example, smart electric grids today can exploit synchronized phasor measurement data. Utilities didn't have these sensors earlier. They can now use vast amounts of sensor data coupled with more powerful computation, communication and digital control devices to sense and respond to changing supply from solar and wind energy sources.

Parallel Computing and Cloud Computing: Likewise, the increase in the power of concurrent computers - both multicore shared-memory systems and distributed computing, particularly in its cloud computing manifestation- allows businesses today to correlate torrents of data from multiple sources. Smart system applications exploit concurrency in many ways, for example by having concurrent threads of computation explore multiple probable futures. Data fusion from geographically distributed sensor networks, such as seismic monitoring systems, use cloud computing.

Web Service Components of Smart Systems: Today, applications can exploit Web services that are powerful components of smart systems. For example, Calais (see OpenCalais.com) offers a Web service that does natural language processing on text; an application sends Calais a piece of text and Calais replies with a detailed analysis of the text, disambiguating terms such as MSFT for Microsoft. Sensors everywhere in the world can access the Google App Engine which can also send alerts to devices everywhere in the world. These services make development of smart systems much easier today than they were a decade ago. And as more of these services are becoming available.

The raw power of IT devices available today enables businesses to build smart systems that could not have been built in the past. Powerful sensors, actuators, communication, storage and computational devices allow businesses to monitor, analyze and respond rapidly to conditions within and outside the enterprise.


Data sources are pervasive.

The mobile phone is a good example of a pervasive source of data. The rate of growth of mobile phones worldwide is staggering. Many mobile phones are equipped with video cameras and have significant processing capability. Sensors, such as stethoscope and ECG readers, can be plugged into some phones today, and many more phones will have these features in the future. Images of wildfires and other disasters, taken by phones and posted in the "cloud" are being used to help deal with emergencies.

Form factors and battery power consumption rates of sensors continue to drop and, partly as a consequence, sensors are used in an increasing variety of applications. Accelerometers are being used in smart walking canes to help the disabled. Electronic IDs are used to track farm animals from birth to consumption (see "National Farm Identification and Records"). Data about a huge variety of items can be obtained from mobile phones equipped with cameras; for example, a shopper can use a phone to scan a YottaMark code on an item and get detailed information about the item's supply chain.

Many Web services provide data sources on a variety of topics. Blog aggregators and indexers, such as Spinn3r.com, provide raw access to blog posts in near real time. The Thompson Reuters Newsscope is a Reuters news archive with stories that have timestamps and metadata tags. A large number of services offer RSS or atom feeds. Social network applications provide near real time data about activities of people. The volume of useful (and useless) data available on the Web continues to increase at a staggering pace.

Data sources are pervasive; the problem faced by business is to use the data wisely and in a timely fashion.

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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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