IT models of business operations have long relied on software abstractions and data approximations, with decisions derived from information systems running within corporate data centers. Other company assets, such as finished goods, equipment, materials, plants, and even many employees, were disconnected or passive participants. Until now.

The goal of IT should be to shift business decisions from reactive to proactive, to predictive, and ideally to automated. In 2010, companies have the opportunity to redesign their IT environments toward this goal – using assets as the starting point. What is an asset? Anything that is important to you and your business.

Through advances in auto-id, sensors, remotes, and wireless devices, it is now possible for any and all business assets to produce data and initiate events. From the simple pinging of current status to the detection of physical movement, changes in temperature, geo-positional tracking, chemical trace reads, biometric identity input, and more, assets today have an unprecedented ability to become sources of information.

Asset intelligence is not just about producing signals. It’s also about responding to them – allowing assets to consume events and automatically execute tasks – all at a much more granular level than ever before. By moving decision and action to the edge, asset intelligence provides a network effect for distributed assets, an embodiment of Metcalfe’s Law, where the value of the network increases exponentially with the number of connected systems or users.

But the key is not just plugging more things into the network or creating more raw data. Asset intelligence also focuses on interpretation and insight – dealing with events and relationships, not just the underlying assets. It’s about signals, not sensors, and the difference is monumental. Having visibility into the temperature and humidity levels of facilities, the location of raw materials, and the status of a production line is interesting. But being able to piece the events together to help make faster, smarter decisions – that’s magic. Realizing that a pallet of paper has been in a room exceeding acceptable humidity levels for five days. Understanding that the saturated paper will cause failures in the pressing machinery at its current settings. Automatically adjusting the machinery controls. Signaling operators to investigate the building’s HVAC system. Turning discrete bits of information into business meaning, leading to action. These are the essence of asset intelligence.

Asset intelligence depends on underlying sensors and data, but that’s not enough. Volumes of raw information do little to help the business. The real value comes from creating an “asset engine” with rules, workflow, understanding of systematic relationships, and the ability to correlate events with business meaning. The goal is signals with business context, predisposed towards action. The net effect? Asset intelligence allows business processes to be re-envisioned to help create value from parts of the balance sheet that have been dormant in the IT landscape. This is a shift from push to pull, from supply-driven to demand-driven models for business decision and action.

History Repeating Itself?

The concept of tapping into devices for automation and improved information is anything but new. Just ask General Motors, who commissioned the first programmable logic controller in 1968. The difference today is in flexibility, usability, and intelligence – interesting from a technical perspective, potentially game-changing when empowered for the business.

Technology Implications

The underlying technology of asset intelligence involves much more than the commodities of sensor hardware and communications devices.

Where To Start?

Most organizations have plenty of physical things and associated data that drive their business. But not all objects lend themselves to the business results of asset intelligence. Start by asking the following questions:

• What is an asset?
• Which assets play a significant role in targeted business processes?
• What information would be valuable to extract from each asset?
• What signals do you already capture, and how?
• What actions can each asset potentially undertake?
• What are the critical interactions or relationships between assets?
• What improvements or innovations could occur with real-time event visibility or task automation at the asset?
• Where is the latency in your value chain?

The analysis should be done across a company’s entire value chain – from receiving dock to shipping dock to customer delivery, from shop floor to accounting to the CEO’s office. Some scenarios will be obvious – like the importance of understanding location, movement, temperature, and contents of a shipping vessel. But others could be more subtle.

• Understanding duress (inversion, vibration, acceleration) of fragile goods or materials across the value chain.
• Managing the relationships between pressure gauges, valves, and pumps of an energy pipeline.
• Tying together physical and IT security systems to track front-gate, fl oor, device, and system access to determine suspicious behavior.
• Providing audit and financial accounting trails of goods and equipment – because every transaction in the enterprise has taxation implications.

Once an opportunity is identified, advancements in sensor hardware and other means of automated data capture make enabling data the easy part. And it can be built on existing sensors and controllers – not the “rip and replace” philosophy of past years.

Building the asset engine is where the complexity lies – allowing proprietary technologies to work together, defining the business rules and correlation services to enable relationships and context, and implementing workflow and security to allow trusted automated decision making. Many IT organizations have already begun investing in these disciplines. It is crucial that asset intelligence fi t into the overarching information strategy and be tied from the outset to business objectives. If sold as a “machine-to-machine” play – focused on infrastructure and sensors – the real potential of asset intelligence will not likely be realized.

Bottom Line

The underlying technology of sensors and wireless embedded devices is an important evolution, but is not in itself compelling. Asset intelligence is the application of those technical advancements to produce real business value. It is closely related to the thrust of the 2010 Technology Trend on Information Automation – the shift from hindsight to insight to foresight.

Asset intelligence breaks-down the paradigm of IT as “collect, aggregate, then decide” to a more real-time “sense, decide, and act.” It empowers more of a corporation’s balance sheet as potential inputs to process and information automation – at ground zero of business operations. And ultimately, it shifts IT from a hypothetical model of the business to an active participant – allowing decisions to be made and insight to be gathered across the value chain.