Image: iStock/Andrey Popov

The more adept that companies become at utilizing their big data, the more value they can derive from the effort. This value initially comes from the easiest return on investments (ROIs) that organizations can obtain from big data. There are ways to save either money or time by using big data and analytics and applying those methods to corporate operational areas.

Examples of this range from avoiding expensive downtime for repairs on commuter trams through predictive (and preventative) maintenance, to monitoring thermostats throughout buildings in order to regulate temperatures and save on energy costs, to attaching sensors to shipping containers to ensure that perishable goods remain properly refrigerated.

SEE: Executive’s guide to IoT and big data (free ebook) (TechRepublic)

When I recently visited with Deon Newman, vice president of IoT marketing at IBM Watson, he pointed to facilities management as an area where many companies are investing in analytics. “Forty five percent of worldwide energy is consumed by buildings,” said Newman. “So when you have a large enterprise like Siemens that has 300,000 buildings around the globe, the company estimates that it can take out approximately $140 million per year in energy costs and 10,000 million tons per year of emissions. This enables it to significantly reduce its carbon footprint through the use of Internet of Things analytics.”

Another company that is adopting IoT analytics is Kone, which produces elevators and people- moving equipment that move roughly one billion people each day. The Kone example is particularly interesting because it demonstrates how companies initially use analytics and IoT data to take out costs and save time, and then move big data and analytics further up the value chain by finding ways to further leverage the data and to generate revenue.

Part of Kone’s strategic vision was to find ways to make its people-moving equipment smarter through the collection of IoT data that machine learning was applied to. For instance, if 10 people are standing in an elevator bay and four of those people want to go to the 16th floor, if they scan their room cards against an elevator scanner, the elevator system could send a car that would directly take these four individuals to the 16th floor without stopping, and then send another car for the remaining six persons who were going to various other floors and stops. The end result is more efficient routing of passengers and greater customer satisfaction.

The Kone example, together with its analytics roadmap, is a perfect illustration of how the big data value chain works.

“At the first layer of this chain, companies look for immediate big data ROI by attacking an operational issue where they can see a quick return of investment,” said Newman. “Once they have proven the ROI from this initial work, they move to a second level, which is the further exploitation of this data through the development of new applications that can further leverage their analytics. The third level is entry into a new line of business or revenue stream from the data.”

SEE: Internet of Things policy (Tech Pro Research)

Applied to a company like Kone, we would see:

  1. initial operational ROI from analytics through the ability to predict maintenance needs for equipment before it fails;
  2. leverage of this IoT data through the development of “smart” applications that effectively route people to their destinations though the use of machine learning and analytics; and
  3. the potential monetization of this data, perhaps by learning about customers and what they care about and then selling advertising that customers would view while they were being transported to their destinations.

“Regardless of the industries that they are in, these are the big data value strategies that companies should be pursuing as they build their big data value chains,” said Newman. “Unfortunately, we still see proofs of concept where there is no specific business case to which they are being applied. It is difficult to secure funding for big data projects without a business case that they will be applied to, and a big data value chain that will provide continuous evolution for ROI. The examples that we are seeing with Kone, Siemens, and others show how these value chains can be built.”

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