The Internet of Things is still in its teenage years, mired in adolescence and grappling with growing pains. A recent report from Gartner said, “A recurring theme in the IoT space is the immaturity of technologies and services and of the vendors providing them.” Architecting for this immaturity poses a major challenge to businesses in every industry.
Despite all the promise and progress of the IoT, most businesses are not (yet) equipped to capitalize on the benefits of this technology. The proliferation of sensors is driving growth in data volume on a scale never before experienced. This demands new approaches to data management and analytics.
Businesses across every vertical need ways, either completely automated or semi-automated, to extract the actionable intelligence from data that is needed for business growth. The companies that overcome these challenges and fully leverage the data at their disposal will be the ones that come out on top.
Ask the Right Questions
Extracting value from IoT data hinges on a business’s ability to ask the right questions. A barrage of raw, unstructured data is worthless. Harnessing IoT data requires a clear sense of what you want to gain. Organizations need to zero in on “high-value, target-rich” data that is easy to access, available in real time, has a large footprint (affecting major parts of the organization or its customer base), and/or can effect meaningful change given the appropriate analysis and follow-up action.
One of the main reasons why businesses are interested in IoT data is its ability to help them optimize their operations. Analyzing IoT data can yield the answers to key operational questions, such as how is the business running? Where are the gaps that need to be filled? How can I best use logistics and product placement? Answering these questions strengthens businesses by cutting down on unnecessary costs, reducing overhead, and driving overall efficiency.
Take the example of an oil and gas company. It might have thousands of sensors on its various pumps and equipment, generating vast amounts of data. The only way to manage that data is to focus closely on very specific problems and on the data that is relevant to those problems. If the objective is energy efficiency, then the company might channel its analytics toward identifying leaks and sources of waste. If the objective is to optimize pump performance, IoT data can provide insight into which pumps are performing the best (and the worst). Where are the bottlenecks and why do they exist? The impact of this knowledge is substantial – in a report from GE, Apache Oil and Gas claimed that if the global oil and gas industry improved pump performance by 1 percent, it would increase oil production by half a million barrels a day and earn the industry an additional $19 billion a year.
Patterns and Irregularities
Another way businesses can leverage IoT data is to identify patterns, as well as irregularities, that indicate risk and inform decision making. For an oil and gas company, this could mean using IoT data to flag a piece of equipment that is malfunctioning. There is no need to analyze data when everything is running smoothly, but you can set up analytics to create an alert when a certain gauge falls below a certain parameter. The ability to see immediately when something is wrong or use predictive analytics to identify a problem before it happens enables businesses to take immediate action. They can shut off the piece of equipment before it causes expensive damages or, worse, compromises safety.
The same potential applies to healthcare. Patients today are surrounded by sensors. “Normal” data is not particularly valuable to healthcare providers, but IoT analytics can flag anomalies, providing insight into why a patient is sick and/or enabling clinicians to act before a crisis hits.
In some business cases, the pattern may be more useful than the exception. For example, retailers can use IoT data to increase their understanding of their customers. Sensors placed around the store collect data about where customers walk and how long they spend in front of various shelves, which provides insight into what drives sales. Retailers can figure out how factors like in-store promotions, staff salaries, and displays affect their top line.
Another key use case for IoT data is personalization. Sensors enable retailers, as well as healthcare providers and other businesses, to collect more data than ever before. This data can be used to personalize the customer experience. For a clothing store, this might mean sending a customer a targeted promotion for items they lingered in front of. For a hospital, this might mean developing a care plan based on a patient’s specific needs rather than relying on more high-level diagnoses.
In all of the examples outlined above, businesses have to know what they are looking for, or at least what they hope to learn and achieve. They then need automated methods for collecting data, integrating data from multiple sources, and extracting actionable insights from that data.
At this current stage in the evolution of the Internet of Things, many businesses are turning to end-to-end solutions to help them accomplish this. According to a report from the McKinsey Global Institute, this is happening for three reasons: the complexity of IoT systems, the limited capabilities of many businesses to implement them, and the need for interoperability and customization. Extracting the most value from IoT data requires an automated ecosystem of sensors, infrastructure, and analytics, as well as humans who can turn those insights into results – in other words, a close connection between man and machine.
Kishore Kumar is CEO of Infore Inc. He is a serial entrepreneur focusing on applications, cloud, mobile health, analytics, and virtualization markets. Kishore was recently the CEO of Pari Networks, which was acquired by Cisco Systems in 2011. Currently, he is the founder of the software defined networking company Nuviso Networks and the social networking company Infore, Inc. Kishore is also part of NS Angel Ventures group, where he is actively investing and advising technology companies in Health/Wellness (Biomedtrics, Hakuna, Moxxly), Analytics (Rental Roost, Gray Roost & Houserie), Cloud (CloudFuze), Big Data (Apervi), Virtualization (Avni Networks), and Process Automation (Quest 2 Excel), among others. In addition, Kishore is actively involved in non-profits and social impact companies in education, sustainable farming, clean energy, and entrepreneurship. Kishore received a BS in Computer Science and an MS in Computer Technology with specialization in Artificial Intelligence.
Subscribe to Data Informed for the latest information and news on big data and analytics for the enterprise, plus get instant access to more than 20 eBooks.