Location and Insight in the Age of the Internet of Things

by   |   December 8, 2015 5:30 am   |   1 Comments

Helen Thompson, Director of Commercial Marketing, Esri

Helen Thompson, Director of Commercial Marketing, Esri

Gartner once again put the Internet of Things (IoT) at the top of its annual hype cycle, meaning that Gartner believes that the IoT is at the peak of inflated expectations. As a result, naysayers have drawn their knives of negativity and are predicting the demise of the IoT due to a lack of a killer app for both businesses and consumers.

I believe this misses the point. The IoT isn’t about a thing, it’s about all things on the Internet. If the IoT truly is about only one thing, then that thing is the opportunity that is to be gained by data – real-time data streams in particular.

Look at all the IoT siblings that are on the hype cycle: autonomous vehicles, connected homes, wearables, advanced analytics, and machine learning. All of these involve billions of objects equipped with sensors that continually throw off data. But there is no value from the IoT without the ability to make practical use of the data being continually created. Geographic data adds perspective and identifies relationships between data points that otherwise appear unrelated and unrelatable.

Mark Bonchek, Harvard’s first doctoral recipient on the topic of social media way back in 1997, explored an interesting concept in the Harvard Business Review in May 2013: little data. Big data is the information that is collected about many people. Little data is information we know about an individual.

Both big data and little data can be enhanced with location information. Geographic relationships can now be parametrized so that we can easily ask questions that help us gain real knowledge about customers as consumers and their habits and needs.

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Where: The Forgotten ‘W’

But how do we pinpoint that individual or smaller community to provide context to the ocean of big data? Geography can provide a where to the who, what, why, and when that we already have collected.

This is easier than it may seem. Every 21st century dataset and transaction contains location information – a customer address, the store location of a purchase, counts of riders on mass transit as they embark and disembark on their journeys.

Think about a shopping list. A few years ago, I had to scan the weekly ads to find the best prices for my shopping trip. Today, by opting in to a store’s loyalty program, I am provided offers each week based on purchases I’ve made previously. The store knows that I frequently buy apples and carrots, so it provides me with a digital coupon for these products. This is little data.

The store also can sift through the purchasing habits of others who shop at the same location as I do and also purchase apples and carrots.  Maybe the store discovers that a majority of these people also purchase mangoes, and offers me a coupon to buy mangoes, too. This is big data.

Thanks to sensors, the store can use algorithms to predict how many additional mangoes might be purchased from this promotion, and stock the shelves accordingly. This is insight from big data and little data viewed through a geographic context.

Providing Context by Connecting Data with Geography

The store can apply a geographic context inside the store during a customer’s path to purchase to drive improvements in customer experience and engagement. It’s possible that soon we not only will receive coupons for what the store expects us to buy, but also will receive reminders as we shop in the produce aisle. Perhaps we will be given an alternative to apples, together with a list of locally sourced ingredients that are on sale, and a recipe for a meal. Not only do we save money, we have a better experience too.

With knowledge of suppliers that is maintained throughout the supply chain all the way to the store, the grocer can substitute ingredients that might be at risk of spoiling soon (those apples are starting to get a little soft) or a higher-value alternative (mangoes) that improves profitability over my regularly purchased apple.

Providing a geographic context to data changes how organizations see and use the data. It also helps organizations gain better insight. Geographic context can be provided through location information that is as simple as a farm name, an address, a GPS coordinate, the owner or supplier name in a manifest, or the number of the vehicle collecting the produce.

Systems with geographic context that feed analytics enable enterprises to join data between disparate systems. The little data approach to these datasets – understanding the where in all data – enables an understanding of the entire supply chain in context.

Solutions via Sensor

Today, sensors are capable of monitoring purchases of farm fresh vegetables and comparing customer preference by supplier and farm. Potential out-of-stocks can be identified, and comparable products can be sourced to respond to market dynamics. Operations can be streamlined and customer buying habits and actions systematized through machine-to-machine and data-to-data learning.

When unexpected events occur, like a supply chain interruption or food contamination, the same geographic context can be applied to minimize impact. Sensors on refrigerated trucks or cold storage facilities can identify failures that could lead to human health risks. Location-specific analysis and big/little data logic, enhanced with geographic context and connections, can optimize the response and mitigate future issues.

Geographic context can help organizations identify all stores where a potentially contaminated product is sold, and other potential contaminations associated with the supplier or source location also can be analyzed. Local health data can be sourced straight from community agencies so past outbreaks or clusters can be identified. Applying geographic context supercharges the value and benefit of data from sensor streams, static stores, and enterprise systems.

Helen Thompson is Director of Commercial Marketing at Esri. Over the past 20 years, Helen has applied her entrepreneurial and technological passion to create a better future through geographically relevant decision making. Geographic context is central to some of society’s biggest opportunities and challenges, including climate change, risk reduction, driverless cars, 3D technology, and global interconnections. Helen is a recognized thought leader, keynote speaker, and expert on spatial theory and location platforms, using her knowledge to advance the understanding and use of spatial technology in business and society.

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One Comment

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