The Benefits of Contextual IoT

IoT or the Internet of Things is new in some regards but established in others. Connected sensors and machines have been reporting data for quite a while.

The process has shed the boring Machine to Machine name and picked up the far more sexy and marketable, “Internet of Things” moniker. But what hasn’t changed is that devices sending data are typically single purpose.

• X is reported by the connected device or sensor.
• X is captured and stored.
• X is evaluated by some rules.
• Variations in X may cause an action to be taken.

Most IoT systems only utilize data from within a closed system. X is seldom combined with Y and Z data from outside the IoT system to derive new insights or enhance the IoT system rules for automated actions.

When other data sources are combined with IoT system data, new insights are derived. You learn things you didn’t even think were knowable. The rules-based actions taken by the IoT system are better informed and require less second-guessing or review. You move from a closed IoT system to a more powerful Contextual IoT system.

Contextual IoT Example

Bottled water service falls into a class of products that are expandable consumables; more will be consumed when more is available. Chips, soda, yogurt and other products share this same classification.

You can sell more of an expandable consumable product if current buyers have more of the product on hand. Making sure a customer always has product increases consumption. “It’s there so I I’ll use more.”

Contextual IoT can help.

For office delivered bottled water, we’ll assume the standard IoT implementation is a set of sensors that report bottle count and volume. How many bottles. How many full. How many empty. A very simple but effective IoT reporting system.

In such a typical, closed, IoT implementation, there is limited data. Client orders 6 bottles of water. When the system reports only one full bottle left, a replenishment delivery is scheduled.

The system is simple and helpful. The capture and sharing of the inventory status data is more efficient than the delivery person running from office to office with a cart of water bottles and checking on physical inventory status. But it’s not as effective as it can be for the customer or the supplier.

Water consumption levels will vary over time. Vacations, holidays, client visits, hosted lunches, late hours worked, and travel schedules will all influence the rate of consumption.

Who is in the office will also change the rate of consumption. You have some who are heavy water drinkers. You have others who make pots of coffee throughout the day. You have still others who hardly ever partake. The mix of who is in the office on any day will influence consumption levels.

The original order quantity could be over or underestimated. It makes more sense to vary the replenishment quantity over time based on consumption than rely on the original order quantity.

Finally, you have this expandable consumable characteristic to consider. When there is more, more will be consumed. When scarcity or out of stock exists, consumption will be reduced.

There are several available data sources that can be combined with the bottle water IoT sensor data to develop a more effective Contextual IoT system. The Contextual IoT system will further optimize delivery efficiency for the supplier. It will also make sure the customer has no periods of scarcity or out of stock and thus increase customer satisfaction and promote increased consumption.

The first data source to add is a database of consumption from the order system. Those data will provide a running calculation of average consumption per week for each client.

Next we’ll turn to public data in the form of standard calendar events like holidays and weekends. This data will give us a consumption adjustment for non work days that we can apply to our average weekly consumption data.

We’ll work with the client to get access to their online calendar. Client calendar data will tell us occupancy levels in the office by day and planned meetings. How many people are in the office will give us additional adjustments for consumption. The calendar data may also tell us changes in the number of employees assigned to this office week to week.

Finally we’ll incorporate an automatic payment mechanism into the system. This will make sure there are funds available for replenishment and eliminate the need to submit invoices, pay invoices or chase down unpaid invoices.

Now we have the makings of a simple but effective Contextual IoT system. The system will recommend replenishment timing and volume based on calculations using readily available data sources.

There are lots of opportunities to make IoT systems more effective Contextual IoT systems. Your smart thermostat could use handsets on your home network instead of a fixed sensor to determine who’s home. There could even be temperature profiles based on which handsets are on the network. A little cooler temp. when it’s just dad. A little warmer setting when the kids are home. Or use a window open message from the home security system to tell the thermostat to idle the HVAC system while we let in a bit of fresh air.

Knowing the Previously Unknowable.

There is another interesting benefit of a Contextual IoT system. You can know and benefit from things that you thought were unknowable.

The sensor or other machine data start the discovery. These data and their reporting frequency give you insights into consumption, use, and other patterns that were guesses before. Connecting those patterns to other data sources can reveal new insights. Like the connection between changes in water consumption and the presence or absence of specific employees.

These changes become knowable and predictable. They offer insights and new attributes that can be used to better forecast consumption patterns at new accounts. They can be used to better target and qualify prospects.

Contextual IoT systems are more akin to programmed trading systems than basic reporting systems. By thoughtfully combining outside data sources with IoT system data, you gain new and unique insights. These added data elements and insights lead to better informed rules for the automation of tasks and improved business performance. And not just once but continuously as you continue to discover newer insights and new ways to apply them.