Operations for a Prescriptive Analytics IoT Strategy
The operation of a prescriptive analytics strategy is usually one limiting factor because it revolves around two core principles — that customer data is reasonably accessible and that you have access to the data science talent needed to turn that data into insight.
B2B IoT — Siloed Data
It is amazing how many companies, both large and small, either have no central place where customer data is stored or have multiple siloed spots where that data exists. Most business-to-business providers (IIoT and commercial IoT) that we encounter have many points of interacting with customer data. There’s the CRM, the ERP, the billing system, an archaic database of serial numbers, and the list goes on.
Often when we dig in to build a prescriptive analytics strategy in IIoT or commercial implementations, there is little to no integration between the data sources, and different departments own each system. Typically deep inside the organization, you’ll find a BI tool that joins some of the data, but it fails to centralize everything in one place.
B2C IoT — Huge Numbers of Consumers
In business-to-consumer IoT, we see a different issue that is no less problematic. Because there are so many individual consumers and so many data points, building meaningful machine learning models requires extraordinarily talented data scientists and software engineers, working together to tailor the models to individuals.
How IoT and Data Science Provide the Solution
What people often forget when creating an IoT product is that they are sitting on a trove of customer data inside the ecosystem they have created. With a little bit of customization when developing the application, you can collect and synchronize customer data, thus solving both the siloed data problem and the no-system-of-record problem.
I’d still recommend choosing a system to house your customer data that is not the IoT cloud, like a CRM. The right choice can vary based on the organization that you’re in.
As mentioned above when talking about B2C specifically, this is all easier said than done. If you go this route, you’re going to be dealing with an unholy amount of data. Every single action from devices at the edge can be tracked and processed. In theory, it sounds like a great idea to just collect all of this and figure things out later, but this can be both expensive and headache-inducing down the line when you want to evaluate the data for other trends.
The right move is to engage with a data science team early in the architecture of your data collection. This group of people will give you both an initial model to evaluate as well as a future-proofed schema by which to evaluate changes needed later.
The other problem we see here is sending constant streams of data directly into the system of record. Unless you want your SaaS expenses to skyrocket, this is a failing strategy. Store the data in the cheapest place possible, and have your data science team build algorithms to send only select information to that system of record.
Finally, you’ll want to get in a cadence with data science to evaluate your algorithms and ensure they’re sending up the right notifications, and there are not more that you’re missing out on.
What Do You Do With All That Data?
Yes, you need your data science team to look at these algorithms on a rhythm, but the next step is how you use that data. This is the million-dollar problem to solve. Don’t be afraid to be creative with solutions to patterns your machines and algorithms see in the data. Engage with a variety of people in your organization. Interview customers about the patterns you’re seeing. Ask probing questions. And then work with your product team to develop innovative solutions to these problems.
An Important Word About Privacy
Prescriptive analytics strategies can provide highly scalable ROI for companies looking to go to market with an IoT device. However, you do not want to get carried away with the behavioral and demographic data you are collecting on customers. Not only is there a question of ethics, but there are also laws, most notably GDPR, that protect consumers against unwanted tracking and data collection.
This means you need to provide a reason and value for collecting and analyzing data. While it might be a headache to some for all the opt-ins they need to collect, this is good for the consumer.
The moral here is: when creating a prescriptive analytics strategy, consult both a lawyer and your conscience. Make sure that the data you’re collecting and the algorithms you’re producing are providing actual value to the consumer of the device, not just so you can sell more stuff.
How Do You Get Started?
IoT devices still feel shiny and new in many markets, but ubiquity is coming. Once everyone has multiple IoT devices at home and at work, the competitive edge will be in the residual value that you can provide to consumers with your prescriptive analytics strategy.
And what I’m truly hoping for? Someone using prescriptive analytics to improve my morning routine.
But I doubt you’ll be able to change my craft-roasted Ethiopian coffee.
Looking to work with a top IoT development company that understands how your product fits with your overall business strategy? Drop a line to our team today.