Analytics and machine learning will become a core feature of your product — that we know for sure. Although the timing is uncertain, eventually market dynamics and customer expectations will require your device to make significant use of data and analytics. Transforming from a device-only company to one with IoT analytics at its core has a number of challenges.

First, the technology is dynamic, involving and confusing. What is AI? How is it different from analytics and big data? What does IoT analytics mean for our customers? All fair questions. Just getting your hands around the terminology and how to frame the challenge within your organization can be difficult.

Secondly, effective IoT analytics projects require collaboration and coordination between different departments (IT, OT, Product, etc.) and roles (executives, managers, engineers,etc.). You’ll work closely with database professionals to access and validate data. Data scientists will help generate insights from raw data. Product managers will tell you what customers really need. Senior leadership will help you prioritize and champion your initiative. Unfortunately, it’s unlikely that all of these disparate stakeholders will have the same understanding of what IoT analytics is and what it means for the business.

Lastly, effective IoT analytics innovation is as much a culture shift as it is technological. Most companies will need to upgrade the way they work. This usually means shorter development cycles, higher-frequency collaboration, more cross-functional orchestration, and faster prototyping. Combined with new technologies, this new way of working can be jarring for organizations with a well-established way of doing things.

These are some of the challenges that keep companies from getting started with IoT analytics. Nevertheless, we know IoT technology will bring massive change, and it’s important to get started. The following 90-day action plan has been gleaned from years of helping industrial and commercial firms jump-start their own IoT analytics transformation.

Get to Know Your Data (0–30 days)

Goal: Inventory data sources, measure data quality, and identify data gaps

Data is to IoT analytics projects as fuel is to rocket ships: you won’t get far without it. Have data analysts explore the breadth and quality of your data from an analytics perspective. Your current data is likely coming from existing business systems (accounting, customer services, quality control, etc.). These systems were not designed to optimize analytics, so your data will be lacking in meaningful ways for the purpose of IoT analytics. That’s okay. It will still serve as a jumping-off point.

Educate Your Team (30–60 days)

Goal: Level-set key team members on what IoT analytics means for your industry, company, and product lines.

IoT analytics is a team sport. You’ll need executives, managers, engineers, and operators working together. When you bring the team together, everyone should have the same goal in mind. To align, your team will need a common vocabulary and understanding of IoT analytics. You will find that IoT analytics is a new enough space that in a room of 10 colleagues, you’re likely to get 10 different definitions of it.

As a first step, provide context for IoT analytics. What is it? Why is it important now? Do this for all your team members. Thereafter, you’ll want to host sessions tailored specifically for roles (executives, managers, engineers, operators, etc.). This will help provide the right amount of detail for your audience.

Create a Prototype (60–90 days)

Goal: Make IoT analytics come to life and build momentum with stakeholders.

Most companies will have several IoT-analytics use cases to choose from. In the first 90 days, focus on one promising opportunity that will help you generate confidence, excitement, and commitment. At the end of this prototype, you should have some feedback from a user/customer, working analytics code, and a business model.

For more on this, check out our piece “3 Elements of a Winning IoT Analytics Prototype.”

Focus on getting the prototyping process down. Most firms go through 5–10 use cases before they get real traction. You want to be able to master this process. This will allow you to quickly cycle through opportunities and find the use case(s) that is a perfect fit.

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By the end of these 90 days, you’ll have learned a lot about your company’s gaps and strengths, and you’ll be off to a solid start towards IoT analytics. Keep in mind that this is new territory. Your company is building new muscles. Nevertheless, the IoT revolution is coming. By starting today, you will give your company a competitive edge and a head start in the coming tech environment. Your future self — as well as your company — will thank you.