The IoT promises a lot of value from new data. But how to treat this data, how to realize the value, and how to incorporate that value into business processing and planning all take planning and work.
The new data can have value internally, in improving business processes and products, and externally, by selling it or packaging it with other services to clients. In either case, it can have significant value: value companies are only beginning to exploit.
Competitors that make effective decisions about data as an asset will thrive in the new environment of the IoT.
What is the process for realizing value from data?
In its most basic form, realizing data value is an iterative process that requires accurately answering three inter-related questions:
- What data do I have?
- What are the possible external and internal uses of data?
- What analytics will turn raw data into something of value?
Identifying a new external need can turn previously low-value data into a revenue stream. Data from newly applied sensors can combine with existing data to enable new business decisions. A new analytic model can draw high-value conclusions from what was previously local process data.
How well do you know your own data?
If you’re like most organizations implementing IoT initiatives, not as well as you need to. There just hasn’t been a need for an accurate, actionable information asset inventory. But that is now essential.
Better sooner than later: as new IoT implementations come online, the volume and variety of data will skyrocket. Best to be ready to categorize, clean, validate, and process that data.
Since the IoT is an ecosystem, this process can include data you share with partners, data provided by customers, suppliers, and data acquired from third parties for other business operations.
Who needs the data?
You can’t sell something if no one needs or wants it. Data is just as subject to the laws of the market as any other product or service. Everyone in the market needs data: the right kind of data, in the right form, at the right time, at the right quality. And at the right price. This is as true for your internal processes as it is for your external customers.
So intensive market analysis, internal and external, is an important part of the process. Don’t assume the data is valuable. Understand what is needed, and see how that matches with what you can provide.
How should analytics be used?
Analytics are used to create actionable information from data, often data originally collected for other purposes. Various types of data can be combined to reach conclusions that one data type could never support. What data product is valuable is determined by the external or internal user, not simply by the fact that you happen to have that data lying around.
Who has started realizing value already?
A number of companies have already started to monetize their data, both internally and externally.
- Selling knowledge: Pirelli sells tire wear data from implanted sensors to fleet managers, who use it for replacement scheduling and efficiency optimization.
- Using knowledge: GE uses hardware sensor data for internal product improvement projects
- Sharing knowledge: Caterpillar, Komatsu, and other heavy equipment makers empower customers while also working to improve their own products
Prepare to monetize
The cycle of data, market, and analytics is continual, seeking new types of data, new uses for data, and new ways of processing and analyzing data. But the result will be a constantly improving understanding of what data you have, what data the market needs, and how to bring the two together.