The IIoT Data Is Already Here, It's Just Not Equally Distributed

Many enterprises find early success at “proving the concept” of industrial IoT, but fail to realize monetary benefits from their investments. Providing value requires solutions that do more than extract data from machines in the field, store it in the cloud, and display it in an app – which is as far as most proof of concept projects go.

Successful enterprise IoT systems must go beyond the machine to cloud pipeline to gain traction. Achieving business outcomes from connected products requires an integrated flow of information throughout the organization, combining and re-combining data from machines and enterprise systems into insights and actions. Unless your initial “concept” includes solutions for data access and distribution challenges up front, you haven’t proven anything.

How Do I Get IIoT Data from my Equipment Up Into the Cloud?

This is how most industrial IoT projects begin. Depending on the complexity of the machinery, the variety, velocity, and volumes of data produced, and the conditions of the environment and networks in which they operate, the answer can range from straightforward to brutally complicated. In the end, however, secure data collection, translation, and transmission is mostly a matter of expertise in identifying appropriate technologies and applying them accordingly.

How Do I Unlock the Value of that Data?

Now the real journey begins, beyond the manual labor of drilling and pumping (to borrow an Oil & Gas analogy) where the properties of the analog “real” world are pulled into the digital domain as individual bits of data. This data must be cleaned, processed, refined, and combined into new formats – a digital transformation – to be used as fuel for the engines of the business. “How do I use this data to create new business opportunities and generate more revenue for me and my customers?” This is where things get interesting.

Filling the Tank

Gathering information from their equipment enables organizations to become more data-driven, but only if they’re able to properly analyze what they collect. Edd Wilder-James, strategist for Google’s TensorFlow machine learning project and former VP of Technology Strategy at Silicon Valley Data Science, makes the problem statement clear:

“The biggest obstacle to using advanced data analysis isn’t skill base or technology; it’s plain old access to the data.”

After overcoming the technological hurdles of data collection, organizations often fail to deliver systems capable of using it for driving the business forward. His research, published in the Harvard Business Review addresses the matter directly. “Embracing data as a competitive advantage is a necessity for today’s business, so why is it so hard to get access to the data we need?”

First, we must understand that it’s not just machine data we’re talking about. A sensor may report the motor is running hot, but unless your system can combine unit temperature with customer, inventory, and service records to trigger an informed action plan there’s not much you can do beyond flash some warning lights or shut down the device.

To provide real value, your solution must connect to and exchange data with your CRM, ERP, and other enterprise systems. Now your IoT system can trigger workflows such as notifying the correct service technician and having the right replacement part ready, according to the SLA assigned to that particular machine and customer. Unfortunately, enterprise data often exists in isolated silos not easily connected.

So what can be done? While conceding that few companies “have the luxury of building a suitable infrastructure from scratch,” Wilder-James implores them to “figure out a way to get there in an incremental way.”

The Obstacle Is the Way

Industrial IoT isn’t just about connecting machines to the cloud. It’s about connecting your machines to your enterprise and creating a central system of intelligence. It’s about aligning your internal operations and teams with your product offerings and giving your business a distinct competitive advantage.

This requires an open, adaptable system architecture capable of incorporating new machines, data types, and tools over time as your system evolves. The good news is you can start small. “Look to identify high-value opportunities,” Wilder-James recommends. “Analyze your business needs, and choose a problem where data could provide a tangible benefit, perhaps in enhancing sales or creating a preemptive incident response.

Each progressive step should build toward an integrated platform for your enterprise data.” With low-hanging fruit like alerting your sales teams when a customer is running your equipment at 100% utilization every day, indicating a ripe upsell opportunity, and easy to understand predictive maintenance scenarios, IoT provides the perfect driving force for transforming your business.

Priming the Pumps

Every organization has some amount of data trapped in silos. Wilder-James notes this as inevitable. Enterprise applications are “written at one point in time, for a particular group in the company, and are optimized for their main function.” That said, whenever the opportunity arises to replace legacy systems or expand into new areas, there are things you can do to encourage data flow and ease integration issues.

“Every new problem has its unique aspects that usually reach back into data acquisition and preparation.”

By taking into account how data will eventually be used by an application during its collection, you can reduce the amount (and expense) of processing that data downstream.

A more pernicious problem for organizations seeking value through data integration is the specter of vendor lock-in. “Software vendors are among the first to know that access to data is power,” he warns. “That is particularly dangerous with software-as-a-service applications, where vendors want to keep you within their cloud platform.” The bottom line? If you want to be a data-driven company, you need to stay in control of your data with the absolute power to choose how and when your data can be accessed and by whom. Avoid SaaS IoT offerings like the plague.

The Future Is Now

IoT is no longer a technology problem. Microsoft, Amazon, and Google have all delivered services making the collection and storage of machine data secure and scalable. Cloud analytics tools abound, with improvements in machine learning and AI coming at an increasingly rapid pace.

The biggest challenges preventing enterprises from leveraging these advances and realizing the value from IoT today are ones of data access, integration, and management. Transforming data into insights and ultimately greater revenue requires unlocking the silos and bringing together information from across the enterprise (while maintaining consistent IT security policies) so these remarkable tools can do their work. An open and adaptable IoT solution can be the perfect vehicle for your enterprise’s digital transformation journey.