According to research firm Gartner, there will be 26 billion connected devices by 2020. Verizon’s The Internet of Things 2015 report revealed that among organizations having integrated Internet of Things (IoT) into their operations, 82 percent reported increased efficiency; 49 percent noted enhancements in product quality; and 45 percent said IoT advances have increased customer satisfaction. But despite the buzz surrounding IoT, we’re only just beginning to hear about actual (i.e., real) industrial or business transformation driven by IoT. With IoT’s ability to extract value from physical assets by making them intelligent, connecting them and using the data they generate to optimize business processes, it’s a wonder why every company hasn’t jumped on the IoT bandwagon. Ironically, one of the most significant gating factors concerning more rapid IoT adoption is the fact that, at least in a business environment, successful IoT implementations are almost always begun as business process automation or business digitization projects with specific business objectives. That is, they are almost never begun as IoT initiatives.

First, IoT is not a well-defined term and can have many different interpretations, depending on your perspective. Certainly, there has been a lot of mainstream press surrounding consumer-focused IoT products, whether that be health trackers, home automation or even connected coffee pots. Due to this, it can often be difficult for those in industries such as manufacturing to think of capital intensive, mission-critical equipment as “things”. After all, these are expensive revenue-generating corporate assets. Even for businesses that do recognize the concept, IoT projects are often thought of in the context of technology initiatives with the goal of connecting machines together and moving data from one place to another.

Instead, companies need to be thinking of IoT in terms of the business value that it can deliver. This is a business-first strategy that is underpinned by technology, but that is not the driving factor. For example, there are many use cases that apply to manufacturing environments. Factory visibility is a key benefit for plant managers, allowing them instant access to the operational parameters and performance of mission-critical equipment. It is now possible to extract and combine data from all manufacturing equipment across locations, regardless of differences in protocols and communications standards. However, viewing that data in a dashboard or mobile application is not enough. IoT can automate decision-making by applying rules-based logic to the real-time data stream. This can be used to synchronize machinery on the assembly line and adjust configuration or calibration settings in response to current operating conditions.

Imagine the typical response to a failed piece of equipment. A technician starts the process by stepping through a set of pre-defined diagnostics and troubleshooting protocols in order to narrow down the possible root causes, locating the necessary parts and attempting a repair. This can be a slow, inefficient process, meanwhile operations are at a standstill. In an IoT-enabled environment, the diagnostic process is automated, eliminating possible root causes through data validation and probability ranking the most likely fix before the technician arrives. The system can also integrate with parts inventories and determine the appropriate skills needed for the repair. This reduces downtime and makes best use of both physical and human resources.

Data analytics also have a place in the IoT strategy of a manufacturing operation. By looking across the entire population of equipment, including historical data, predictive algorithms can identify the leading indicators to a failure, reducing the business impact of downtime. Maintenance and repair of equipment transitions from reactive to proactive, enabling the plant to schedule the work at a time that is best for the business. Advanced analytics can also be used for optimization of machine performance, whether the goal is to increase output, minimize materials usage or reduce defects. By establishing a data model based on desired machine performance, IoT technology can not only identify underperforming equipment, but also provide prescriptive remediation steps. Depending on the environment, these actions can be automated in real-time to the desired assets, eliminating the need for human intervention.

All of this has the potential to improve businesses outcomes, but how does one go about architecting and deploying a complete IoT solution? The first step is to clearly identify the business use case that the solution is targeted to address. For the situations described above, this could be smart diagnostics, condition-based maintenance, predictive failure or asset optimization. In addition to the use case, definition around the scope of the deployment and the desired outcome is required. A common pitfall is to attempt too much on the first iteration. While it is important to have a vision of the desired future state, it is equally important to start small in IoT deployments in order to build confidence in both technology and business value.

Next, recognize that the skill sets needed for an IoT initiative may not reside in your organization. According to a recent Gartner research report, 80 percent of IoT projects will take twice as long as planned due to factors such as poor due diligence, skills shortages or inadequate sourcing practices. In many ways, IoT is the convergence of several technologies, including embedded software and controls, real-time automation and data science. A challenge in the industry is that many vendors supply one piece of the puzzle, putting the selection process and integration burden on the customer. However, firms do exist that can supply both the technology and talent to implement complete IoT solutions. This is an area where experience and breadth of knowledge counts.

Finally, keep in mind that IoT projects are mainly focused on the value of data and by nature require the involvement of many disciplines within a business. In manufacturing environments, this translates into more cooperation between those who manage Information Technology (IT) and Operational Technology (OT) systems. Data ownership and privacy issues also means the involvement of legal and corporate governance groups.

IoT as both a technology and business concept is building momentum and companies of all types can benefit from its virtues. The path to success requires focusing on business value, understanding the necessary components for a complete solution and early involvement of cross-functional teams within the organization.

Dave McCarthy is Director of Products at Bsquare.