The Internet of Things is creating massive opportunities across line of businesses. Much like the commercial internet, the IoT is set to transform virtually all existing business model and vertical industry, creating new opportunities for all players. Availability of new data sources and analytics tools will define the contours of the co-operate IoT roadmap. However, current expectations among businesses that stand to benefit from these opportunities are more guarded. Many leadership teams are unsure of what IoT means, how it differs from the dynamics of the past and how to define new business strategies. The collaboration and mastery of new skills, such as those within the IoT will be essential to business success. An organized, disciplined approach to moving forward with IoT is essential to that success.
Businesses need to define their IoT strategy and goals. Doing so will determine how quickly they need to move and how they balance the risks associated with market leadership against the risk for followers and late adopters. No single enterprise possesses the full range of skills required to address the challenges associated with IoT. As a result effective IoT strategies will necessarily involve some degree of participation in alliances and business partnerships. IoT concepts are different from the usual business, this calls for new pools of expertise. Partnerships may be sought and some businesses might create an “IoT Centre of Excellence- COE” to leverage expertise across multiple applications and business units, apply consistent change management techniques, evaluate implementations projects both internally and among competitors. The COE helps to
· identify and apply IoT best practices
· enable change management
· re-think business models
· manage human resources
· evaluate IoT maturity assessment and IoT governance
Despite the networked nature of IoT solutions, IoT is not IT. IoT is more like industrial technology, or operations technology than information technology. It’s for this reason that IoT occupies a different domain than IT and why the IoT buying process is decentralized. IoT and IT must Collaborate and Coordinate. In today’s corporations, IoT is a collaborative effort by three different organizations,IT, the business units, and the primary value creation functions such as operations, manufacturing, marketing, and engineering. An enterprise must define its vision and overall IoT strategy before embarking on an IoT Journey. It is important to evaluate motivations. Are motivations and interests purely economic, or are they for societal benefits? What is the overall market and competitive environment in which the business plans to engage? What possible change will occur over the years? This strategy should reflect the extent to which the enterprise plans to shift towards IoT and the timeframe at which the shift will take place. Some enterprises will invest limited resources in adoption, other see it as a monumental shift and have invested massively in IoT programs. Each strategy must set out a vision, goals and guiding principles appropriate to the enterprise’s view point to shape its overall approach to IoT. When the strategy is perceived to be important, it can be beneficial to appoint a senior stakeholder from within the enterprise management team to lead and co-ordinate the IoT initiative across the enterprise. This ensures consistency and minimizes chances of pursuing conflicting goals or investing in less than optimal supporting infrastructure.
Enterprises will also need to define appropriate guiding set of principles for collaborating with partners and working within the overall IoT ecosystem. They will need to identify the capabilities they are likely to need to engage in industrial iot projects. It is best enterprises adopt a well-defined and clear approach to IoT and appoint a dedicated leader to navigate the IoT journey to improve chances of successful outcomes.
In general, IoT systems consist of six essential stages and technologies. Enterprises need to understand these components to successfully manage any IoT projects.
1. Cold- path analytics: Most enterprises lack the expertise to fully exploit IoT generated data. To do this, it is necessary to engage the services of machine learning experts to analyse a collection of data that are stored and in silo. “siloed” data are data available to a particular group or unit in an organisation. Exploiting this data helps identify patterns and trends for later analytics.
2. Device, Sensor, and hardware management: This is an essential part of IoT projects. Enterprises must identify sensors that work well with equipment. This includes ability to apply upgrades, security settings and also work with other physical tools that may be available in the hardware component.
3. Gateway and Edge communication: Sensors will need to communicate with processing platforms known as IoT platforms. The data can either be processed on the cloud or edge. Enterprises are starting to make decisions using edge computing. Edge computing is a system that allows data to be processed close to the device generating the data. It allows for faster analysis on data.
4. Hot path analytics: Hot path data involves known trends and patterns. This means a set of data has been exploited i.e cold path analytics to understand what insights and happenings are obtainable. With this identified, machine learning algorithms can be applied on sensor data.
5. Data storage and advanced analytics: Data collected into an IoT system is kept for longer periods of time. This allows for development of additional analysis and insights. Usually, data is exploited periodically for additional insights that may be used for reporting and development of complementary algorithms.
6. Reporting and dashboards: IoT data will need to be fed into reporting and dashboard tools to provide visual representation of status, happenings and impacts on the business.
Many IoT use case require significant amount of data to understand patterns compared to normal operations. When initiating an IoT project, enterprises should begin with a data project. A proper check on the frequency of data collection and the IoT goals and objectives should be considered. For instance if the goal requires leveraging predictive maintenance by identifying anomalies but the equipment provides data only few times in a day, the goal doesn’t match the operation. Predictive maintenance use case is a time series intense situation. Data should be gathered in seconds. Having a small amount of historical data isn’t sufficient to create machine learning models to support IoT goals. Enterprises will need to start with the data and sensors available to create the initial cold path analytics and machine learning models and then add additional sensor for more data.
A major challenge in IoT implementation is the “Hardware’’ part. Usually, questions like how many things need to be connected, how many sensors arise. Enterprises should consider the problem to be solved. IoT is not a solution looking for problems. Considering the questions of hardware above make IoT a solution looking for problems and not a solution to existing problems that it is. Goals can be broken into smaller goals. For instance, a manufacturing company looking to leverage Industry 4.0 in Nigeria- predictive maintenance use case- might be considering connecting all the equipment on the shop floor at a go. This makes the project cumbersome, complex and risky. If one or two critical assets combination can be considered first, the use case is proven, additional equipment and sensor can be added to gain more insight.
Successful IoT projects focus on business problems and innovation can be driven leveraging the technology. The use case first approach is a good way of measuring a successful IoT project.