- November 21, 2019
- Posted by: admin
- Categories: Big Data Analytics, IoT
Industrial IoT (IIoT)
With more than 86 percent of businesses increasing their investments for the Internet of Things in 2019, it is no wonder that IoT is the foundation for global digital transformations today.
But what is set to grab major headlines 2019 onwards is Industrial IoT or IoT?
IIoT is the new wave of industrialization – Industry 4.0. Powered by big data analytics, Artificial Intelligence, machine learning, and advanced smart devices, IIoT is all set to bring in major changes and open up vast economic opportunities in the fields of manufacturing, supply chain, and various other industrial processes. Analysts have estimated an economic impact of IIoT which might cross $14 trillion over the next 12 years. (Source)
However, analysts feel that for returns-oriented investments in Industrial IoT, a well-defined strategy seems to be missing at present.
Enterprises need to clearly outline their data strategy
It is crucial to implement proper strategies to extract data correctly and benefit from actionable insights. This is because Industrial IoT involves heavy investment, so the failure of analytics strategies can turn out to be expensive.
For that, companies first need to plan which data to collect for analysis. There are certain critical considerations for planning their data strategy, like:
- Keeping the outcome in mind – With a view of their specific needs, companies need to understand which data-driven insights are useful for them.
- Determine business objectives – Along with determining their specific goals, the company also needs to plan how they will use their data for achieving those business goals.
- Inspect any data gaps – A gap in data can play spoilsport in the Industrial IoT world – it can impact outcomes and trigger errors. To prevent this from happening, it is important to ensure how to plan seamless data flow to a central repository and keep the data secure.
- Back calculations – Businesses need to find out what kind of data must be extracted to achieve a certain outcome – for this, backtracking is a way out. Identifying the correct nature of missing data can be immensely profitable because companies can fill in gaps effectively thereby attaining the desired results.
Adopting a model-driven strategy
To outline or define their data strategy companies, a model-driven strategy for IoT can be of great help. In this approach, specifications of the IoT system, the plan of data implementation and data analytics visualizations are all displayed clearly to outline the context between them. This will help the company’s data experts to validate the entire functionality of the system by simulating how end-users will interact with it. It will also bring to the surface any underlying defects that need to be addressed and fixed immediately.
With Industrial IoT focusing mainly on data analytics, experts have found that there is a gap in the utilization of this data that is being extracted from IoT devices. Most companies have managed to utilize 10% or less of this data for their business benefits, according to reports by McKinsey. (Source) But with proper strategies, this gap can be bridged to leverage the full benefits of IIoT.