Industry 4.0 is all about the Internet of Things and Big Data Analytics. Analysts across the board are talking about the enormous economic opportunity of Industrial IoT, predicting 30 billion connected devices by 2020 and a potential impact of more than $14 trillion over the next 12 years.

According to Forbes, Industrial IoT data analytics is consistently a top driver for why companies invest in IoT. Companies look to data for improving product performance, making data-driven product decisions with a better understanding of their customers, and gaining visibility into system usage.

But while the hype surrounding IoT is focused on data analytics, the reality is that most companies are only getting a fraction of the value from the data of their connected systems. A study by McKinsey found that 54% of companies utilized 10% or less of this information.

This gap between data expectations and reality may seem extreme, but it can be bridged.

Understanding the data challenge

How do we explain why in a gas rig with 30,000 sensors, only 1% of data is being used for decision making? Or why Industrial IoT data management remains a leading challenge for companies year after year?

To answer these questions, we first need to understand how we got here.

IoT development and adoption has surged dramatically in a very short time frame. In the past 2 years alone, almost 5 billion new devices were connected worldwide.

And while the growth of IoT has been drastic, many of the early IoT adopters have been taking a “makeshift approach to their early IoT initiatives.” Companies are moving forward with industry 4.0 initiatives such as predictive quality and predictive maintenance, without an Industrial IoT framework for supporting it or the right partners to ensure successful implementation.

When it comes to data, a clearly defined strategy for how to collect the right data and get actionable insights from it, is rarely in place prior to product development.

Define your data strategy

The price tag of Industrial IoT or industry 4.0 is too high for companies to fail at extracting greater value from its data.

Ahead of deciding which analytics will be applied to production line data, companies must first ensure that they will be collecting the right data to analyze.

To do so, we have to rethink how to formulate a data strategy at the outset of industry 4.0 planning.

A successful data strategy must take into account 4 critical things:

1. Business goals- What are the benefits you want to get from your IIoT initiative and how do you plan to put your data to work for achieving those goals (e.g.: improved quality, reduced unplanned downtime, increased throughput)?

2. Start with the end in mind Which data-driven insights will you need to extract in order to fulfill your needs? What will the dashboards look like?

3. Work backwards- What data needs to feed into your analytics in order to get to the data-driven insights?

4. Assess and define the data gap- What sensors do you have in place, and which are missing? How do you get the required data to a central repository at the required intervals and how do you get your PLC, SCADA, and data historians to communicate via the internet protocol while keeping it secure?

A model-driven approach  

Defining an Industrial IoT system’s functionality and its data strategy simultaneously requires a model-driven approach to IoT. This approach creates context between the Industrial IoT system spec, its planned data implementation, and visualization of data analytics – by displaying them all within the model.  

Such an approach allows you to simulate how users will engage with the system, in order to validate the system functionality and address any issues ahead of development.

Interested in learning more about how to leverage a model-driven IoT platform to get the most value from your data?

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