Actionable Steps to Prepare for the Industry 4.0 Era

Companies that build the ability to collect, refine, and act on data will ultimately lead the Industry 4.0 disruption. Start building a data-driven organization now by bridging the skills gap.

The promise of Industry 4.0 is centered around the way we collect, store, process, and analyze data to enable better decision-making—at lightning speeds. But we’re not there yet.

Most industrial operators have mastered data collection and storage through their supervisory control and data acquisition (SCADA) system and historian, but they have relatively simple means of processing it into useful insights. It remains trapped within their systems and unused.

An organization’s ability to unlock insights from its collected information relies on its in-house capability—that is, the skillsets of data analysts paired with the specific domain knowledge of the industry. Unfortunately, we’re competing with other industries for the same data science skillsets, in particular the marketing, finance, and insurance industries, which are also trying to squeeze insights from their collected data.

While this skills gap is being filled, organizations can build their data capability and competitive advantage by building a basic understanding of data—contextualizing data, working out how to leverage third parties, and training specialist staff in data analysis.

First, make data everyone’s business

At its core, data must be understood at all levels within an organization—from data collection at a machine on the shop floor, data entry and administration, data analysis and presentation, right through to knowing how to interpret the information to make the right decision.

Contextualize the data

Without context, data becomes meaningless. Structure data so it can be found and understood, and add real-world correlations to data in logs and historians. Data contextualization will reduce the level of domain knowledge required to understand what you have. Providing a consistent model between the different data sources will allow data to be connected at a later date. Being able to link the actions in your maintenance database with the actual real-time performance of the plant is essential to deriving insights on the impact of maintenance programs.

Expect to leverage third-party providers to process data, and make it secure

Sometimes the business isn’t the best place to get insights from its own data. In many cases, vendors and integration solution providers with the data, knowledge and/or install base can derive insights better than the end user. So much so that we’re seeing a transition in the vendor/solution provider service model: They’re broadening their support from scheduled maintenance services and responsive breakdown callouts to include real-time operational monitoring and analytics services.

It’s the low-lying fruit that organizations can take advantage of now while they develop their in-house data capacity for the future.

But in order to achieve this, the third party will need access to internal data. So you’ll need to start planning and developing guidelines around IT security, access, and data ownership of such setups.

It takes time to determine an approved architecture, place it into requirements, and ensure it makes it into the procurement language so that the next piece of equipment or service-level agreement (SLA) can be established with the right base. But it is well worth the time; without this planning, you either miss out or lose control of your own data security to whatever the vendor thinks is best.

Upskill your staff

Instead of waiting for the new breed of data scientists to arrive, then waiting for them to get a clear understanding of your industry sector, organizations should be upskilling their own domain knowledge specialists now.

There is a growing range of analytical tools that are making it easier to take the first steps of analyzing your own data in more complex ways. We have been able to use some of this specialized process data analytics software to lead technical personnel through creating simple models and matching them against operational situations with as little as one week’s worth of training.

Universities are working through the same challenge around responding to the industry gap. We’re seeing more data analytics degrees that are 100% online, have flexible study hours, and focus on recognition of prior learning (RPL). This means that working professionals with strong domain knowledge can complete a degree faster, with fewer obstacles.

Unlocking data’s potential

Despite it being some 12 years old, Clive Humby's revelation “Data is the new oil” is more potent than ever.

If left unrefined, data can’t really be used. Companies who build the ability to collect, refine, and act on data will ultimately lead the Industry 4.0 disruption. And central to this is in planning to acquire skilled data professionals.

Beyond filling this future skills gap, you should be able to start building a data-driven organization now. At the most basic level, every organization should capture data, add context, and plan for it to be analyzed by external providers and upskill specialist staff in-house.

Written by:  Steven Bailey, Principal Engineer, Sage Automation, for Automation World.