Data governance in digital manufacturing has been a challenge for organizations preparing for Industry 4.0. It becomes urgent and complex in businesses transitioning from a traditional industrial model to a smarter approach.
By adopting disruptive technologies such as Big Data, Machine-to-Machine, and the Internet of Things, industries begin to deal with a massive volume of data. And this informational contingent needs to result in relevant decision-making.
The shift toward what has come to be called the Fourth Industrial Revolution, when conducted correctly, helps reduce costs, increase productivity, and deliver a series of other benefits. Therefore, data governance becomes highly important in manufacturing.
So, how can you prepare for this new reality? Read on to find out!
What does data governance mean in digital manufacturing?
In general terms, what we call data governance is the management of the availability, usability, integrity, and security of data used within a company. Solid initiatives include a governing body or board, a defined set of procedures, and a plan to execute them. Therefore, it is a strategy.
Consequently, data governance in digital manufacturing is nothing more than total mastery over the volume of data generated, processed, stored, and controlled in industries that have already understood the value of digitizing their processes.
How to do it?
Industries looking to prepare to map information flows and data analysis techniques already have a series of methods at their disposal. These are guidelines that facilitate governance and pave the way for better results.
Check out what major IT research institutions, such as Gartner and Capgemini, suggest below!
1. Recognize that data is not solely the responsibility of the IT team
It is essential to rethink business leadership in light of proper data governance. This should not be a concern exclusively for the IT team, since digital manufacturing goes beyond technical details.
The entire organizational structure must be configured for strategic data management, including defining roles for everyone involved in day-to-day operations, as well as in the use of informational flows for business development.
2. Establish how data should be managed
Another important step is the formulation of a clear vision on how the data strategy should be managed. This involves hiring a team of experts to design and execute the strategy.
Often, this guidance cannot be handled by the internal team, making it necessary to hire experienced vendors (consultancies, for example) on the subject.
3. Rely on COBIT 5 to align IT with business objectives
Establishing good information technology control to ensure it consistently contributes to operational and business objectives is also very important.
A great way to do this is to anchor your data governance strategy in COBIT 5, the framework developed by ISACA to help companies govern and manage IT efficiently.
After all, COBIT 5 helps tackle major challenges in regulatory compliance, risk management, and the alignment of IT strategy with organizational goals.
4. Identify tactics to drive data management
Another fundamental step for implementing data governance in digital manufacturing ideally is identifying ways to boost the strategy. In other words, for the initiative to be reliable and yield tangible returns, it is necessary to think about the required processes, procedures, and guidelines.
Security protocols, for instance, will need to be adopted to ensure that no industrial information is leaked or compromised by malicious actors.
According to Gartner, security is the most significant data governance challenge for organizations planning to adopt or currently implementing IoT solutions.
“The event-driven nature of data in many IoT implementations — constant streams of the same sensor readings, for example — means that typical data retention policies — usually keeping everything stored — are less effective. Therefore, a different philosophy is required regarding which data to keep and which to discard.”
5. Encourage leadership proactivity to identify bottlenecks
Leaders must also act proactively in identifying gaps and weaknesses in the data governance strategy. Latency, service downtime, lack of applications or equipment. All of this must be under control to drive the strategy forward.
6. Modernize key areas
Finally, experts point out the need to modernize areas that are fundamental to data governance. This movement involves, for example, accelerating the adoption of technology-related skills and roles.
The roles of Chief Data Officer (CDO) and Chief Analytics Officer (CAO) are examples of how digital technology reaches a more sophisticated level in organizations that elevate their data governance in manufacturing management, as they introduce new working methods and concepts that make the strategy more effective.
Considering the tips gathered here, do you believe your industry is on the right track for data governance in digital manufacturing? To avoid missing upcoming posts and to follow more insights on the subject, subscribe to our newsletter!
