Mastering data governance initiatives in the age of IIoT

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The Industrial Internet of Things encompasses internet-connected devices that companies use within their organizations.
Improved data governance is a necessary goal to strive for regarding the broader Internet of Things, but it is exceptionally important for the industrial realm.

One mistake companies often make is that they collect data with IIoT devices without having clear goals for using that information to support business objectives. In other words, they might start gathering data because such an option is available to them but not know how to apply it to benefit the company because they have not thought so far ahead.

In contrast, when companies invest in IIoT devices after formulating well-defined data governance policies that dictate the future use for the information, it should be easier for them to remain competitive. That's especially true when competing against businesses that have not prioritized data governance.

More is not necessarily better when it comes to data collection practices. Instead, companies must look at how the information they get could translate into more value for their organizations and customers. That strategic approach should help them maintain competitiveness regardless of their industries or focuses.

Organizations could think about the most pressing problems that cause difficulties for their business as a start. The next step is to figure out how data from IIoT devices could address those challenges.

Data analysis is one of the trends shaping the IIoT, and that reality will likely continue into the foreseeable future. Now, companies are evaluating how they can use information instead of just collecting it. That change of thinking could increase competitiveness, especially when data governance policies are established right away.

Lacking a Solid IIoT Data Governance Policy Could Reveal Proprietary Information

Most IIoT gadgets both send and receive information about processes that occur within the scope of the businesses that use them. However, concerning distributed data, companies must ensure it doesn't reveal information to recipients that could highlight trade secrets. For example, many IIoT sensors track various actions that happen in assembly lines.

If recipients can extract details from information that tells them how companies go about making their products and what helps them stand out, businesses will discover their operations are not sufficiently locked down from outside parties. While some of those entities might not seek gain from the information, others may try to mimic certain practices.

When that happens, the increased competitiveness mentioned above becomes less prominent and may no longer be relevant at all. However, keeping sensitive information secret is not straightforward. That's because it takes substantial forethought to figure out how to spend money on IIoT equipment that works seamlessly together.

Besides ensuring those gadgets work together to achieve larger aims, companies must also investigate ways to safeguard the data so it doesn't reach outside parties that shouldn't see it. When creating a data governance policy, businesses need to incorporate all IIoT devices currently used, as well as update those guidelines if new equipment gets added.

Proper Implementation of IIoT Technologies May Lead to Better Quality Control

One of the advantages of the IIoT is that it could automate many tasks formerly handled by humans, and do so quickly. When that happens, it's easier to stay on top of necessities like quality control. For example, IIoT-enabled equipment could automatically calculate the burst pressure of a pipe or tube, whereas it might take an employee several minute to make this calculation, especially if the formula isn’t one he knows off the top of his head.

Such knowledge is essential for companies to have to maintain the quality of the items produced and, in some cases, avoid emergencies in the future. Moreover, bringing automation into the picture reduces the chance of mistakes.

However, any efforts to improve quality control could also become ultimately ineffective without data governance. That's because data governance could lead to enhanced future decision-making that boosts the effectiveness of the items produced. That's only true, though, if there is a well-defined data management strategy in place to handle huge amounts of information.

Data governance helps tackle issues like duplicate or incorrect data so the people who analyze the information and put it to use can feel confident in it, especially when reporting to corporate leaders. When the data is trustworthy, it's more valuable for applying to decision-making, whether for quality control improvements or otherwise.

Data Governance Supports Cybersecurity Measures

It's crucial for companies of all sizes to be aware of cybersecurity risks and to realize that reliance on IIoT equipment could increase the number of attack points for hackers to exploit. Although data governance relates to factors other than cybersecurity, it supports better overall security at an organization.

Prioritizing cybersecurity puts companies at a reduced risk of hacks that could disrupt business operations and cause reputational damage. Data governance goes hand-in-hand with cybersecurity by restricting access to sensitive data and maintaining better consistency when working with the information, among other things.

Industrial cybersecurity is the responsibility of everyone who works for a company that uses IIoT equipment. People need to take risk-based approaches to keep things secure and follow industrywide best practices. However, things like organization size and structure could affect how data governance fits into a cybersecurity plan, too.

Companies Cannot Overlook Data GovernanceThis overview emphasizes why businesses must implement data governance during any proposed or actual efforts to use IIoT equipment.

Being mindful of data governance needs immediately lets companies avoid going back and altering their policies later after shortcomings become apparent.

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