With new technologies from robotics to IoT devices, the manufacturing industry has experienced a drastic transformation. Most of these advanced technologies rely on data to help manufacturers make smarter decisions and optimize their operations. Data-driven manufacturing comes with streamlined workflows, high-quality products, improved supply chains, and optimized resource management, but it doesn’t come without challenges. Whenever you’re dealing with large amounts of data, you also have to think about data management, data centralization, storage issues, cybersecurity, and ensuring compliance. However, it is easy to overlook these issues and focus solely on the positive aspects of data collection.
Here’s how manufacturers can develop smarter, more secure data management strategies that will allow them to gain valuable insight while minimizing the risks that data governance brings.
Breaking Down Data Silos
It is not unusual for manufacturing enterprises, especially those with outdated organizational structures, to experience data silos.
The sheer volume of data generated each day by various endpoints can easily lead to a database flooded with duplicates and errors. When your data storage system is siloed the vital pieces of information can easily fall through the cracks.
With different departments accumulating data relevant to their line of work, it can be easy to lose track of the bigger picture and end up with many different pockets of data, inaccessible to different teams and departments.
Data is only useful if it can freely flow through your organization and can be easily accessed by everyone who has the right permission.
By breaking down data silos, you’ll allow your teams to work cohesively and more efficiently. That’s why every good secure data management strategy starts with creating a unified and centralized data storage system that helps prevent and break down data silos.
While it’s possible to retroactively clean your data by using scrubbing software to make it more accessible and suitable for further analysis, it’s a much better option to start to have a company-wide data management policy from the beginning.
By clearly defining roles, assigning ownership, and giving your employees clear instructions on how to handle data, you’ll have a much more sustainable strategy that will allow you to collect better, more accurate data.
Auditing and Classifying Data
If you want to make the most of your data, it also helps to audit your existing database and determine exactly which types of data you’re storing. Once you audit your data, you will be able to organize it and create a comprehensive data classification system.
Data classification will not only help ensure that your data is well organized and easily accessible, but it will also help you determine appropriate access-level and create a protection plan for each cluster of data.
Depending on the sensitivity level, there are different regulatory requirements when it comes to storing, protecting, and even deleting your data.
The GDPR requires complete transparency and minimal data collection. This means that you clearly have to specify which data you’re collecting, why you’re handling it, how you’ll store it, and try to accumulate as little data as possible.
On the other hand, some of the data, while extremely sensitive, can also be crucial in case any legal issues occur, and might be needed as evidence, so you’re obliged to archive it for a certain period of time.
For example, your communication records can contain sensitive information about your employees, business partners, and clients, you must adhere to a required retention period. In that case, the easiest way to ensure compliance is by using email archiving solutions.
Manual data retention can lead to errors and oversights that can cause serious legal issues, so it’s better to rely on archiving software that allows you to automatically set retention periods and take the guesswork out of data compliance.
Mitigating Security Risks
There are two major types of data security threats. The first one comprises external attacks, such as phishing scams and malware. Luckily, most manufacturing enterprises have no issue protecting against such threats, with many data security tools and software at their disposal.
On the other hand, there are data security threats that come from within the company. While these threats are almost as common and not less damaging than the external ones, internal security risks often get overlooked.
In fact, careless or uninformed employees are the second most likely cause of serious security breaches, right after malware.
It is essential to establish a corporate culture of cybersecurity awareness and educate your employees about potential threats. With clear guidelines and well-defined policies, manufacturers can regulate data storage, control access to sensitive information, and improve protection.
A good data management policy should clearly define safe practices for handling sensitive data, and prevent not only employee errors and leaks, but also ensure compliance with regulatory requirements.
The more manufacturing companies rely on new technologies and analytics, the more vulnerable of an asset big data becomes. That’s why data management is crucial for ensuring this asset stays protected and accessible.
Alex is a passionate tech blogger, internet nerd, and data enthusiast. He is interested in topics that cover data regulation, compliance, eDiscovery, information governance and business communication.