In today’s factories the development of the 4.0 Industry allowed the introduction of technologies aimed at the exploitation of data. The data we’re talking about comes in the form of information about productive processes that are useful to control the productivity activities and react to possible new requests coming from clients or changes related to the market.
Predictive maintenance, resources provisioning and supply chain optimization are only some examples of activities where Big Data is useful to succeed. This considerable amount of data has to be protected and treated in a safe way, given its critical potential for the success of businesses. But, which are the data you should protect and which are the data you should share? We’ll cover this topic in this article.
The introduction of devices belonging to the Internet of Things, that are everyday more common in today’s factories, have brought to the creation of data streams that, if not properly managed, cannot provide the promised competitive advantage. Here’s where Data Governance comes into play. Through what can be defined, in a very simple way, as an activity of data administration, it is possible to understand which are the data that need to be protected and which, on the other hand, need to be shared to optimize the work and the results of the whole productive process.
Data Governance: the meaning behind the term
The more is the amount of data extracted from industrial productive processes, the more is the need for organization required to make them useful: this is the only way to actually become a smart factory. That’s why we talk about Data Governance: to identify, collect, organize, store and, in other words, manage data is a fundamental activity to realize the optimization of business processes. In detail, the Data Governance Framework can be defined as a system of business rights and responsibilities related to the informative processes that work thanks to predetermined and collectively accepted models. These predefined processes describe who can intervene, who has access to what information and when, if needed, proactively act through predetermined procedures.
Data that needs to be protected through Data Governance
GDPR is the recently introduced general regulation on personal data protection that every business had to implement. The regulation defines that personal data collected from users have to be protected, which means that they cannot be published or become accessible to third parts without a previous consent explicitly released by the people involved. What’s the definition of personal data? GDPR includes in these category of data all the information that directly or indirectly identify or allow the identification of a physical subject, which include data that “expresses physical, genetic, mental, commercial, cultural or social identity of those physical subjects”. For example, the phone, the credit card, the account data, the license plate, the client number or the address are all personal data. There’s more: the term “personal data” has to be interpreted as widely as possible, so less explicit information have to be included too, such as work hours registration (that include information about the moment a worker starts and finishes his work day) or an user’s IP address. So, every time you have to deal with personal data, it is mandatory to implement a privacy information notice that users have to agree to.
The data that need to be shared through Data Governance
When dealing with productive processes it is very unlikely to collect personal data. Data coming from machineries are in fact not defined as personal data since they’re not related to people. With the introduction of Big Data (big streams of data regarding the owned machineries and processes), business of all sizes are exploring the way data analytics can help them make strategic decisions and obtain a competitive advantage. Considered its crucial function, data needs to be protected from external attacks, but at the same time has to be shared within the business. The Data Governance activity needs to be therefore able to:
• standardize processes
• track data
• protect data from external attacks
Considered that the amount of data can be really huge and that the data con refer to multiple departments within the productive process, businesses have to become able to build a collaboration framework among the business’ teams by obtaining not only the consent from the upper management, but from every worker: everyone should feel included in a data-driven business culture. An example of data sharing from a Data Governance point of view can be made if we think about the pharmaceutical sector. In order to realize a determined product, raw materials have to be stocked inside the reactors, which are constantly monitored with probes. Until recently those probes were not connected with each other: this means that a worker had to manually verify the activity measurement data and only after that define the orders for supplies. Today, thanks to the IoT, the network and Data Governance it is possible to make probes automatically collect and send data – through encrypted protocols – to the control room that manage the productive process. But that’s not all: those data can be accessible through the business cloud, allowing for example the visualization to the management board, even if it is located on the other side of the world. This is a good example of data management and sharing that can consistently impact on the productive processes.
Big Data, Big Data analytics, Data Governance… The solutions coming from the 4.0 Industry are defining a future in which technology will become increasingly important in terms of processes optimization, security and cost reduction. In a context where the data collection and the correct usage of the information obtained, specific roles such as data scientists are mandatory, since the “science of data” will become a crucial factor in the determination of a business success. If you want to implement an Industry 4.0 strategy, but you don’t know where to start, click con the image below and read our free guide “5 steps to develop an Industry 4.0 project”.