Industry 4.0 is evolving with a quick pace, by adopting new trends and tendencies, such as improved human-machine cooperation, sensor technology, IoT, and Artificial intelligence.
However, if we look in more detail, every trend is closely related to data collection, analysis, and interpretation. And all of them are being adopted by industrial enterprises with the purpose to enhance monitoring, evaluation, decision-making, risk analysis, and planning processes. But what sits in the foundation of data analytics?
The most straightforward answer is “Data collection and storage,” as exactly Data Lakes provide numerous benefits for storing industrial data.
So, let's see in detail what are those benefits and why Data Lakes have become a trend in Industry 4.0.
The collected data don't have to be processed, organized, nor filtered.
As Data Lakes do not have strict structure, shape, or composition, they enable the collection of all kinds of data: those include unstructured, semi-structured, structured, relational, and others.
What is more, all these data can be collected simultaneously from all types of devices, such as machines, IoT networks, sensors, and WiFi access points. They are being stored at a single place, in their original, raw format, without any processing, organizing, or filtering.
On the other hand, as Data Lake does not support any definite structure or schema, the collected sets can be easily changed, without imposing any risk that the data storage will be compromised.
In addition, there is flexibility in the extraction of data, as many different tools can be used. So if a particular data set is extracted from the Lake for analysis, raw copy of the same set continues to be stored in the cloud, where it is once again accessible and can be reached, if needed.
For example, if the machine supervisor needs to have access to specific type of information, he can simply enter the Lake and extract the data he needs, without compromising its raw copies in the cloud. This way, the next person who will need the same data will be able to extract it in its raw format and synthesize it in a manner suitable for him. And yet, raw format copy will still remain present in the Lake.
Data democratization
Another benefit that Big Data Lakes provide to the business entities operating in the Industry 4.0 is the fact that all the collected data, which is stored, is fully accessible to all the internal stakeholders of the organization. In other words, data democratization is allowed.
By granting access to all the company's internal stakeholders, including the various departments, machine supervisors, team leaders, key partners, and management, the communication within the enterprise is facilitated, the tasks of the different departments are optimized, and any unnecessary functions are forgone.
For example, if the quality control supervisor of an industrial plant needs to have access to a particular set of data, with Big Data Lake, it will not be required for someone else to prepare data report beforehand. As a result, time and cost are saved. Time, because if someone else has to prepare the data report, this process will delay his other daily tasks, while the quality control supervisor will lose time waiting. And cost, because time is money; thus, by optimizing and foregoing duplication of functions, the business works faster and more cost-efficient.
Data democratization benefits both centralized and decentralized companies operating in the Industry 4.0. It gives transparency to all the operations within the organization and establishes a level of trust between the different departments and subunits. In addition, the management has a continuous overview of the processes, based on unbiased, uncompromised information. This allows the company leaders to make data-driven and evidence-based decisions and plans, as well as to assess future risks.
Real-time decision-making
If a company uses data-warehouses to store its collected data, it has to be pre-filtered, optimized, and structured. Therefore, the information has already been processed and prepared to be used for a certain goal. In other words, the purpose of the data is defined before it is collected and stored, however, this takes away the opportunity to make real-time decisions on the data purpose.
Contrary to the data-warehouses, Lakes collect and store information without pre-defined purpose. This way, if the business entity identifies new opportunities in the motion of its operations, it can instantly pick consistent, yet raw data from the Lake, and analyze it according to its goal.
Data Lakes support not only SQL (compared to data-warehouses) but multiple complementary languages in order to satisfy any advanced analytics requirements. What is more, sophisticated algorithms are utilized to structure large quantities of consistent data and deliver high-quality analysis. As a result, no time is lost in collecting new, goal-oriented data-sets.
The opportunity for real-time goal-setting in terms of the collected data is essential when it comes to quickly changing business environment, such as Industry 4.0. By constantly having industrial data on hand, the management can immediately analyze any information regarding supply chain, demand, operations, sensors, IoT, and many others. Moreover, it is flexible to quickly identify and tackle critical issues, challenges, and react to changes in technology, customer requirements, and supply chain operations.
Inexpensive to collect
Storing various types of data, by processing, filtering, and categorizing it beforehand is expensive, and all those structured data require structured storage. It is not surprising that such storages are costly, especially when the data quantities are huge and constantly increasing.
As Lakes are highly flexible, they are low-cost, yet efficient. They make data collection inexpensive, as this decreases the overall yearly costs, contributes to higher ROI, and enhances the revenues. According to a study conducted by Aberdeen, organizations who implemented a Big Data Lake had 9% higher organic revenue growth, compared to similar companies using alternative storages.
Conclusion
Having in mind the mentioned 4 aspects of Data Lakes, we can conclude that they have become a trend within the businesses operating in Industry 4.0 for a reason. Having huge quantities of easily accessible and flexible data, which can be customized immediately and for any purpose, provide numerous advantages: enhanced monitoring and control, competitiveness, optimization of tasks, lowered costs, flexibility, transparency, and growth.
