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The advanced guide to the implementation of Big Data Management in 4.0 Industries

Laura Pisano,

 

The advanced guide to the implementation of Big Data Management in 4.0 Industries

With the fourth industrial revolution, Big Data Management, which can be defined as the activity of managing the considerable amount of information coming from the dynamic productive processes of smart factories, is becoming a topic of increasing importance in a context where a growing number of businesses is trying to be part of the Industry 4.0.
Using intelligent automated systems to allow communication between machineries and productive environments is a complex process, but how can you implement with success a Big Data Management system from a Industry 4.0 point of view?

Let’s see how it’s done with this guide to the implementation of Big Data Management in 4.0 Industries.

 

 Discover how to develop an Industry 4.0 project in just 5 steps!

 

Thanks to Big Data Management, your company can today become a real smart factory and optimize the dynamics linked to digitalization, artificial intelligence, Internet of Things and cloud computing, thus being able to obtain performance improvements in a large number of areas: administration, marketing, but also workflows.

Big Data Management and Industry 4.0: the evolution of industrial technology

Thanks to Big Data Management, your company can today become a real smart factory and optimize the dynamics linked to digitalization, artificial intelligence, Internet of Things and cloud computing, thus being able to obtain performance improvements in a large number of areas: administration, marketing, but also workflows.
Industry 4.0 and Big Data Management are the next step in the evolution of industrial technology and managers, executives and data scientists have now at their disposal an incredible amount of real-time information that can be used to improve the efficiency and the productivity of every operation. In the fourth industrial revolution, in fact, the main role is played by robotics, cloud computing and the Internet of Things (IoT), that allow to reduce the storage and data management costs with the automation of most of the productive processes. Let’s see now which are the different phases of a Big Data Management implementation strategy.



1. The importance of the sources used in data collecting

The first thing you have to think of in order to have success in Big Data Management is to analyze the sources used to collect data, that are necessary to optimize the interaction among machineries, software and internal and external stakeholders.

The information stream generated by the different devices and the IT department is immense and uninterrupted, especially if we consider that to it has to be summed up with the data coming from the Internet, social media and e-commerce platforms. But that’s not everything: in today’s companies, data come also from sensors, chatbot, various tracking systems and customer care channels, with all of this sources constantly evolving and bringing information that allow the realization of predictive analysis to optimize all the business processes.

Being able to manage, analyze and understand which are the principal sources for your business data is the first step towards a successful Big Data Management strategy.



2. Correct data interpretation is key to an optimized usage

An efficient communicational exchange implies that the two interlocutors speak the same language. Not being able to correctly decipher and decode the Big Data in a 4.0 Industry context means to make the dialogue between different machineries impossible.

Imagine having to manage terabytes of information without a Big Data Management integrated system and having to work with different file formats that are not compatible with each other: the result would be total chaos, even if you have an ETL software that identifies the file format, extracts it and translates to transfer it to other devices.
It also often happens that the data is not structured and does not integrate in the company database, especially if it comes from e-mail messages or customer care communications. Working on the normalization of data is essential to allow the productive ecosystem to speak one single and shared language.



3. The need for a storage fit for Big Data Management in the Industry 4.0


To think from a Industry 4.0 and Big Data Management perspective means to focus also on the storage infrastructure of your company, trying to realize an IT architecture based on business data flexibility and portability.
Until now, IT departments were focused on data storage on their Data Center, while the new 4.0 Industry context makes this effort useless: data is now useful as close as possible to its source, which means as close as possible to the machinery, the sensor or the IoT device that generated it.
For this reason, the implementation of Big Data Management in your company needs to start from choosing a storage that allows dynamism, portability and flexibility to your data, which can be achieved, for example, with a cloud storage system.


4. Big Data Management, Data Science and Data Intelligence


It’s not possible to fully cover all the aspects of Big Data Management if we don’t consider Industrial Analytics, which are all the techniques and technological tools that support the business in every Business Intelligence, Data Visualization, Simulation and Data Analytics activity. These systems are all necessary to support the company in finding the right data and all the hidden meanings behind it by working in a way that is similar to the Big Data Management basic mechanism: elaborate the data coming from IoT systems of the production line and the supply chain that are connected to the IT and OT systems.
In the 4.0 context every data is analyzed to improve the productivity capacity, the efficiency and the business continuity, allowing to find new business opportunities that can be found in the after sales phase or in the realization of new products or services.
It’s important that you understand that Big Data are not static nor predictable, so the system you use for Big Data Management has to be flexible and dynamic enough to allow your company the use of real-time data that can be used for both interactive analysis and AI, Machine Learning and Data Simulation systems alimentation. This is the most advanced and effective phase of new Data Science.
It should be clear by now the importance of adequate storage structures even in Data Science, since the traditional infrastructures represent today an obstacle for the development of businesses in the Industry 4.0. A solid storage system is the space in which data live and the base for your Big Data Management activity, plus it ensures the ideal structure for your company in terms of speed, performance and security.


Industry 4.0 and Big Data Management: the change starts with you


You have know understood how Big Data Management implementation is essential in the Industry 4.0 for optimizing the work organization, generating advanced provisional models, optimizing production process and much more.
The only way to fully exploit all the opportunities that these new technologies can offer is to have an holistic approach to change, a strong leadership and the full understanding of how every data can influence business processes. To this day, a lot of industrial businesses settle for perfectly functioning tools, that are however unable to be used for the innovation purposes of the industry 4.0: choosing to adopt new technology and trying to obtain successful results through it means that you have to change your mindset first.
In order to obtain all the benefits that come from the Industry 4.0 and the perfect implementation of Big Data Management it is necessary to make significant changes that have to be made in small steps, but that lead to a final result that is every day more appreciated by leaders: learn how to do it with our free E-Book "5 steps to develop a Industry 4.0 project"!

 

Industry 4.0 guide

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