Big Data: How Data Changed The Production Process

Big Data: How Data Changed The Production Process

Big Data and the production process: The fourth industrial revolution has arrived, and companies that do not adapt to new market dynamics tend to eat the dust of competition.

In this way, the internet of things, robotics, artificial intelligence, and other technologies have become standard practices in daily corporate life. Among the various innovations available, we will discuss big data in industry 4.0.

Do you know what Industry 4.0 is? The phenomenon is explained by the implementation of non-traditional technologies for managing innovation, optimizing processes, and improving the efficiency of the productive capacity of the business.

In this context, big data companies are increasingly gaining relevance and competitive intelligence in the digital market.

Thus, only the implementation of enterprise big data can deal with all this information strategically and efficiently. Want to understand better how this works? Let us explain!

What Is The Influence Of Big Data On Industry 4.0?

More and more companies need to find ways to store, interpret and generate insights from this diversity of information dispersed in clouds to make the decision-making process more assertive and effective.

In this way, the digital transformation that characterizes this new era in which we live has made traditional tools for collecting and reading data, such as Excel spreadsheets and business intelligence (BI) platforms, obsolete.

Therefore, in this context, big data’s influence in Industry 4.0 arises. After all, only this technology can deal quickly and reliably with all this volume of records and with the current speed of information circulation.

Theorists designate 5 V’s to characterize big data :

  • The volume responds to the large data capacity that the technology can store.
  • Speed represents the agility with which the technology can collect and process the records.
  • Veracity indicates the ability to filter the quality of the correct data used.
  • Variety responds to the different sources of records, which can be internal and external.
  • The value represents big data’s relevance in generating buy and innovation for organizations.

Therefore, all these characteristics guide how companies should deal with strategic information and make the industrial process more efficient.

What Are The Examples Of Big Data In Industry 4.0?

Several examples of big data analytics in Industry 4.0 can help your business reduce costs and become more profitable.

The practice, in the end, consists of cross-analysis of data from different sources (for example, inventory control x product sales indicators x) aimed at delivering intelligent solutions through measurable metrics and indicators.

Thus, the primary data sources that support the operation of big data in Industry 4.0 are:

  • Social Data: is the data collected from user interactions on social networks, Google searches, and actions with other company digital channels. It is mainly through them that it is possible to develop the customer journey map, capable of tracing the target audience’s consumption patterns and behavioral profiles.
  • Enterprise Data: these are inputs made available by the company at all times, such as human, financial, and productive resources data and other records. They are fundamental to aligning the company’s operational capacity with current demands.

Let’s give a practical example of how the cross-analysis of these two sources above can optimize the productive processes of companies that use big data.

Integration Between The Shop Floor And Business Intelligence

Imagine the following situation: the productive sector is momentarily oriented to intensify the production of a product x according to a previous analysis of the needs of the brand’s customers.

However, a new analysis by the commercial team, also supported by big data and artificial intelligence for sales, collected important metrics that point to a greater tendency of purchases of product y to the detriment of x.  

To reach this conclusion, data automation tools were used to monitor information on social networks and internet portals, observe sales numbers, and diagnose internal performances.

Therefore, the automated crossing of all this information guides the productive sector to reformulate its operating strategy, investing more in producing products y. The example also highlights the need for real-time records monitoring and periodic diagnostics for constant process alignment.

Also Read: How To Use AWS For Big Data?

Techno Team

We are a power packed team of cults who love to explore new technologies and bring all latest news to our viewers.

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