Big Data in the Industry 4.0: How to Turn Data into Profit

By: Gabriela Pederneiras  1/14/2019 

 Industry 4.0 is already present and active. In 15 years, the forecast is that it should move around $ 15 trillion. In some industrial segments, the innovations brought about by this concept will have the potential to increase production volumes by 2050 and reduce costs by up to 13%, according to a report released by BP .

One of the pillars of the fourth industrial revolution is Big Data, which is nothing more than the collection and understanding of a large volume of data. This information can either be organized in numerical and non-ordered tables, having various formats such as audios, videos, written documents, payments, among others. What matters is that they can be brought together, organized, analyzed and that the result is insights for the industry.

According to a survey conducted by PricewaterhouseCoopers (PwC), which interviewed more than 2,000 companies from 26 countries and different industry sectors 4.0, 72% of them feel that using Big Data and analyzing this information will improve the relationship of industries with their customers. In addition, 86% of respondents said they expect to have lower costs and higher revenues over the next five years because of Industry 4.0 and the new concepts it has introduced into the market, such as Big Data.

How Data Analysis Works in the Industry

The benefits of Big Data for the industry are countless. At all times the production and commercialization of industrial products and services generate information that can be analyzed to identify patterns and anomalies. In this way, it is possible to optimize the process, focus productions, collect data from all machines, operators, robots and sales. In the end, this generates a detailed analysis of the entire industrial chain, which brings positive impacts to the company's management and decision making.

For this to be possible, the data are collected and analyzed in stages.

The first of these is called data preparation. At this stage, the information is prepared, separated, organized to follow a standard of analysis. Without this, the volume of unorganized data generates unnecessary work and instead of facilitating the process, creates a barrier in it.

The second step after preparing the data is to critically analyze the information coming from them. The so-called data mining, is the phase of identifying patterns and anomalies that may base strategic decisions for the industry. Many companies stop at this stage and with the insights they generate they plot plans to streamline processes, improve machine utilization and minimize costs. But for those who want to continue the process, it is possible to teach machines to read the data patterns and thus make decisions. This is called Machine Learning.

Five Big Data Basics
To run effectively Big Data needs to obey five standards, also called the Big V's five V's: volume, speed, variety, truthfulness, and value.

• Volume refers to the amount of information generated by industry. If analyzed well, the data in abundance saw allies of the companies. 
• Speed is how often this information is produced, its analysis needs to follow that flow to be done effectively. 
• Variety is the property that explains the different ways a data can come to analysis. 
• Truthfulness ensures that data that is collected at high volume and high speed is real and has a sound footing - if the information is contradictory, it is not possible to make the correct analysis. 
• Finally, the value of the operation should be taken into account to calculate the cost-benefit of such an analysis. 

Practice
If the industry produces or collects data and uses it as a source of information, it already uses Big Data. But if the pillars and steps listed here are applied, the benefits can be enhanced.