All successful companies are already familiar with and use it. We're talking about Big Data, and you've probably heard it thousands of times in recent years, but... do you really know how the industry leverages this technology? Are you aware of the great power contained within these 7 letters? If you read this article, you'll discover the aspects of Big Data that are truly useful for your smart factory.

Your factory is talking to you, but don’t worry, everything is fine. In fact, more than fine. It does so in the form of data, and it does so continuously. A lot of what it tells you will make your life easier when it comes to managing.

That is why we must not let these “words” be carried away by the wind. We need to retain them, process them, and make decisions based on what they tell us.

The factory communicates a huge amount of information to us daily, so we must be prepared to assimilate large amounts of data. To achieve this, we have Big Data technology.

From here, you will learn everything that it contributes to the industry 4.0, as well as its basic functioning and some practical application cases. Are you ready to listen closely to your plant?

What is Big Data?

The term Big Data refers to the technology that allows, in a systematic way, working with large sets of data. We are talking about amounts of information that cannot be processed by traditional tools. Among the functionalities of Big Data, we find:

• Data capture.
• Storage.
• Analysis.
• Search.
• Exchange.
• Transfer.
• Visualization.
• Query.
• Update.
• Data privacy.
• Data sources.

The concept of Big Data is based on 5 basic pillars:

1. Volume: the amount of data that is collected and stored will determine whether we are talking about Big Data or not.
2. Variety: we refer to the different types of data that Big Data works with. Previously, structured data (which has a fixed format) was used in a relational database. The proliferation of semi-structured and unstructured data (such as multimedia content, emails, etc.) led to the need to work with them and extract all their value.
3. Velocity: data is continuously generated, so it is important to highlight the ability to process it in real time.
4. Veracity: the data must reflect the reality of what is happening.
5. Value: the data must be useful for the purposes set by the users.

It is not necessary to be a technology specialist to understand how Big Data works. The steps that make up the operation of a Big Data system are:

1. Create a strategy: we must be very clear about the purposes for which we want to implement Big Data in the company. Additionally, it is important to know the resources we have and those we will need.
2. Choose the data sources: these are very varied. Data is being created constantly, and we must carefully select those that generate valuable data for the company. Among them, we have: information from sensors and… IIoT devices, open data, information from websites and social media, data lakes, etc.
3. Data storage and management: this includes actions such as reliable access, integration methods, ensuring data quality, control, storage, and preparation for analysis.
4. Information analysis: we extract value from the data by identifying patterns, correlations, and other useful insights aligned with our strategy.
5. Finally, with all these conclusions, managers and responsible parties make informed management decisions.

When we are in in industrial environments, the amount of information generated in each process is immense. Moreover, it continues to be “produced” as long as there is activity. In many plants, this happens 24 hours a day, 365 (or 366) days a year.

Therefore, the opportunity to detect areas for process improvement is constant. And, of course, it applies to all components of the supply chain —from raw materials to the finished product—.

In general terms, we can say that the application of Big Data in industries optimizes processes, improves productivity and product quality and increases profit margins.

How do we achieve this? One key factor is installing sensors at critical points in the plant. These sensors vary in nature depending on the variables we want to measure:

• Temperature sensors.
• Pressure gauges.
• Humidity level sensors.
• Manufacturing speed gauges for a part.
• Energy consumption.
• Geolocation through beacons or other technologies.
• Etc.

Beyond thinking about manufacturing lines, Big Data in industries is also applicable to other more generic aspects of any company. For example, social media user comments or received emails can be analyzed. In this way, marketing and sales strategies can be refined.

Applications of Big Data in industry

Big Data is a very versatile technology, so many areas of an industrial company can benefit from it. Its most prominent applications are:

• Logistics and transportation.
• Production processes.
• Quality control.
• Understanding market trends.
• Human resources management.
• Cybersecurity.
• Optimization of energy consumption.

Big Data in logistics and transportation.

The increase in road traffic, the greater relocation of warehouses, fluctuations in fuel prices, business internationalization, and the rise of e-commerce are logistical trends where Big Data plays a role.

Here, Big Data systems work with information obtained from vehicle GPS, traffic data from official institutions, mobility data of people and materials in warehouses, product supply information from customers, etc.

Big Data in production processes

Within the manufacturing processes themselves, data analysis is crucial to, for example, prevent mechanical failures in machinery. In this case, Big Data technology is combined with artificial intelligence to shape the predictive maintenance.

In this way, we will be able to anticipate the occurrence of critical failures. These failures could halt operations or create defective products with no value, leading to significant financial losses.

Big Data in quality control

In addition to studying the machinery and the systems involved in production, we must also monitor the condition of the product itself. We are not only referring to the final product but also to the raw materials and intermediate products.

In addition to having sensors like the ones we have mentioned, in the field of quality control real images captured by cameras can also be used. These devices are capable of capturing many images in a short period of time, which are then analyzed with artificial intelligence.

In this way, products can be sorted based on quality criteria. This allows them to be discarded or directed for other purposes. For example, depending on the degree of ripeness of a fruit, it can be packaged or sent to be made into juice.

Big Data for understanding market trends

The industrial sector is greatly affected by the ups and downs of demand. This has recently been very evident with the health crisis. For example, demand in the pharmaceutical sector has multiplied in recent months; another example can be seen in the agri-food sector, where deciding the right time for harvesting is key to maximizing profit.

To control these trends, we need to work with historical data, as well as social, economic, environmental, political, and many other types of factors.

Big Data for human resources management

This technology is used for various aspects of human resources. From selecting the most suitable candidates to achieving a perfected management of personnel and ensuring that employees feel comfortable in their roles, reducing turnover.

An example can be seen in Amazon. Through its “Amazon Connections” program, which collects real-time information daily from brief questions asked to its employees. Another example is the technology company Xerox, which managed to reduce its employee turnover rate by 20%.

Big Data for cybersecurity

Many industries work with truly innovative products and processes, so they don’t want anyone stealing their secrets. On the other hand, a cyberattack could paralyze their entire operation. This creates an urgent need for protection against cybercriminals.

Here, we must also consider the collaboration between Big Data and Cloud Computing technology, as in most cases, the information is stored in the cloud. Big Data systems analyze all the information collected, looking for potential vulnerabilities and irregularities.

Big Data to optimize energy consumption

Furnaces, refrigeration chambers, machinery of many types, air conditioning systems… all running continuously. Can you imagine the electricity bill? Controlling electricity consumption in an industrial environment is no small task. Fortunately, Big Data can lend a hand here as well.

To achieve this, the concept of a smart grid comes into play. These work with distributed IoT sensors that collect consumption data for the Big Data system.

The latter works together with artificial intelligence. With all this, electrical consumption is monitored in real time, ensuring that the supply is adjusted to the demand. This balance helps reduce electricity costs.

Tools for the application of Big Data in industrial companies

Sure, Big Data is currently very necessary in the industry, but… how do I apply it? Where do I start? Asking these kinds of questions is the most natural thing in the world once we know we need to rely on this technology.

To prevent this from becoming a continuous headache for industrial managers, at Sixphere we offer solutions that harness the full potential of the vast amounts of data generated by a factory.

First of all, we can talk about Polaris Industry, which integrates technologies such as Machine Learning, IIoT, and Big Data, among others. Polaris Industry is built upon a 5-layer architecture:

• Iot & Proximity.
• Core.
• Business Services.
• Data Engine.
• Smart Cloud Factory.

As you can see, one of these layers is called the Data Engine. It is a transversal layer that collects, structures, and processes data, making Big Data essential for its operation.

Polaris also has a module specifically developed for the agribusiness environment, called Polaris Farm. It is tailored to the specific conditions of the sector, especially due to its outdoor nature.

In this way, greater control is achieved over both crops and livestock. Polaris Farm is currently under development, driven by our progress in the European DIVA project, aimed at the digital transformation of the agri-food sector.

We must not forget about our product Polaris Tx, designed for the digitalization of the supply chain. With Polaris Tx, all members of the chain will be informed in real-time about everything happening in production. This allows them to improve management while ensuring security and privacy.

As you can see, Big Data is essential for any industrial company that wants to continue growing. Its adaptability, combined with the results it delivers, are the key factors behind this.

To make this step easier and make the plant even smarter, at Sixphere we have solutions that know how to listen to your factory and understand everything it tells us that is relevant. Are you going to keep turning a deaf ear to it?

Do you want to know what we do and how we do it? Visit our success stories and ask us anything you need to know.