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The Importance of Big Data Analysis in Industries

Big Data is essential in constant evolution scenario in industries. Understand all aspects of this technology and applications that already exist today.
Big Data and Data Analysis

When we talk about Big Data, we are referring to an application capable of storing and handling a large volume of data from different sources (in daily life, the most common sources of data are social media, websites and videos). These data have large proportions when applied in an industry, for example.

The tools and technologies that industries are using today are not always the same. However, it is known that the data collected in a productive environment can be extremely valuable if evaluated with attention and speed.

In this article you will understand how Big Data is part of Industry 4.0 and is essential for the evolution of any company. In addition, we discuss the difficulties and benefits of applying Data Analysis and Big Data strategies in industrial environments.

The role of Big Data in Industry 4.0

In Industry 4.0, data is extremely important as it allows other pillars to exist and have their specific functions as well. Together with Big Data, which has the function of enabling the storage and processing of information and data, we have a second pillar of Industry 4.0 called the Internet of Things (IoT).

IoT makes it possible to capture data from different sources that can be processed using a Big Data solution and then used for decision making in the industry. With IoT, huge amounts of data are available to interpret and use. The storage and processing of this data happens through Big Data solutions. The data contained in the storage platforms are used later for industry decision-making.

Putting in a practical example: consider that a product is being manufactured on a large scale and while this happens, the Big Data central is receiving much of the information related to the manufacturing process of this product, such as the data collected by sensors.

In another application, data collection from social media is happening ate the same time. Consumers are writing comments about their preference for the product in a smaller format.

With Big Data, production planners can access industry data as well as social media data to get information and adjust production specifications agile and proactively.

Without the information, you would only understand this consumer preference when your competitor started to make more products in small format and outstrip your industry in terms of sales, or you would simply lose consumers without understanding exactly why.

Following this idea, a new way of making decisions begins to be part of the intelligent industrial environments: data acquisition in high volume, high speed and variety.

The challenges of using Big Data in industries

When deciding to use Big Data technologies in an industry, those responsible for the implementation and subsequent system maintenance have major challenges. However, if these challenges are evaluated and overcome with organization and planning, they become part of a great process of innovation.

1. Quality of collected data

In a process with integration issues, the information may become inconsistent due to the small problems that occur in each process. And when a Big Data strategy begins to be used, it is common that problems, duplications, and errors are found.

With poor quality data, no cloud storage, and no guarantee of reliability, inconsistent reporting and analysis can be generated.

And to avoid this situation, it is important to use a quality system that filters the data and organizes it in a logical way, identifying possible problems before the data is stored.

2. Definition of the Big Data Project

In order to improve industrial processes, to use Big Data is an alternative with many positive aspects. However, for your application to succeed, you need to have a team of people that are responsible for the tool that is being used.

This team will be extremely strategic, and should have the knowledge and expertise to define what information, in what format, is important to the business.

This is necessary because it is not possible to evaluate all the data generated in a complex environment as an industry. It is necessary to define a project with paths so that this information reaches its final destination (reports and decisions) in a satisfactory way.

3. Acceptance of employees

The human being has a tendency to remain comfortable and in a comfort zone. When starting a new project, which will naturally result in changes in the way everyone work and live their routines, managers may need to spend more time on training sessions.

By doing this, all users of the new systems and forms of data collection are aware of their responsibilities and for them to understand which are the positive sides of this change.

4. Skilled workers

When a new tool or process begins to be used, it is essential that more operational operators and employees have complete knowledge and ability to seek for better solutions at all times.

This is not an easy task because many times these employees deal with many people, and conducting training to ensure a homogeneous knowledge is a great challenge. Professionals with knowledge in statistical analysis, data architecture and design, for example, are scarce in the market.

However, it is necessary to organize and invest in the qualification of the professionals so that besides accepting the new format of work given by Big Data, they can also contribute actively in this process.

The 5 Vs of Big Data

Big Data acts by making use of some important pillars, which help us to understand its importance in any productive process.

1. Volume

As already mentioned, Big Data handles with a high volume of data, so that insights and positions are taken based on a lot of information from different sources. To perform a good analysis of the information collected, having a high volume of data is a great advantage.

2. Velocity

As the whole system becomes faster with the use of Big Data, the decision-making processes need to have the same velocity.

3. Variety

During this article we have mentioned different sources of data that may exist; besides it, the data can be obtained in various formats. Imagine an entire industry that produces a product to be offered in supermarkets. The data generated in this chain will come from the production machines, financial transactions, sales, repercussion in social media, among many others.

4. Veracity

This is an essential pillar of Big Data, because in cases where the data generated is not true, or does not really demonstrate the reality of that industry, the whole system loses credibility. Therefore, good planning and design of how the information will be used is very important.

5. Value

Finally, a pillar that is quite controversial in any innovation project. In order to understand the value of Big Data, one must consider it as an investment, so measurement methods should take this into account. The potential of a smart industry that applies Big Data for data analysis is very large, and the value for money needs to be considered as well.

Benefits and advantages of using Big Data in industries

Using Big Data in industries allows a transformation of the different pieces of unique data into knowledge that can be used to improve processes, and consequently products and services.

One big advantage of applying Big Data to production chains is having the power to identify planning errors. Alongside this, industry managers can verify the results at any time, and even make more assertive projections for the future.

Among the benefits of using Big Data in the industries, we highlight 6 below:

1. Velocity for information delivery

With system integration and IoT, large amounts of data are collected. And by using Big Data technology, the velocity in which this information can be accessed and used is much higher.

2. Monitoring of equipment in real time

By receiving information from the equipment used in production, it is possible to identify scenarios that result in production downtimes. Thus, once these scenarios have been defined, preventive rather than corrective intervention becomes practically automatic.

3. Identification of bottlenecks in the productive process

At the same time that information about equipment is collected, the final products can be traced back to the end consumer. This allows a complete analysis of the production process, regardless of the stage in which it is found. And with these data at hand, it is much easier to make accurate interventions to improve the quality of the system as a whole.

4. Fast and correct decision making

There is no argument against data. If you have in front of you data showing that a part or process step is not working well, a decision that is required is quickly and easily made. That is why it is essential that the data is always correct and consistent with reality.

5. Costs reduction

As a consequence of small actions taken in the industry, the positive results are being added and the consequence always ends up being a reduction of costs. These are operational costs, production losses or even consumer complaints.

6. Greater integration among sectors

With the need to cross information from different sources, a natural consequence is an approximation of the different sectors of the industry. Much is expected of multifunctional employees, who are able to carry out projects with people from different areas of the company. With the use of the industry 4.0 and its pillars, there is a great opportunity for the development of these professionals and more and more quality in all internal projects and processes.

Industrial applications of Big Data

As you may be wondering, there are many possibilities of applying Big Data in different industries (health, general services, governments).

Since the start of an industrial operation with Big Data is labor-intensive, the focus of projects should be on options that add value to the business.

Some examples of Big Data application are:

Improvement of manufacturing processes

McKinsey and Company owns a Big Data case in the manufacture of pharmaceuticals. One company used to manufacture vaccines and blood components in 50 to 100% yield range with an identical manufacturing process. By using Big Data, the team was able to segment the manufacturing process and identify a process that affected performance. As a result, it was possible to increase vaccine production by 50%, resulting in savings of $ 5 to $ 10 million per year.

Custom product design

Tata Consultancy Services has one case of a company that has its highest revenue making custom products. From Big Data, this company analyzed customer behaviors and understood how to deliver the goods profitably. In this way the company was able to change its way of manufacturing to lean manufacturing and to understand which products were viable for its production.

Better quality assurance

Intel, a computer processor manufacturer, uses Big Data in its production to optimize the quality process of final products. Initially it would take 19,000 tests for each chip manufactured!

By using Big Data, the company was able to significantly reduce the number of tests to ensure quality, resulting in a saving of $ 3 million in manufacturing costs, which if extended to other production lines could reach $ 30 million.

Supply chain risk management

Another example is to use Big Data to evaluate possible risks, such as delivery of raw materials. From the analysis of data it is possible to understand if there are meteorological problems during the logistics of some raw material, being then possible to estimate delays or better measurement of the final product delivery deadlines.

Consumer focus

Coca Cola is a giant industry that uses Big Data to evaluate its consumers. In a certain country, the company has put on the market a machine of soda that allows the people to make blends of flavors.

That’s how the company collected precious information about the behavior of its customers, creating new flavors like Sprite Cherry and Sprite Cherry Zero.

Industry closer to the final consumer

In the automotive sector there are interesting applications such as a company that produces parts and is able to warn the owner of the car, through IoT and Big Data, that the oil change must be done, for example.

This allows companies to get closer to the people who are using their products in a collaborative way and always improving the consumer experience.


Finally, considering all the benefits and challenges of Big Data, and going through many examples of application of this technology today, one can see that it is a predominant tendency in large industries.

Its use is not simple, but if well executed it has many positive results in the short, medium and long-term.

In order to have space in this new scenario of digital transformation and use of Industry 4.0, Brazilian companies need to be persistent in their plans and carry out a lot of training so that when intelligent processes are happening, it is a more natural adaptation of all people involved.

Are you already paying attention to Big Data tendencies within Industry 4.0? Share your experience in the comments section below!


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