{"id":10948,"date":"2019-06-23T00:44:33","date_gmt":"2019-06-23T03:44:33","guid":{"rendered":"https:\/\/industriall.ai\/blog\/?p=10948"},"modified":"2022-01-04T13:23:16","modified_gmt":"2022-01-04T16:23:16","slug":"the-importance-of-big-data-analysis-in-industries","status":"publish","type":"post","link":"https:\/\/industriall.ai\/blog\/the-importance-of-big-data-analysis-in-industries","title":{"rendered":"The Importance of Big Data Analysis in Industries"},"content":{"rendered":"\n

When we talk about Big Data<\/strong>, 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.<\/p>\n\n\n\n

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<\/a><\/strong> and speed.<\/p>\n\n\n\n

In this article you will understand how\nBig Data is part of Industry 4.0 and is essential for the evolution of any\ncompany. In addition, we discuss the difficulties and benefits of applying Data\nAnalysis and Big Data strategies in industrial environments.<\/p>\n\n\n\n

The role of Big Data in Industry 4.0<\/strong><\/h2>\n\n\n\n

In Industry 4.0, data is extremely\nimportant as it allows other pillars to exist and have their specific functions\nas well. Together with Big Data, which has the function of enabling the storage\nand processing of information and data, we have a second pillar of Industry 4.0\ncalled the Internet of Things (IoT).<\/p>\n\n\n\n

IoT makes it possible to capture data\nfrom different sources that can be processed using a Big Data solution and then\nused for decision making in the industry. With IoT, huge amounts of data are\navailable to interpret and use. The storage and processing of this data happens\nthrough Big Data solutions. The data contained in the storage platforms are\nused later for industry decision-making.<\/p>\n\n\n\n

Putting in a practical example: consider\nthat a product is being manufactured on a large scale and while this happens,\nthe Big Data central is receiving much of the information related to the\nmanufacturing process of this product, such as the data collected by sensors.<\/p>\n\n\n\n

In another application, data collection\nfrom social media is happening ate the same time. Consumers are writing\ncomments about their preference for the product in a smaller format. <\/p>\n\n\n\n

With Big Data, production planners can\naccess industry data as well as social media data to get information and adjust\nproduction specifications agile and proactively.<\/p>\n\n\n\n

Without the information, you would only\nunderstand this consumer preference when your competitor started to make more\nproducts in small format and outstrip your industry in terms of sales, or you\nwould simply lose consumers without understanding exactly why.<\/p>\n\n\n\n

Following this idea, a new way of making\ndecisions begins to be part of the intelligent industrial environments: data\nacquisition in high volume, high speed and variety.<\/p>\n\n\n\n

The challenges of using Big Data in industries<\/strong><\/h2>\n\n\n\n

When deciding to use Big Data\ntechnologies in an industry, those responsible for the implementation and\nsubsequent system maintenance have major challenges. However, if these\nchallenges are evaluated and overcome with organization and planning, they\nbecome part of a great process of innovation.<\/p>\n\n\n\n

1. Quality of collected data<\/strong><\/h3>\n\n\n\n

In a process with integration issues,\nthe information may become inconsistent due to the small problems that occur in\neach process. And when a Big Data strategy begins to be used, it is common that\nproblems, duplications, and errors are found.<\/p>\n\n\n\n

With poor quality data, no cloud\nstorage, and no guarantee of reliability, inconsistent reporting and analysis\ncan be generated.<\/p>\n\n\n\n

And to avoid this situation, it is\nimportant to use a quality system that filters the data and organizes it in a\nlogical way, identifying possible problems before the data is stored.<\/p>\n\n\n\n

2. Definition of the Big Data Project<\/strong><\/h3>\n\n\n\n

In order to improve industrial\nprocesses, to use Big Data is an alternative with many positive aspects.\nHowever, for your application to succeed, you need to have a team of people\nthat are responsible for the tool that is being used.<\/p>\n\n\n\n

This team will be extremely strategic,\nand should have the knowledge and expertise to define what information, in what\nformat, is important to the business.<\/p>\n\n\n\n

This is necessary because it is not\npossible to evaluate all the data generated in a complex environment as an\nindustry. It is necessary to define a project with paths so that this\ninformation reaches its final destination (reports and decisions) in a satisfactory\nway.<\/p>\n\n\n\n

3. Acceptance of employees<\/strong><\/h3>\n\n\n\n

The human being has a tendency to remain\ncomfortable and in a comfort zone. When starting a new project, which will\nnaturally result in changes in the way everyone work and live their routines,\nmanagers may need to spend more time on training sessions.<\/p>\n\n\n\n

By doing this, all users of the new\nsystems and forms of data collection are aware of their responsibilities and for\nthem to understand which are the positive sides of this change.<\/p>\n\n\n\n

4. Skilled workers<\/strong><\/h3>\n\n\n\n

When a new tool or process begins to be\nused, it is essential that more operational operators and employees have\ncomplete knowledge and ability to seek for better solutions at all times.<\/p>\n\n\n\n

This is not an easy task because many\ntimes these employees deal with many people, and conducting training to ensure\na homogeneous knowledge is a great challenge. Professionals with knowledge in\nstatistical analysis, data architecture and design, for example, are scarce in\nthe market.<\/p>\n\n\n\n

However, it is necessary to organize and\ninvest in the qualification of the professionals so that besides accepting the\nnew format of work given by Big Data, they can also contribute actively in this\nprocess.<\/p>\n\n\n\n

The 5 Vs of Big Data<\/strong><\/h2>\n\n\n\n

Big Data acts by making use of some\nimportant pillars, which help us to understand its importance in any productive\nprocess.<\/p>\n\n\n\n

1. Volume<\/strong><\/h3>\n\n\n\n

As already mentioned, Big Data handles\nwith a high volume of data, so that insights and positions are taken based on a\nlot of information from different sources. To perform a good analysis of the\ninformation collected, having a high volume of data is a great advantage.<\/p>\n\n\n\n

2. Velocity<\/strong><\/h3>\n\n\n\n

As the whole system becomes faster with\nthe use of Big Data, the decision-making processes need to have the same\nvelocity.<\/p>\n\n\n\n

3. Variety<\/strong><\/h3>\n\n\n\n

During this article we have mentioned\ndifferent sources of data that may exist; besides it, the data can be obtained\nin various formats. Imagine an entire industry that produces a product to be offered\nin supermarkets. The data generated in this chain will come from the production\nmachines, financial transactions, sales, repercussion in social media, among\nmany others.<\/p>\n\n\n\n

4. Veracity<\/strong><\/h3>\n\n\n\n

This is an essential pillar of Big Data,\nbecause in cases where the data generated is not true, or does not really\ndemonstrate the reality of that industry, the whole system loses credibility.\nTherefore, good planning and design of how the information will be used is very\nimportant.<\/p>\n\n\n\n

5. Value<\/strong><\/h3>\n\n\n\n

Finally, a pillar that is quite\ncontroversial in any innovation project. In order to understand the value of\nBig Data, one must consider it as an investment, so measurement methods should\ntake this into account. The potential of a smart industry that applies Big Data\nfor data analysis is very large, and the value for money needs to be considered\nas well.<\/p>\n\n\n\n

Benefits and advantages of using Big Data in industries<\/strong><\/h2>\n\n\n\n

Using Big Data in industries allows a\ntransformation of the different pieces of unique data into knowledge that can\nbe used to improve processes, and consequently products and services.<\/p>\n\n\n\n

One big advantage of applying Big Data\nto production chains is having the power to identify planning errors. Alongside\nthis, industry managers can verify the results at any time, and even make more\nassertive projections for the future.<\/p>\n\n\n\n

Among the benefits of using Big Data in\nthe industries, we highlight 6 below:<\/p>\n\n\n\n

1. Velocity for information delivery<\/strong><\/h3>\n\n\n\n

With system integration and IoT, large amounts\nof data are collected. And by using Big Data technology, the velocity in which\nthis information can be accessed and used is much higher.<\/p>\n\n\n\n

2. Monitoring of equipment in real time<\/strong><\/h3>\n\n\n\n

By receiving information from the\nequipment used in production, it is possible to identify scenarios that result\nin production downtimes. Thus, once these scenarios have been defined,\npreventive rather than corrective intervention becomes practically automatic.<\/p>\n\n\n\n

3. Identification of bottlenecks in the productive process<\/strong><\/h3>\n\n\n\n

At the same time that information about\nequipment is collected, the final products can be traced back to the end\nconsumer. This allows a complete analysis of the production process, regardless\nof the stage in which it is found. And with these data at hand, it is much\neasier to make accurate interventions to improve the quality of the system as a\nwhole.<\/p>\n\n\n\n

4. Fast and correct decision making<\/strong><\/h3>\n\n\n\n

There is no argument against data. If\nyou have in front of you data showing that a part or process step is not\nworking well, a decision that is required is quickly and easily made. That is\nwhy it is essential that the data is always correct and consistent with\nreality.<\/p>\n\n\n\n

5. Costs reduction<\/strong><\/h3>\n\n\n\n

As a consequence of small actions taken\nin the industry, the positive results are being added and the consequence\nalways ends up being a reduction of costs. These are operational costs,\nproduction losses or even consumer complaints.<\/p>\n\n\n\n

6. Greater integration among sectors<\/strong><\/h3>\n\n\n\n

With the need to cross information from\ndifferent sources, a natural consequence is an approximation of the different\nsectors of the industry. Much is expected of multifunctional employees, who are\nable to carry out projects with people from different areas of the company.\nWith the use of the industry 4.0 and its pillars, there is a great opportunity\nfor the development of these professionals and more and more quality in all\ninternal projects and processes.<\/p>\n\n\n\n

Industrial applications of Big Data<\/strong><\/h2>\n\n\n\n

As you may be wondering, there are many\npossibilities of applying Big Data in different industries (health, general\nservices, governments).<\/p>\n\n\n\n

Since the start of an industrial\noperation with Big Data is labor-intensive, the focus of projects should be on\noptions that add value to the business.<\/p>\n\n\n\n

Some examples of Big Data application\nare:<\/p>\n\n\n\n

Improvement of manufacturing processes<\/strong><\/h3>\n\n\n\n

McKinsey and Company owns a Big Data\ncase in the manufacture of pharmaceuticals. One company used to manufacture\nvaccines and blood components in 50 to 100% yield range with an identical\nmanufacturing process. By using Big Data, the team was able to segment the\nmanufacturing process and identify a process that affected performance. As a\nresult, it was possible to increase vaccine production by 50%, resulting in\nsavings of $ 5 to $ 10 million per year.<\/p>\n\n\n\n

Custom product design<\/strong><\/h3>\n\n\n\n

Tata Consultancy Services has one case\nof a company that has its highest revenue making custom products. From Big\nData, this company analyzed customer behaviors and understood how to deliver\nthe goods profitably. In this way the company was able to change its way of\nmanufacturing to lean manufacturing and to understand which products were\nviable for its production.<\/p>\n\n\n\n

Better quality assurance<\/strong><\/h3>\n\n\n\n

Intel, a computer processor\nmanufacturer, uses Big Data in its production to optimize the quality process\nof final products. Initially it would take 19,000 tests for each chip\nmanufactured!<\/p>\n\n\n\n

By using Big Data, the company was able\nto significantly reduce the number of tests to ensure quality, resulting in a\nsaving of $ 3 million in manufacturing costs, which if extended to other\nproduction lines could reach $ 30 million.<\/p>\n\n\n\n

Supply chain risk management<\/strong><\/h3>\n\n\n\n

Another example is to use Big Data to\nevaluate possible risks, such as delivery of raw materials. From the analysis\nof data it is possible to understand if there are meteorological problems\nduring the logistics of some raw material, being then possible to estimate\ndelays or better measurement of the final product delivery deadlines.<\/p>\n\n\n\n

Consumer focus<\/strong><\/h3>\n\n\n\n

Coca Cola is a giant industry that uses\nBig Data to evaluate its consumers. In a certain country, the company has put\non the market a machine of soda that allows the people to make blends of\nflavors.<\/p>\n\n\n\n

That’s how the company collected\nprecious information about the behavior of its customers, creating new flavors\nlike Sprite Cherry and Sprite Cherry Zero.<\/p>\n\n\n\n

Industry closer to the final consumer<\/strong><\/h3>\n\n\n\n

In the automotive sector there are\ninteresting applications such as a company that produces parts and is able to\nwarn the owner of the car, through IoT and Big Data, that the oil change must\nbe done, for example.<\/p>\n\n\n\n

This allows companies to get closer to\nthe people who are using their products in a collaborative way and always improving\nthe consumer experience.<\/p>\n\n\n\n

Conclusion<\/strong><\/h2>\n\n\n\n

Finally, considering all the benefits\nand challenges of Big Data, and going through many examples of application of\nthis technology today, one can see that it is a predominant tendency in large\nindustries.<\/p>\n\n\n\n

Its use is not simple, but if well\nexecuted it has many positive results in the short, medium and long-term.<\/p>\n\n\n\n

In order to have space in this new\nscenario of digital transformation and use of Industry 4.0, Brazilian companies\nneed to be persistent in their plans and carry out a lot of training so that\nwhen intelligent processes are happening, it is a more natural adaptation of\nall people involved.<\/p>\n\n\n\n

Are you already paying attention to Big\nData tendencies within Industry 4.0? Share your experience in the comments\nsection below!<\/p>\n","protected":false},"excerpt":{"rendered":"

Big Data is essential in constant evolution scenario in industries. Understand all aspects of this technology and applications that already exist today.<\/p>\n","protected":false},"author":5,"featured_media":10949,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false},"categories":[101,82],"tags":[],"yoast_head":"\nThe Importance of Big Data in Industries - Blog IndustriALL<\/title>\n<meta name=\"description\" content=\"Big Data is essential in constant evolution scenario in industries. 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