Big Data: The Importance of Data in Industrial Processes

It is impossible not to connect the importance of Big Data to the dawn of a new era. Nicknamed the oil of the 21st century, Big Data has greatly helped not only the industrial sector but many others in the way they manage their businesses.

So, what is Big Data?

There are several versions about the origin of the Big Data concept and the start of its applications. One of the best known traces back to NASA, which in the early 1990s began using the term to describe immense, complex datasets that pushed the conventional limits of the computing of the time. The concept gained momentum in the early 2000s, when analyst Doug Laney articulated the now widely used definition based on three Vs:

• Volume: organizations collect data from varied sources, including financial transactions, social media, and information from sensors or data transmitted from machine to machine.
• Velocity: data is transmitted at an unprecedented speed and must be handled in a timely manner.
• Variety: data is generated in countless formats, from structured (numeric, in traditional databases) to unstructured (text documents, email, video, audio, stock quotes, and financial transactions).

Depending on its use, other dimensions may be considered, such as veracity, complexity, and variability, among others.

Big Data itself is just raw data. However, with the use of software and analysis techniques, it is possible to extract useful information and insights for managing the process being analyzed, uncover new patterns, and explore questions that had not yet been asked.

Decisions made from data are far more accurate and grounded in reality, moving away from strategies based on intuition or guesswork and gaining agility in course corrections.

A rapidly growing market

According to Forbes magazine, the adoption rate of Big Data analytics reached 53% among the companies it surveyed, up from 17% in 2015.

Worldwide, the Big Data market grows by around 10% to 14% per year. Projections point to growth from US$42 billion in 2018 to US$103 billion in 2027.

To give an idea, some companies collect more than ten thousand data points every 250 milliseconds. That is why adopting digital strategies for Big Data analysis can be critical: in addition to boosting productivity, studying this information enables more assertive processes, cost reductions, and even the forecasting and prevention of operational problems.

A success story: Meritor

Meritor, a manufacturer of transmission systems, brakes, and other components for commercial vehicles, is a good example. When evaluating suppliers, customers take into account the number of parts rejected per million (PPM). To improve its performance on this metric, Meritor quintupled the volume of data collected and began tracking defect rates not only by product batch, but also by individual production operations. It also decided to distinguish parts rejected by customers from those rejected by suppliers, which made it possible to assess the quality levels of its own sources.

The result? In 2013, the company's rejection rate was 139 parts per million. In the first quarter of 2014, with the company working to improve the traceability of production problems, the rate dropped to 67.

Opportunities to improve manufacturing

The opportunities to enhance manufacturing with Big Data are virtually endless. Among them:

• Consumer-focused product offerings: the focus is entirely on the customer. To know them better, it is possible to explore technologies such as Artificial Intelligence, which through machine learning help industry professionals understand the exact profile of their consumers.
• More accurate decisions: information can be used to optimize operational tasks and support far more precise decisions, with complex analyses of production pace and of how well a product is received in the market.
• Anticipating scenarios and problems: it is possible to anticipate scenarios by gathering data about the consumer market and conducting evaluations, which allows companies to experiment with and test new products and solutions.

According to NewVantage Venture Partners, Big Data is delivering its greatest value by cutting expenses in 49.2% of cases and by driving innovation and new business lines in 44.3% of cases.

In summary: gains for industry and customers

By processing large volumes of data, industry benefits just as much as customers:

• More efficient production lines
• Real-time monitoring of the production process
• Accuracy in data analysis
• Predictive identification of problems
• Improved forecasting of product and production demand
• Decentralization of the production cycle
• Industry closer to the end consumer
• Customized manufacturing
• Reduced time to correct problems
• Greater integration across sectors and levels of operation
• Higher quality in decision-making