Big data is a big term that can mean a lot of different things, depending on who you ask. Everyone thinks of something slightly different when they hear the phrase, and almost everyone is “right.”

With the amount of data swirling around the world wide web–which grows daily at a staggering rate–information about virtually anything can be gathered in massive volumes and then dissected and analyzed.

As of last year, nearly half of all businesses say they’re using big data, while over 80% of those businesses say they are satisfied with the results they are generating from their big data programs.

WHAT IS BIG DATA?

Big data is a new term, but in reality, it’s a longtime practice. Gathering and storing large amounts of information for education or analysis has existed as long as books and libraries.

However, the concept of using computers to process massive amounts of digitized data is relatively new. Industry analyst Doug Laney canonized the definition of big data in the early 2000s by outlining the three Vs that characterize big data:

• Volume: Businesses and organizations gather data from a wide array of sources, depending on their intended use case. From business transactions to social media actions to gathering sensor and machine data, new computer systems can autonomously gather, catalog, and analyze these actions to provide grouped data or actionable insights.

• Velocity: An enormous quantity of data is being generated in the digital era at rapid speeds. A key component of the big data era is the ability to rapidly process the amount of data generated and gathered. Modern computing power allows organizations to process large amounts of data at unprecedented speeds.

• Variety: Data comes in all shapes, sizes, and formats. The big data era is characterized by the ability to view all possible inputs and sources as forms of data and the ability to process all of that data according to the user’s needs; whether you are cataloging music, studying transactions, or refining manufacturing processes, big data can effectively handle inputs no matter the format.

HOW COMPANIES ARE USING BIG DATA?

Big data programs constantly analyze every aspect of a business’ operations or customer-facing efforts. By studying everything from what people do when they visit a website to where the largest inefficiencies are in a production or communication chain, big data can help businesses in virtually every aspect of quantitative business processes.

These include decreasing expenses through efficiency gains, identifying opportunities for innovation, launching new products or adding revenue based on consumer analysis, increasing process speed by studying in-house and customer-facing processes, and transforming the business to be more relevant and competitive for the future.

All of these practices can help create a data-driven culture, which ultimately enables a business to reliably and repeatedly monitor and analyze its own operations to operate more efficiently–all without some serendipitous “a-ha moment.”

Companies might use big data to “observe” how people use their websites. Even without violating your privacy, they can observe patterns of what people click on, see how long viewers spend on a given page, or A/B test ads to see how they perform. This can help them revise their websites to serve customers better, to create stronger CTAs that lead to more transactions or longer time spent on pages, or even to understand what products  people are most interested in versus what they actually end up purchasing.

Big data can also refine much larger processes, from major manufacturing operations to small internal tasks.

HOW BIG DATA CAN IMPROVE MANUFACTURING

Some manufacturing processes are standardized and relatively streamlined, with minimal variability and potential for only the slightest gains moving forward. Robotic manufacturing of straightforward steel components, for example, may be nearly fully optimised given current technology and big data analytics.

On the other hand, organic or variable products like biopharmaceuticals, may display a variation in yield of 50 to 100 percent. These products are typically ‘living’  things like vaccines, hormones, and even synthetic blood components. While they may have some synthetic ingredients, they are all typically manufactured using live, genetically modified cells, which means that they will inherently demonstrate variability just as living creatures might.

In most of today’s manufacturing techniques, these variable-intensive processes are monitored by production teams, even if computer systems are used to gather the data–which can often span more than 200 variables ranging from ingredient purity to output chemicals.

The massive variability in results can create a myriad of issues, from unpredictable production costs and product quality to more serious risks like impotent or overly potent vaccines and medications.

Recent implementation of big data analytics allowed a top-five biopharmaceutical manufacturer to markedly increase its vaccine production without incurring any additional expenditures.

By studying each phase of the production process, it was able to gather data about each step and the materials used before identifying every inefficiency in order to increase yield solely through process efficiencies.

WHY BIG DATA MATTERS?

Big data is less about how “big” the data is and more about how you use it. As long as the sample size is large enough to create meaningful data sets or observable patterns, the importance of big data lies less in its size or even gathering ability and more in the ability to analyze and process that data to create insights that were not previously available.

Big data can enable cost or time savings by identifying unseen inefficiencies, can help brands identify new product needs by studying consumer behavior, and enable truly informed decision-making by allowing businesses greater visibility into their own processes than ever before.

Advanced big data enables everything in the digital age to be safer, smarter, and more efficient. As an enterprise accrues an ever-growing database of patterns and “normal” behaviors, big data analytics can determine root causes of failures and identify problems in near real time.

When customers search for given products but exhibit behavior consistent with reluctant spenders, big data can issue them personalized coupons that create a win-win for the vendor and customer by providing meaningful savings that trigger an actual sale.

Big data also acts as one of the greatest security systems by identifying any out of the ordinary behavior, not only through traditional security behavior like “looking” for unusual users but also by noticing any behavior or order of operations that appears to be inconsistent with traditional use cases.

THE FUTURE OF BIG DATA DATA

Big data is becoming a more integral part of a wider array of industries every day. As more specialists learn the potential for big data analytics as a tool to not only gather but process all types of data inputs, organizations that do not deal in traditional “data” formats are recognizing the potential and need to implement data programs to remain competitive in the evolving digital landscape.

While most people think of big data as a collection of data points, the biggest advances and best use cases all revolve around the speed and complexity of processing that data.

Looking towards the future, brands should envision not only what they might accomplish by gathering pertinent data sets, but also to what could happen if they could make repeat, real-time inferences based on the data that they do gather.

With fast processing and the ability to gather everything from where web users hover their cursors on your website to which chord progressions are most common atop the pop charts on any given day in history, big data can and will continue to transform how we perceive the world around us, all while eliminating waste and unnecessary expenditures.

Previously published in Industrial Marketing, Used with Permission

Bio:

Anna Kucirkova speaks 3 languages has a passion for kids and writing. While she has been to many places in Europe and SE Asia she still wants to explore the rest of the world.