Today, many companies are facing challenges in adopting data analytics. The digital era requires full compliance of businesses with the new terms and conditions dictated by digital space and huge volumes of data inside. It is the only way to survive in the modern market.
In this article, we will overview techniques that will help you drive business efficiency and deploy all the power of Big Data. Only proven tactics backed on the first-hand experience of S-PRO experts and senior leaders of major companies. Keep abreast of the best and get ready to dig into the actionable tips on how to use Big Data in business.
Big Data: Quick Retrospective
In the mid-1990s, the need to understand trends, preferences, and patterns in the large databases when people interact with each other and different systems gave birth to the concept of Big Data, first used for huge volumes of data.
In 2001, Doug Laney expanded the concept, including the growing variety of Big Data created by companies and the velocity at which that data was being generated. That time, the world has first heard the term “3Vs of Big Data,” including three factors: volume, velocity, and variety.
To stay competitive, modern businesses must adapt their hosting services to the growing volume of data. This fascinating Big Data statistics prove why.
- By the year 2022, 90% of enterprise strategies will consider information as a crucial business asset and analytics as a critical competency. (Gartner)
- The global Big Data market is forecasted to grow to 189.1 billion U.S. dollars in revenue in 2020. (Statista)
- Data is growing even faster than expected, and in 2020, it is forecasted to reach 1.7 megabytes of new information generated every second for every person on Earth. (Forbes)
- The amount of global Big Data is predicted to grow up to 5.2 zettabytes by 2025. (Leftronic)
According to Rita Sallam, VP at Gartner, data and analytics leaders should investigate the prospective impacts of Big Data and adapt business models and operations according to them. Otherwise, they are taking the risk of losing a competitive advantage to those businesses that do it.
How to Use Big Data In Your Business?
In order not to be abandoned by the markets, companies should learn how to use Big Data in business correctly. Here are just a few use cases.
Dialogue with Consumers
Big Data allows companies to get a better understanding of customer experience and, consequently, make the right conclusions and maximize the value delivered to a target audience.
According to Vince Campisi, chief information officer at GE Software, the company should always be focused on customer excitement and delivering value to the target audience. To reach an outcome that is the most positive for both a business and customers, you should know how to efficiently manage a data set. If you do it, your customers will bring you new sources of data.
Big Data lets you collect information from social networks, web visits, call logs, and many other sources, opening up tens of new amazing opportunities of interaction experience enhancement. You can start delivering personalized offers to your customers, reduce the buyer’s churn, and manage issues proactively.
According to Zoher Karu, VP at eBay, modern businesses face a big challenge that spins around data safety and what is shared vs. what is not shared. Backing at his own experience, Zoher Kary states that customers want to share their data if there is the value returned.
Machine Learning and Continuous Intelligence
AI and machine learning have become the hottest topics today. Big Data is one of the most significant reasons why. Nowadays, we can not only program machines but also teach them. With the assets of Big Data at our disposal, we can advance machine learning models and use them in business.
Machine learning together with augmented analytics empowers continuous intelligence, real-time analytics integrated within business operations, processing current and historical data to prescribe actions in response to events. It is an innovative way to automate decision-making.
Rita Sallam, VP at Gartner, says that Continuous Intelligence is a great opportunity for analytics and business intelligence specialists to help companies make smarter decisions in 2020.
Global giants like Procter & Gamble and Netflix, utilize Big Data to forecast the target audience’s demand. These businesses create predictive models for new products and services by breaking down key parameters of past and current products and services and modeling the connections between these parameters and the prospective commercial success of the offerings.
Furthermore, P&G employs data gained from targeted customer groups, social networks, and test markets, to shape a clearer vision of new products.
Companies use Big Data to gather the buyers’ feedback. It helps them get a deeper understanding of how target audiences perceive their businesses. It empowers brands to make the right conclusions and take the needed actions to re-develop products.
Analyzing information from social media, you can make many useful discoveries about your audience’s feedback. It also allows you to classify data according to targeted groups and get insights about each of them. Moreover, you can break down the data according to different geolocation groups and demographic parameters.
A large number of marketing techniques aimed at gathering, processing, and analyzing the buyer’s behavioral patterns and payment information open a whole sea of fantastic opportunities for Big Data in eCommerce.
Big Data can personalize the online shopping experience and maximize conversions. Search and big data analytics let eCommerce businesses enrich data to improve desktop and mobile search experiences. Besides, predictive models and ML can help companies analyze log data to predict consumer preferences and make products even more personalized and likely-to-buy.
Perform Risk Analysis
The company’s success significantly depends on general economic and social factors and events. Sometimes, they can even determine the enterprise’s destiny. Big Data allows conducting predictive analysis, scanning social media feeds, and newspaper reports. It helps you always stay updated about the latest trends, developments in the industry, and important events that can affect your success. It enables you to prevent some risks or, on the contrary, immediately benefit from a situation on a global market.
The incredible possibilities of Big Data do not end here. Big Data tools can empower you to map a whole data landscape across the venture and analyze all possible internal threats. Being armed with this data, you can be sure that sensitive commercial information is secured and preserved by regulatory requirements.
Many enterprises, particularly those that manage financial data, credit and debit card information, have switched focus on Big Data as the best solution to ensure data safety and protection.
Some algorithms can turn factors anticipating mechanical issues into data sets, like the year, make, a model of equipment, log entries, sensor data, engine temperatures, etc. An in-depth analysis of these attributes of possible failures can help businesses prevent serious problems that may happen during manufacturing, for instance. Analyzing this data, organizations can provide maintenance timely, more effectively, and raise equipment uptime.
Undoubtedly, Big Data is still playing a significant role across various industries globally. Like Clouds, Big Data is a buzzword today and seems to be the same in the coming years.
When it comes to understanding your customers and adjusting your approaches to their needs, Big Data can be a defining factor in terms of modern digitalization. The right data, analyzed at the right moment, can bring businesses the skyrocketing success. In this article, we have described a few practices that will help you do it.
Do you want to learn more about how to use Big Data to drive your business efficiency and enhance process automation? Our team provides custom Big Data solutions to complement your current business model with technologies capable of mapping out and presenting massive amounts of data.