Big data has become a game changer in modern industries over recent years. Almost every company within every industry collects data, but it is the refinement of data into actionable intelligence that holds the key to success. The use of analytics to uncover patterns in large volumes of data allows organizations to improve business efficiencies and find new ways to serve their customers. The clever use of machine learning algorithms and analytical solutions allow costs to be reduced and revenues to be boosted. With global revenues for big data analytics software about to top €166 billion, the use of big data analytics certainly relates to big business opportunities. In this article, we’ve highlighted five of the leading industries paving the way with examples of how big data analytics is being applied.
Banking and Security
Banks have long had access to vast quantities of data, which has the potential to provide insight into how their customers live their lives. Historically, the primary challenge the industry has faced is how to extract real value from the large volumes of data. It is the newer generations of analytical tools which are helping them turn a corner, making banking one of the areas predicted to see tremendous growth from big data. More powerful analytical software will help firms in the sector establish correlations in customer behaviour, analyze historical records, and determine trends and new market opportunities.
One company leading the way within the banking industry is American Express (Amex). The long-time CEO, Ken Chenault, is a champion for the transformative impact that big data holds. Amex not only deals with vast quantities of data but data is core to the firm’s historical DNA. Amex has a closed loop system whereby it issues its own cards, acting as both the issuer and acquirer. Amex is able to analyse trends on cardholder spending, build algorithms to attract customers and match merchants with the right customers. Recently Amex has started looking at customer behaviour using machine learning models. By integrating over 100 variables, using sophisticated predictive models, the company is able to understand which customers will move to competitors and who will remain loyal. In their Australian market, they are now able to forecast 24% of the accounts that will close within the next four months. This gives the company the power to take measures to retain those customers.
The healthcare industry is another sector that has access to vast amounts of data. However, traditionally the industry has struggled with using the data to reduce the rising cost of healthcare, in part due to the inefficiencies of legacy systems. The digitization of health records is enabling the industry to make huge leaps forward with regards to big data analytics. Using health data of populations or individuals has the potential to prevent epidemics, cure disease and cut costs. With the use of healthcare data analytics, prevention is truly becoming better than cure, with the ability to pick up early warning signs of serious illnesses making a considerable impact.
Aetna is a medical company that uses big data to provide patients with quick advice about their ailments. All individuals need is to log in to their site, provide their details and list their symptoms. The system is able to list nearby facilities that can help in the case of a crisis. An example of this is Aetna’s use of big data analytics to predict and prevent type II diabetes. It is very likely that metabolic syndrome presents itself first, a group of five risk factors that may coincide. Any adult with more than three risk factors is five times more likely to develop diabetes. The use of big data can predict the one-year risk of developing the syndrome and can offer ways to mitigate the risks of further complications.
Government agencies are renowned for storing student data, from student behaviour to test results, for the use of statistical analysis. However, the application of big data analytics, to truly unravel the learning process, holds some exciting changes for the education industry. Big data has the potential to revolutionize both the way students learn and the way teachers teach. Evaluating how students understand is the greatest responsibility of an educator. The challenge is the integration of data from multiple sources and vendors, but the opportunities are vast.
The Learning and Management System deployed by The University of Tasmania in Australia is an excellent example of the use of big data analytics in education. The university has over 26,000 students using their system. The system tracks when they log on, how much time they spend on various pages, as well as the overall progress of each student over time. The system is also used to evaluate teacher effectiveness. A teacher’s performance is measured against multiple variables such as student demographics, aspirations and behavioural classification. The outcome is that the University is able to tailor its teaching accordingly to improve results and the experience for students and teachers alike.
The retail industry is evolving rapidly from traditional bricks and mortar retailers to modern e-commerce traders. The way that consumers shop is changing and the line between online and offline is blurring. Retail, as with our other industries, has access to an enormous amount of data. Data comes from loyalty cards, POS scanners, RFID and more. The application of big data analytics enables retailers to take this data and make vast improvements to customer service and to ensure they match the right person to the best product. Retail data analytics is becoming the new norm, allowing the forerunners to deliver unprecedented value to their customers.
Target, the second largest department store in the US, has used big data to create a pregnancy detection model. Target’s Guest ID allows it to track purchase history, credit card use and survey responses along with many other consumer activities. This data was supplemented by purchased demographic data. Target then compared shoppers who had registered on their baby-shower registry with their guest ID. This allowed them to pinpoint changes in shopping habits based on the stage of their pregnancy. By applying these behaviours to all shoppers, Target was able to detect women who were pregnant without being notified, and from this they created their pregnancy prediction model. By offering specific promotions to their pregnant customers, the company was able to grow its revenues by €23 billion in 8 years. There are of course privacy considerations to be taken into account, but it is an incredible application of big data that offers food for thought.
The manufacturing industry is highly competitive and innovative technologies have allowed them to increase production capacity and streamline operations. Big data analytics is now providing a further way for businesses to boost the productivity and profitability of their operations. The manufacturing industry has traditionally lagged behind with the analysis of data due to a lack in IT capabilities. However, cheaper computational power and advanced analytical software are giving them the opportunity to make huge leaps forward. Predictive maintenance, performance analysis and improved strategic decision making are set to make a big change.
Intel is a company that has been harnessing big data in the production of its processor for some time. Each chip has to undergo around 19,000 tests when it comes off the production lines. Intel has used big data analytics to reduce the number of tests needed for quality assurance. Data was analysed from the manufacturing process to reduce the length of tests and focus on specific details. The outcome was a €3 million saving in manufacturing costs for a single line of Intel Core processes.
Businesses need to take advantage of the opportunities offered by big data to allow them to innovate and compete. Interpreting data provides the opportunity to vastly improve customer service, boost production efficiency and to develop new data-driven products and services. As we’ve demonstrated, examples span across all industries with countless potential applications. Big data is here to stay, and with the rise in digital technologies, the amount of data businesses can capture will only continue to soar
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