Unless you’ve been living in a cave for the past couple of decades, you know how important data has become virtually every business and every consumer on the planet. How much data is out there? A recent calculation estimates that the entire digital universe will reach 44 zettabytes in 2020. For perspective, that’s “40 times more bytes than there are stars in the observable universe.”
The key, of course, is knowing how to mine that data, analyze it, extract value from it, and apply it to a tangible business solution. Companies across industries and government organizations are using big data analytics and data science technologies to change the way they operate and to create solutions that impact people in almost every conceivable way.
According to a study by Accenture, 79 percent of executives feel that companies not embracing data analytics will lose their competitive position, and 83 percent have pursued big data projects to seize competitive advantage. The stories they are now telling about their successes are exciting. Here are four industries that are using advanced data analytics to thrive in a data-driven world, and important skills you’ll need to be a part of it.
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Industries Using Data Analytics
Retailers have always been laser-focused on putting the right products in the hands of the right consumers. Predictive data analytics are now being used to not only offer to buy recommendations (such as the next best offer on a shopping site) but also to hyper-personalize the entire customer experience online. An Adobe study found that companies that prioritize and personalize the customer experience are 3 times more likely to exceed their top business goals. Based on past user behavior, companies can dynamically offer promotions and content to keep users engaged.
Analytics is also used to optimize pricing. According to a PWC study, 60 percent of consumers believe pricing is the most important factor in purchasing a product. To address this, Walmart developed its analytics hub called Data Cafe that leverages more than 40 petabytes of customer data to understand buying trends at its stores on the fly. An algorithm can identify a sudden drop in sales of a particular item and offer more incentivizing pricing to boost sales back up.
Developing efficient agriculture methods is vital to every country, and data analytics are now changing the way farmers grow and provide food. One key example is in Africa, where agriculture is the largest industry, but much of the land is currently underutilized. Farmers in Africa often lack the financial resources to invest in machinery, fertilizers, and technology to optimize their crop yields. Still, it’s hard to get loans from banks who don’t have great visibility into farmers with no credit history.
Research collaboration at the MIT Institute for Data, Systems, and Society (IDSS) is building a data-driven platform to analyze risk-sharing to upgrade farming practices. Data science helps predict the value of advanced farming practices (such as different types of fertilizers or irrigation systems) to incentivize lenders to provide lower-risk loans. By using data science and machine learning techniques, they can quantify the predicted value of added resources as well as the probability of success. Underperforming farms, in particular, stand to gain the most from these data-driven agriculture programs.
Predictive analytics is having a big impact on the banking industry as well. Much like retail, banks are learning to consolidate internal and external customer data to build a predictive profile of each banking consumer. Financial institutions can use the insights they gather to provide consumers with value-driven services that are customized for each individual, rather than pushing out mass marketing programs that treat all consumers alike.
Three key examples were recently cited in a McKinsey study:
- A European bank trying to boost retention of inactive customers turned to machine learning algorithms to predict which customers were most likely to reduce their activity with the bank. The data-driven program helped create a targeted marketing campaign that lowered customer churn by 15 percent.
- A US bank used advanced analytics to study discounts private bankers were offering to customers, proving that unnecessary discounts were being given too often. The problem was corrected and drove eight percent higher revenue within a few months.
- A top Asian bank used big data to analyze customer information such as demographics, products purchased, transaction data, and payment tendencies. By discovering patterns in the data, it created 15,000 micro-segments to target customers more accurately and increase the likelihood to buy by three times.
4. Government and Public Sector
Public sector organizations across the globe are using data analytics, natural language processing (NLP), machine learning, and speech and image recognition to solve problems before they erupt into crises. A report by Deloitte highlighted various examples, including:
- Crime—The Durham, NC police department used AI to observe patterns and relationships in criminal activities to identify crime hotspots so police can intervene quicker (reducing violent crime in the city by 39 percent in seven years).
- Human trafficking—The Defense Advanced Research Projects Agency (DARPA) developed a platform to monitor dubious online ads and connect them to human trafficking crime rings. This data-driven technology has helped agencies locate and rescue millions of victims and prosecute traffickers.
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