Why You Need Big Data: Big Data Use Cases in 8 Industries

Big data analytics can help predict customer behavior, drive sales, identify fraudsters, and prevent industrial accidents. We will tell you how you can use big data in different areas and show big data cases from real companies.

Big data in industry: predicting accidents and optimizing production

Predictive analytics. Nowadays, IoT systems are often introduced in production: they install sensors on equipment and in premises, and then analyze the data collected by them. This data is big data, it can be used to monitor the condition of equipment, simulate production processes, identify and prevent failures.

Reducing product costs and optimizing production. If you collect a lot of data about the operation of machines, the percentage of scrap and each stage of production, and then analyze them, you can understand:

  • under what conditions marriage occurs most often;
  • which stages of production are spent most of the time and why;
  • what product tests are of little use and do not provide new information;
  • how you can optimize and speed up work at individual stages;
  • how to use fewer consumables.

All this helps to reduce costs and reduce production costs, which means, increase profits.

Search for new deposits. When extracting natural resources, deposits often have to be looked for almost blindly. However, with the help of big data analysis, it is possible to detect patterns, study the state of soils, the presence of underground voids, the temperature of rocks – and thus effectively search for promising deposits, comparing new areas with already known analogues.

Big data in logistics: cargo transportation planning and route optimization.

Transportation planning. In logistics, the transportation of goods is influenced by many different factors: the loading of warehouses, traffic jams, the state of the fleet of cars, the location of gas stations. When all these factors are brought together, collated and analyzed, routes and delivery times can be planned more efficiently to avoid transport downtime.

Reduced delivery time. Taking into account various factors in the transportation of goods helps not only to plan cargo transportation, but also to reduce delivery time: choose the shortest routes, avoid traffic jams and difficult sections of the route, save gasoline.

For example, in logistics there is a “last mile problem” – it costs about 28% of the total shipping cost. This happens because the driver has to drive into yards, look for parking, stop and turn around.

Big data in retail: personal offers and optimization of product display

Increased sales. Information about customer behavior in a store or on a website is big data. Based on them, you can assume what exactly people will buy and use this to increase sales:

  • offer suitable related products while shopping;
  • arrange promotions and discounts on goods that are relevant at this time for most customers;
  • send out personalized discounts and offers, for example, offer young mothers discounts on baby products;

For example, online retailer Amazon uses big data for its product recommendation system. Their system is based on machine learning – it takes into account the behavior of other shoppers, your previous purchases, the season, and dozens of other factors.

As a result, 35% of all sales on Amazon generate recommendations, and 86% of service users claim that recommendations influence their buying decisions.

Optimization of the display of goods. Big data can also be used to position products on shelves: analyze customer preferences, product information, shape and color of packaging to boost sales.

Big data in finance: assessing solvency and improving service quality

Assessment of solvency. It is important for banks to issue loans only to those who will definitely be able to repay them, so as not to incur losses. Big data analysis helps to analyze customer solvency and assess risks.

Improving customer service. Banks also use big data to make personalized offers to customers. It’s like in online stores, only banking products and services are “featured products”.

Big data in HR: hiring employees and preventing layoffs

Hiring employees. Early recruiting often requires weeding out those who have little or no interest in the job. This problem can be solved using big data: collect information about candidates and resumes, identify patterns in them, use this data to develop scripts or train robots and neural networks.

Optimization of HR strategy. Companies often analyze customer behavior, and the same principles can be used to analyze employee behavior: track their performance, overtime, signs of fatigue or burnout.

Google has a People Analytics department that analyzes big data related to employee behavior. They have several successful big data use cases:

Back in 2002, the company analyzed the work of thousands of managers and created “8 strategies for the behavior of Google managers.” Strategies are now regularly supplemented and used in hiring and training employees.

Analysts constantly monitor the behavior and condition of employees: how much they earn, how often they stay late at work, how effective they are. Based on this, a decision is made on additional payments or the extension of vacations.

Special algorithms warn that a particular employee will soon want to quit. This helps managers to react in time.

Big data in medicine: disease forecast and patient data collection

In the medical field, in the future, big data can be used for diagnostics and treatment, most interesting projects are still at the development or testing stage, but there are already implemented ones.

Predicting diseases. If you collect enough data about patients, you can make assumptions about what they are sick now or may be sick in the near future.

Maintaining a patient database. Many patients have a long medical history that is often kept by different hospitals and doctors. To see the full picture, you need to collect data into a single database. With the help of big data technologies, you can not only organize such a database, but also set up convenient search and analytics in it.

Big data in education: helping you choose courses and preventing dropping out

Help in choosing courses. In education, big data projects help students with career guidance: they analyze their abilities and help them choose a direction of study and a future profession.

Prevention of deductions. In the United States, 400 thousand students a year are expelled from universities. To solve this problem, Virginia Commonwealth University analyzed dropout data and built an algorithm that identifies students at risk.

The system notifies when a student becomes problematic. And then they work with him individually, for example, they offer a transfer to another course or the help of a tutor. At the end of the semester, the number of students who completed the course increased by 16%.

Big data in marketing: increasing profits and attracting customers

Creation of commercially successful products. Big data on customer behavior can help predict demand and determine whether a product will be successful before launching a product to market.

For example, Netflix uses such technologies. More than 150 million people use this platform to watch movies and TV series. The company analyzes the behavior of customers: which TV series they watch, which they throw, which moments they rewind. This helps to better understand the psychology of viewers and competently recommend new series to them.

Targeted advertising and reduced customer acquisition costs. Big data helps you better target audiences and deliver targeted ads more precisely.

For example, retailer Ozon uses big data for targeted ads and product recommendations. For this purpose, user logs are collected on the website and in the mobile application – they record everything that they have viewed, scrolled through, and clicked on. Based on the data, a forecast is made: whether the user is planning a purchase, what category of product is likely to interest him. Relevant products are shown in targeted ads.

Ozon also tested shelves of recommendations for various products. The users were divided into two groups: for the first recommendation, they were manually compiled by experts, for the second, they were collected automatically based on log data. As a result, sales in the second group were 10 times yours.

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