Everyone is looking for new ways to turn data into money (monetize data assets). Everyone is on the lookout for new ways to extract value from data. However, data ingesting and modelling are merely means to an end. The goal is not simply to generate more reports, dashboards, heatmaps, knowledge, or wisdom. The goal is to make fact-based decisions, guide machine learning, and take action. Another goal is to empower users to do data discovery and insight generation without involving IT teams so-called User-Driven Business Intelligence
Industry or Functional use-case
- What do you really want to accomplish?
- Enhanced customer loyalty?
- Improved customer engagement?
- Increased wallet share through cross-selling?
- New clients?
- Reduced attrition?
- Data processing that is both cheaper and faster?
- In other words, what is the application?
Big Data Workflow
Big Data use-case by Various Industries
According to Gartner’s 2019 CIO Survey, data and analytics, as well as AI/machine learning, were named the top game-changing technologies by 48 percent of healthcare provider CIOs. Big data and analytics are transforming many industries, and one of the most visible areas where big data is transforming processes is healthcare. New patient data sources can provide deeper insights to help healthcare providers improve care quality and streamline operations.
Breaking down data silos to combine medical data from multiple sources and get a comprehensive view of your business from one source, rather than various, disparate sources, is one of the most difficult challenges of big data in healthcare.
A truly data-driven healthcare analytics solution should be able to connect all of your data sources and analyse structured, unstructured, and real-time data. Your insights are not as effective as they could be unless you can incorporate all of your patient data, including diagnostic information, doctor observations, and real-time data from medical equipment.
To target its customers, the insurance industry has always relied on data analytics. Statistics are used by various insurance companies to segment their customers, including travel insurance companies, health and life insurance companies, property and casualty insurance companies, and so on. Accident statistics, policyholder personal information, and third-party sources all aid in categorising people, preventing fraud losses, and optimising expenses.
The shift to digital platforms has created new sources of information that can be used to understand a customer’s complex behavioural patterns and precisely determine his or her segment. Big data, in the insurance industry, refers to unstructured and/or structured data that is used to influence underwriting, rating, pricing, forms, marketing, and claims handling.
Travel and tourism is an evergreen industry. People in general enjoy travelling. People’s insatiable desire to travel has fueled the growth of startups in the travel industry.Many new travel agencies or businesses are entering the market. And there is no denying that an increase in travel will result in an increase in the generation of data associated with it.
As a result, travel companies collect and store massive amounts of data. They collect data on transactions, flight paths, customer data, check-ins, and so on at every stage of the travel journey.
Big data assists in efficiently combining, managing, and storing all of this information. It also helps to make the customer feel more appreciated and better served, which leads to increased revenue.
The gaming industry is currently on the rise. With over 2 billion players worldwide, the gaming industry is a source of enormous revenue that is expected to grow further. With an increasing number of users, the amount of data to be processed grows. Users’ playing time, interaction time, quitting point, activity peaks, results, scores, and so on provide a wealth of data for analytics.
Understanding the value of data for gaming optimisation and improvement drives specialists to look for new ways to apply data science and its benefits in the gaming industry.
One of the most widely discussed topics today is the discovery, consumption, and implementation of energy. Renewable and reusable energy is extremely important at both the individual and business levels. Today’s energy consumption is massive. Today, the energy sector powers and supports every process.
Every entity requires more energy than ever before, and they want it at a low cost. It was a difficult task in the past, but the advancement of Big Data and Analytics has made it a real possibility. Big Data enables businesses to collect, store, and analyse massive amounts of data (terabytes and petabytes). For years, the power and energy industries have worked with big data and routinely processed large amounts of information. Companies can either set up the infrastructure on-premises and use Big Data with internal consultants, or they can use Big Data in the cloud and have the cloud provider manage the entire infrastructure. Whatever path businesses take, the value of Big Data is undeniable.
Big Data is brimming with valuable, unanswered questions! The real challenge is distinguishing the actual predictive indicators — the signal from the noise — in the data. Similarly, big data analytics will make new scenarios possible.
Organizations will need new technology with clear implementation strategies, iterative test-and-learn environments, and data science talent to compete.
Companies that compete on analytics and data-driven services tend to experiment with a large number of small models or iterate quickly on large amounts of data. This allows for rapid data exploration in order to identify unknown relationships and trends in order to develop new products and services.