Data is the new gold. This idea is not new but that doesn’t make it any less true. A treasure chest of deep, actionable insights, big data has the power to fuel digital transformation across industries. We’ve examined some of the real-world big data use cases to check how businesses put their data to work and adopt their best practices.
Forward-thinking leaders are implementing big data initiatives to take advantage of a range of benefits, from enhanced customer experience, through improved enterprise-wide performance, to new revenue streams.
There is definitely no shortage for the use of big data. In this article, you’ll discover how data-driven decisions help companies gain a competitive advantage and read about the big data use cases from the following industries:
- Media and entertainment
Of all the industries that are likely to benefit from the use of big data analytics, telecom is best placed due to the sheer amount of data flowing through operators’ networks. Telco operators have access to exceptionally valuable data sources, including network data, customer profiles, device data, geolocation data, customer usage patterns, and more. Fueled by these insights, telecoms can increase profits throughout their value chain.
Major communication service providers (CSP) such as AT&T, CenturyLink, Swisscom, T-Mobile, and Vodafone have already adopted big data initiatives into their telecom software solutions. This has given them a variety of benefits, including forecasting demand, planning network capacity, and gaining a 360-degree customer view. And with the battleground for customers as competitive as ever, improving customer experiences is on the front burner.
Two-thirds of CSP respondents identified customer-centric objectives as their organization’s top priority.
Big data use case in telecom
One of the use cases for big data analytics is the continuous monitoring of network conditions and customers’ equipment in real time in order to predict potential failures and address the problem before users reach out. The largest telecom operator, AT&T, collects 30 billion data points per hour to assess network quality. This process helps the company save hundreds of thousands of dollars in repairs while ensuring uninterrupted service and top-notch customer experiences.
With a world population of over 7 billion people, a changing climate and the depletion of viable farmland, modern-day agriculture faces significant challenges. To cope with these, the industry is embracing promising new technologies, such as IoT, cloud computing, big data, and analytics. Smart sensors and connected devices produce an unprecedented amount of data, which reinforces decision-making and has ushered in the era of smart, data-driven farming.
AgTech companies are making use of big data’s predictive capabilities to achieve and/or streamline the following:
- Analysis of soil types and fertility levels
- Optimized resource use
- Increased crop yields
- Forecast of climate conditions
- Decreased downtime and reduced costs
- Management of supply chains
Big data use case in agriculture
To maximize yield potential, farmers need to take into account a myriad of factors, including weather, soil quality, moisture and nutrient levels, seed placement, frequency and dosage of fertilizers and pesticides, and so on.
John Deere, a global leader in precision agriculture, has created an entire ecosystem that seamlessly connects agricultural equipment fitted with sensors and a cloud-based portal to monitor activity in real-time, analyze performance, share insights, and make data-driven decisions on what to plant, where and when.
Source: Digital Initiative
The scope for big data analytics use cases in finance and banking is immense. Traditionally considered a data-intensive sector, the financial industry is awash in data, both from internal structured sources (transaction data, trading systems, market data, securities data, etc.) and unstructured sources (social media, news and articles, customer feedback, and even third-party databases).
There are many ways that financial institutions leverage predictive analytics capabilities to transform themselves and gain a sustainable competitive advantage.
Advanced customer segmentation. Leveraging abundant customer data such as demographics, behavior patterns, device data, and more helps financial institutions create detailed persona profiles.
Real-time stock market insights. Machine learning algorithms can analyze stock prices, as well as social and political trends that impact the stock market, to help analysts make informed investment decisions.
Security and fraud management. Real-time big data processing allows financial institutions to stay on top of any suspicious activity and identify fraudulent transactions.
Accurate risk analysis. By taking into account both traditional and non-traditional data sources, ML-powered algorithms can better predict credit default risk and streamline the underwriting process. Find out how a FinTech company leveraged our services to develop a SaaS lending platform that evaluates a potential borrower’s credit score and manages the entire loan lifecycle
Find out how a FinTech company leveraged our services to develop a SaaS lending platform that evaluates a potential borrower’s credit score and manages the entire loan lifecycle
Big data use case in finance
As the risk of customer defaults rises, accurate credit risk assessment remains a key focus for financial institutions. Online loan provider Kreditech leverages data-driven analytics to improve credit scoring and balance risk appetite.
In addition to conventional data, Kreditech uses all available data points from an applicant’s social media posts and connections, the company website interaction pattern, device and location data, eCommerce activity, and more, amassing over 20,000+ pieces of information. Then, the ML-powered scoring engine crunches through the collected data to determine the applicant’s eligibility for the loan — all in a matter of minutes.
With a variety of options available today, consumers easily switch brands at the slightest dissatisfaction. To win buyer loyalty and stay ahead of the competition, retailers make use of big data to gain an understanding of shopping behavior and thus become truly customer-centric. The Global Big Data Analytics in Retail Market size is expected to reach $14.1 billion by 2026, rising at a market growth of 23.4% CAGR.
The Global Big Data Analytics in Retail Market size is expected to reach $14.1 billion by 2026, rising at a market growth of 23.4% CAGR.
It comes as no surprise that brands rush to invest in retail data analytics. In the era of the connected customer, retailers sit on a data goldmine that can be used to enhance customer experiences and improve revenue streams. Other benefits include:
- trends and demand forecasting
- dynamically optimized pricing
- marketing campaign effectiveness analysis
- cross-selling and upselling
- supply chain visibility
- streamlined back-office operations.
Learn how we helped a global retailer gain visibility into its customer engagement ecosystem through a scalable data management solution and powerful data visualization tools
Big data analytics use cases in retail
The retail behemoth Amazon surely knows what big data is, as the company hosts its estimated 1,000,000,000 GB of data on more than 1.4 million servers. One of the main ways that Amazon uses its data is through its recommendation engine. By observing touchpoints such as what items customers look at and buy, and whether they leave reviews, Amazon is able to deliver highly personalized recommendations. In fact, 35% of Amazon’s revenue is generated through the engine.
However, it’s not only global retail chains that make use of big data analytics. Small businesses, too, can benefit from a data-driven approach to their operations.
Pendleton & Son is a local butcher located in London. After decades of premium service and steady customer base growth, the store found itself competing against a newly opened supermarket on the same street.
To combat falling revenues, the Pendletons fitted their store window with simple sensors to monitor footfall and measure window display effectiveness. The new insights allowed them to better tune their messaging and upgrade displays. What’s more, the data also pointed to increased foot traffic in the evening hours, prompting owners to try opening at night and serving food to people returning from pubs at that time. The strategy proved successful, driving an additional revenue stream for the business.
Of all the industries that make use of big data analytics, healthcare has the potential to gain the most. Clinical research, digitized medical records, medical imaging, advances in genomics, and a variety of mHealth solutions and wearables have all led to a tech revolution in the healthcare industry.
Healthcare analytics help make sense of these massive data sets and drive not only positive but also life-saving outcomes. By relying on data-powered insights, physicians are better equipped to predict the risk of a patient developing a disease and accurately diagnose the condition, which reduces healthcare costs and improves patients’ quality of life.
Source: Data Flair
Learn how a cloud-based IoMT solution helps predict health risks and deliver personalized patient care
Big data use case in healthcare
Apixio, a leading provider of healthcare analytics services, leverages machine learning to take clinical decision-making to the next level while improving operational efficiencies. By extracting and analyzing health records, the company empowers healthcare providers with granular insights into patient health. In 2018, Apixio’s ML-powered platform analyzed 4.5 million health charts, reducing the time and effort to code health charts by 80%.
Media and entertainment
The digital media industry is evolving at a rapid pace. Over half of the global population now uses social media; live and on-demand video streaming is also on the rise. To effectively compete in this shifting landscape, media and entertainment (M&E) companies need to generate value throughout the content delivery lifecycle while ensuring seamless omni-channel experiences for customers.
That’s where big data analytics comes in. Today, M&E brands can track cross-platform content consumption habits, viewer engagement, social media activity, subscriptions, response to marketing campaigns, and more. Once extracted, enriched, and analyzed, this data helps brands in a number of ways:
- Creating a single customer view
- Predicting audience behavior
- Delivering highly personalized content
- Reducing customer churn
- Increasing customer lifetime value
- Ensuring more efficient content discovery
- Enhancing ad targeting
Big data use case in media and entertainment
A perfect example of a big data use case in media and entertainment is Netflix. The streaming giant leverages big data analytics to meet the needs of over 195 million paid subscribers. To that end, Netflix implements ML-powered algorithms that analyze customer behavior, preferences, and viewing patterns to come up with customized offers — 75% of what people watch on Netflix is based on personalized recommendations.
The bottom line
Information appears to be a game changer, giving rise to numerous use cases for big data across industries and domains. With the ability to provide deep actionable insights and uncover hidden patterns, big data analytics models help businesses deliver more customer-centric services, streamline their front and back office operations, and drive new revenue streams.
With hands-on experience in big data engineering, Intellias enables companies to build up their business intelligence capabilities and get a leg up on competitors. Contact our experts to start creating value from your data.