These examples in five industries demonstrate the value of advanced data analytics across different disciplines and the benefits it delivers for different use cases.
Advanced data analytics capabilities are more accessible than ever. Organizations in a wide range of industries can now collect, store, and analyze rich data at scale without needing massive IT footprints or hard-to-find expertise. With the right set of tools, it’s possible to abstract away so much technical complexity and accelerate time-to-insight.
The challenge is knowing how to incorporate different tools into a single streamlined data ecosystem. This is where Amazon Web Services (AWS) shines. AWS offers solutions across the end-to-end data management lifecycle, from ingestion to visualization. Everything teams need to leverage advanced data analytics exists under one roof. That means IT leaders don’t have to stitch multiple technologies together. As a result, we’re now seeing a proliferation of use cases for advanced data analytics across various sectors.
In this article, I’ll highlight advanced data analytics in five industries: healthcare, retail, finance, manufacturing and entertainment. My hope is that this article will spark new ideas for companies within these industries, as well as inspire those from other sectors to pursue similar ideas.
Advanced Patient Data Analysis in Healthcare
Advanced data analytics capabilities are paramount in the healthcare industry. Healthcare providers have access to tremendous amounts of data with enormous potential. The key is being able to consolidate data from different sources – smart medical devices, electronic medical records (EMRs), physical documentation, etc. – and identify patterns that lead to valuable insights. When this happens, both personalized medicine and population health improve, as clinicians can make better decisions based on real-world data.
We worked with one healthcare organization that wanted to implement a data lake on AWS to eliminate errors tied to duplicate errors and speed up complex analyses. We set up a new landing zone and implemented several AWS solutions, including AWS Glue, to revamp the client’s data processing pipeline. With the new AWS infrastructure, the organization’s researchers can now execute complex analyses much faster without compromising security.
See also: It’s Time to Stop Treating Predictive Analytics as a Data Science Project
Advanced Customer Behavior and Sales Forecasting in Retail
In the retail space, advanced data analytics is unlocking new levels of sales forecasting precision based on past customer behaviors. Retailers now can track highly nuanced activity across acquisition funnels that span both physical and digital spaces – web pages, mobile apps, brick-and-mortar storefronts, and more. Those who can combine information from all these sources and build cohesive customer avatars can better predict what buyers want and meet those needs faster than competitors.
In the retail sector, we partnered with an eCommerce flower business that wanted to upgrade its online shopping experience. Our team implemented Amazon Personalize, a machine learning service, to deliver real-time, relevant product recommendations to shoppers based on past behaviors or certain attributes. Our client doesn’t have to maintain an in-house data science team and the implementation continues to improve over time.
Advanced Risk Assessment and Fraud Detection in Finance
Mitigating risk and detecting fraud are huge priorities for financial services groups today. Cybercriminals have more planes of attack than ever, and malicious GenAI will continue to be a problem for years to come. Finance companies have a responsibility to help keep their customers’ identities and financial information safe. This means catching fraud and risky behavior quickly before it evolves into bigger problems.
We’ve worked with many financial services organizations, including one in the gift card resale niche, that wanted to minimize errors, fraud, and inefficiencies tied to the shopping experience. We developed a new Point-of-Sale (PoS) solution that benefited both the business and customers immediately before a busy holiday season by ensuring safe transactions through a secure and easy-to-use tool.
Supply Chain Optimization in Manufacturing
Manufacturers are using advanced data analytics to optimize their supply chains and gain visibility into distributed operations. There is also a lot of opportunity for manufacturers to make their supply chains and overall operations more efficient by increasing visibility into all activities, including those without any human involvement. Strategically deployed IoT sensors empower leaders to develop more resilient and adaptable supply chains and facilities that don’t crumble under pressure.
We completed a project for a manufacturer of building safety systems that wanted to develop a new earthquake sensor software product using IoT technology. We used services like AWS Lambda and AWS IoT Device Shadow to build a reliable IoT infrastructure that aggregates and analyzes data about potential catastrophic events from disparate devices. The new solution is designed to handle high volumes of information and simplify device management for our client.
Personalized Content Recommendations in Entertainment
The entertainment industry has been using advanced data analytics for years to improve content recommendations. Going forward, entertainment platforms will continue to refine their algorithms and incorporate more data points that further personalize end-user experiences. This process of continual fine-tuning is essential for organizations in this space. Customer loyalty depends on being able to deliver high-quality recommendations.
Many of our past analytics projects have been with organizations in the media and entertainment space. We worked with PBS to create a personalized recommendation engine on top of a sophisticated MLOps capability. Even after the project, PBS’ recommendation engine continues to improve as it gathers more data from users.
