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From fashion to financial services: How big data is transforming businesses

At a recent ‘Big Data for Industries’ roundtable at our UK headquarters, my colleagues and I discussed how different sectors are challenging convention to capitalise on the opportunities offered by data analytics. Many interesting points were debated, so I wanted to take the opportunity to share some of the key take-aways from our discussion through this blog post.

In today’s data-driven economy, businesses across industries are harnessing big data analytics to cope with the economic pressures they face, innovate through new services, and to gain a competitive edge. Financial services, scientific research and retail are prime examples of sectors where data analytics is propelling fundamental changes in how things are done and generating long-term positive outcomes for organisations and the society as a whole.

In financial services, big data is freeing up businesses from the constraints of years of data hoarding by simplifying analysis and enabling more meaningful insights. This enables organisations to sharpen customer service, liquidity risk management, business predictions, claim management, fraud detection and compliance.

For example, a leading health insurance company uses SAP HANA to analyse data for more than six million hospital cases each year to identify potential health risks to its clients, improve patient care, and offer preventative measures that extend life.

One of our banking customers uses SAP HANA for profitability analysis across 31 markets in Europe. Before, the company ran its analysis on a monthly basis, covering one market and one product only. This took hours. Thanks to SAP HANA’s in-memory capabilities, the company is now able to analyse all products and 31 markets simultaneously, drill deep into individual customers, and to run the analysis on demand, in a matter of minutes. This has had a fundamental impact on the business, making it more agile and enabling faster decision making.

While many banks have embraced technology, a recent study by SAP and a group of leading academic institutions found that more needs to be done to close the gap between regulators’ expectations and banks’ ability to meet compliance and reporting requirements. The study identified mobile, in-memory computing and cloud as the biggest technology trends to shape banking in the future.

Big data is helping to advance people’s understanding of the planet’s biodiversity too. SAP is currently working with the International Barcode of Life (iBOL) project to help accelerate the building of a database containing DNA barcodes for every species on the planet. The database hosts more than 400,000 species at present, but to identify all the species on the planet (estimated at between 10 million and 100 million), iBOL is looking to expand the number of people contributing to the research. SAP and iBOL have developed the LifeScanner app, available on iTunes, to crowdsource the collection and analysis of DNA barcodes. Anyone can use the app to collect a tissue sample or whole organism, send it off for analysis and get a species identification using DNA barcodes. The published data will be made available to researchers and students for analysis through SAP HANA and SAP Lumira.

Data analytics will soon be used to tackle fraud in the food supply chain too, as mislabelling continues to plague the industry. It can be hard to tell apart dry oregano and basil, for example, because certain herbs and spices can look very similar after processing and packaging. To help address this challenge, SAP and Tru-ID are exploring solutions to increase visibility in the supply chain using SAP HANA and DNA-based verification testing.

In retail, big data is both a necessity and a strategic advantage, and data analytics plays a key role at three levels: store placement, assortment strategy and customer engagement.

Think about a high-end clothing store at an airport. At the top level, thanks to big data, the location of the store is not random – it’s close to a gate with a high proportion of affluent passengers to maximise sales. At the second level, the store uses data to choose the products sold based on the type of shoppers that walk through the doors at different times of year – for example ensuring that the store is stocked with children’s t-shirts during school holidays. Drilling deeper, the third level is how the store uses data on its customers and its stock to sell more effectively. This could include making recommendations based on what the customer bought on their last visit, or even what the weather is like in their destination. 

Big data analytics is empowering retailers to meet the expectations of their increasingly discerning customers, who expect a connected shopping experience – both online and offline. The popularity of online shopping means that consumers are used to being given recommendations based on their previous purchases, and receiving discount codes to thank them for their loyalty. Data analytics enables retailers to join the dots across their online, offline, mobile and social channels to create a real time 360° view of their customer and provide a better omni-channel customer experience.

Banks, scientists and retailers are leveraging big data to simplify how they operate, enable faster decision making and to serve their customers more effectively, demonstrating the growing value of data across industries. It’s all about challenging convention and asking yourself, “Is there a better way of doing this?” For organisations wanting to turn the masses of data they gather into meaningful, actionable insights, the opportunities are endless.

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