Data Analytics Use Cases: How Businesses are Creating Value from Data

It’s no surprise that so many businesses are transforming the way they capture and use data. Data-driven organisations are 23 times more likely to acquire customers, six times as likely to retain customers and 19 times as likely to be profitable as a result.

It’s no longer just global tech giants like Google, Amazon and Facebook who are creating strategies to gain a competitive advantage from their data. Digital technologies have transformed the way customers interact with businesses across every sector, generating huge volumes of data for organisations that they’ve not had access to before.

We take a look at some of the industries and companies leading the way in the way they creating value from their data.

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The banking and financial services sector is one such area that is being revolutionised by advances in big data analytics in a number of ways.

Fighting Financial Crime with Data

In the UK alone, banks are spending £5bn p/a to combat financial crime. And with one in ten insurance claims fraudulently filed, the world’s leading insurers are also increasingly using artificial intelligence and machine learning algorithms to analyse millions of data points enabling them to quickly detect fraudulent transactions that would otherwise go unnoticed by humans.

Using Big Data to Manage Investments

In the investment management sector, robo-advisors are redefining the way investments are managed. By providing digital financial advice and investment management services based on algorithms that automatically allocate, manage and optimize clients’ assets, investment managers can keep human intervention - and costs - to a minimum.

The large investment houses are increasingly getting involved with Vanguard, Schwab, Fidelity, Merrill, TD Ameritrade and others all offering robo-advisor services.

Data-Driven Risk Management

Data analytics is also being increasingly used by banks to develop risk management solutions.

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Singapore-based UOB Bank has been using big data to improve its ability to react quickly to new risks. It has launched a system, made possible by in-memory technology, that reduces the time taken to calculate its total-bank risk (value at risk) from around 18 hours to just a few minutes, and they aim to be able to conduct real time stress tests in the near future.

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Morgan Stanley also successfully implemented big data technology within its existing business models. By enhancing its big data processing capabilities, the US bank has been able to increase the volume and quality of analysis it conducts on its portfolio, using AI to recognise patterns and improve its financial risk management.

Data analytics and AI in other sectors

The energy sector is also being transformed by big data analytics with AI solutions enabling energy companies to perform a wide range of functions from failure probability modelling and outage detection and prediction, to optimising the performance of assets and improving the customer experience with real time billing.

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BP have invested in a cloud-based AI technology provided by Grid Edge that enables customers to predict, control and optimise their building’s energy profile. Using data including weather forecasts and expected occupancy, customers are able to use predictive energy management technology to adapt their energy use, leverage periods of high renewable power generation, and effectively use their building’s flexibility in energy demand and generation like a giant battery, to reduce costs and carbon emissions.

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The aviation industry too has been revolutionised. Connected, fully-digital aviation is giving rise to new business models that further improve customer experience, sustainability and efficiency.

Aerospace giant Airbus has been developing pioneering solutions to unlock the potential of big data in aviation. 50,000 sensors on their Airbus A350 were collecting 2.5 terabytes of data every day, but less than 0.5% of this was being used and analyzed by industrial companies. So they launched their Skywise platform which connects different stakeholders – airlines, manufacturers, suppliers, etc – on a digital server to exchange and analyse data from different environments –
the shop-floor, inflight operations, maintenance, and others. Working with the likes of EasyJet, Emirates and AirAsia their aim is to enable greener, cheaper and more comfortable travel, with airlines benefitting from better operational performance and aircraft availability, and manufacturers and suppliers able to continuously improve efficiency and develop new services.

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In the FMCG sector, AI, ML and predictive analytics that feed on big data are creating a range of new customer experience and engagement use cases, with many using ML to personalise marketing content to consumers based on their preferences and habits.

Coca-Cola has previously outlined how much it relies on data, using customer data not only to drive product development, but also to enable the creation of precision and programmatic marketing content tailored to audiences’ lifestyles and passions.

One of the key challenges faced by Coca Cola, as with any global brand, is making sense of the sheer volume of data held, which can be overwhelming.

Their approach has been to start small with data and make sure it’s used with a clear purpose in mind, as Justin De Graaf, their former Head of Data Strategy explained, “Our teams focus on key outcomes that we believe data will help deliver, and then we build a business case to support it.”

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