2018 was a big year for data, with scandals breaking out across the globe. From Timehop falling victim to a cyber-attack and losing the personal information of 21 million users to Strava revealing the locations of secret military bases across the US, it seemed no data was safe.
However, the biggest scandal of 2018 and the ‘watershed moment’ for society’s understanding of personal data was the discovery that Cambridge Analytica had been harvesting the data of 100 million Facebook users without consent and using it for political purposes.
These scandals have brought privacy issues and the use of personal data to the forefront of the public eye, and acted as a huge wake-up call in how our data is used by businesses across the world.
All industries collect and use data. When used in the right way, data analytics can fuel growth, increase efficiency and optimise customer experiences. However, how our data is gathered and stored is becoming much more important to us as consumers. Have you ever wondered how different industries use our data? Here’s a breakdown…
Big data is expected to grow faster in healthcare than any other industry. According to a report by the Society of Actuaries, 47% of healthcare organisations are already utilising big data and predictive analytics to improve their services. Big data is used within the healthcare industry for a multitude of reasons, with one of the key factors being reducing human error. By establishing patterns in patient health records, big data can be used by medical practitioners to work out the best treatment to prescribe a particular patient. As well as being used to treat patients, big data analytics have the ability to predict health issues before they arise. By tracking information such as patient heart rates, sleep habits and glucose levels, big data technologies can evaluate datasets and mine for information to pre-empt potential medical problems. Big data is also used largely in healthcare to reduce costs. By using predictive analysis, hospitals are able to anticipate situations such as daily admission rates to help with staff allocation and reduced waiting times; all helping to save money.
From social media metrics to sensor data and transaction information, retail organisations use big data to gain a competitive edge in a crowded industry. Retail organisations use big data to understand their consumer base on a deeper level, review industry trends and make business plans and decisions. Data driven insights are key to improving consumer conversion rates, personalising campaigns and lowering customer acquisition costs. With customers interacting with shops through social media, e-commerce sites and in store, it means that much more data needs to be collected. Once the relevant data has been acquired, it can be used within the retail industry to find what motivates consumers, how they behave, their preferred products and the best platforms to reach them. This behavioural data is key in improving customer acquisition, driving consumer loyalty, providing personalised services and increasing profits.
From your salary to the ins and outs of your savings account, banks know exactly where your money is going. By tracking your spending, banks are able to create a precise analysis of your financial background and sell you additional personalised products based on your spending. Tracking the spending habits of individuals also helps to create patterns in finance, making it much easier to detect fraudulent behaviour. With many customers taking to social media platforms to express their views rather than directly contacting the bank’s customer support centre, big data tools are used to sift through this public data and recognise when the bank is mentioned to help gain an understanding of consumer opinions and make improvements on demand.
So there we have it, just a few uses for big data across three of the largest industries in the world. Fraud detection and reduced hospital times sound great, but how would you feel if the data used for them was also used to decline your insurance application? The truth is, there’s a big grey area around what we are and aren’t happy for our data to be used for.
How much is too much? Where do you draw the line?