Which is more important – understanding what happened to your business last week, or understanding what’s happening right now? Well, both can provide useful insights that you might be able to use to improve your customer experience, make better products and services, or create efficiencies in your business processes. But there’s a strong argument to be made that nothing is as vital as understanding what’s going on in the here-and-now.

Real-time analytics is about capturing and acting on information as it happens – or as close as it’s possible to get. This involves streaming data, which could come from cameras or sensors, or it could come from sales transactions, visitors to your website, GPS, beacons, the machines and devices that operate your business, or your social media audience.

This real-time streaming data is increasingly important in many industries. Financial service companies use real-time analysis of transactions to spot fraud and halt transactions before they take place. This had saved them millions that used to be wasted on tracking, canceling, and rectifying damage that could only be detected after the fact when a complaint was received. Netflix uses this form of data to make on-the-fly decisions about what customers want to watch next, based on what’s popular right now. Meanwhile, Facebook uses it to identify and remove dangerous content like fake news and abusive posting from among the 4.75 billion posts that are made each day. This would be impossible without real-time data and analytics.

Working with real-time data is often an advanced use case for businesses that requires a mature data strategy. But tools and platforms available today mean uses are being found for real-time data outside of the domains of financial services and Silicon Valley.

In one of my favorite use cases, the Wildlife Insights program between ZSL and WWF, uses machine learning to analyze video footage taken in South Africa’s Kruger National Park to automatically raise alerts when suspicious activity is detected that could suggest a danger of poaching.

The reason that real-time data is so valuable to business is that, in a world where we are creating 2.5 quintillion bytes of data every day, not only is it expensive to store old data, but the shelf-life of data is shrinking. The most valuable insights are always going to be in the most up-to-date data. If your competitors are ahead of the game when it comes to tapping into this resource, and you’re still working with months-old, static or batch-processed datasets, you’re clearly at a disadvantage.

Walmart – the world’s largest retailer – clearly understands this. It set out to build the world’s biggest private data cloud, capable of pulling in 2.5 petabytes every hour. But that data only represents transactional sales made over the last few weeks. As Naveen Peddamail, a Walmart senior analyst, told me, “If you can’t get insights until you’ve analyzed your sales for a week or a month, then you’ve lost sales.”

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Applying smart AI technology – typically machine learning algorithms – to real-time data sets brings businesses closer to achieving what Gartner analysts have termed “continuous intelligence.” One example here would be in site security – where machine vision-equipped cameras can be used to sound alarms when suspicious activity is detected. In fact, Shell uses a similar system to automatically spot customers smoking on their forecourts and warn them that it isn’t a good idea! Similarly, in a manufacturing environment, cameras can be trained to look for signs of wear-and-tear or other warnings that malfunctions could be imminent.

Those are all examples of internal data, but external data can be captured or analyzed in real-time, too. Delivery and logistics operations can tap into traffic or weather data streams to assist with route planning, enabling them to react to changes in situations as they happen. And a number of companies provide real-time anonymized location data captured from smartphones that they provide to retail and events businesses to help with predicting people’s movements. Real-time data is also often used in point-of-sale systems, particularly in e-commerce, to attempt to cross-sell and up-sell more products and services to customers that have already made a decision to buy something.

Micro-Moments

One of the most powerful ways that companies can start to use real-time data is to help to identify who their customers are right now. We understand that there are times when people are consuming content because they want to be entertained or they’re researching ideas, and there are times when they are ready to pull the trigger and make buying decisions. As a recent survey by Microsoft found that humans have an average attention span of eight seconds (less than a goldfish), hitting potential customers with the right message at the right time is crucial. If you don’t, by the time they are in market for whatever you’re selling, they’ve probably forgotten all about you!

Real-time data and analytics are quickly becoming the best way for companies to “be there,” making connections in your customers’ minds between your products and their needs at the time they need them. US pharmaceutical chain CVS uses data to build micro-moments for its customers by tracking the movement of medication through its supply chains and sending out real-time alerts, both when their medication is ready to be collected and when it needs to be taken. As well as improving patient experience and satisfaction, they found that it also improved rates of compliance with doctors’ orders, leading to improved healthcare outcomes.

As mentioned, real-time analytics is often seen as an advanced use case because it involves fast-moving data that’s often messy or unstructured. Necessary procedures such as data validation and cleansing will need to be completed on the fly, which inevitably means more processing power and, therefore, expense.

But the advantages of getting it right can be huge. Particularly if you are in a cutthroat market, knowing that you are operating on information that is as current as possible means you can be confident that your predictions and decisions are going to be sounder than those of your competitors.

Keep an eye on any future trends in data science, AI and technology by signing up for my newsletter, and check out my books ‘Data Strategy’ and ‘Business Trends in Practice.’

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