To one extent or another, every organisation in today’s world recognises that big data is key to its future. That hasn’t always been the case, of course. Only in the last decade or so has there been a big focus on big data — or data in general. Prior to that, ‘data warehousing’ was the prerogative of organisations that had to deal with vast quantities of data as part of day to day operations, such as banks, insurance firms and pharmaceuticals. Now with the dawn of increasing digital capabilities and the appearance of the Internet of Things (IoT), organisations from every sector are eager to explore how they, too, can generate business intelligence from the information they collect from customers.
If you look at the internet pioneers — Google, Facebook, Ocado, Netflix, Amazon, etcetera — they have loads of data and, with the exception of the ‘odd’ data breach, they tend to use it well for marketing purposes, to access and engage customers, and to sell value to clients. This has led B2C and B2B organisations to realise that if they understand their customer base – their needs, wants, desires, and buying behaviours – they can influence purchasing decisions and win loyalty.
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Consumer brands like Coca-Cola and Heineken use data and artificial intelligence bots to support everything from product development to advertising campaigns. Big data analytics and machine learning models are helping media service providers such as Netflix and Amazon Prime predict what their customers will enjoy watching. And marketing teams at fashion brands like Burberry use big data as a blueprint for a digital infrastructure that will automatically nurture customer relationships by personalising the shopping experience both online and on the high street.
There are even toys on the market that embrace data. Thanks to sophisticated language processing, advanced analytics and ML, the modern Barbie doll can listen and respond to a child in under a second, choosing between 8,000 lines of pre-programmed dialogue. With every interaction, these dolls learn more about their owners, storing information to remember that child’s favourite food, TV show and colour, proving that the Barbie brand is taking its original 1984 tagline seriously — “We girls can do anything”.
Data alone, is not enough
Data alone cannot help organisations thrive, of course, but the insight gleaned from it can transform the way businesses operate. Before that can happen, however, big data needs to be organised and presented in a way that will enable business leaders to make better decisions about how they manage their operations, supply chains, costs, employees and customers. The problem is that, despite the business world’s recognition of big data’s power, most organisations struggle to bring all their different data sources together, let alone harvest it for genuine business insight. In fact, Forrester predicts that data governance is going to be one of the biggest challenges corporates around the world have to tackle in 2019.
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Effective data governance can enable intelligent real-time business decision-making that will, in turn, drive organisations in a more profitable direction. One of the best approaches when it comes to unleashing big data’s potential is investing in a data lake: a central repository that allows organisations to collect everything — every bit of data, regardless of its structure and format — which can then be accessed, normalised, explored and enriched by users across multiple business units to reveal patterns across a shared infrastructure. The advantage of this approach is that organisations can gain end-to-end visibility of the enterprise data and actionable business insights. The disadvantage is that the data has to be kept up to date, which takes time and effort.
Another downside is the GDPR compliance and data security risks that are associated with depositing the entirety of an organisation’s business-critical data into a data lake. Under GPDR rules, organisations are liable for protecting employee and customer data. If the data is compromised, the penalties are high. If you bring your data together in one place, there’s an argument that this can make it easier for hackers to penetrate your network. Because a lot of this data will be hosted in the cloud, however, there are industrial strength security platforms in place. Even so, organisations still need to address their vulnerabilities to ensure their online environments are armed with multiple firewalls.
This is perhaps a step removed from the key issue — that the majority of organisations are struggling to bring their data together, let alone make sense of and protect it. Most retailers in the UK are still grappling to understand their customer base. Banks are a bit further on the journey but there’s still a way to go. Organisations that are highly acquisitive often have no idea how data moves in and out of those acquisitions, so no view of their customers across the whole of the group following periods of M&A.
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An exception
Travis Perkins is one of the exceptions. Having been very acquisitive over the years, the UK’s largest builders’ merchants needed a solution that would improve efficiency and enhance its customer offering. It invested in a new enterprise software solution that would integrate its many co-dependent platforms to provide the company with real-time data and reports. As many as 40 of Mitra’s digital transformation specialists integrated numerous downstream systems so that any changes made to company records could filter down into related data source and supporting applications. This means data will be kept up to date more easily and minimise the time spent on database admin. The HR team, for example, now only has to update employee data in one core location. For customers, this new approach to data management has resulted in an improved experience because employees working in Wickes, one of the group’s retailers, can check product information in a heartbeat.
There’s a large push around machine learning and its capabilities to understand and analyse large sets of data in order to create algorithms and make forecasts. ML is a key driver for creating data lakes because these make it easier to analyse what’s in there in order to come up with predictions.
Demand for intelligent tools that improve data analytics and governance will only increase, as organisations streamline corporate information architecture with tools that help to embed machine learning into business operations, ultimately providing a simplified, richer customer experience. But before that can happen, firms will need to put more building blocks in place to meet ML’s promise, starting with the most elusive — data.
