Now that the infrastructure to store and process vast quantities of data has become mainstream, the initial wave of hype around ‘big data’ has subsided.

Having access to big data is no longer a differentiator; it’s now the way you exploit your data that sets you apart.

The race is on to get to the point where extracting maximum value from your data is standard. This is all very well for digital native companies that have built their entire infrastructure to achieve this, but it’s much harder for others that need to transform their legacy systems before truly exploiting big data. Many organisations currently sit somewhere on that journey, having made investments under the heading of ‘Big Data’. Some are frustrated that they haven’t gained the value they expected, and others have had success, but are now wondering how to build on it.

The goal of big data is being able to assemble and interrogate data across every aspect of your business, so you can zoom out to understand large scale phenomena and zoom in to understand an individual or to customise an experience – all in a timely way. Many organisations can already do this, but it’s often a slow and painful process that doesn’t support real-time decision-making.

Successfully realising the full value of big data means you can make better decisions, faster and more cost-effectively, supporting more efficient internal performance. And by bringing AI and machine learning into the equation, you’re able to better allocate resources to match demand, moving to preventing events rather than just predicting them. Combining AI and machine learning with ‘human in the loop’ input allows the automation of complex processes that would otherwise be too costly. This frees people from tedious and time-consuming tasks to do something more productive.

From a customer point of view, you’ll be able to use customer profiles and behaviours to tailor your propositions to their needs. You’ll create a rich dialogue with your customer to fine-tune how they’re using your products, offering enrichments where appropriate. And, as your strategic insight into your customer’s experience increases, you’ll be able to target your activity and investment to support your business goals.

Big data is currently fulfilling its potential in a wide range of industries and applications, from drug discovery and genomics, programmed trading and consumer fraud detection in financial services, through to transportation logistics and high-end engineering, such as sensing on modern jet engines. So, what are the challenges businesses like yours need to overcome to achieve similar success?

Achieving high-quality data analytics isn’t a switch that you can just flick and watch the value flow through the organisation. The right tools and basic data infrastructure need to be in place to get the most from any investment in data. In order to be ready to implement AI and machine learning, we need to move how we deal with data from an art into a science. It’s essential too, that businesses set their data analytics firmly within emerging legal and ethical frameworks, to make the most of their AI and machine learning.

Tools now exist that make the process of developing the models that power AI and machine learning more disciplined, reproducible and traceable. Businesses must be able to manage all the inputs that go into developing their models, including the data they ‘train’ the models on, the structure of the models themselves (so they can be adapted or re-used) and anything created along the way that might be re-used at a future date. This transparency means that the results of AI and machine learning can be held accountable to regulatory and ethical systems.

Currently, many firms only pay lip service to the fact that data is a company’s biggest asset, and this needs to change. To really exploit the benefits of big data and analytics, data needs to run through everything as an integral part of the organisation’s people, culture and skills. The litmus test of success will be when every area of your company has data as part of its strategy.

It’s the time to prioritise getting the right talent to realise your big data goals, and to make sure data skills are evenly spread throughout your organisation. Right now, it’s likely you have islands of expertise, patchily distributed. Changing this will probably involve a broad programme of upskilling and empowering your workforce to create digital citizens who appreciate the importance of accurate data and act as accountable curators for it across its use journey. This data-skilled workforce is essential to achieving positive outcomes throughout every aspect of data, from entering and consolidating it, through to distribution.

When big data becomes business as usual – and when we stop referring to ‘big data’ – we’ll know the size of the data has stopped being the main problem and its adoption journey has come to an end. What will the data environment look like at that point?

We predict that a thorough understanding of the value of data throughout its lifetime will become the norm for businesses. We’re moving into a world that will prize the cutting-edge models and processes that run on the top of data, rather than the data itself.

To get to this point, data must join the trend towards digital first capabilities. A cloud-based data approach will mean that the appetite for gathering data for the sake of it will fade. Instead, businesses will shift to a more cost-effective methodology of dipping into a data lake common to the industry and going back to get more data as and when it’s needed. As companies move to buying in data and mission-critical services as software-as-a-service tools, they’ll find that, increasingly, analytical capabilities are built in. Potentially, this could generate a regression back into a siloed way of working.

The promise of big data and analytics is to give everyone and everything information superpowers, while protecting privacy and our social values. What’s evident now is that making this a reality will involve a careful blend of factors in an environment that both values and respects data. Action needs to start today on upskilling the workforce, making timely, good quality data available and putting in place appropriate infrastructure and tools. 

Discover how your business can exploit technology and innovation both now and in the future by downloading our ‘Winning the innovation race’ brochure.