Five key principles for data insight success

How can you use data to grow your business? Ian Bobbett, Chief Data Officer at Crimson and George Lynch, Head of Tech Advisory at NashTech (both part of Nash Squared) look at 5 key principles for success. This article first appeared on ComputerWeekly.com. 

It’s a moment that no Chief Data Officer (CDO) or other technology leader wants to experience – when a new BI data dashboard is presented to the exec and the reaction around the room is, “But that just doesn’t feel right!” 

In today’s business environment, one thing that is not in short supply is data. But managing that data, and turning it into accurate decision-useful insight, is much harder to do. It’s an issue that is more relevant than ever right now as businesses look to deploy AI and Generative AI solutions into their business – data is critical to success. 

The data challenge is a theme that consistently comes through in Nash Squared’s annual Digital Leadership Report. The 2023 report showed that only one in four digital leaders rate their organisation as very or extremely effective at using data insights to generate more revenue. This is despite the fact that half of digital leaders see data & analytics as one of the top two deliverers of return on investment. 

Across the piece, businesses routinely struggle to get the value they should be getting from data. The picture is similar across small, medium and large enterprises – although medium-sized businesses seem particularly squeezed, with significant volumes of data but not the sophisticated systems to deal with it that some of their larger counterparts have been able to invest in. 

So, given these issues, how can businesses begin to get their hands around their reams of data and use it to maximise performance, productivity and insight? 

Here are five key principles that we see as fundamental to success. 

1. Begin with the impossible. And work back from there 

A great data strategy vision needs to be led by the business, not IT, as ultimately it is the business that will be using the data. IT has a critical role to play, but to really make the strategy have impact it needs to come from people who have no preconceived ideas about how it would work, how easy or hard it is to achieve, and – at least initially – cost. It is IT’s role to reverse engineer the desired outcome, and then work with the business to get a balance between dreams and reality. 

2. There must be a ‘value exchange’ for the data strategy to work 

Another fundamental is that people across the business must have ‘skin in the game’ and a clear incentive to make data work. If capturing, curating and updating data doesn’t help a business area owner achieve their targets and objectives, it’s simply unlikely to happen. It’s when there is a value exchange – when multiple parts of the business benefit in their own different ways from particular sets of data – that the strategy will actually be translated into everyday reality. 

This means that, when mapping out the data strategy, it’s essential to consider what the priorities and needs of functions across the business are – and where and how they overlap, interconnect and mutually reinforce each other. These are the points of value exchange and should be supported and enabled by the data architecture design. 

3. Data is more than a technical exercise – with many supporting features that need to be right 

The value of data lies in how it’s used – so it follows that a successful data strategy requires focus on the efficiencies and opportunities that surround it. This needs to be supported by governance – having clear standards in place around how data is managed, stored and curated. There needs to be clear ownership of data, with the relevant teams taking responsibility for data that falls within their remit. 

There must be sufficient support and training in place too, so that staff are equipped with the skills they need to handle the data. Across all of this, what is perhaps the key is instilling a data-oriented culture – the acknowledgement that data matters and that it is everyone’s responsibility to treat it carefully and properly. When data is treated like this, every day across the entire business, it is surprising what a transformative effect it can have. 

4. Chief Digital Officer as a change agent not just a technical guru 

Needless to say, the role and leadership of the Chief Digital Officer (CDO), or other responsible technology leader, is critical. Too often, we tend to see CDOs appointed or viewed as technical experts. Clearly, good technical knowledge and experience is important – but at the same time, there is an IT team that the CDO can draw on for that. 

More important, in our view, is that the CDO has the ability to work as an agent of change, who can help forge the connection between commercial/performance aspects of the business and data & technology. This is the leap that organisations are really looking to make. 

It also means that the CDO should have a direct line to the top. In an ideal world, they will regularly be speaking directly to the CEO and/or Board, rather than through the mediation of the CIO or CTO. They need this direct exposure for their own purposes after all – to be effective, they need to be aware of and involved in many key business decisions and challenges. 

5. AI makes good data even more important 

And then there’s AI. The huge interest in deploying AI (including generative AI) solutions just elevates the importance of good data even further – because without that, AI investments simply won’t work. AI holds enormous potential, and the growing excitement around solutions such as ChatGPT has put predictive analytics and machine learning right back on the agenda, but it’s vital to make sure you’re not trying to run before you can walk.