Data as the new status quo

Becoming an intelligent enterprise isn’t a question of how much data or how accessible it is. It’s a question of how prepared your culture is to embrace it.
Sarah Ronald

Sarah Ronald

Passionate about technology and psychology, Sarah founded Nile in 2006 to improve how everyday digital products and services are researched, designed, and communicated. Today a team of 50, Nile specialises in regulated industries and is especially focussed on the Financial sector. She has been a special adviser to both the British and Scottish Government, and served on boards for The Design Council, Service Design Network and The British Interactive Media Association.

At the end of last month, I (Sarah Ronald) sat down for a roundtable discussion over breakfast with some of the UK’s most interesting and innovative thinkers. Together, we shared insights, perspectives, and solutions that could help us navigate how data is transforming the way services and business processes are designed.

In the Fourth Industrial Revolution, data can undoubtedly be a company’s greatest asset — but increasingly, firms are finding that they have so much data that they’re paralysed by it. So how can organisations effectively use data to modernise and make smarter decisions? And how can they do so responsibly when there is no clear playbook or ethical guidance in place?

The human vs tech challenge

In exploring these questions around the table, there was broad consensus that the biggest challenge around data is people, not technology. This is supported by a survey published by the Harvard Business Review in February. The survey found that while three-quarters of US companies are investing in artificial intelligence and big data capabilities, fewer than 30% have successfully established a data culture. Of those surveyed, 93% of business leaders said the biggest obstacles were convincing staff to engage and adapting their organisation’s existing processes.

It’s a story that’s all too familiar. New methodologies and technologies like machine learning, robotic automation, and blockchain are incredibly exciting, but it’s easy to get carried away with the hype instead of using them to design solutions that overcome problems and deliver organisational value. In fact, these technologies often end up as solutions looking for a problem.

Machines may be getting smarter, but they’re still a long way from understanding meaning like a person does. In healthcare, they can be trained to do useful tasks like optimise processes or identify patterns in data — but without the input of a doctor, the technology has no way of knowing whether it has identified a cancerous tumour or an anomaly in the global price of avocados. This is why the key challenge for organisations hoping to pivot to a futureproof, data-driven approach more often lies in the labour market than the data centres.

Data in the belly of the beast

A flood of new accelerators and incubators focused on data have launched in recent years. Here in Edinburgh, Royal Bank of Scotland has invested in a £1 million Data Academy to help upskill its staff in the latest data techniques. These collaborative hubs are an important tool for incumbent companies to attract the boldest and brightest ideas from the next generation.

With the right talent, tech-focused innovation can happen extremely quickly in these environments. The biggest challenges begin to emerge when data-driven approaches are scaled outside of a lab and the organisation’s immune system kicks in. As excited as leadership often is to talk about data, they aren’t always equipped to promote or foster a culture that can readily receive it.

There is a strong trend towards organisations aiming to become intelligent enterprises. But data isn’t a perk that can be tacked onto existing business models and procedures. The strengths and weaknesses of data need to be fully grasped before existing operations, policies and structures are redesigned around it.

The truth is there are no perfect models when working with data. All models can have errors or limitations, and educating the business workforce around those characteristics is key. There may be gold in the unprocessed facts, raw numbers, figures, images and text stored by organisations, but often new capabilities are required to define how to make the most of it and identify where decisions could and should be made.

At its core, data is fundamentally cross-functional and requires teams to work in new ways. For enterprises to build a truly effective data culture, decision-making must be devolved while remaining shaped by principles from the top. The appetite exists for this change, but there aren’t yet any clear roadmaps for how to make the transition.

Over the coming years, we’ll see much more content and material for organisational leaders to learn about transitioning to data cultures. The Transformation breakfast meetup will continue to be an active part of the conversation and I’m looking forward to learning more and sharing lessons learnt with the wider community.


Thanks to Lee Wilson from the Bayes Centre for presenting to the roundtable and providing her insights. The Bayes Centre is Edinburgh and Lothian’s innovation hub for data science and artificial intelligence. In her role as Director of Innovation Partnerships, Lee identifies opportunities for data driven innovation where we can collaborate with industry partners to successfully adopt new technologies.

Additional thanks to Laura Thom from Pathfinder Consulting for co-hosting the Transformation breakfast with me.

Further reading