Data: frustrated potential 

The UK has high hopes to become a data-driven economy, laid out most strikingly in the government’s AI Opportunities Action Plan earlier this year. Behind pledges to make it easier to build AI infrastructure and create jobs in this new revolution is a more vanilla reality. To realise the vision, more UK workers will need to get used to working with data to identify the best AI model inputs and use cases in their work.

 

This change can be enacted but isn’t without hurdles. In examining the frictions and pain points felt by UK-based analysts who work with data day in, day out, we see these hurdles in play. And in addressing them, a path towards empowering UK workers to work with data on a much larger scale is established.

 

 

A fragmented analyst toolbox

Our latest survey of analysts working in the UK had plenty of good news. Almost every respondent reported use of AI in their role leading to increased job satisfaction over the past year. A significant grouping report experiencing a greater strategic significance in their roles thanks to the time savings and improved reporting capabilities AI enables.

 

Beneath the surface, however, there are signs of continued drags on working with data. Over half of analysts spend between 6 and 10 hours a week on data preparation and cleaning data. That’s a significant manual burden. It’s telling that 78% of UK analysts are still using spreadsheets to clean and prep data for analysis. Sheet formats rely on manual formatting and lack sophisticated automation capabilities that make quick work of data prep and cleaning.

 

Tool sprawl is a bigger concern. Most (60%) of UK analysts are using 3 - 4 tools/platforms for data preparation and analysis today, with that number rising to 5 - 6 for 27%. But most say they’d rather use 1 - 2 tools/platforms in their job.

 

While analytics remains burdensome and spread across tools, it’s hard to see how working with data can play a larger role in UK firms. There’s an inverse to this. Technology change paves the way for getting more employees incorporating data and analytics into their work.

 

 

The myriad benefits of a platform play

Modern cloud-based platforms for data analytics make it possible to consolidate the tools used to work with data, with APIs and connectors making light work of pulling in internal data to build workflows that automate processes and carry out analytics. This can save hundreds of hours lost to manual data management.

 

Crucially, platforms that offer low-code/no-code interfaces transform how organisations democratise access to data. When users can build analytics workflows for their own work with a simple drag-and-drop, it’s easier for finance, HR, marketing, and operations to autonomously analyse data, test hypotheses, and make evidence-based decisions, without relying on overburdened analyst teams.

 

Wide rollout and uptake of analytics platforms in organisations can even accelerate AI adoption and use cases. This is multipronged. On the one hand, platforms come with genAI features for end users to build workflows and generate reports from data with a natural language prompt. On the other hand, they facilitate building workflows that visually map out exactly which data inputs and rules define an internal AI use case, so that compliance with a company’s AI governance policies can be signed off on.

 

This is the ideal scenario, but technology isn’t a single make-or-break factor for becoming data-driven. Without a data-literate culture, there’s plenty that can frustrate scaling analytics, even with the very best data infrastructure.

 

 

Power in a data-literate culture

Democratising access to analytics will struggle to result in action if employees beyond analyst teams can’t define analytics use cases for their own work. The ability to do so requires a familiarity with the foundations of working with data. Training opportunities, combined with education, prove the best solution to instil a culture of data literacy and bring employees on board with the journey of technological change.

 

Take data visualisation, a core concept for modern analytics, as an example. Members of the team don’t just need to know how to use a platform to visualise their workflow. They need to know the “why” – the principles underpinning effective visual communication. The “how” and “why” framework applies to teaching all aspects of working with data, including genAI use cases.

 

Training and education are never ‘over’. A culture of data literacy in practice means knowledge sharing around working with data and celebrating the results of analytics and AI initiatives are constants in a workplace. Nurturing and building such cultures requires leadership. UK firms should think about what this means for them. If becoming more data-driven is an objective written on a page, but nothing more than that, then that objective won’t be hit. A designated internal leader, with the experience of working with data to define the best analytics use cases and support teams as they scale up their use cases, will make real progress towards these objectives.

 

 

Taking the right steps to make a vision a reality

For a starting point to make the UK a more data-driven and AI-first economy, look no further than the frictions those working with data today are already coming up against. Too much manual time drags and tool sprawl are holding back the full potential of analytics and working with data. In modernising data infrastructure with platforms to automate analytics workflows, organisations can unleash that potential while also opening the door to increased analytics and AI use cases for a wider set of data-literate employees. 

 

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Rishi Kapoor is Head of WW Partner Sales Engineering & Solutions at Alteryx

 

Main image courtesy of iStockPhoto.com and mbortolino