A host of expert speakers shared the tactics they’re using to grow data-driven cultures in their organizations at Data & Analytics Live: Europe
EasyJet’s data and analytics journey will be familiar to data-focused executives across the globe.
The airline has made huge steps forwards in recent years, developing a data strategy that aligns with corporate goals and delivering ‘quick wins’ to get the business on-side.
But as EasyJet Director of Data Science and Analytics Ben Dias said at this year’s Data & Analytics Live: Europe virtual summit, it’s what happens next that will determine whether the company achieves its ‘data vision’.
He explained: “I said from the start when I joined the company that we’ll never be the world’s leading data-driven airline unless everyone is having access to data and self-serving a lot of analytics themselves.”
The question of how to get enterprises to this level of data maturity was a key theme at this year’s event. Across three days, many of Europe’s top data leaders shared how they’re advancing data cultures within their organizations.
Breaking Up Data Silos is a Key Challenge
One major obstacle that stands in the way of many organizations becoming fully data-driven relates to the early adopters that are so crucial at the start of any company’s data transformation.
These people are among the first to acknowledge that data is a key business asset. But with this acknowledgement comes the temptation to hoard it for themselves.
“One of the key issues I see that’s common across all industries is really breaking up the data silos,” noted Hiren Jani, Head of Data and Analytics at Bank of International Settlements. “People still treat data as a personal asset, like ‘marketing data’ or ‘sales data’.”
“The data is not treated as an enterprise-wide asset,” he continued. “Breaking those silos is the key – and making that data accessible to everyone.”
Dias agreed that providing everyone in the organization with access to the right data is a key step on the path to data maturity. Once the foundations are in place and ‘quick wins’ have shown people what data can do, the next step is to empower more and more staff with data-driven tools.
Democratize Data with Caution
However, Dias cautioned that rushing to ‘democratize’ data too soon can backfire. Data science in the wrong hands can be dangerous. So, it’s important to understand an organization’s data maturity level and match the technology staff have access to with their ability levels.
“[Say] you want to forecast your sales for the next three months,” Dias said. “Your data may not be linear. But anyone these days can download a Python library and build a forecasting algorithm – a linear regression – and it won’t crash. It will give you an answer. Imagine using that to drive your business and the answer being way off.”
For this reason, many organizations are finding that staff data literacy levels are a barrier to reaching the next stage of their data transformations.
“Do we have the right skills in the organization?” asked Kinnari Ladha, Global Head of Data Science, Analytics and BI at travel company TUI. “Are we investing in our people and our skills? It’s essential that we do that.”
Ladha noted that upskilling staff is a continuous process. Programs must be in place to ensure new staff have the right skills and ensure the company keeps up with the pace of progress.
Change Management Requires Empathy
Research from Harvard Business Review shows that the most successful data leaders dedicate more than half of their budgets to change management initiatives.
As Valentina Zolotukhina, Head of Business Control and Analytics at subscription car ownership start-up Care by Volvo, noted, that’s because staff can be reluctant to abandon ‘tried and tested’ ways of doing things. It takes emotional intelligence to get people to see the benefits of new ways of working and ensure they don’t feel criticized or threatened.
“How can we support them and encourage and help and make them succeed?” she asked. “The answer here is, ‘Baby steps.’ Really increase the level of support from the very beginning.”
She said scheduling one-on-one time to work with her team members on the same data science challenges has helped to ensure everyone progresses together.
“We scheduled time to work on the same model shoulder-to-shoulder, building the same calculation flow,” she recalled. “I was able to transfer my knowledge in real-time and make sure my team member was excited about the results.”
Dias agreed that executives must be sensitive about how they pitch new data-driven tools to ensure they’re viewed positively, and not as a threat. So long as companies do that, he said the key to getting people to engage with them and develop their skills is to provide good ‘data role models’.
“It’s about finding those cheerleaders,” he said. “Those people who are forward-thinkers who would jump at it if you suggested something.”
All the speakers at this years conference agreed that starting small is key when embarking on the road to data maturity. But as organizations navigate this next stage of this journey, many of Europe’s data leaders are turning their focus to skills development to encourage their organizations data cultures to grow.