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Women working on their computers in a board room.Women working on their computers in a board room.

Empowering frictionless data literacy in the workplace

RMITO, Merkle, Monitor Deloitte, and Microsoft came together to discuss data in the workforce.

“One of the most important things that drives decision-making is trust,” said Steve Wayne, Chief Data Officer at Merkle Australia. “If people don’t trust the data, they won’t leverage the data.” 

Steve was one of three-panel members at last week’s wonderful RMIT Online industry discussion: Empowering Data Literacy In The Workplace. The topic, as you may have guessed, was data. How we’re using it, how organisations can leverage it for their own strategic gain, and where some of the challenges might come from over the next few years.

The event was held at Chin Chin in Melbourne’s CBD. Our three-panel members were Steve, Alex Papli (a Data and AI Specialist from Microsoft) and Maria Grisanti (a Senior Strategy Manager from Monitor Deloitte). RMIT Online’s own Kade Brown acted as moderator, leading our panellists through some of data’s emerging issues: improving data literacy within the workforce, the importance of storytelling and visualisation, and how AI might reshape the data landscape. 

The key challenge that emerged from the talks was this: in a world awash with data, how can we sift through the numbers and help businesses make better decisions? It feels like we’ve got more data tools than ever before, but a lot of businesses still struggle when it comes to extracting value. It’s often hard to see the forest when you’re surrounded by trees. 

Guests and panellists speaking at event, and posing for a camera opportunity.
I think we get so caught up in volume of data – how many gigabytes and terabytes we have – that we forget the important bit: what are we actually trying to solve? Alex said. As leaders, we need to focus on that. What are we trying to get out of this information?

Steve agreed, pointing out that data literacy isn’t just confined to the ‘data team’ anymore. “Everyone’s responsible for data policy now. It’s no longer just the insights team. So we need to start thinking about data literacy, working out a common language so that everyone’s on the same page. Often people just capture all the data and think, ‘Great!’ But it’s making sure you serve that up in the right fashion. That’s where the real magic lies.”

These days, any discussion about data visualisation and storytelling quickly turns to generative AI. So what role did our experts think artificial intelligence might play in data storytelling?

“AI is an interesting one,” Maria said. “I think a lot of organisations are still wary of integrating generative AI, meanwhile you’ve got everyone at home talking to ChatGPT!"

“The big thing for me is maturity. Studies have shown that a lot of businesses in Australia and New Zealand are still low on the data maturity ladder. Only about 30% of businesses here are insights-driven. Over 60% of companies are aware of data, but they’re still not making the most of it. So before jumping into AI, I think businesses still need to focus on the fundamentals, like upskilling their talent and improving overall data literacy.”

It's the classic cart-before-the-horse scenario. Artificial intelligence is a powerful analytical tool, but without those foundational literacy skills, it’s going to be hard for teams, especially teams with less technical data fluency, like marketing and sales, to reap the full benefit.

Who needs these skills? Short answer: everybody, Steve said. It’s important that everyone on the team understands data – it’s critical.

And you don’t have to look too far to find industry use cases.

“Take BMW Mini: they started out with a marketing strategy that drove awareness of their brand. But by using the right data capture points, they found that what really mattered was the devotion people have when they actually drive a Mini. They come out of a test drive wanting to buy. This totally changed BMW’s marketing strategy. It’s now all about getting people behind the wheel of a Mini, because they know that’s the fastest route to purchase.”

Alex pointed out that one of the big leaps we’re seeing with generative AI is being able to translate qualitative data into quantitative data – without the associated mind-numbing data entry. “A simple example might be customer surveys and feedback,” he said. “We’ve always known what our customers think of us from the EPS, but we don’t always know why. Being able to run generative AI over the top and quickly summarise vast amounts of feedback, it’s a huge opportunity for companies to improve their service offering.”

RMIT Online would like to thank our panellists – Steve, Maria and Alex – for participating in a lively discussion. The event was a huge success, and we’ll be looking to host more industry panels in the future. 

Explore RMIT Online’s Data Storytelling course here, or explore our range of Data Science and Analytics courses.

This article was originally published on 25 September 2023