Marketing panel: Making data meaningful to people

An ADMA Data Week 2020 panel discussed data visualisation and how to empower people to see data and help make the complex, accessible.

How do marketers comprehend what their data means? It’s a constant challenge marketers face and with the emphasis on data-driven decision in marketing growing, it’s becoming more and more important. 

At this week’s ADMA Data Week 2020, a panel facilitated by Trisca Scott-Branagan, head of personalisation, group marketing, at ANZ, and featuring VP Thrive Global, Matthew Riccio, DBM client director, Gazal Kapoor, and JLL global director BI strategy, Fi Gordan, discussed ways of improving data literacy.

Thrive Global’s Matthew Riccio said there are three main points to focus on. The first is to know your audience, which includes not only their data fluency and their data literacy, but also what specifically they're hoping to get out of the data and kind of what message they're looking for and expecting. 

The second point is knowing the message you'd like to convey to each of the audiences and focusing on that. “An individual will need to know the aggregate for a business unit or for the organisation, whereas a CEO might only care about that aggregate level. Understanding that and not necessarily introducing unnecessary data or visuals that might offer opportunities for confusion or for misinterpretation of your overall message,” Riccio said.

The third is to consider the flow and whether or not additional information or the context might shift the messaging itself. “We might sometimes include text or a reading slide or whatever else might be helpful so that we know by the time the data is represented we've led the user on that journey and they know what to expect by the time they get to it,” he said.

Striking the right data balance

How far to go in simplifying data and the risk of oversimplifying data is something that those who work with data to present certain findings need to manage. JLL’s Fi Gordon said a balance needs to struck.

“We can over communicate, either by cluttering a dashboard with too much detail and too many visualisations making it difficult to comprehend. This can be overwhelming for our stakeholders. But on the flip side, you can over simplify by aggregating numbers, which can hide underlying trends people should know about,” Gordon explained.

“We're trying to understand the problems we're trying to solve. We use design thinking, working with stakeholders to distill information and then rather than approaching a brief from hundreds of metrics people believe they're actually interested in, we spend time trying to understand how they will use the data, why and what action they will take from the data.

“We make the experience simple for users by using a template, which creates consistency and familiarity with the look and feel, so they know how to navigate around the dashboard, which increases the trust and brand awareness."

Working with data, the panel all agreed, is part-science and part-art because it involves rules, testing, analysis and feedback. Equally it’s about telling stories, finding things that resonate and showing meaningful information.

Art and science

DBM’s Gazal Kapoor said the first rule she teaches people coming to data analysis for the first time is that it's art and science.

“I start with the statement that data visualisation is like a language with its own grammar and rules that we can all agree on. So that's the science. But at the end of the day it boils down to storytelling, which is a creative process. So that's the art part,”  Kapoor said. 

“On the science side, you have to know what you're trying to say. You have to ask ‘what is my key message?’ Once you have your key message, and that in itself is a very detailed process to go from exploration to explanation, then the data visualisation techniques will get you there."

But while the rules of the science part are clear, there's an art part, which comes from and storytelling.

"Where are you at? Once you decide your message, this is my protagonist, this is my hero, you make sure that everything else fades away to tell that story. The biggest thing you can bring from a storytelling point of view is the passion," Kapoor said. "Like any other storytelling, it applies to data visualisation as well: Bring your passion to your audience.”

Data in and of itself won’t do very much unless there’s buy-in from the c-suite right down to the people who need to understand and take action based on the data. ANZ’s Trisca Scott-Branagan posed the question that “if culture eats strategy for breakfast, how do you create a data culture within your own team and across the organisation?” 

Gordan said if you're seeking establishing a great culture is to find what you're trying to achieve. “And what are your vision and values for your team? How does that align with your organisational values? Identify what you want your team and company to adopt, and most importantly, to find the risk of not changing,” she said. 

“McKinsey studies show that breakaway companies are two times more likely to obtain strong executive alignment on analytics vision and strategy, and four times more likely to embed analytics into the organisation. So once you have your vision and values, make sure you're securing the executive endorsement and know how you’re going to communicate that to people."

Riccio said the biggest behaviour changes come from data buy-in at the top.

“When you get that top down, it's beyond just cultural permission, it's cultural encouragement. It's something that allows it to become viral, where everyone wants to be a part of it, and there's that very supportive kind of network for everyone to get engaged and learn more about it. And that really is where we've seen the biggest behaviour change,” he said.

“It’s also embracing the fact that not everyone loves data the way that data analysts do. And not everyone will be as accepting up front. It is important to acknowledge that and show that enthusiasm and passion for it, and help others who are not as fluent or as literate in data and visualisations."

Kapoor agreed data coaching is important. “It ultimately boils down to the user, you can be passionate, you can do everything right by the book, but you have to keep the user in mind. And if, for whatever reason, they like pie charts, a pie chart it will be to start with," she said.

"The culture doesn't change overnight. You have to show small wins and sometimes you have to pick your battles."

Follow CMO on Twitter: @CMOAustralia, take part in the CMO conversation on LinkedIn: CMO ANZ, follow our regular updates via CMO Australia's Linkedin company page, or join us on Facebook: https://www.facebook.com/CMOAustralia

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