The most obvious, perhaps, is helping a business to focus on data as much as instinct. But the other is helping those employees that actually have data chops to get to know how the broader business works.

Marketers, specifically, need to understand what they can expect from a data and analytics department.

Debby Cannon, Head of Supporter Data at National Trust, says, “The biggest challenge we’ve faced has been working out how the skill sets and approaches of the very mathematical people fit together with those of the qualitative research people. It has been a case of getting people to work together, to start to see the benefit of each other’s disciplines.”

So what steps can businesses take to create a data-led culture? Here are some tips taken from our new report, Working Effectively with Data Teams.

Working Effectively with Data Teams – A Best Practice Guide

The CEO must lead

A 2016 McKinsey Global Survey found that executives in high-performing companies most often rank senior management involvement as the factor that has contributed the most to their analytics success.

Previous McKinsey research found that CEO involvement in analytics correlated strongly with business success. Forty four percent of respondents from high-performing companies said most of their data and analytics activities were sponsored by the CEO – this figure was just 16% in low-performing companies (where CMOs and CIOs were more likely to lead).

The CEO, therefore, needs to champion analytics, both in its current and in its future role in the business.

Data professionals should use qualitative narratives

Not everyone enjoys a spreadsheet of numbers or a dashboard with little in the way of interpretation.

Data professionals should be using qualitative narratives – i.e. talking like ethnographers. Asia Miles, which runs the loyalty programme for airline Cathay Pacific, has a team of ‘data artists’ to do just this.

Adrian Hado, Head of Insights, Analytics and Customer Experience Design, told Econsultancy’s research team, “Data science is all about experimentation, and data artistry is all about expressionism. The data artists are obviously not going to replace the data scientists, but our role is not about just sharing spreadsheets, it’s about telling a compelling story.”

Break down those silos

Analytics needs be considered from the beginning of the marketing workflow.

Lovehoney’s Head of Ecommerce Matt Curry describes a very simple measurement protocol of questions that any commissioning department has to ask at the start of any project:

  • What metric are we trying to change with this project?
  • Are we currently measuring it?
  • Do we have enough historic data on that metric to be able to perform a comparative analysis?

Such simple measures can improve communication and break down silos. Andrew Leigh, Insight Manager at Waitrose, says:

“So much is down to communication. If we just sat on our own doing our own thing and didn’t tell anyone what we’re doing, they wouldn’t see the advantages or why we would do it…”

Develop the ‘liaison’ role

Not dissimilar to the data artist role at Asia Miles, the liaison between analytics and marketing is a key role in fostering data-led culture. This pragmatic role is sometimes tasked with pushing back on both sides of the divide, ensuring actionable insight is prioritised.

James Alexander performs this role as Decisioning Director at Sky.

“There is increasingly a need for people like me to help co-ordinate these things. There is a risk that, if marketers aren’t close enough to the systems or the data to understand what’s possible, they can be gamed into signing up to a solution that promises the world, but in reality falls far short – not because of the theory but the practical implementation challenges.”

Unite data silos

Of course, this can be a technological challenge, bringing sources of customer data together. Software is not always designed with data sharing in mind and businesses may be still locked in to legacy vendor tech.

Silos of data can create political issues, too, with some departments historically protective of their particular data sources.

Prioritising a central data hub is a vital step in creating a data-led culture.

Set KPIs to drive collaboration

Business-wide KPIs can encourage department heads to collaborate, to determine how they will be met.

Adrian Hado at Asia Miles describes how this approach works: “As a group, marketing, IT, finance and ourselves know what our business goal is for the year, but it’s just a big chunk of miles. So first of all, we work with the marketing team to see how many customers we have got, how many customers we need to get, and how much we need to grow the value per customer of our existing customers by in order to hit these financial targets.

“Together with marketing, we ask where we’re going to source the new customers from. Then we go back to the data and see what our really good customers look like, where they came from, and which campaigns worked, so we can do more of those campaigns. And we find as much information as we can possibly gather about what these customers look like and where we can get them from. Then if we need any help from IT, that’s the point at which they get involved. So, we’re all in it together.”

In summary

Each of the points made in this article places relationships at the heart of the challenge. Breaking down cultural barriers between analytics and the rest of the business is mostly about people – that’s what makes it tricky, but incredibly worthwhile.

Learn more

Econsultancy’s Best Practice Guide to Working Effectively with Data Teams