The telltale signs of data mismanagement, and how brands can avoid it

The challenge of mastering data, marketers are rapidly coming to discover, is that it’s not a quick fix. Salesforce Datorama’s recent ‘Marketing Intelligence Insight for the UK’ report would suggest that marketers are becoming more at home with the use of data, but most would also agree that they are quite some way from taking full control of it.

This is something of a concern, as many marketers would also agree that data is at the heart of their growth strategies, with 90% of those surveyed in the research stating that leadership had shifted their companies’ priorities towards marketing-led growth at least “somewhat significantly”. A further 44% use data as much as possible in decision-making while a quarter (26%) use it “almost always”.

Yet more than half of respondents (56%) would also agree that data mismanagement actively hampers their future plans and is, in fact, the top barrier to growth cited by the research. But it needn’t be that way and marketers are already working to tackle these challenges: 62% of respondents claim good or excellent progress in their marketing analytics investments and over half are tracking ROI or aligning KPIs across teams. So the foundations are being put in place.

Download Salesforce Datorama’s ‘Marketing Intelligence Insights for the UK’ report free here

Pets At Home’s chief data officer, Robert Kent, is encouraged. “There were a few comments in the research that reinforced our belief that [the source of our recent growth] is the focus over the last couple of years on how we use data to better serve customers, encouraging them to take on more services from our ecosystem.”

Some businesses describe themselves as digital natives; we would describe ourselves as data native.

Russell Davies, Bulb

Clearly, motivation to get data under control is strong. Among the many defining characteristics that distinguish digitally native challenger brands from the ‘old guard’ is not just their confidence with tech-driven solutions, but also the apparent ease with which they gather, integrate and manage data.

Russell Davies, VP of marketing for one such challenger, energy brand Bulb, recognises the different competencies and difficulties that new and established companies have, and that big brands can’t always solve problems by acting like agile startups: “Some businesses describe themselves as digital natives; we would describe ourselves as ‘data native’. I don’t think we could usefully advise a business that’s been going for 50 years because we’re not faced with those issues.”

Problems identified in the research that come under the umbrella of ‘data mismanagement’ include the amount of time spent cleansing data, difficulties in connecting data from multiple sources, in-house skills and the sheer volume of data to deal with.

The first and last points are inextricably linked. The amount of data available to marketers is more voluminous than ever before and triaging it is becoming one of the burdens marketers face. It can be a complex cost-benefit analysis for the organisation, as Arcadia Group’s data director, Simon Kaffel, acknowledges: “Companies need to focus on [data] governance and it’s a difficult one because it’s a cost that can’t be attributed to any commercial value. But, down the road, you’ll realise that value because you’re looking at quality data and it’ll make you more efficient.”

Close the skill gap

Governance doesn’t just apply to the rules governing data set out by regulatory bodies, it’s internal governance too, Kaffel insists. Making sure that the company sets parameters for its data needs and sticks to them.

“Pragmatism enables us to deliver around the Pareto rule – to generate enough value from where we are; working with the business to shape the art of the possible. It’s a misapprehension that we need more data. If you’re not realising the value, you’re not going to focus on data quality or the pragmatic governance that has to happen. There is as much information as we need.”

Critically, Kaffel adds: “What we need is more smart people that can translate that data in the right way.”

The idea of smart data people can strike fear into business leaders’ hearts, and the Salesforce Datorama research identifies skill sets as a significant challenge: 35% struggle with employee resources. We have often heard of the huge salaries and small pool of truly gifted data-focused individuals that companies are supposed to fight for to gain the upper hand but this is not necessarily the case, especially in Davies’s explanation what it means to be data-native.

What we need is more smart people that can translate that data in the right way.

Simon Kaffel, Arcadia Group

“Data is across the business in all positions. Not everyone is a data scientist but everyone has a data scientist in their team. Not everyone is 100% data-literate but they’re certainly data-friendly.”

This is certainly a ‘softer’ view of the data-focused side of the business – one that has at least a data awareness but is focused on the wider business needs. Indeed, Kent feels this is more a ‘linguistic’, rather than scientific, skill: “One of the prerequisites is bilingual staff – people who can talk the language of the business and the customer. Not just analyse the data, but explain it in story terms to a business layperson. It’s become a real lookout for us in our recruitment campaigns.”

But not every business can access the skillsets needed internally or via recruitment, as Kaffel warns. “There are a raft of really good data people who get both sides of the [business and customer] equation. A lot of people say they do [data] but that isn’t necessarily so. Sorting the wheat from the chaff isn’t as easy as it once was.”

Third-party help is a prerequisite for most companies at some point. Kaffel’s rule of thumb here is “working with third parties that I trust, who get the business problem and are willing to work with me to resolve it”. But outside help won’t achieve everything on its own.

“You can’t just hire a consultancy and six months later it will be solved. You have to prioritise resources and the board ought to be paying attention,” Davies insists. He is fond of the aphorism, ‘It’s not complicated, it’s just hard’. “At the moment, if only 5% [of your organisation] are data literate, you just need to get to 90%. If you need to train people, then that’s what you have to do.”

Neither is technology the cure-all for data mismanagement, according to TSB CMO Pete Markey: “Technology will help but not everyone has the appetite to invest. And technology doesn’t solve everything. It’s how we operate as a team and work with technology and agencies to make the most of the ecosystem [that matters].”

Indeed, there are three key elements to a successful data strategy – technology, people and processes – and none of these will enable businesses to unlock the power of their data alone. They need technology to help automate processes, and ensure data harmonisation and cleanliness. This will ultimately contribute to the data-literacy of all teams, and allow them to share and collaborate on insights. Finally, this changes the processes that they have in place when it comes to accessing and using data for performance measurement and better customer experiences.

Find the ‘North Star’

The key to unlocking data for a lot of organisations still wrestling with it is putting it in the context of business challenges and objectives. As Kaffel states: “It isn’t about data professionals driving the business. It’s about the business driving how it wants to proceed through data.”

“The starting point is to step back and look at what you’re trying to achieve. Set a vision statement – what is the end goal. Then, where are we today and what gaps will prevent us getting there?” Markey suggests.

He adds that establishing a ‘North Star’ is critical – the vision that will galvanise the organisation. But, he adds, it has to have some real tangible outcome: “You can’t beat a vision that has commercial value. Whether the business wants to grow here or there, or make the budget more efficient – those are all great questions that feed the vision.”

Davies understands that legacy systems and disparate data sets that don’t often get along can derail efforts (more than a third of the survey respondents found unifying data difficult) but there are still options. “Find somewhere – a division – where you can make it work and show how well it performs.”

It can take time, but for Kent, Pets At Home’s North Star was clear from the outset: “It was one of the first things we recognised we needed to do – over the last 12 to 18 months, forming the customer view and unifying it across product lines. Understanding what customers need from us and knowing that whether we’re contacting them through direct mail, email or via the app, we have a constant voice across channels and we do that through matching data.”

Markey feels that TSB is already an efficient data-led organisation but there is still plenty scope for improvement. “We use our data very effectively and can provide opportunities to have conversations with our customers through our branches, the app, telephone banking and paid media. But it’s not done in real time and if you visited the branch this morning and went on the app this afternoon, we wouldn’t be able to connect that.”

Not for long, however: “We will do a lot more in real time,” he promises.