Over the last few years, many marketers have noticed a transitional change in their roles. Rather than focusing on the more creative aspects that have traditionally defined marketing, they are instead transforming into data scientists; analyzing reports, cleansing data sets and building in-depth profiles for each of their customers.

But are these new data wrangling responsibilities what marketers signed up for when they started their career? According to a new report from BlueVenn, not only do over 40% of marketers have to digest 21 or more sources of data, many are now spending as much as 80% of their day working with it.

Having to adapt to this new data-led approach to their job – while maintaining the more creative components that drew them to marketing in the first place – is a challenge that many need to understand better. Not least the MarTech industry.

To learn more, we surveyed over 200 senior B2C marketers to determine how they are handling the ever-increasing deluge of customer data, and then identify their biggest struggles.

Bringing multiple data sources together in order to form a coherent Single Customer View (SCV) has long been regarded one of marketing’s ‘holy grails’. Yet unified customer data is still a long way off for many – as many as 82% of marketers have yet to achieve an SCV, with 62% saying they have to deal with customer data from numerous disparate silos.

High volumes of data and disjointed data cause other problems, too. For example, over half (54%) of marketers claim that poor quality data has damaged their ability to provide more targeted campaigns, while 27% claim that they lack the skills to analyze it anyway.

So, given that we found 72% of marketers believe data analysis is the most important skillset for them to acquire over the next two years, what can be done to improve the situation? Do marketers, as the marketing press so frequently suggest, need to re-skill into data science? Or is this expectation misplaced?

If we’re being honest, the majority of marketers aren’t clamoring to retrain in data science in order to continue doing their day-to-day jobs. That said, they are equally reluctant to hand precious marketing data over to others. The IT department may have the analytic skills, but do they have the marketing savvy to put the data to best use? An external data agency may have them – but they also come with a considerable cost.

If customer data needs to stay with the marketing department then there needs to be a middle ground. Out conclusion was, rather than turning marketers into data scientists, MarTech vendors need to step up. After all, who are these marketing tools really for?

This is where the case for a Customer Data Platform can be made. By both removing the need to analyze data by hand and creating a single interface from which multiple data sources can be managed, marketers can do much of the work of data scientist using automated data management processes.

True, we might not yet be at a stage where marketers can press a single red button and the platform does all the data crunching for them. Nevertheless, as far as resolving many of the biggest issues they face – without extensive retraining – a CDP sounds like the compromise that our report suggests marketers are crying out for.