Data. It’s something modern marketers both have too much, and not enough of, which is why every agency, media company, and brand realizes getting a handle on marketing data management is essential to success. So, where does the road to better marketing data management start? We interviewed NinjaCat CEO, Paul Deraval, to find out!
Paul Deraval is the CEO and co-founder of NinjaCat, a digital marketing performance management platform built for agencies, media companies and brands. After working a few years in the agency world as a software developer, Paul shifted his focus to analytics in 2014, and since starting NinjaCat, he’s been obsessed with building a product that helps clients prove the value of their work through better marketing data management.
“The current state of marketing data management can be complicated without the right tools,” says Paul. “And the challenges around data are different for brands, agencies, and media companies.”
“Agencies have to analyze and monitor data, ensuring targets are hit, report on campaigns, share the raw data with clients that may want to run their own experiments.”
Paul goes on to describe the marketing data challenges for brands and media companies dealing with internal and external stakeholders and complex infrastructures.
“When marketing data has to escape out of a Frankenstack, a patchwork collection of tech solutions, the pathways can be circuitous, the quality may be suspect, and the entire process is nearly impossible to automate or scale,” says Paul.
Many modern marketing departments are heavily dependent on data teams to help them access and use marketing data, or they take it upon themselves to build a custom warehouse, but then there is 100% reliance on data teams for transformation, storage, and portage.
Paul goes on to mention one of the main problems with marketing data management is junky data.
“If it’s garbage in,” says Paul, “then it’s garbage out.”
“A vast percentage of a data scientist's working day is not spent on analysis, pattern finding, refining algorithms or building training sets, but cleaning and organizing, foundational data wrangling,” mentions Paul.
“What I’m seeing as the next evolution of marketing data management is less manual work, more point solutions, and better ways to tackle ingestion, transformation, and quality.”
Paul believes all of the hype around machine learning/AI should be approached cautiously, since the tech is relatively new and the real workhorse platforms will eventually outpace the currently over-crowded marketplace.
“My advice on being successful with marketing data is, ‘focus on the meaningful, not the monotonous.’ says Paul. “If you can stay grounded in strategy, simplify your process and streamline your capabilities with marketing data, there is no limit to what’s possible.”