A Look at Marketing's Biggest Data Challenges of the 2020s
A Look at Marketing's Biggest Data Challenges of the 2020s
Marketers won’t have major problems piping and storing data this decade. In fact, it will be “relatively easy and commoditized” because of cloud software innovations. We’ve figured out what to do with big data, in other words.
The big challenge will be ensuring your data’s freshness, provenance, legal permissions, exclusivity, distilling and activating the data. How well you do those things will determine how successful your data initiatives are in this decade.
Scott Brinker, author of the Chief Marketing Technologist blog, and Jason Baldwin, global head of product management of WPP, shared those insights in a report released in October, “Martech 2030” (PDF).
“I think the problem with most martech deployments is not a function of a lack of data. It’s a function of a lack of good marketing,” Brinker told CMSWire.
We’ll dig into these two areas of what Brinker and Baldwin called data competitiveness — (1) the source of data and (2) how effectively marketers can distill and activate that data — in this story and another at a later date.
Bringing Dark Data to Light Is Only a Start
We’ll start with the mighty marketing challenge of distilling and activating data. The first big problem? Many organizations never activate the data they capture. According to a report by IDC and Seagate, 44% of data available to organizations goes uncaptured, and 43% goes unused.
Getting better at capturing data is one thing. But, as Brinker and Baldwin noted in their research, the distillation and activation — “putting it to use” — is where the magic happens.
Data grows in value in two dimensions, they found:
The degree to which it is distilled into information, knowledge and insight.
The degree to which it is activated in the organization, from reporting to decision-making and, in a “Big Ops” environment, driving automated reactions. A major concept behind Brinker’s and Baldwin’s research is marketers need to move from the mindset of Big Data to Big Ops. Big Data, they found, “was a revolution for handling the enormous volume, variety, and velocity of data flowing through organizations today.” Big Ops, meanwhile, will be about “managing the growing volume and variety of apps, automations, processes, and workflows operating in brands and agencies on top of that universe of data.”
Data distillation is a combination of knowledge and insight, information gathering, augmented data, processed data and raw data. Data activation is a process of managing stored data and the ability to report, analyze, make decisions and ultimately drive automated reactions, Brinker and Baldwin reported.
“The first dimension is your data intelligence,” they found. “The second is your data reflexes. These two dimensions intersect to determine how valuable data ultimately becomes. Data may be distilled to insights, but are they fed into the right decisions at the right time? Data can be merely processed, yet can it immediately trigger a helpful automated response for a customer?”
Brinker shared that many marketers have a problem of truly unlocking data they capture because it’s fragmented. Customer success teams have their own data sets, and marketing, marketing research and product teams do, too. “There’s just so much opportunity to unify the data that people already have access to and make it more actionable,” Brinker said. “That’s where I would start.”
Christopher Penn thinks so, too. Penn, co-founder and chief data scientist of Trust Insights, said the people and processes side of activating and distilling marketing data is just as important as the technological aspect, he said.
The first problem: People. Penn shared a story of a sales team not divulging their data sets to any other functions. That’s a people problem. Oftentimes, people aren’t capable of working with these massive data sets. Smart Insights reported earlier this year that analyzing customer data/insight, data analysis and reporting and data and database management were among the most prevalent skills gaps for marketers.
“At the risk of being obnoxious and elitist, people as a whole, and marketers included, are just functionally innumerate,” Penn said. “… They left math behind when they left school, and they’ve not picked it up since.”
Technology won’t fix the problem, Penn added, and too many marketers emphasize tech over people and processes. “The idea of marketing technology puts too much emphasis on the technology and not enough on the people and the processes around it,” Penn said. “The machines can crunch crazy amounts of data, and there are all sorts of really cool stuff out there. We work with a bunch of different AI tools, but at the end of the day, you still have to do something with that data.”
Start a Quest to Become Data Fluent
And that means marketers need to get better at becoming data-fluent, he said. Many marketers don’t have the skills needed to be able to work with data as fluently as they should. That ultimately comes down to training, talent development and hiring the right marketers with the right skill sets.
“Data is a language,” Penn said, “which means that you have a bunch of people who are illiterate in this language stumbling around trying to do stuff and doing really badly at it. The resolution to it is not more technology. You’ve got to have processes in place that enforce the rigor needed in order to get the results you want.” Your marketing team’s methodology for extracting insights from data is critical and something that is a “market opportunity for companies that are progressive and smart,” Penn added.
Developing your own recipe internally for how you process your data can be a competitive advantage. Your marketing goals should be, Penn said, to use data better than your competitors can, out-optimize them in search, outbid them in Google Ads, etc.
One of the largest remaining sources of dark data for marketing is around content effectiveness and efficiency, according to Eve Alexander, senior director of product marketing for sales and marketing software provider Seismic.
“While marketing teams operating at the top of the funnel have long had the tools to distill and activate data,” she said, “increasingly the spotlight is turning to product marketing to be able to provide similar intelligence and activation at the bottom of the funnel. In the next decade, content creators will be required to not only have the know-how, but also the applications, tools and data sources, to do the same as their top-of-funnel colleagues.”
Or, as Brinker told CMSWire, data shouldn’t just reside in a “data lake.” It should be a “data waterpark” with all kinds of activity.
Be Practical About Your Data Readiness
Susan Sauter, chief marketing officer of CRM services company Customertimes, said the aspirational concepts of Big Data and Big Ops need to be seriously considered by any company looking to compete in a data savvy landscape.
“That said,” she added, “we have observed that most companies are not ready to jump multiple steps in their own maturity curve at one time. With that in mind, companies should evaluate their current state of readiness and develop a practical roadmap to work toward these ideas in order to create a continuous improvement cycle.”
She cited the example of leveraging all the internal customer data across the enterprise first before extending to external sources. Then, identify the most valuable elements that can be actioned and ensure that they are actioned within their various systems of engagement.
“Organizations that do not put their data to use are doomed to fail,” Sauter added. “They can invest time, money and valuable resources without a plan to action those insights, leading to a negative ROI.”