Though the worlds of data scientists and chief data officers are in many ways only tangentially related to marketing research, some recent reading about the two got me thinking about the roles of language and terminology in MR.
A few years back the folks at Gartner coined the term “citizen data scientist.” In a recent blog post (“Citizen data scientists and why they matter”), Gartner’s Carlie Idoine notes that the company defines a citizen data scientist as “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.”
Idoine’s post contains a list of the traits of a citizen data scientist. These remind you of anyone else you know?
- possessing a contextualized vision of the organization
- can apply analytic techniques to business problems
- has an appetite for what matters relative to business priorities
- been around the block and has connections
- unique perspective of individual business areas
- able to go to bat to justify business value
- involved hands-on in multiple analytic areas and activities
I’m not suggesting that researchers get new business cards with “citizen data scientist” on them but it struck me how neatly the above also describes the skill set of an effective marketing researcher. And it made me wonder if “data scientist” (citizen or otherwise) might sound more impressive or effective than plain-old “marketing researcher” to an internal partner.
Creating a common language
In his article, “What makes a successful chief data officer?” Raconteur’s Rupert Goodwins quotes Nic Orton, chief data officer for LexisNexis Risk Solutions, a data analytics organization, on the role of language and his job. His success, he says, depends on creating a common language for data between business units inside the organization and, beyond that, to customers and partners.
For example, data quality is judged in seven dimensions, including completeness, validity and consistency. “When you see your language being used elsewhere in the organization, it builds confidence that there’s clarity across the organization, which means we build trust in ourselves that we are using data correctly,” Orton says. “That becomes visible outside the organization and builds trust in relationships.”
Are you hearing and seeing your data- and research-related language being used elsewhere in your company? Perhaps your firm has a uniform, well-understood set of terms and constructs for communicating about research but if there are problems, it might be worth looking at the language being employed – by everyone involved. Are internal clients asking for one thing from you and getting another? Might they not be coming to you with their business questions because they don’t think answering them is part of your purview, based on your job title or department name?
If you want to drive the narrative and control (or at least influence) how you are seen by your internal audiences, you certainly need to take the lead in determining the language used to describe what you do and the deliverables you provide. That said, I fully understand the enormity of trying to change the culture in an organization of any size, especially the size of some of those where many of you work.
For me, it gets back to Orton’s idea of a common language about data between business units and between the organization and its customers and partners. If things are not functioning as smoothly as they might, spend some time looking for disconnects. Because nothing erodes trust like poor communication and, playing off of Orton’s point, nothing has the power to build trust and solidify relationships like clarity and shared expectations.