Recently, Opher Kahane from Origami Logic wrote a great post about why Marketing data is so difficult to use. It’s a great post and if you are a marketer and haven’t read it, you should.
Much as we love the post, it also highlights an issue that is particular to Marketing and Technology. Many of us in the Claritix team have served other lines of businesses such as Finance, HR and Operations and almost never have the business folks in these functions needed to know or care about the technical complexities involved in providing them with the business results.
So, why is it different for Marketing? Is it that the world of Marketing is inhabited by geeks who are curious about the technical definition of each element in the data that they need to consume?
On the contrary, most marketing folks who we meet (excluding a select few geeks especially here in the Silicon Valley) really couldn’t care two hoots about how and why the data is complex. As a VP of Marketing at a large B2B Hi-Tech company told us this afternoon:
“We have been using XYZ (a very well known Marketing Analytics solution) for many years and every time I go into their dashboards, I get a headache. I cannot understand all the technical hoops that they make me jump through”.
Sure, the fact that the Marketing Stack is complex and that there are various idiosyncrasies in each source system could impact the quality of the analyses that the Marketers get. But we at Claritix believe that this is precisely where Data Analytics as a Service (DAaaS) comes into play.
It should be the role of the DAaaS providers to hide the complexities from their end users. Of course, we can and do talk about how difficult it is to get the data in order to convince customers that they need our services but once we are engaged, our users should never have to worry about the fact that the analysis they need to send to their VP tomorrow morning needs data to be pulled from ‘n’ different systems, each with its own mood and personality.
If you are a Marketer, we would love to hear your thoughts about how necessary you think it is for you to deal with the details of data complexity.