The IT industry is filled with buzzwords. Often, we create these words or phrases in an attempt to make it seem like something is new, different or unique. Take big data for example: The Global Language Monitor recently crowned that term as the most confusing term of the decade (along with dark data). Will data scientist follow suit? I realize that data scientist isn’t just a phrase or buzzword, it is a job description, but it still is a relatively new term used to describe an emerging and critical need for companies large and small to marry the analytical expertise (usually a professional analyst) with the business domain knowledge (for example, a marketer). Despite this need, however, companies still can’t seem to fill this seemingly necessary void because there aren’t enough people with the required skills.
Want to know why? Because it requires someone with a balanced brain, utilizing both left and right side almost equally to balance art with science, creativity with logic, MIT with Juilliard. It doesn’t exist because there are so few people that can truly balance both of those disciplines, understand them and apply them. Consider personality tests like Meyers-Briggs. Typically, people show clear tendencies toward one or two particular, somewhat related personality attributes, while clearly lacking in others. Instead of trying to force people to think in ways that their brain just wasn’t wired to, what about enabling them with emerging types of technology that not only perform the analytic heavy lifting, but also translate the results in plain language. This idea seems to make more sense than forcing a marketer, who is skilled at crafting a message that will resonate with an intended audience, to figure out what base rate fallacy, bias or binomial distribution is.
Do I think there is merit for companies to train existing employees to think like this? Sure. Do I think universities should continue to create new curriculums to teach these skills? Absolutely. But I also think that another solution exists for the vast majority of the workforce that haven’t yet mastered these skills.
Yes, I work in the software branch of IBM and I have a background in advanced and predictive analytics, so you might claim this blog to be self-promoting pap. You’re certainly welcome to your viewpoint, and I won’t argue with you, but I will implore you to look further and see if my suggestions could function as a bridge between brain hemispheres.
I joined SPSS (now IBM) five years ago and, today, here I sit at the Smarter Commerce Summit in Tampa, Florida. I’ve seen new products announced and demonstrated that combine hardcore analytical tools with line of business applications that don’t require advanced programming skills or a PhD in statistics or to use. These are business applications, like marketing automation platforms, with embedded advanced analytic techniques built to perform sophisticated segmentation and predictive scoring, enabling more intelligent and personalized marketing by engaging customers in context. I’ve also seen 40 year old predictive modeling technologies turned into a platform where a user can literally ask a question (for example: “Who is the best person to send this particular campaign to?”) and get a response back in common language, steeped in statistical rational.
Instead of bridging this gap with a single person, why don’t we encourage a partnership between our marketing and IT talent, utilizing these emerging technologies as a common language to unite the two groups? Let’s use a new common language to bridge these two often disparate groups: data security, privacy, governance, integration and management can continue to be the concern of the IT organization, and customer acquisition, satisfaction, lifetime value, ROI, SEO and conversions continue be the concern of the marketer, with each group keeping a close eye on the activities of the other. This should be a collaboration, where marketing has the ability to choose sophisticated analytical tools to gain actionable insight and IT can ensure the quality and security of the data that is being analyzed.
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