Reach beyond the traditional Data Scientist role to ensure your analytics deliver maximum impact to the enterprise and its leadership.
Professional adventures of late have dragged me back into the world of data analytics, a business space I hadn’t been regularly inhabiting for the past several years. I’m thrilled and dismayed to learn that for all the legitimately exciting promise of Big Data, the workaday reality of that business hasn’t advanced dramatically in my absence. In fact, I sat through a presentation during which a sharp management consultant listed the key identified drags on efficiency in data warehousing, soliciting solemn nods from most of the executives in the room, only to reveal that the list he was quoting was generated in 1999!
The hard work of managing data remains a whole lot of hard work:
- Identifying what data is available
- Determining how to gain access to it, at a dependable cadence
- Transforming the data so as to play nicely with the larger portfolio, while minimizing dumb-down
- Taking tough decisions about what data should be retained for the long term, and at what cost
- Exposing that data to business users who can wield it intelligently for the betterment of the enterprise
It’s that last bullet that I’d like to drill-into during this post, because that’s really where the rubber meets the road. All the rest of the hard work and expense in this process only exists in order to create a product that positively impacts our businesses. In some cases, this value is generated automatically through technology feedback mechanisms updating user experiences in real-time. For 90%+ of companies, however, consumption of these hard-won data products is by a business professional attempting to utilize that data for insight that leads to action. And that is where we so often hit the skids.
Beyond Data Science
I’ve been told that one of the hottest professions at this moment is Data Scientist, and I believe it. I sat with an Insurance industry executive recently who mentioned that his company had a plan to hire 400 of these professionals over the next 15 months. AWESOME.
While I’m sure some of these people will be fantastic communicators and deliver just the value their business is looking for, I fear that many will not. No doubt we need smart, computationally-minded resources to put our data through its paces and squeeze-out maximum utility. But, from the perspective of that last bullet above, they still only gets us to the three-yard line. If this intelligence can’t be packaged-up into a delivery that is clear, contextual, and actionable, very few business leaders will be equipped with the skills and/or time to make that conversion happen on their own. And until it does — we can’t ROI on all that effort.
If this intelligence can’t be packaged-up into a delivery that is clear, contextual, and actionable, very few business leaders will be equipped with the skills and/or time to make that conversion happen on their own.
With all this in mind, I recommend that our data analytics growth plans include engagement of not just data scientists, but some data shamans, as well. That’s a silly title, but let’s unpack it a bit.
Dictionary.com defines shaman as:
“n. (especially among certain tribal peoples) a person who acts as intermediary between the natural and supernatural worlds, using magic to cure illness, foretell the future, control spiritual forces, etc.” [http://www.dictionary.com/browse/shaman?s=t ].
In practice, the shaman is a leader who brings pivotal soft skills to the tribe. Operating as a trusted advisor to the chief, the shaman endows tribal decisioning with context: historical, strategic, spritual. The shaman is charged with thinking and operating beyond the strictly logical, discerning what is underneath the obvious — delivering that knowledge in a manner that is engaging, instructive, and inspirational. To me, this sounds very much like the resource we need in those pivotal final steps of our data analytics pipelines.
Power of the Narrative
Human history leaves no debate about narrative structures as the most effective channels through which to communicate complex concepts in a widely understandable way. From the earliest pictographs, through the assorted parable traditions of major world religions, to the duelling spin of our current political climate, stories strike us most deeply, providing an immediate and lasting facility for information. Narrative structures have the potential to inhabit our data with meaning, elevate it to the visceral, and engage faculties beyond the neocortex in its consideration.
Working closely with the scientists and other professionals in the data hydration pipeline, the data shaman digs deeply to understand the frame of reference within which that data exists. Marrying this with an informed understanding of the host business and its larger market, the shaman converts data analytics into narrative format, attaching potential actions to represent an assortment of upside/downside options. Actionable intelligence that business leaders need in order to make smart, timely decisions.
Sourcing the Resources
Where will these fantastical resources come from? Three sources, I expect:
- Some will develop organically, based upon the skills and traits already baked-in. We’ve all known those unique and in-demand resources in our firms who are inexplicably able to bridge the gap between groups, syntaxes, and agendas. Those folks are always invaluable, and some will choose to wield their awesomeness in support of data analytics.
- The content world is full of individuals who excel at chunking-down complex concepts into understandable narratives: investigative journalists, technical writers, business analysts… Some of these will be drawn to data analytics as a place where their skills can best shine.
- The data scientists we already employ wield invaluable domain expertise. For those that don’t naturally fall into category #1, we can augment their skillsets through training in narrative communication. Blogging, mythography, screen writing… Don’t laugh, a data scientist on the far side of a Robert McKee seminar [ https://mckeestory.com/ ] can become a lethal weapon for business.
Analytics with Impact
So much of the data analytics regime exists in a realm of deep technical detail. But the team that produces that data for the betterment of the enterprise must understand the importance of making a hard pivot to the right brain during the final leg of processing. Most senior business leaders aren’t comfortable dwelling in the world of data interpretation, and those who are can barely afford the time to do so in our swiftly changing environment. They require trusted resources to parse that data into contextualized information, presented in a narrative format that can be easily understood and internalized. Data management is a team effort, from the gathering, to the ETL, to the delivery. And data scientists play a pivotal role in that process. But leave some space on your org chart, and in your imagination, for a data shaman or two to help package it all up into insight that provides meaning to senior leadership and facilitates action from the enterprise.
Organizing people, process, and tools for scalable delivery — VP, Digital Operations, Univision Communications, Inc.