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Emerging data solutions and trends have a common thread: the power of the collective in which the final product is bigger than the sum of its parts. Companies leading the data and analytics charge have recognized that the power of a collective applies to more than technology. They are also mindful about maximizing the potential of a much more accessible resource: their staff.
Indeed, conversations around organizational strategy today emphasize the need for teaming models which bring together diverse skills and resources to enable data science, data governance and stewardship and the digital enterprise. As these conversations progress it’s important to think through how to best harness that collective power. While face time and common objectives are important they are not the only success factors.
As a member of the SAS Best Practice team I see the power of the human collective come to life in every presentation, eBook and paper I write. As fine as my ideas sometimes are, they would not be as well-received without collaboration and input from my team. That team includes a very smart, creative genius named Dave. Dave’s primary job? Keep my natural proclivities as an engineer in check. In other words: ensure the content we create is visually intriguing, consumable and – let’s be honest – palatable to a broader audience. Most of whom do not find naked facts and figures compelling.
Here are some teaming principles we’ve learned in our work together:
Allow Time for Reflection: As a Group and Individually
The process starts with an initial brainstorming session. I provide the raw concepts and any rough ideas on conveying them: often in the form of analogies or metaphors. We walk the concept, team members ask many, many questions and bat around initial top-of-mind ideas. We then retreat to our own dugouts. Thereby providing much needed time to reflect and ponder before reconvening with the broad group. In the next meeting, Dave presents refined visuals (i.e. completely refactored graphics – seriously, the man can make a stick figure look good), potential refinements and changes are discussed. So it goes until the end product is ready for public consumption.
Play to Team Member’s Strengths
Yes, Dave and I are a team. One that recognizes our individual strengths and optimizes them relentlessly. This unlikely collaboration between a chemical engineer and a graphic artist results in an end product far superior to anything we would create alone. A more important outcome? Mutual learning. I’ve become more creative and Dave’s business domain expertise has substantively improved.
Invite – In Fact Incent – Dissension
Our most productive conversations often start with: “that’s great, but…” Interestingly, concepts initially thought to be non-starters frequently win out in the end. Oft modified, but still recognizable. The lesson learned? When an idea is new or contrary to the mental model you are perhaps unwittingly attached to, time to sleep on it can change your perspective. Allowing time for rational thought after the gut-level “no way that works!” reaction subsides. All of which assumes you’ve given divergent points of view and opinions a seat at the table. Of course, am much as we enjoy a good debate, we still have a product to deliver. Which brings us to the last point.
Pick the Best, Implementable Idea
Enough said. Or is it? I enjoy debate as much, if not more, than the next guy. That said, we recognize that constructive debate is a means to an end: delivery of content that helps our clients move the needle on their business. Very often, this means that the most creative or high concept idea doesn’t win. If we can’t execute in a timely fashion, the idea is for naught. No matter how clever if the concept requires too much explanation, same goes. While we don’t limit ideas up front, we do integrate routine checkpoints to validate not only the concept but our ability and willingness to put it in action. This is a discipline data science and governance teams must also embrace.
Organizations embracing bigger and better approaches to data and analytics are well advised to deliberately maximize the power of their resource collective. While the change management conversation goes far beyond these simple principles the best intentioned data science, data governance or stewardship program – indeed any cross-functional endeavor – can’t function without their incorporation.