by Chris Pehura – C-SUITE DATA
It’s our moment. There were twenty of us, a mixture of executives, consultants, and senior directors sitting in the conference room. We’re there to present the new direction we as a company are taking. We weren’t starting off on a good foot. A lot has happened recently. We got our lumps from those market analysts. We’re going through a massive layoff. And a well-respected executive resigned. Many in our audience aren’t coming from a good place. Who could blame them?
We were ready. To back up our narrative we got everyone we needed in the room. I opened up the conference bridge. Over three hundred from across the country chimed in to listen to what we had to say. For six hours we presented the financial and strategic benefits for our new direction and what we must do to realize those benefits. With our due diligence, we walked through the evidence. We were prepared; and we have Data Science to thank.
“Data science is an interdisciplinary field including processes and systems to extract knowledge and insights from structured and unstructured data. Data science spans mathematics, statistics, data analysis, visualizations, and predictive analytics. Data science focuses on giving us the correct answers.”
With Data Science we were able to construct an end-to-end model of our company strengths and where we should be making our investments. This allowed us to take our blinders off and see how to correctly interpret our market position, our operations, and how we currently achieve our revenue growth. Data Science, when we allowed it, helped remove our internal biases. Data Science gave us the correct answers.
But our audience didn’t want the correct answers. They wanted the right answers. To get those, we took the correct answers from our Data Science models and used them as evidence. Then based on that evidence we determined those right answers.
“If we didn’t do this, we would have found ourselves in a much bigger political whirl wind. When things get political we debate on what is the right thing to do. To shorten the debate, many of us are prone to taking evidence that supports our position, rather than basing our position on the correct evidence.”
To get the right answers for our audience we created a narrative that navigated our Data Science model from end-to-end. We took that narrative and summarized it to get our key message. With our key message we developed our five most memorable outcomes for our new direction. To achieve all of this was an art. This art was Language Arts.
“Language Arts is the study and improvement of reading, writing, speaking, listening, and visual literacy. Language Arts teaches us to influence, argue, and debate based on interpreting proof such as statistical, numerical, phonetic, and symbolic proof. Language Arts focuses on giving us the right answers.”
Using the tools in Language Arts we refined our narrative, message, outcomes, and supporting evidence. With nine different essay approaches we confirmed we were using the correct evidence and closed all the gaps in our narrative.
“We constructed our six-hour presentation based on the correct evidence. We then enriched our presentation leveraging nine different essay writing approaches.
Cause and effect
We repeated the process until we were all ready to present with a strong unified narrative.”
With our due diligence, we became a unified front to our audience of three hundred plus. We answered all of their questions fully and consistently. We negated the finger-pointing. And most importantly we kept the presentation on track and on message. And this built the confidence our audience needed to accept our narrative and gain their support for what needed to be done.
We won the support of our audience with our narrative. A narrative we build with Data Science, streamlined with Language Arts, and delivered using the correct evidence. The kind of presentation we gave, our political whirl wind, and our on-edge audience is all too common in corporate America. Strategic change is ever-expanding in scope and frequency. It’s very clear we need a new kind of leader; a leader that knows how to gather the correct evidence and use it to present the right answers; a leader amalgamated with Language Arts and Data Science.
Just how much art do we need in Data Science?
We need all of it.
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