I am an introvert. Here’s how I went from fearing authority to speaking on stages.

“I was Patient Zero of losing a personal reputation on a global scale almost instantaneously.”

^^ Monica Lewinsky at a Women’s Day presentation I attended in 2017. 

Yes, that Monica Lewinsky. The woman who, by some POVs, almost brought down Bill Clinton’s presidency because she kept a certain blue dress. 

Her presentation was so well crafted on the subject of cyber bullying, I couldn’t stop watching. But it also felt like a warning: visibility can be bad. Making yourself into a household name might not be the business windfall you think

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Analytics On The Bleeding Edge: Transforming Data's Influence

Analytics teams are named for the silos and limitations within which they trap themselves.
Paid Media. Owned Media. SEO. BI. Customer Service. Data Warehousing. Email. And, a thousand other silos (depending on your company size).
One outcome of this reality is that while every team works hard to do their very best work, it is rare that they earn strategic influence from their work. That’s not really surprising, if your view of your scope is narrow… Your impact will be narrow as well.
The other dimension to consider is most Analtyics teams kick into gear after the campaign is concluded, after the customer interaction has taken place in the call center, and after the funds budgeted have already been spent. When you only look backwards, it limits your ability to have an impact.
Finally, few analytics teams obsess about predictive analytics in a way that allows them to dictate future action. This is a huge miss… Left to their own accord, how many companies will make the same decisions data would recommend? Astonishingly few.
Transforming Data’s Strategic Influence.
The above-observed realities were on my mind as I took on a new role to lead Global Strategic Analytics. This time around, my goal was for the analytics team to chart a very different path… To solve for expansive influence, before, during, after, money is spent by the organization.
A key part of how this manifested in our work was doing truly super-advanced machine-learning powered analysis to answer hard questions that few can successfully. This is of course exciting and very cool.
But the difference in the team’s impact comes from the combination of an audacious vision and putting together the people-process-structure that powers our desire for data

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