Once again, let’s get out of the spreadsheet and discuss other things that we need to manage this world with reliable data.

Today, we look at what it takes to fully own a data-driven role. Many of us are shocked when we take on a new position and discover how data-heavy it turns out to be—and the politics we’re drawn into.

It’s always a pleasure to meet with someone like Keidra Chaney, fellow bassist,  fellow data nerd funkateer, and Social Media Strategist. We recently met to prepare for a course we’re co-teaching on getting more from Google Analytics by using Excel. During our planning, we shared stories about teaching people to work with data and one concern that came up was that a lot of people who are responsible for data never expected their role to be so data-heavy.

  • An employee is promoted to supervisor and suddenly there’s all this performance data to manage.
  • The person who seems to know a lot about social media is assigned a new official role of Social Media Mystic Poobah. Uh Oh! Loading auto-tweets into HootSuite is one thing. Downloading and analyzing YouTube analytics in Excel are a different matter.

I and others can show all the neat-o tricks in Excel but

Dealing with data is political
Data exists in relationship to human expectations
Data drives decisions

After all the rows and columns of raw information have been poured into whatever BI tool, that’s really when the arrows start flying and fun begins for the person responsible for the analysis and output.arrows

  • The phone rings, and someone who doesn’t like the output asks, “are you sure this is right?”
  • One department wants to use the data as evidence against another department.
  • Hecklers dismiss it all as crazy and wrong, and they have a list of two whole anecdotes as evidence.
  • Someone else uses the results to obnoxiously gloat to someone else, “I told you so!”

All of that comes with managing data. Oh! And these, too:

  • Someone else uncovers a real error in the analysis.
  • The analyst uncovers their own errors—after having reported the initial bad results.
  • Numbers don’t add up and the analyst has no answer for why not; and hours of investigation only lead to dead ends.

What’s the answer?


Clean data doesn’t stand on its own. A pivot table and line chart don’t elicit unanimous agreement from stakeholders. Data results have to be delivered to decision-makers who may not like the results. For those “messengers” who weren’t expecting to work with data, a mentor is needed even more than tech skills.

I’ll never forget Thomas. He stepped up as a mentor when I had my first analyst role. He noticed my lack of confidence when I’d get called in to explain my numbers. Thomas had helped me with the analysis and checking the data quality. We knew the stuff was right but I was intimidated by the VPs, CFO, CEO and other alphabets. (Being an analyst is a high visibility position; something I hadn’t expected.)

Thomas warned that my intimidation translated as a lack of confidence. And that made everyone else less than confident in what they had to report to their higher-ups. So, I had to quickly grow a spine and own my work.


Anticipate The Questions That People Are Going To Ask
If a number wildly varies from a trend, someone is going to ask why. If the answer is “I don’t know,” then show that you’ve put some effort into digging into why, and that the variation is not a surprise.

Data Is Always Accurate “As Of Now”
Data is dynamic and is never perfectly clean. We’re constantly updating old data. One day you get notice that someone mis-coded something in a report over the past few months, and now it’s corrected. All of that old data has to be revised, and everything related has to be adjusted accordingly.

There’s no shame or embarrassment in that. It’s just a reality of dealing with data.

Be Confident But Not Defensive
When we’re wrong we just have to own our own mistakes. Check out the story where I miscalculated someone’s commission payment.

When we’re right and people challenge the data, it can’t be taken as an affront to one’s competence. We’re all on the same team and want reliable data. Sometimes results are just hard for people to swallow, and they react by asking a bunch of questions.


Big thanks to Keidra for sharing an understanding that Excel, SalesForce.com, Google Docs, a 99-cent calculator and a notebook, are all just tools. There’s way more to keeping this world’s data clean and reliable. It’s only reliable and useful if an analyst can stand by it. Unfortunately, many analysts aren’t prepared, especially in this new world where data is oozing from everything.

Keidra and I can teach Google Analytics and Excel to new analysts but only a mentor can teach how to manage the context of politics, workplace culture, expectations, doubts and drama.