Research that should worry every data scientist / analyst

Sep 15, 2022
researching an idea

I founded DecisionViz in 2012 as a remote-first company.  Most client work was done asynchronously – we met for a short time, made decisions, then rejoined as needed.  I leveraged technology to create an experience where clients felt like they had more contact and access to me.

10 years later, remote work is now common, though in its early stages.  While most of us have had “Zoom fatigue,” the new ways of working are exciting.  

However, I recently read a study that should concern every data scientist / analyst.

According to Killing Time at Work '22 by Qatalog and GitLab:

“One might expect those who work asynchronously to feel the least presenteeism pressure.  However, the opposite is true, with 70% of those who always work asynchronously saying they felt pressure. This suggests that those working asynchronously are worried people won’t recognize that they are working, even in organizations where async work is possible.”

Do you feel this kind of pressure?  Why am I so concerned?

It’s hard getting people to trust data; now we have to worry whether they trust us?

The work of uncovering insights from data does not usually follow a specific path and is not easily “observable.”  In fact, more work may be done in what does not produce an insight.  The environment is set for the pressure of presenteeism to escalate. 

The question of “how does one demonstrate they are present?” has existed while working in the office, but it’s now in the spotlight with being remote.

Here are three suggestions to boost the visibility and progress of your work:

  1. Like Thomas Edison said about inventing the lightbulb, “I haven’t failed, I’ve just found 10,000 ways that don’t work.”

    Document and have team reviews of what you haven’t found (i.e. analysis that yielded no results) is equally important and can lead to new ideas.  This kind of documentation will help others avoid spending time investigating certain ideas.
  2. In the same way software developers have code reviews, have analytics reviews.  We get so deep into the data, it’s too easy to get stuck on a problem or become too focused (myopic) that we miss clues to follow. 
  3. Take breaks.  Yes, it’s counterintuitive, but your productivity and effectiveness decrease the harder and longer you work.  I follow the Pomodoro Technique, which advocates for 5 minutes breaks every 25 minutes.  

Management Has A Unique Data Literacy Challenge

Since management doesn’t necessarily come from the ranks of data analysts or data scientists, their data literacy may be low in understanding how the work gets done.  

Unfortunately, this issue is rarely discussed.  Data literacy efforts focus on learning skills for working with data or technology to manage data catalogs and governance. 

With high demand for data analysts and data scientists, team leaders and upper management should mutually set clear expectations and work to lower pressure.

How are you feeling about remote work?  How is your management behaving?  

Comment below and be part of the conversation.


This is why I created the Design To Act ® framework for companies to scale data literacy.  If you’re not opposed to discussing your challenges >> schedule a call 📞

Rewriting How People See Data™


PS Stephen Few’s book The Data Loom lays out a framework for the work of data analysis.

**  Here’s the link to Killing Time at Work '22

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