Three Steps to Joining the Data Generation
Historically, most people have adopted a “mweh” attitude when it comes to data. While they may need it to do their jobs, they are essentially disengaged. Rather than seeking new ways to put data to work, many spend half, or more, of their workdays dealing with mundane issues--correcting errors, seeking confirmation for numbers that look suspicious, resolving differences in data from different systems. It is as if everyone’s job has two components:
Their jobs and
Dealing with mundane data issues so they can do their jobs.
A huge waste of talent.
Further, it is no secret that employees are beyond burned out, resigning in record numbers. Exacerbating this, many harbour legitimate fears that statistics, data science, artificial intelligence and/or digital transformation will replace them.
Yet “data” presents an unprecedented opportunity for people to empower themselves, seeking the facts and using them to make things better in their personal, civic, and professional lives. Data is there for everyone, regardless of profession, age, or level of training! I encourage everyone, and I mean literally everyone, to empower themselves with data. This article urges everyone to join “the data generation,” a rapidly growing group of people that is doing just that.
Meet The Data Generation
Most people are remarkably tolerant of time wasted at work due to bad data: Sales people spend a couple of hours a day correcting bad leads from marketing; Finance people spend even more time tracking down discrepancies; and managers delay making important decisions because they can’t trust the reports they need to make those decisions. It as if dealing with bad data is simply part of the job.
The day-in, day-out indignities associated with the pandemic, political misinformation, and challenges in finding a new normal, have fuelled a growing intolerance. Some people have been concerned with the big issues such as how many really died from Covid and was the election stolen. But for most people, it is closer-to-home issues that have brought a new urgency to high-quality data and analyses. Getting answers one could trust to basic questions like:
“What percentage of people in my neighbourhood have been vaccinated?”
“Your website says you have light blue spray paint. Where is it?”
“Can I trust this report about a philandering local politician I found on social media?”
“Will the gifts I ordered arrive in time for the holidays?”
simply won’t wait.
In response, more and more people have quit tolerating bad answers and empowered themselves to find fuller, more complete, accurate, and relevant data and use it to make things better in their personal, civic, and professional lives. In doing so, they join “the data generation.”
It is easy to identify data scientists, other data professionals, and “that guy” who could explain the number better than anyone else, as members.
But the heart and soul of The Data Generation is “regular people:”
The intern in an Indian animal shelter, using data to identify areas where urbanisation threatens people and wild creatures.
The third-grade teacher who shows their class how to make simple graphs, even if it is not in the curriculum.
The manager who (finally) stops rationalising all those conflicting reports and seeks out the root causes.
It appears to me personal issues, not work issues, are the dominant reason the people have joined the data generation. This stands to reason--after all, nothing has the immediacy of issues like, “what do I have to do to keep this eight-year old, in school, in the face of Covid?”
There have never been more opportunities, or a greater need, for individuals to use data to make unique contributions in their personal, civic, and professional lives.
You have the Power
For some people, joining the data generation means conquering their fears. For challenging the status quo is always hard and it is even harder with data. After all, data is so elusive, nuanced, and technical.
Yet, people have far more power than they think. No one is stopping you from plotting your teenager’s grades to see if a bad score is an anomaly or a the latest in a very concerning trend. No boss ever says, “Whatever you do, don’t get to the bottom of those quality issues” or “Stop! We’re proud of our slow, ineffective, and inefficient process.” Even better, as a companion article (“You Must Put ‘Regular People’ Front and Center in Your Data Program”) explains, data programs cannot succeed without regular people. Said differently, when it comes to data, your company needs you more than you need it.
Bob Pautke, a Cincinnati-based career advisor, observed, “Too many people have great ideas, but they’re afraid to speak up. Data empower them—it’s not just your opinion when you have the facts to back you up.”
Embrace this power and use it to overcome your fear!
1. Pick Something That Interests You
It is fascinating to me that companies are loaded with problems that can be attacked with small amounts of data and basic analyses. By and large, they have eschewed such problems in favor of big data, artificial intelligence, and machine learning. In my view, they’ve missed the boat, creating opportunities for the data generation. Pick something that interests you, such as “it seems like I waste a lot of time waiting for meetings to start,” or “why is it so hard to follow-up with patients?” and follow the steps outlined in “How to Start Thinking Like a Data Scientist” and “To Improve Data Quality, Start at the Source” or other good templates. These provide step-by-step instructions for completing a project in just a few weeks. Successfully completing one project will build your confidence for the next, larger project, and the next, and the next. (And to dispel your fears, these templates allow you to work “below the radar.” So, if you fail no one will even know!)
2. Use what you learn in your personal life at work. And Vice Versa
The methods one uses to embrace data at work and are exactly the methods you need to embrace data at home. So apply the lessons you learn at work to your personal life, and vice versa to reinforce one another. To illustrate, the first step in any data quality project involves understanding who the customers are and what they need. This means active listening. While I learned these skills at work, they proved even more valuable as I tried to be a good parent to my teenagers. Conversely, I used skills gained in clearly explaining data science to my children to better communicate results to senior managers.
3. Think through “What’s in it for me?”
As you gain some experience with data, it’s okay be a little selfish, asking yourself, “what’s in it for me?” Some simply experience a special satisfaction in conquering their fears of data and learning something new. Others use their successes with data to better positions themselves for promotion. Conversely, some dispel their worry that failing to jump on board would limit their careers down the road. And many find a primal joy, almost like the joy youngsters feel in learning to ride a bike, in taking on a “sacred cow,” something that had always annoyed them at work. And so forth.
So think it through. Then craft the series of projects you work on to get what you want!
Contrary to the belief of many, data and data science are not reserved for an elite few, skilled in mysterious arts. While it is true that “the more you know, the more you can do,” an increasingly large and important segment is demonstrating that they can do plenty as well. If you’ve not done so already, join the data generation!
ABOUT THE AUTHOR
Dr. Thomas C. Redman, “the Data Doc,” President of Data Quality Solutions, helps start-ups and multinationals; senior executives, Chief Data Officers, and leaders buried deep in their organizations, chart their courses to data-driven futures, with special emphasis on quality and analytics. Tom’s most important article is “Data’s Credibility Problem” (Harvard Business Review, December 2013) He has a Ph.D. in Statistics and two patents.
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