If you’re like me, then you’ve had (many) experiences working with people who aren’t data savvy. This isn’t meant to be judgmental. It’s just a statement of fact.
Even in today’s modern economy, with all its emphasis on technology and data, most employees are not comfortable in working with data. According to a recent report by Accenture and Qlik, while 87% of employees recognize data as an asset:
It’s not surprising, then, that one of the most ubiquitous challenges data professionals experience is working with colleagues who are not as technical, or data savvy, as they are.
As a data professional, it can become tiring to work with people who just don’t get how leverage data. But the good news is that there is something you can do to make things better: Help your non-technical coworkers to become data professionals (or, at least, data professional adjacent) themselves. This helps your colleagues and your company to become more data driven, which, in turn, enables you to work more effectively and efficiently. You spend less time on answering the same questions or fulfilling the same requests and more time on doing high-value, analytical work.
As the adage goes, “Give a man a fish and you feed him for a day; teach a man to fish and you feed him for a lifetime.” That’s the approach you need to take to help your non-technical coworkers become data professionals. You need to empower them to become more comfortable interacting with and leveraging data in their responsibilities.
I’ve been in the position of being the data professional having to empower my less technical colleagues to unlock their inner data professional. It’s hard but possible — and deeply rewarding to witness your colleagues’ transformation. Let me share with you key lessons I’ve learned along the way to do this successfully.
It all starts with knowing your audience — in this case, understanding your colleague’s data background and what her professional goals are. This becomes the foundation on which you build your entire effort to unlock her inner data professional.
Among the questions you need to consider are:
Treat your non-technical coworker as you would a customer, because, in a way, she is your customer. You are “selling” her a tailored service that will develop her data skills and fluency. Answering the above questions will orient you as you serve your colleague.
For data novices, relevant metrics are especially important, because they offer direction in terms of which activities/outcomes to prioritize and give visibility into whether the data-driven approach is working or not. You should always collaborate with your non-technical colleague to determine together the metrics to be used. Doing so secures your colleague’s buy-in on the data-driven approach and establishes accountability on her end.
When determining the metrics, keep in mind that they should be:
Once the metrics have been defined, the next step is to make the data accessible and digestible. For non-technical folks, it’s not productive to just work with a data dump/export or by querying the actual data source; instead, the best medium for them to consume data is in a collated dashboard that summarizes the data in a self-service format.
The dashboard can be beautifully designed in a BI platform (e.g., Looker or Tableau) or hacked together in Excel. It doesn’t have to be sophisticated or extravagant — it just needs to be simple and easy enough for your coworker to use and contain all the jointly established key metrics. Be careful when building the dashboard, because your conception of a good dashboard might be different than your coworker’s. Don’t fall into the trap of building something for yourself and let that distract you from your true audience, that is, your coworker.
The advantage of a dashboard is that even non-technical people can self-service their data needs. Encourage her to regularly check the dashboard so that data becomes more familiar and less intimidating. Encourage her to consistently ask herself how her or her team’s daily activities impact the metrics, for better or for worse, so that she can develop an eye for drawing insights from data.
Trust is earned in drops and lost in buckets. Specifically, trust in data is earned primarily through: 1) consistent accuracy of data, and 2) reliable history of data driving positive outcomes. Skepticism around the value of data and a reluctance to apply data are not uncommon in data novices, and inaccuracy is the quickest way to feed their skepticism and reluctance. Therefore, it is essential that you cultivate your coworker’s trust in data by providing accurate data, whether in the form of a dashboard or an ad hoc spreadsheet.
Practical steps you can take to avoid this headache are:
I hope that you not only find the above tips useful, but are now inspired to bring life to your data-thirsty working environment. Good luck in your quest to help your coworkers unlock their inner data superheroes!
Sungwoo Chon is a business strategist and operator who has worked at both startups and public companies to build, scale, and optimize their go-to-market and product operations. Get in touch at sungwoo@hyperquery.ai.
