I have been lucky enough to work in many data organizations in my career to date, so I have seen a diversity of data literacy skillsets and approaches to using data. From very commercial sales and consulting organizations to internally focused product and engineering teams, each group sat on a different point along the spectrum of data literacy.  

What was one challenge that was common amongst most of these groups? Data anxiety.

In almost every case, the teams could see the potential value of data, but there were individuals who sensed a barrier separating them from fully realizing the benefits. Perhaps they had felt overwhelmed by poorly communicated insights in the past, or they had never been equipped with the basics they needed to build confidence with data. In any case, because of this data anxiety, these people preferred to keep data and data literacy at arm’s length versus embracing it and moving towards self-service analytics. In my experience, this dynamic is more prevalent within less technical organizations.

Moving beyond Metawork with Self-Service Analytics
If you fail to deal with data anxiety, you get what I call metawork. What is metawork? As the word implies, it is work on top of the work; unproductive, inefficient, non-value-adding effort that can be hard to quantify, manage or escape. It is not a concept that is unique to the world of data, but certainly a common symptom of data anxiety.

When you have a dynamic where individuals push back against having to conduct even a small amount of data analysis on their own, and data analysts then push back on the user requests they see as trivial, the conversation can begin to cycle down. The focus shifts from where it should be – solving business problems – to a metawork spiral of unproductive framing meetings, endless back-and-forth iteration, and a breakdown in healthy business relationships. There is no true value-producing effort; it is just… metawork.

If you find yourself trapped in a metawork spiral, there are a few ways to shut it down:

  1. Focus on the business problem. A good data team should focus on solving major business problems – either opportunity-seeking or pain-resolving. To do this effectively, they need to understand the business, and the business needs to understand the value they provide and where to plug them in (early and often).
  2. Build two-way trust. Business users should trust data teams to help them solve problems, but data teams need to create confidence among non-technical users that they can build their own literacy to handle everyday priorities. 
  3. Empower users to handle the basics. Off-the-shelf self-service analytics tools have improved significantly over the past few years to empower nearly anyone to lean into their basic data literacy and create what they need, iterating rapidly without needing to back-and-forth constantly with a data team. Read: no SQL code-writing required. 
  4. Save data teams for the big problems. If business users can get answers to basic questions (and get them quickly) using self-service analytics tools, they will trust their own data skills and call on data analysts only when they are grappling with tough, complex challenges. Complexity should be measured by the problem, not the solution.

Ultimately, you want every team member to know they have a role to play regarding data – it is not something to outsource or avoid – and that without evolving their own skills, they will hold back the business. There is only so much analyst bandwidth to go around, and everyone needs to raise their game on the data journey.

Somehow this is one of the tougher nuts to crack in data analytics. As so many have pointed out, success with data is not about technology or platforms, it is about change management. Get everyone thinking of themselves as data analysts, even at the most basic level, and it can be a game-changer for your organization. 

After all, no one really wants to spend their time doing metawork; with the right approach, you can move toward a self-service analytics model where everyone can play their part with data to create real value for the business.