You’re busily working away and the phone rings. Instantly you’re distracted by a pressing issue that needs your attention. You stop your current task and focus on attending to the interruption.
The problem requires some documentation hidden away on your network store, so you go hunting. The information can’t be found and you call a colleague. They are now interrupted. Between you, the answer is found and you then spend another five minutes crafting a response to your client.
The client receives an answer, their problem is solved, you go back to your original task. It takes you a few minutes to figure out where you were and get back on track - but finally you’re in control and moving forward.
Then the phone rings again, another problem...
Does this situation resonate with you?
It certainly does with me, particularly in my past life as a technical manager. I was constantly fielding calls from internal data customers who were querying reports or data feeds. Calls would come in from newspapers and media outlets who purchased our data. All the while, I was trying to do my "value added work" but tackling "non-value added" interruptions.
The problem with these interruptions is that we seldom recognise them as "non-value added;" they are simply classed as "getting stuff done" or essential admin. Quite often they are requests from customers or stakeholders, so they require a great deal of focus.
A big problem with managing these kind of data-related issues is the problem of "setup time" and its impact on our productivity.
Setup time stems from the world of quality management and, in particular, the Lean methodology. Basically, it’s the time taken to prepare an environment for value-added work. It originates from the world of manufacturing. For example, if you were about to press a sheet of metal for fabricating car parts, it's the time taken to calibrate the various pieces of equipment and generally get the environment ready for operation.
In an office environment we are impacted by setup time whenever we need to deal with these non-value added data problems. When we finish firefighting we have to get our environment back into a ready state, and this can take several minutes. Now consider a small team of 10 knowledge workers and consider how much setup time and non-value added time are consumed each week by fielding data problems. This time can be easily tracked using a simple spreadsheet, and it’s something I’ve introduced in past organisations to help us identify the optimal areas for improvement. By identifying not only the type of defect that was passed to you for resolution but the task you were working on at the time, you can identify the impacts of poor data quality far more accurately.
Why not try it for yourself? Log your daily value-added activities, interruption activities and setup times to see how poor data quality affects your daily productivity. Scale this up over a year and you’ll have a compelling case for action, particularly when you get your other teammates involved.
