A Reddit post reminded me that Data Scientists often struggle with time management and priority management. There is a customer expectation of fast turnaround – especially in Analytics. Data is complex. Supporting tools are only starting to catch up with their engineering equivalents. This post shares time management and priority management tips for data scientists that I have learned in building teams. The temptation is to lean on automation and ‘better tools’ but the reality is that discipline and assertiveness will have the biggest effect.
Three challenges to effective time management and priority management for data scientists
The main challenges to effective time management and priority management for data scientists are:
- Allowing stakeholders to invade team time.
- Allowing stakeholders to invade priorities.
- Lack of internal processes and discipline cause high communication and coordinate overhead, reducing the team’s effectiveness.
Time management and priority management tips
Preventing invasion of team time
These tips are straightforward and aim to move disruptions to controlled times on the team’s schedule.
- blocking team technical time in calendars allows data scientists to engage in several hours of focused work.
- no meeting blocks or even no meeting days again allow productive data science to be done.
- office hours help stakeholders who have questions meet with a team member on the team’s schedule.
- an equivalent of ‘first line support’ allows dedicated team members to respond to quick fire requests without the whole team being disrupted. Done on a rotation, this is an efficient way for mature teams to defend their core working time.
Preventing invasion of team priorities
Firstly, it is impossible to prioritise without a searchable list of the active work and incoming work the team faces. Many teams are pulled in different directions because they cannot communicate their current work and their priorities to stakeholders.
- Workflow tracking can be as simple as a spreadsheet or as complex as modern workflow tracking software depending on the size of the team, the nature of the work and the number of stakeholders.
- Maintain backlogs of work that the team are aware of but hasn’t been started yet. This allows you to measure and communicate the work on the team’s plate as well as constructively discuss what should be done next.
- Educate on how to say ‘no’. This is difficult, involves and cultural change and is often something junior team members struggle with. It is much easier to say No when you can be clear on the day’s current priorities, when the work waiting on the backlog can be discussed and when there are other avenues for a solution such as the office hours and first line mentioned above.
These tips involve discipline from the team to always write up requests as well as training in how to write requests clearly in a way that the deliverable is understood.
Improving team internal processes
Even with the tools of prioritisation and time management in place, data science teams, by the nature of their work, are often far less effective than they could be. Changing data, changing understanding of business processes, a cultural lack of awareness of version control and release management, a habit of ‘solo’ development in notebooks and personal spaces all contribute to burdensome internal communication. The incorrect reaction is often bespoke configuration documentation to try and keep the team in sync.
Convention is a far more effective approach that configuration for coordinating the team’s activities and outputs. Fortunately, Guerrilla Analytics can help with principles and practices to help data scientists adopt conventions that allow them to operate more effectively with minimal overhead.
You must be logged in to post a comment.