Agile working: Common practices to help customer insight teamsby
Continuing his series on Agile working for customer insight teams, Paul Laughlin shares common Agile practices performed by successful analytics teams.
Continuing our series reviewing how data, analytics and insight teams can achieve Agile working in practice.
In my first post on how to achieve Agile working in practice, I focussed on four principles that were needed. Principles of attitude and culture, in order to have the right mindset and approach to working this way.
Continuing with this series has been driven by the feedback I have heard from a variety of data & analytics leaders. Those working in business today are telling me that this challenge is still very much a work in progress. Senior leaders want a more agile business, but it’s not a quick fix to achieve.
To help, in this post I focus on common practices that I’ve observed in analytics teams implementing this approach.
Agile working in practice: Common practices
At the start of my first post on agile working, I referenced the five most popular agile methodologies. But, whether you are implementing Scrum, Kanban, or one of the others, certain practices appear to be needed by most methods.
So, whatever nomenclature you prefer, watch out to ensure you are implementing:
1. A visible ‘backlog’ of prioritised work
Every member of the team can see new work and any significant changes needed. One key here is having real clarity as to commercial priorities & drivers, so that requests for work can be compared & prioritised.
2. Tickets for the units of work needed
Work is broken down both to suit time periods for sprints and to divide the different skills needed. One key here is the ability to diagnose early on the work units needed from different data, analytics & other skills – worth planning out common work units in advance (based on experience). Plus, the use of tools like Competency frameworks to raise awareness of skills really help with allocation.
3. Public boards to track progress
Internal customers, sponsors & wider team have a common view of priorities and progress. Such transparency is key to the culture needed for this collaboration. One key is to consistently use this to support stakeholder management & sponsor conversations. Driving expectation of them using the board too.
4. Planning short sprints together, with bidding for units of work
A regular rhythm, e.g. 2 weeks, is established as the duration for delivery and cycle for new priorities. One key here is the cultural attitudes I praised as needed in my first post, another is to focus more on collaborating in order to achieve delivery, rather than more rigid planning.
5. Stand-up meetings for the whole team to share progress & challenges
Regular opportunity, e.g. daily, to spot issues early and collaborate to fix. Once again the attitudes and culture I shared previously are crucial to this working well. Managers also need to ensure that they encourage openness about issues or mistakes. Good news reporting is the very antithesis of agile working.
6. Post-sprint reviews to learn from what worked & what did not
Time is taken post-delivery to identify any lessons to be learnt for future sprints. One key here is to focus on systemic issues. Depersonalise criticism and work on improving how everyone works together to improve the system for future work. Any underperformance by individuals should be handled separately. Use one-to-one chats with managers (where possible giving close-to-the-moment feedback).
I have documented such a simple list to help reveal that a significant amount of agile working is not ‘rocket science‘. You may well have more success by simplifying to ensure everyone gets it, rather than an over complex purist approach.
In the final piece in this series I will share the four drivers of success that I and other writers have observed - those behaviours and attitudes that differentiate those who achieve successful Agile working.
I work with exceptional leaders & their teams, so they can master the people side of data & analytics.
That means helping them maximise the value they can drive from using data, analysis & research to intelligently interact with customers. It also means developing teams & enabling them to sustain their improvements through...