How to be an indispensable asset as a Data Practitioner (Analyst, Scientist, Engineer…)
Data can be confusing and daunting. It’s the role of the thoughtful data practitioner (using this term to englobe everyone working with data) to make it ‘digestible’ for the target audience! That’s how data and analytics could be really actionable.
While this sounds simple, not many really get the gist of this subtle skill. Any data person gets exposed to this aspect early in their career when they see how difficult it can get to communicate the right message with data. Those who persist and get to the point where this skill is smoothly integrated in their data/analytics work, are those who end up become the real data assets of any data focused company and culture. That should be the objective of any data person working with data to help others making more informed decisions and build more intelligent systems.
Practical Examples
- Your reviewer needs to know more details about the patterns you found to understand how you solved them
- In a PoC project, less is usually more. Focus on solving the road blockers and don’t overload your focus on others’ with too much noise about non-relevant metrics/numbers
- Your data stakeholder cares a lot about the system (architectural changes for instance) and the reliability of the metrics being built
- A business stakeholder cares about reliability of the data and that the outcome connects to their other pieces (like other data sources they use). They couldn’t care less about how you’ve built this (technically)
- A well thought-through visualisation is an art and when done right it can fast-forward the thinking of everyone to the end goal like making a decision
- An executive couldn’t care less about micro level KPIs. If you share too many of those, you an end up loosing their trust. They need to take quick intuitive decisions. for them definitely less is more unless they ask for more
Some pointers how to develop this subtle skillset
- Don’t be stubborn about what you built. Always be willing to adjust, correct, refine and improve!
- Observe how your audience is interacting with your data/analytics. Put yourself in their shoes with their context to really get how they perceive your deliverables
- Proactively ask for feedback to further refine. Don’t ask yes/no question as those get you lazy useless answers. Ask open ended questions and let the other person tell all what they have to share.
By the end of the day, your added value is not what you delivered but rather how what you delivered served others. Don’t take too much pride in deliverables, but rather on what was achieved thanks to them!
