This is Part 5 in a series about Collective Data Empowerment. If you want to get the whole series and accompanying tools in an ebook, go here. So, you’re a data executive. You’re on a mission to build a data-driven culture. You spend your days leading organizational change, building relationships, choosing vendors, and other “senior management things.” But how often are you actually doing data work? Or closely watching individual data projects as they unfold? The best data leaders want to see how the sausage gets made. You’ll know what’s broken, what’s going well, and what to do about it. Here are three ways to get the truth.
1. Do data project ride-alongs
Our research has revealed that typical data projects contain seven stages (see image above). In a data project ride-along, check in with stakeholders at each stage and observe how their tools, objectives, needs, collaborators, and challenges change along the way. Here are some questions we find helpful:
- Who is involved at this stage? What are their roles? Are they data practitioners, subject matter experts, or…?
- Is anyone missing from this stage? Who? Would adding their perspective or skill improve the project? Which aspect (e.g., speed, reproducibility, etc.) How?
- What would make this stage more efficient? What are three ideas to consider?
Do enough of these and you’ll have a good sense where things are flowing, slowing, working, and breaking. You’ll feel the friction and hear some ideas worth investigating. Consolidate your findings, share them widely, and discuss them openly. Make sure to involve less-technical people, too, or it’s not Collective Data Empowerment!
2. Conduct data project post-mortems
Many companies do post-mortems to squeeze more knowledge out of every project and apply lessons learned to the next project. You don’t have to lead every data project post-mortem. Nominate someone to be your data practices champion, teach them the steps, and pass the torch. Here are some things to keep in mind for your first data project post-mortem. Make your people part of the process Project participants should share what they learned with others and listen respectfully to their collaborators. Do it within a week of completing the project Speed is essential to improving data practices. The memories are fresh, and participants can immediately apply what they learn. Make them blameless, like Google does
These post-mortems are blameless because we assume everyone makes mistakes from time to time. Post-mortems aren’t criminal investigations, they’re an affirmative process designed to make us all a little smarter.
–Ken Norton, Partner at GV
For our own post-mortems, we use a slightly adapted variant of Google’s post-mortem template. Use our version as is, adjust it to fit your team, or adapt to each individual project. Either way, you’ll want to make sure you capture lessons around these main elements: Project overview This should have your goals, objectives, and success criteria listed for the project. Don’t forget to log whether the project was successful or not! Accomplishments What went well? Which outputs are most useful beyond the project? Name the top three project highlights. Be specific here. You want to document and share what great data work looks like with everyone else, right? Improvement areas Now, what went wrong? How can we avoid or address those issues moving forward? Do we know the root causes of any project delays? What are the immediate and long-term effects of these issues. Specificity is important here, too. Lessons learned What can we carry forward in our own work that will help us? How can other parts of the company learn from our successes and failures? Document and share. Action items What are some outstanding action items remaining from the project? Do they have clear owners assigned to them? What does the follow up plan look like? Cross all t’s and dot all i’s. However you archive and distribute these lessons learned is up to you, but make sure they’re accessible, easy to understand, and actionable.
3. Read and sign the Manifesto for Data Practices with your teams
After you’ve done some data project ride-alongs and post-mortems, you will know much more about your company’s actual data practices. Specifically, what to improve and what makes them great. The last piece to bring everything together is a commitment to doing things better. Enter the Manifesto for Data Practices. The Manifesto for Data Practices is a set of four values and twelve principles that we believe describe the most effective, ethical, and modern approach to data teamwork. Leaders from diverse backgrounds including academia, business, journalism, open source, and the public sector co-authored the Manifesto to build a shared understanding of modern data teamwork. Consider taking the Manifesto as is or adapting it for your needs. When asking others in your company to join you, remember this. The most important thing is a public declaration of your commitment to improving data practices. When you do so, others will join in solidarity. If your organization signs the manifesto, you’ll be joining 1,500 others who already made the same commitment. Read it, sign it, and share it here. Our team has also designed a repeatable workshop based on the Manifesto, called Practices for Better Data Teamwork. Feel free to take the slides and run it yourself, or let us know if you’re interested in collaborating on a custom workshop for your company or organization: email@example.com.
There and back again
When you’ve done these activities, share them to empower your colleagues to empower others. Adapt them. Reinvent them. Whatever you do, don’t let the momentum die.
Ready for more? Get right to it and download the complete guide to building a data-driven culture through Collective Data Empowerment.
Check out the rest of the Collective Data Empowerment series!
- You can’t build a data-driven culture without your hidden data workforce
- The high stakes and staggering opportunity of data-driven culture
- 16 patterns you see in pre-data-driven companies
- Finding your way with Collective Data Empowerment
(Editor’s note: This post was updated on 9/19/2018 to add new resources and reflect the completion of the initial series of posts.)