A Step-By-Step Playbook to Grow Your Organization’s Analytical Maturity

Implementing in real life the different frameworks we’ve seen

.. And we’re back on our articles on Analytical Maturity! One of the feedback that I received on the previous installments was that those articles might be too high level and it would be great to have something slightly more actionable. So that’s what we’re going to do today: make the whole thing more actionable via a step-by-step on how to grow an organization’s analytical maturity.

Let’s start with step #1: Shutting up and listening!

Step #1: Shut up and listen

There is this great TED video by Ernesto Sirolli called “Want to help someone? Shut up and Listen”. In it, he explained how, as a 21 years-old, he worked for an Italian NGO which tried to “teach Zambian people how to grow food”. They successfully grew magnificent tomatoes in the valley down by the Zambezi river — but, as Sirolli puts in his own words:

When the tomatoes were nice and ripe and red, overnight, some 200 hippos came out from the river and they ate everything. And we said to the Zambians, ‘My God, the hippos!’ And the Zambians said, ‘Yes, that’s why we have no agriculture here.’

Whenever you want to grow your organization’s analytical maturity, the first step is going to shut up and just listen. You need to listen and understand the pain points, the different jobs, the data needs, what has been done in the past to solve those pain points, etc. Not only will this give you a better perspective of the lay of the land, it will also give you a better understanding of the people that are part of your organization.

“Listening” In the context of a medium or large organization means actively gathering information. To do so, you have a couple of efforts that you can run:

• User Interviews: Deep dive into the individual experiences of your team members to gather qualitative insights into their daily challenges, data usage, and specific pain points. By engaging directly and in a small setting, you create an open channel for honest feedback and nuanced understanding of the data needs across your organization.
• Running Surveys: Surveys (especially anonymous ones) give you the ability to collect a broader range of insights from a larger segment of your organization. This not only helps in identifying common themes and areas of concern but also serves as a tool for establishing benchmarks for satisfaction and data literacy levels within your organization.
• Joining Team Meetings: Participating in regular team meetings across different departments allows you to observe firsthand how data is currently being used in decision-making processes, identify gaps in data availability or accessibility, and note the common questions or misconceptions that arise when data is discussed.
• Shadowing People’s Jobs: Try immersing yourself in the lives of your users. This will provide you with a raw look at the operational challenges faced by teams and individuals, offering invaluable insights into how analytical tools and data are integrated (or not) into everyday workflows.

Using those different methods, you can do a lot of great listening that will give you a comprehensive understanding of where your organization stands in terms of analytical maturity and where the most impactful improvements can be made. This is a very foundational stage — part of the diagnosis phase, that will help you shape your strategy.

Now should you listen to everything everywhere all at once? If you don’t want to end up like Jobu Tupaki — it is important to make sure to adopt the right lens during your exploration.

• In terms of depth, some problems will require you to use a microscope (because they are very technical, very complex, and very sensitive) while some will be better observed from afar (because they are low priority and don’t necessarily require your involvement). I don’t have any ready-made framework for that here, but part of your diagnosis phase is about getting this understanding.
• In terms of width, using the framework “Tools / Processes / People / Culture” is usually a good starting point. You want to have a good understanding of the people of your org, their skillset, and what motivates them; you want to understand their processes and how they work together and with the other stakeholders; and you want to be clear on the tool they use.

Step #2a: Prioritize, build a roadmap and communicate

Once you have a good understanding of the different organizational pain points, and why they exist, you can start preparing a strategy. As I mentioned in a previous article — following Richard Rumelt’s framework, a good strategy is made of 3 elements: a diagnosis, a guiding policy, and an action plan.

At this stage, thanks to all your listening from the previous section, you should feel well-equipped to start documenting needs and start pinpointing where the gaps are in your current organization. Hopefully at this point creating the following “mappings” shouldn’t be an issue:

• Skill set mapping (who inside the team can do what)
• Activity / Project mapping (who inside the team is doing what)
• Process mapping (how we decide what to do and how it is being done)
• Tools mapping (what tools do we use to do what we want to do)
• Stakeholder mapping (who outside of the team cares about / can influence what we do)
• Goal mapping (what are the top goals of the different teams and how they relate to each other)
• Etc.

From experience, as you put together those different maps, you will start encountering interesting insights about your team and your organization, and more generally you will start understanding how the whole “system” works together. It is usually after this stage that you realize that some tools are being used because some skillsets are not available, some processes exist as a workaround around faulty tools, etc. Basically this is at this step that you’ll find out about the little inefficiencies. And so hopefully after this long mapping exercise, you should see a few areas for improvement — and this is where you need to move from exploration to action:

• Adopt a Prioritization Process: Establish a clear and transparent method for prioritizing the needs you just documented. This could involve criteria such as impact on business goals, frequency of the issue, or the ease of implementation. Consider frameworks like MoSCoW (Must have, Should have, Could have, Won’t have this time) or ICE scores (Impact, Confidence, Ease) to methodically assess each need.
• Prioritize Accordingly: Use your chosen prioritization process to rank the needs from most critical to least. This step ensures that resources are allocated effectively to areas with the highest potential for impact on the organization’s analytical maturity.
• Create and Share Your Roadmap: Adopt a clean roadmap, with clear deliverables and a clear timeline. Communication is key in setting expectations and building support for your initiatives. Share the prioritized list and the rationale behind the ranking with your stakeholders. This transparency helps in managing expectations, securing buy-in, and fostering a collaborative approach to improving the organization’s data capabilities.

By following these steps, you create a focused and strategic roadmap that addresses the most pressing data needs within your organization. This roadmap not only sets clear expectations but also aligns your data initiatives with the overall business objectives, ensuring a cohesive and effective approach to enhancing analytical maturity.

Note that while the diagnosis gave you the areas for improvement, sometimes it is not necessarily clear what the solution should be. There are two frameworks that I like to use in those kind of situations:

• Action > Information > Vision: A lot of the time, your vision will come from additional information that will come from you taking action. You do something and learn from it, and this helps you shape your vision. So if you are not sure where to go, take the first very little step you can — from there you’ll be at a different venture point, and most likely the next steps will appear.
• Innovation > Quantification > Orchestration: This framework that comes from “The E-Myth Revisited: Why Most Small Businesses Don’t Work and What to Do About It” by Michael E. Gerber is great for when you try to optimize a process: just select a piece of the process, “innovate” (i.e. change it), quantify this innovation (is the process better with this change?) and if the quantification is positive, orchestrate (i.e. remove the human decision making component out of it).

So if you are not sure about what the solution should be and what the deliverable in your roadmap should be — either break your problem down and focus on the very next step, or run a pilot for a potential solution.

Step #2b: Get some quick wins to build credibility

This step — while optional, is generally recommended. If you need to build some credibility, you might want to prioritize some quick wins, as this will allow you to build trust and put you and your team on a “momentum” of deliveries.

• Identify Low-Hanging Fruit: Look for projects or improvements that can be implemented quickly and have a visible impact. These could be simple data quality fixes, automating repetitive manual reports, or providing access to a new, valuable data source. The goal is to find changes that require minimal effort but yield significant benefits.
• Leverage Existing Tools and Resources: Utilize the tools and platforms already available within your organization to implement these quick wins. This could mean creating new dashboards in existing BI tools, using automation features in your data platforms, or simply optimizing current data processes with better practices.
• Celebrate and Communicate Successes: Once you’ve achieved these quick wins, make sure they are well communicated and celebrated. Use internal newsletters, meetings, or any company-wide communication channels to highlight the improvements made. Sharing success stories not only builds your credibility but also demonstrates the value of data-driven decision-making to the entire organization.
• Solicit Feedback and Suggestions: After implementing quick wins, ask for feedback from the users. This not only helps in understanding the impact of your efforts but also engages the wider team in the data improvement process, potentially uncovering more opportunities for quick wins.
• Repeat the Process: Building credibility is an ongoing process. Continue to look for opportunities for quick wins even as you work on more complex, long-term projects. This iterative approach ensures continuous improvement and sustained engagement from your organization.

By focusing on quick wins, you can rapidly demonstrate the value of your data initiatives, earning the trust and support needed to tackle more ambitious projects aimed at elevating your organization’s analytical maturity.

Step #3: Get sh*t done, communicate, document everything and train everyone

Now that your strategy has been set up, it is time to abandon the “spectator” mode and switch to “get-sh*t-done” mode.

• Execute: You did your roadmap, you know what needs to be delivered when — get on with it. One of the greatest pieces of advice I read when I became a manager for the first time was to be very protective / intentful with my time. Thanks to the roadmap you know exactly what needs to be delivered when, so you can make sure you save enough time every day / every week to make it happen. As there is always a plethora of fires that you need to attend to in any company, having a clear and robust roadmap helps you make sure you are not just solving for the present, but building for tomorrow.
• Communicate: I am a big believer in the “build in public movement”, which is about documenting your journey and sharing it as it is in public. I see two benefits here: (1) Regular updates, whether through meetings, email updates, or dashboards, help maintain transparency and it fosters a culture of trust and collaboration. It also helps put yourself / your team on your stakeholders’ map and ultimately grows the “luck” of your team (the more visible you are, the more people know about you and your mission, the more likely they are to reach out if they see some areas for collaboration). (2) This is a great forcing function to document your journey, allowing you to track the different changes, and understand the impact of your decision over the long term.

By executing your plans efficiently, maintaining open lines of communication, and investing in documentation and training, you create a foundation for sustainable data-driven growth. This approach not only improves your organization’s analytical capabilities but also embeds a culture of continuous learning and improvement.

Step #4: Start again (but improve the process as you are doing it)

Because life is just an eternal cycle:

• Reflect and Review: Once you’ve completed a cycle of listening, prioritizing, implementing quick wins, and executing larger projects, take time to reflect on what worked and what didn’t. Gather feedback from stakeholders, analyze the impact of your initiatives, and review the effectiveness of your communication and training efforts.
• Incorporate Lessons Learned: Use the insights gained from your reflection phase to refine your processes. This could mean adjusting your prioritization criteria, finding more efficient ways to execute projects, or identifying better methods for engaging and training your team.
• Update Your Roadmap: With new information and a better understanding of your organization’s needs, update your roadmap to reflect current priorities and new opportunities for improvement. This iterative planning ensures your efforts remain aligned with the organization’s goals and adapts to changing circumstances.
• Keep Communicating: Based on feedback, refine your communication strategy and training programs. This could involve introducing new formats for your newsletter, experimenting with different training methodologies, or leveraging technology to make learning more interactive and engaging.
• Embed Continuous Improvement: Cultivate a culture of continuous improvement within your team and the broader organization. Encourage ongoing feedback, promote the sharing of ideas, and recognize contributions to the data-driven culture.

By treating analytical maturity as a continuous journey rather than a destination, you foster an environment where learning, improvement, and innovation are part of the daily routine. This iterative approach ensures that your organization remains agile, responsive, and increasingly data-savvy.

Conclusion

In wrapping up this playbook on elevating your organization’s analytical maturity, remember: the journey is cyclical, not linear. Just like in those open-world video games (*wink* my previous article), where the adventure never truly ends, and there’s always a new quest around the corner, your path to a data-driven culture is ongoing.

And that’s the main important idea from this playbook really: growing an organization’s analytical maturity is an ongoing journey, and the most important part here is to make sure you get on a “delivery momentum” so that you are always moving forward.

And the more you are moving forward, the more cycles of “listening > diagnosis > roadmap > execution” you go through, the more the path to analytical maturity will be defined. So, keep iterating, keep improving, keep transforming your organization into a more data-savvy, decision-smart entity, and more importantly, have fun doing so.

Hope you enjoyed reading this piece! Do you have any tips you’d want to share? Let everyone know in the comment section!

And If you want to read more of me, here are a few other articles you might like:

• How to Build a Successful Dashboard
• Building Analytically Mature Organizations (AMO)
• What Sets Great Data Analysts Apart

PS: This article was cross-posted to Analytics Explained, a newsletter where I distill what I learned at various analytical roles (from Singaporean startups to SF big tech), and answer reader questions about analytics, growth, and career.

A Step-By-Step Playbook to Grow Your Organization’s Analytical Maturity was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.