#266 Leveraging Decades of Information Architecture Learnings to Do Data Well - Interview w/ Akins Lawal
Data Mesh Radio
Focusing on User Experience in Data Products
This chapter emphasizes the importance of prioritizing user experience in data products and the challenges that come with it. It highlights the need to make goals easily achievable for users and prioritize people and their actions when working with data.
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Akins' LinkedIn: https://www.linkedin.com/in/akinslawal/
Schema for Success: https://www.schemaforsuccess.com/about
In this episode, Scott interviewed Akins Lawal, a Data Strategist. To be clear, he was only representing his own views on the episode.
Some key takeaways/thoughts from Akins' point of view:
- Far too often in data, people move to build _something_ instead of focusing on building good information architecture specific to the task at hand and the organizational goals and capabilities.
- Good information architecture isn't about tech, it's about the principles and practices of how you're going to structure your data/information.
- Keep going back to good product principles in information architecture: your focus should be on what are you trying to accomplish over what are you trying to build.
- ?Controversial?: Organizations need to focus far more on hiring for learning capacity instead of only for current skills. The world is changing too quickly to try to focus on specific skills for many data-intensive jobs.
- Leadership buy-in ends up being the number one determining factor of success for projects and transformation according to many studies. Trying to proceed - even with the greatest plan ever - without that buy-in greatly reduces the chances of success.
- Maturity models can be extremely helpful but they sometimes don't tell the full story. Look for pockets of maturity in your organization and see what can be copied/replicated and what can't when improving the maturity of other areas of the organization.
- !Controversial!: There is something different about information exchange in person rather than entirely virtual. It is far more likely to create a deeper understanding if you can collaborate and whiteboard. Scott note: I'm 50/50 on this. A big question is cost benefit of mental energy, travel, etc. - better return doesn't always mean better return on investment.
- Let people know "that data is more of a tool to empower … that also needs to be woven into the organizational fabric."
- Reward people for (appropriately) sharing their data. At the start, accolades can be enough but if you really understand incentives, it should become something more deeply rewarded.
- People, process, tools but it always comes back to people being the most important.
- You can focus on and improve your data culture no matter the size of an organization from two to 200K.
In Akins' view, many more people need to slow down a bit to really consider what they are building, why, and for whom. Far too often, data people - and tech people in general - want to build something but we need to focus on good information architecture. We need to consider a lot about the specifics of why we are building something and also who will use it and how in order to maximize positive outcomes.
When starting with good information architecture, Akins recommends asking a lot about what the system will do and why. Map out the user flows even, how will people access information. That way, you can start to back into what kind of systems and approaches will support what you are trying to accomplish - it's not about what you are trying to build, it's about what are you trying to accomplish and building something that can help.
As you start to plan out your information architecture, Akins recommends you start finding your champions - you need people to rally around to get things moving forward. Then you plan out your process - there are many different tried and true processes for sharing context/information but it's important to find one that works well with the use case and your organization. You should be looking for happy mediums between all involved because no one will get everything they want if you're doing it well.
In Akins' view, if you want to become data driven as an organization, you need to focus on hiring learners, not just for current skill sets. Data - how to analyze it, leverage it, share it, etc. - is a lifelong problem/challenge. New tech and approaches always emerge. You want someone focused on staying up-to-date on best practices, not specifically proficient in one tool that could be close to obsolete in a few years.
Leadership buy-in is far and away the most important factor to new initiatives succeeding, according to multiple studies from groups like HBR and McKinsey. So Akins recommends to make sure you are aligning with those leaders and showing them the benefit of improving your data initiatives. A big reason so many data initiatives fail is that lack of buy-in and support. The best laid plans are still far less likely to succeed without support from above.
Akins talked about how maturity models can be a very helpful tool for finding what's already working in your organization relative to data work. You can find the patterns and then assess if you can make those into repeatable patterns for the rest of the organization or not. It's all about creating a situation where things can mature.
In person collaboration for Akins just kind of 'hits different' - you are better able to exchange context if you're in the same room and able to whiteboard. Potentially that's around driving meaning and trust? Virtual tools still have not caught up to the in-person collaboration capabilities. So if you aren't in person, he believes you will likely need to meet more often just to ensure you really understand each other.
Akins then talked about the importance of leveraging data to empower people and weaving that data understanding and empowerment into the fabric of the organization. How do you empower people to make more and better decisions with data? That's how you move towards being data driven.
Something Akins likes for driving data sharing and usage is incentivization: how are you rewarding people for sharing and leveraging data? Leaders should be giving teams and people that are sharing and using data well lots of accolades and potentially other rewards so others want in on the action.
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