Advocating for Ethical AI Practices and Creating a Movement

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Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.

Wendy's LinkedIn: https://www.linkedin.com/in/wendy-turner-williams-8b66039/

Culstrata website: https://www.culstrata-ai.com/

TheAssociation.AI website: https://www.theassociation.ai/

In this episode, Scott interviewed Wendy Turner-Williams, Managing Partner at both TheAssociation.AI and Culstrata and the former CDO of Tableau.

TheAssociation.AI is "a global nonprofit business organization …focused on bridging the disciplines of AI, data, ethics, privacy, robotics, and security." It is focusing on things like networking and knowledge sharing to drive towards better outcomes including ethical AI.

Some key takeaways/thoughts from Wendy's point of view:

1. Right now, we try to break up the aspects of data into discrete disciplines - and then work on each completely separately - far too much. Privacy, security, compliance, performance, etc. Instead, we need to focus on the holistic picture of what we're trying to do and why.
2. Communication is key to effective data work and driving value from data. Hire product managers and focus on the why. Break through the historical perceptions of data as a service organization. Drive to what matters - outcomes over outputs - and focus on delivering value.
3. "What's the point of being focused on the data if you don't understand the business that the data is supposed to be used for?"
4. ?Controversial?: "There is no transformation without automation." If you want data to play a part in transforming the business, you need to focus on automation. Data related work can't be toil work or most won't even do it.
5. "You will never be as successful as you can be as a data organization if you're not able to influence your IT partners, your product teams, your business teams."
6. For far too many companies, data is just an afterthought. It's not the core around how they build out initiatives. When you bolt-on the data to any aspect of the business instead of integrate it from the start and build with data in mind, it's far less impactful. You're always playing catch-up. Make data a forethought.
7. In many respects, data has become a 'four letter word' to lots of people - meaning it has a bad connotation.
8. There are a lot of internal politics around data. Data can mean power and it can also give people perspective on your team's performance. Try to work towards removing the politics if possible but also good luck… 😅
9. There is so much data in many large organizations that execs can't make sense of it. They often don't understand what data they will need to support their decisions or how to get in place the data they do know they need.
10. There's also often a disconnect between strategy and targets/feasibility when it comes to data. There may be a strategy of grow X product with a target of 'grow X product 15%' but there isn't a good reason why 15% is the target. It becomes a dartboard instead of data feeding into creating the goals.
11. Execution and tactical decisions are powered by data far less often than they should be. There is far too little thought or process around strategy and tactics enabled by and about creating data.
12. Many line of business or domain leaders are simply not great at data. They may be able to leverage insights but they don't get the information cycle, especially sourcing necessary data. Data teams need to partner with them effectively - that is definitely a two-way street.
13. ?Controversial?: Relatedly, too many data people are focused on the data work itself instead of the impact of the work. There needs to be a better understanding of what teams are trying to accomplish with the data work. It's not about the pipeline, it's about the goal of the work and the impact.
14. You need internal processes and clear delineation of ownership or you will have multiple teams measuring the same things and getting different answers.
15. Far too often, people are myopic in focusing on their own job instead of how they fit into the bigger picture of the organization and delivering value to customers. That leads to teams not considering how they exchange information internally, only focusing on their own usage.
16. ?Controversial?: Data teams need to spend more time creating their own data around the impact of data work and impact of issues like data downtime. Move past the service-only/cost-center perspective.
17. Lack of data fluency, especially among execs, causes so many issues. If people don't understand data, they don't understand how much they can trust it and thus won't rely on it.
18. Relatedly, there is a significant lack of understanding of upstream and downstream data and business processes and needs that could be fixed by better communication. What do you need from your upstream and how are your downstream users leveraging your data? Communicate!
19. Automating data work enables business partners to identify "business choke-points" and address them.
20. ?Controversial?: You can't have AI without really understanding your business processes and how data supports those, how people combine data and their degree of trust and understanding of the data.

Wendy started out with her perspective that in some respects, data has become "a four letter word". There's so much data and everyone is trying to use it but everyone also feels inundated. Instead of being data-driven, we are data-flooded or data-dragged. And there is a major lack of tying the data work to the actual strategy and execution. Where do we need data to support our decisions? We need a strategy to get that data in place.

Relatedly, Wendy sees the major breakdown between strategy and goals when it comes to data. There may be a strategy to grow a product but how much growth is feasible, a good target? Why is that growth feasible? What does the data say about growing that product, e.g. the market dynamics and your positioning in the market? So when goals are set, it is a 'finger in the air' type guess as to how much it could grow or worse, simply how much leaders want a product to grow. And then what data do we need in place to enable the team managing that product to actually be able to grow it that much? How do we enable them to make smart tactical decisions?

Basically, it's a lot of things looping back on each other. We need data to set good strategic decisions. But we need a strategy to set up our ability to capture and analyze that data. We need data to make better tactical decisions. But most companies lack the ability to make good tactical decisions to get the necessary data in place. It's top-down driven but far too often the ones who understand what needs to be done for and with data are too far down in the organization and there's a communication gap. Thus, there is a significant lack of being data-driven. We have to admit the problem first. How to fix all of that is another fun process 😅

For Wendy, far too many organizations have data as an afterthought. And that leads to subpar understanding of what's actually happening with the business and lacking the information to fine-tune their strategic decisions. There isn't a strong strategic connection flowing from the business strategy to the data work. Execs aren't spending the time to really follow-through on exactly what the tactics should be to get the right data in place. Scott note: we literally have a panel on doing that, tying the data work to the business strategy and vice versa 😎 episode #251

In many parts of many organizations, e.g. Marketing, Wendy sees there being very competent people who just don't really understand how to do data well. They need a great partner. Should the marketing leader be focused on what data sources they need and why? Or should we be able to translate their needs into the work? But first, we need to actually be able to partner and they need to understand their needs. Data people can supercharge their efforts but the business partners need to lean in. Scott note: in data mesh, part of the role is enabling them to get better. We need people to up their fluency but doing data mesh or not, everyone starts somewhere and we need to help them level up.

On the flip side, Wendy also sees how often data people are stuck focusing on the data work instead of the business aspects of what that data work is tied to. Without the business context, all you are doing is pushing 1s and 0s. What do business partners need and why?! There needs to be the ability and the courage to just hammer out the understanding differences or the problems will persist.

Wendy also gave some specific examples of too many cooks in the kitchen relative to certain measurements. Instead of there being one official perspective or measurement for something like usage of a cloud product, in a previous role there were many measurements across engineering, finance, marketing, sales, etc. And every single one was different because they all used slightly different methodologies and even sources. So when they tried to look at success of the product, everything told a different story. And when they tried to have a simple bill the customers could understand, it was just not possible. While single source of truth is a complicated and overloaded term, one official source of truth for a question is something you should be able to rally around.

A big problem in many organizations is people are only focused on their own job and lose sight of the bigger picture and especially how they play into that bigger picture of the organization's success according to Wendy. Even if your role isn't directly improving the customer experience, your work can have a positive impact on that if you drive towards that goal. Sometimes, politics around data also gets in the way of collaboration across teams and lines of business.

Wendy talked about another persistent problem in data: the service model. If your data teams are only focused on supporting other teams, you can lose sight of your big picture impact as well as the impact of bad data. She believes data teams need to spend more time creating their own data around their impact and also quantifying the costs of data issues. What are the actual impacts to the organization? And do execs outside the data team understand data well enough to understand those impacts? If they don't understand data, can they even trust it enough to rely on it?

Circling back to the bigger picture, Wendy believes that teams can drive significant process improvements if they just understand the impact of their work - especially through data - upstream and downstream. What do they actually need from others? Who is consuming their data and why? What impact will changes have? How are communications set up to prevent issues and create strong understanding and trust? And then of course, try to automate as much as possible to lower the burden on everyone involved in the data flowing around :) As part of that, please just hire good product managers 😅

Wendy said, "You will never be as successful as you can be as a data organization if you're not able to influence your IT partners, your product teams, your business teams." Data is a team sport, data is about making the organization better. You need others to play with you or it won't work.

When thinking about actual business transformation around data, Wendy said, "There is no transformation without automation." Historically, doing data work has required a lot of effort. The business side just wants to leverage the data, help them automate as much as possible. Otherwise many - most? - business partners won't want to engage with the data and leverage data to improve their work - it's too much effort. Also, removing the friction from data work helps people identify the friction in general business processes. So automating the data work allows them to more easily identify and then address "business choke-points".

For Wendy, too many aspects of data work are treated as wholly separate disciplines instead of treating it as all part of one whole. Security, privacy, compliance/regulatory, performance, etc. We have to shift it left but also stop trying to treat them as discrete challenges to overcome instead of interoperating aspects of a working, scalable solution. Think data by design 😎 That's why she created TheAssociation.AI, "a global nonprofit business organization …focused on bridging the disciplines of AI, data, ethics, privacy, robotics, and security." She said, "there is no security, there is no privacy, there is no ethics, there is no AI without data," but we also need organizations to actually implement their policies into their data and data work. There isn't going to be ethical AI without someone leading that charge and TheAssociation.AI is looking to push that effort forward.

In wrapping up, Wendy circled back to the start. What is the point of doing data work. She said, "What's the point of being focused on the data if you don't understand the business that the data is supposed to be used for?" Being a data leader, especially the CDAO, is VERY tough because you often don't own much of the infrastructure if at all and have to do your work essentially via influence. But if you build the right relationships and understanding of the business, you can still have a major impact and drive significant value for your organization.

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