Awesome. Hello. Uh give folks just a few seconds here to hop on. I have a full 20 minutes of content, so I'm just gonna get started pretty shortly here. Awesome, great.Well, thank you so much for, for coming to modeling and ethical data culture um Here at the Chief and tech summit at The Woman In Tech Global Conference. My name is Cindy Lynn pronounced she, they, I am uh first and foremost, the principal at Cindy Lin Consulting um here at City Land Consulting. I believe in empowering the impactful, ethical and data driven decision making that grows organizations. I offer services um to that end in data culture, data, product management, and business intelligence. And I'm also available as a fractional data leader. I'm also um the member of the speaker team at L A Tech for Good. Um We work at the intersection of social impact and technology. Um We foster social change by teaching equitable and ethical data practices so that we can empower individuals to use data, more responsibly building a more data, building, a more equitable world. Um I first became involved with this organization after attending their uh workshop on this topic in December of 2020. And since then, it's really changed how I look at data.

And it's uh one of the things that I'm I'm committed to is having a positive impact um in my day to day data practice. So uh let's get started. Uh So, uh for those of you who are new to the concept of a data culture, I think um all organizations have a data culture um because it's just the infrastructure processes and practices around how data is used to make decisions. And so most organi all organizations use data in some way and have some kind of practices around it. But there's a big difference between having us data culture and having a strong data culture. And so a strong data culture is one that is used to make strong decisions, um decisions that are made faster, that have the outcomes that they want. Um And so I'm going to talk to you about how you implement a strong data culture. Um So you can get that outcome um using the data culture framework. So the data culture framework is one that I developed and that you see at a basic level here. Um it has three different layers starting with your organization um and then looking deeper into the department and at the individual level. And so there must be some um work around building out each one of these different layers.

Uh And there must be investment in that and for each of these different layers, there's also certain principles that I think are really important when it comes to building up this culture, this this framework. Um The first at the organizational level is that you must be building trust, instilling trust in the data that's being presented and that it's, it's important that data is accurate, that's being shared. Um so that it can be used to align your organization against what is important towards.

Um uh that everyone's working towards. What is the goals that you're working towards? Leaders need to be, make, need to make sure that they are um good at storytelling so that they can connect, what is what they're seeing with the data, with the reasons behind it so that your team understands this is what I'm experiencing and that's how it's getting reflected in the data that's being presented.

Um And also uh leaders need to make sure that there's strong data governance, meaning that how data is accessed and analyzed has been standardized across the company. So there's not three different uh ways of calculating something like an engaged user. There's one definition that everyone uses across the board. So that's at the organizational level on the departmental level. Um You need to be building tools that are specific and relevant to the departments that you're building them for so that they are integrated and processes that the team is already employing to run their operations. Um And then using those uh tools within that process. You're building a community of team members that are comfortable using data in their day to day lives and using that to communicate with each other and having a strong uh having their own language in which they engage with data and their work. And finally, at the individual level, you need to be making sure that you're um recruiting and developing strong data talent so that people have the skill set in order to go about analyzing the data that they are looking for. You want to make sure that you're sparking curiosity within those individuals and making sure that they are uh asking questions that ultimately um getting the answers to will help them make better decisions.

And ultimately, if you do all these things um and create a strong framework for data culture within your organization, you're going to empower individuals to be able to answer their questions themselves and ultimately make those strong decisions. So that's the data culture framework.

If you're interested in learning more, I've actually written a whole blog series about it in progress. They come out on Thursday. So there's much more on these specific topics that you're more than welcome to look at. And that's on my website, you'll see that at the end. Um And so if you're interested in um implementing a data culture, uh a strong data culture within your organization, I have some ideas of data culture projects that you might consider um within uh kind of structured in those three layers of the framework. I'm not going to talk about all of them. I'm just gonna talk about one each. Um at the organizational level, you might consider something like um what I call the data gym, which is something that I implemented at a previous organization with my team. Um where we presented uh a once weekly uh meeting that was available to everyone in the company to attend on some data topic. Um Usually it was uh us answering a question that a team member, a fellow team member in the organization had asked us. And so not only showing them that uh showing them the answer to that, but showing them how we got there so that they felt like they could easily see how you go from A to Z, right at the team level. I think operational tools that are powered by data can be especially useful and demonstrate the power of something like a data culture um in tools like a client health score or machine status.

These are tools that help your team understand uh where they can spend their limited time and have a big impact if your um tools are analyzing, you know, all of your clients and saying these are the ones that are in most need of attention. Um That's going to be really helpful for your team that you know, is spending all this time trying to figure out what to work on. So something like that can be really useful and finally, at the individual level, um the one I want to highlight here is data coaching for managers. So this is really helpful for organizations that are um in growth mode, maybe promoting from within um strong individual contributors to managers.

Um Oftentimes I've seen that those individuals are excited to manage but don't necessarily have those strong data skills to be leading. And so are failing to make things like Roy or business cases to the executive team. And so don't get promoted further up the ladder because they're not able to kind of make that connection. So that's why I think things like data coaching can be especially useful for those individuals. So these are just some examples of data culture projects that I think can be impactful. And if you have your own ideas, I'd love to see them in the chat. So, um you know, if you go about implementing a data culture, you start to see a strong data culture form at your organization, you'll start to see that data is powerful and some guy once said, um or, or woman um depending on which university you're living in uh with Great Americans, great responsibility.

And so uh it's important for us to think about the kind of impact that we're having when it comes to using data. Um And so I think that, you know, as we look at the societal issues that surround us day to day. Um It can be embedded in how data is analyzed and presented and whether intentionally or not, it can compound existing biases and continue to limit access to people already at the margins. And so I want us to commit to um using as a tool to underline how deep oppression goes and create solutions to ensure justice rather than um just letting it happen and, and not paying attention to it. So, um to this end, I want to cover two topics that are or two concepts that I think are really critical to understand as we start to talk about an ethical data culture. The first is data equity. Um This definition here really resonates with me. Um Data ethics refers to the consideration through an equity lens of the ways in which data is collected, analyzed, interpreted and distributed. It underscores marginalized communities unequal opportunities to access data and at times they're hard from, they're harm from data misuse.

Um So obviously, it underscores uh this quote underscores um not just the impact that uh data equity has but also um the different areas in which you should be thinking about data equity. Um And I think that's reinforced in the second quote here um from the Feminist Manifesto published at mit um Equity is both an outcome and a process. So, of course, we're working towards a more equitable future, but we need to make sure that equity is built into all the steps that we take in order to get to the equitable future. So, data equity, um the other concept I want to cover is data ethics. And so this quote comes from um Harvard Business School's blog. Um It says that data ethics encompasses the moral obligations of gathering, protecting and using personally identifiable information and understanding how it affects individuals. Data ethics asks, is this the right thing to do?

And can we do better by Harvard, Professor Justin Tingley? So this is uh again, emphasizing how important it is for us to be thinking about how we use data because it has profound ramifications for the people that were um whose data we have collected. Um So those are, you know, two concepts to keep in mind. And so as we redefine what an ethical data culture is, I define it as um an ethical data culture recognizes and offsets harm experienced by marginalized individuals and organizations in the infrastructure process and practices around how data is used to make decisions. Now, you'll notice that second half is still the same as the first as the initial definition I I gave you. But um in this one, we are specifically focusing on making sure that we're um supporting those marginalized individuals and organizations and not, and, and contributing to justice and, and not injustice. So um we'll talk about uh from here, I want to talk a little bit about um some of the ways in which these concepts manifest and how we use data.

Um So you can be more careful about uh going forward and using data in your organization and your data culture. So the first one I want to talk about is data security. Um This is uh a manifestation of data ethics, of course, um We are probably all familiar with this topic. We probably are very invested in making sure that data remains private and secure. Um But uh I think there are more ways of looking at this than you might think or may immediately come to mind. Um And so, of course, this is the insurance of safety and security of personal information. Um And so I want to present a situation in which you may have some trans students at a school that maybe feel supported, feel comfortable being out in that school. Um But may have trans transphobic caregivers at home. This is a reality um in which I um I was working with um when I was at a previous organization that focuses focused on care for students um in that space. Uh You know, we wanted to make sure that we are creating a welcoming and open and accepting environment for people to be whoever they were.

Um And so we allowed opportunities for things like um capturing preferred names, pronouns and gender stored in our student information systems or the information system we were working with. Um And on top of that, there are um it was really critical that we thought about how data was shared to individuals that are in these students lives, that may not be supportive of the students themselves. And so it was very important that we thought about how we protect that data, how we share that data and ultimately, how do we protect Children because um there might be certain situations in which that information being shared might lead to an unsafe environment for those Children.

So it was really critical that we thought about the ways in which that information was being presented, right? And so while this is not a typical example of how we ensure data security, this is something that is very real and thinking about how you share data on the platforms that you're working with um or in other interactions, thinking about like not just on the product side um about those communications that might go back and forth.

But in case like, you know, caregivers call in and someone on the support team uses a preferred pronoun or uh uses the preferred name or a pronoun um that maybe the caregiver is not aware of. So it there are a lot of ways in which you think about how data can be um shared that may be inappropriate. So that's the first one. The next thing I want to talk about is under representation in data sets. Um Here, aggregation and poor data collection practices. Can hide the experiences of the most marginalized. So we want to think about that. Um The example here I have is um looking at immediate income split two ways. So first we have Asian American income in 2018 being about $20,000 higher than the national median. Um But so that paints one story, but if you were to split out Asian Americans into more specific ethnic groups, you see there's a much different picture for different groups within Asian America. Uh And so, you know, you see Burmese Americans are making $36,000 a year, significantly lower than the Asian number and as well as at the national level. So if I was to make decisions about things like where resources are being allocated using one number versus using another or using one set of data versus another, I would probably be making different decisions on how to, on how to allocate those resources.

Um So making sure that your aggregation levels are, are, are at the right level is really important here. Another piece of that is making sure you know, if you're collecting data that excludes certain groups, like if you're collecting gender data, that only includes male and female, you're excluding people of different gender experiences. Um non-binary like myself um gender, non conforming or trans individuals who may identify a certain way. And if they're not represented in the data, you can't tell the story of them. You can't share what their experiences are. So that's also something to think of as you're collecting data on the flip side of that overrepresentation in data sets can also be a problem. Um So thinking about how data is captured can overrepresent groups and then use as truth, um use as truth rather than as a reflection of that process. And so we see problems with that, especially in things like crime statistics. Um If you think about it, crime statistics are a measure of enforcement, not of crime. And so it's a reflection of that uh law enforcement process.

Um And so as a result, we see things like this, right, where uh this is pulled from the prison policy initiative where um Black and Hispanic individuals are much more likely to experience use of force when interacting with the police and much more likely to be arrested once and multiple times compared to um other groups.

So this is definitely of high concern. Um as you think about um what is actually being captured here over policing, it's not the fact that um you know, these populations are uh more likely to do crime. Um because what you're gonna uh what if I was a policymaker? It was an uninformed policymaker, I should say and I saw this, I might say, oh, we need to send more police to these neighborhoods that are full of black and brown people. And um that might, that would probably lead to a cycle of additional policing higher crime statistics and just the cycle getting repeated and we'll see this in effect and how detrimental it can be on the next slide. Um because um as we have all seen, um A I machine learning are at the forefront of our industry now and I don't think they're going away anytime soon. And so those are often built, those are built upon data sets. And so if those are built upon core data sets and design algorithms can go um and affect people's lives if they go unchecked for discrimination. And so using that, um example, from before I have a story to tell, um this is a story from uh Mount Stroud. Um You can find this on the verge. It's called heat listed. It tells the story of um uh Robert mcdaniel who was uh flagged by Chicago Police Department's predictive policing algorithm as being likely to be involved in a shooting.

Um And it didn't list whether or not he would be a victim or a shooter. Um It just said that, you know, given all the data that we have, he's going to be likely involved. Um Chicago PD started showing up to um his home repeatedly and the community uh jumped to the conclusion that he was um an informant for the police and um that led to him being involved in not just one but two separate shooting incidents um in which he was the victim. And So um you can see how things like um bad or data, like crime statistics can lead to additional violence when used incorrectly. So just thinking about how we um set up our algorithms to be just and non discriminatory. So those are just some examples or, or these are kind of the main areas in which we can kind of think about how data equity and ethics present in the work that we do. Um And so thinking about how that connects back to your um ethical data culture. Um Here again, are some ideas on how you might go about making sure that you're um practicing an ethical data culture. On the um also based on the data culture framework. At the organizational level, you can think about data ethic metrics that can be used to measure impact at the team level. Um You uh can look into integrating data equity tools like the data, nutrition labels.

Um That's a tool that gets um discussed in the L A tech for good um data equity workshop. So we'll talk about that more in a second. And at the individual level, maybe you just introduce these concepts during data training. Um So that people are familiar with them, I think that everyone works with data. And so um the more people who are familiar with um asking these types of questions, understanding how flawed systems can be if they're not thoroughly understood um is can be a real big impact, right? And so um when you're thinking about building out your data culture, um everyone is involved with that. And so make sure you're pulling from everyone to build one that reflects their values. Uh I just want to end with um uh highlighting L A tech for goods, leading equitable data practices workshop. Again, this is um a super useful tool. It's the workshop I attended that kind of got me started down this journey of data equity and ethics and you can learn more at that website or you can email hello at L A Tech for good.org.

Um They're an awesome organization and you really um the workshop is really great because it gives you um you can bring the work that you're, you're currently working on and you can apply the tools that they give you. So you can have um you know, hands on direct feedback on what it is that you're working on. And then myself, I am a consultant um like I mentioned at the beginning, so feel free to reach out if any of these topics are, are something you want to discuss further. I'd be super excited to learn more about what you're working on and how I can help. Um And if data culture is something that you're thinking about, like I mentioned, I have my ongoing blog series on data culture which you can find at both my website and on linkedin. Um And um I uh you know, again, specialize in data culture and data product management and in business intelligence as well as operating as a fractal data leader for organizations that need services like that. So if you're interested, feel free to reach out. And with that, I'm over 20 minutes, but I would be happy to answer any questions that you may have. Thank you. Thank you, Jacqueline. Uh a question I often get asked. Maybe I'll talk about that in terms of implementation, data culture, implementation um is what to work on.

First. Um One thing that I uh I think has been really helpful is using that data culture framework. And when I'm kind of getting a, a lay of the land during that, uh what I like to do in the first stages of implementing a data culture is just getting an assessment done of what's going on with that organization and how that compares to tho those different parts of the data culture framework.

Um So I can kind of see, OK, there's uh you know, low trust but everyone who is using that data um to stay aligned on what the goals are. So at least there's, you know, consistency in terms of uh what are the goals for the organization, whether or not they trust it is a different story. Um And so that is AAA really helpful way of understanding kind of where to go with your implementation and you can, you know, build a plan off of that. Um To, to figure out where you want to go to. OK. Any other questions, I'll go back to the slide. Like I said, I'd really love to connect. If I'm not already connected with you, I actually recognize some of the names here. Um But if we're not already connected, please definitely shoot me an email or connect with me on linkedin. Um Love to hear what you're working on right now and um just, you know, open for a coffee, virtual coffee. All right. Fantastic. Well, um I'm gonna hop off then. I don't see any more questions. Um but uh I hope you have a wonderful day and I hope you're having a lovely conference. Have a good one. Bye. No, no.