MetaDAMA - Data Management in the Nordics
This is DAMA Norway's podcast to create an arena for sharing experiences within Data Management, showcase competence and level of knowledge in this field in the Nordics, get in touch with professionals, spread the word about Data Management and not least promote the profession Data Management.
-----------------------------------
Dette er DAMA Norge sin podcast for å skape en arena for deling av erfaringer med Data Management, vise frem kompetanse og kunnskapsnivå innen fagfeltet i Norden, komme i kontakt med fagpersoner, spre ordet om Data Management og ikke minst fremme profesjonen Data Management.
MetaDAMA - Data Management in the Nordics
3#10 - Inga Ros Gunnarsdottir - Innovating Diversity and Inclusion in the City of Reykjavik through the role of CDO (Eng)
"We believe that by making data more accessible, the city will become more transparent and accountable to the people that we serve."
In our latest MetaDAMA episode, we're joined by Inga Ros Gunnarsdottir, the Chief Data Officer (CDO) of the City of Reykjavik, who's at the forefront of a transformation towards data-driven innovation of inclusion and accessibility. She walks us through her fascinating journey from engineering at L'Oreal to shaping the future of data use in municipal services. Her insights reveal how simple text, visuals, and a focus on digital accessibility are revamping the way citizens interact with their city's data.
As we navigate the terrain of digital transformation, Inga Ros delineates the distinct roles of a Chief Data Officer versus a Chief Digital Officer, highlighting the intricacies of their contributions to a city's digital ecosystem. Reykjavik's Data Buffet serves as a prime example of how open data visualization platforms can enhance not just transparency and accountability but also literacy in a society hungry for knowledge. She also shares compelling stories of data's impact in classrooms, planting the seeds for a future where every citizen is data-literate.
We wrap up our conversation with a deep dive into the nuances of creating data visualization tools that adhere to digital accessibility standards, ensuring that everyone, regardless of ability, can partake in the wealth of information available. The discussion traverses the significance of maintaining the Icelandic language in data communication and the imperative of ethical data collection practices, especially concerning marginalized groups. By the episode's end, it's clear that the key to unlocking the full potential of data lies in the simplicity and clarity of its presentation, an ethos that Inga Ros champions and we wholeheartedly endorse. Join us on this journey to discover how Reykjavik is rewriting the narrative on data inclusivity and the profound societal transformations that follow.
My key takeaways:
- Think about how you make data available - design thinking, finding new was to visualize data is important for inclusion.
- Its the responsibility of public sector to make as much of their data openly accessible.
- The role of CDO is important, because you need someone to see the bigger picture and how data effects everyone.
- Managing data, especially for public services, comes with a social responsibility.
- The difference between a CDataO and a CDigitalO - data requires a different skill set than digital transformation.
- Data professionals need to ask the correct questions in a service design process.
- Data access and ownership should be discussed already at the design phase.
- People have expectations towards digitalization in public sector: you want to access the data you need at the time you need it, from where you are.
- «Data is a valuable societal asset, where we all have the shared responsibility to ensure data quality.»
- Data quality is a precondition for using data to its purpose and its potential.
- You need to think digital universal accessibility, when it comes to data and visualization.
- With data stories the city of Reykjavik uses visual, verbal and sound effects to convey messages through data.
- There is a focus on using accessible language, and to not over-complicate texts.
- Data, especially in the public sector, has not been collected and curated with trains AI language models in mind.
- There is a great risk that historical biases and previous lack of awareness is transmitted into our models.
Data Buffet:
- Open data visualization platform and an open data portal.
- Make as much of the city’s data easily accessible.
- Access to a wide variety of correct and reliable data is an enabler for innovation in societal services.
This is Metadema, a holistic view on data management in the Nordics. Welcome, my name is Winfried and thanks for joining me for this episode of Metadema. Our vision is to promote data management as a profession in the Nordics, show the competencies that we have, and that is the reason I invite Nordic experts in data and information management for talk. Welcome to today's episode of Metadema, and I'm really excited for this one because I finally have someone from Iceland again on the show. This is the second person from Iceland and she is the chief data officer of the city of Reykjavik. Welcome, inga. Thank you so much.
Speaker 1:So we have a really interesting topic today to talk about. We talk about and this is a mouthful so innovation of diversity and inclusion through the role of the chief data officer. So we're going to talk a bit about the role of chief data officer, try to get an understanding of what the role is about, why it is important, and then we're going to talk about the initiatives that the city of Reykjavik is doing with data and finally, concluding it with diversity and inclusion, which is our main topic for today. So, as a quick intro to the topic and I feel like it's a bit of Spider-Man, peter Parker with great power comes great responsibility.
Speaker 1:Data has become a driving force for many parts of our lives, and this becomes really clear in social settings where data can dictate how we utilize public services, like in the city of Reykjavik, and Inga is with us today to talk about how they are, as chief data officer, in the driving seat for innovating public services through data. So with that comes also the importance of data ethics and the social responsibility that is imminent. So how can we take this responsibility, especially as CDOs, and use data to help people or even build a better society? So welcome Inga, and please introduce yourselves.
Speaker 2:Thank you. That's a great question. To this date, the common narrative around data is that it's really complex and often hard to understand, leaving us with the feeling that it can and should only be exploited by experts. But we at the city of Reykjavik want to change this narrative. We want to show people that data is for everyone, and everyone should be able and have an equal chance to use data to make data-driven decisions, whether it is in relation to their profession or in their private lives. Our approach is that we want to take the responsibility away from the user, so that the user shouldn't have to be an expert in data or a specific field in order to understand the data being presented to them.
Speaker 2:We're therefore focusing on user-satric approach when it comes to presenting data. We are focusing on delivering data in the form of data stories written in simple text, supported visually by pictures and graphs, and specifically designed with the digital accessibility in mind. By designing data visualization with multiple accessibility requirements in mind, we are simplifying our visualizations and making them more relatable to all. We are creating our own visualization components for graphs, where we, for example, take into account the screen reader ability. By designing our own components, we can coordinate the visual representation of the city's data across multiple platforms. We are doing this because the data and digital accessibility supports social inclusion for all people, independent of their age, sex, ethnicity or social status.
Speaker 2:I especially believe that in the public sector, data is highly valuable societal asset and it is in our responsibility to make as much of the city's data open and accessible to our citizens and employees as possible. It is our belief that by making data accessible, the city will become more transparent and accountable to the people that we serve, and by encouraging available and shareable data, the city provides the opportunity to drive innovation and encourage people to create innovative ways to tackle new and existing problems and drive innovative citizens' satric services. This also has the potential to increase efficiency and effectiveness within the safety operations and services, and I could go on and on and on.
Speaker 1:This is fantastic and there's so much happening, and this is exactly why I wanted to talk to you, but before we deep dive into the topics, tell us a bit more about who you are and how you got to the stage where you are at now in your career as chief data officer for the city of Farkilik.
Speaker 2:Yeah, my name is Ingar Ops. I am a mother of three wonderful children, and how I came to this position is kind of like it just kind of happened by accident. I did my engineering studies both here in Iceland, where I took my bachelor's degree in operational engineering, and then I moved to Denmark where I did my master's degree in product development and innovation engineering, and so I was never like focused on data or data, specific jobs or anything like that. But being a foreigner in Denmark, it was kind of hard getting a job after I graduated and in the end I landed a job at L'Oreal and Copenhagen as a master data expert. I don't know why I applied for this job initially because I had never heard of master's data experts before and I had no idea what they did. So I had to do some research before I applied for the job. But I landed that job and there I kind of found my passion for data and realized how important data is and all the possibilities that we can Introduce to like better societies, driving better innovation and stuff like that. And that's how I somehow came into the world of data.
Speaker 2:But in my earlier years I was a gymnast.
Speaker 2:I was a part of the Icelandic national team for over 10 years in artistic gymnastics.
Speaker 2:I was also selected as the sportswoman of the year and co-poor, which was my hometown at the time, at one point in my career, and I think that there is where my perseverance comes from, where also my maybe need for trying new things, like also things that I didn't scary because I'm stepping out of the box and doing things that I'm not sure that I know how to do.
Speaker 2:That can also be scary, but being a gymnast teaches you the importance of preparation, to not let yourself, doubts, fears and failures defeat you, and that you need to believe in yourself, because nobody can do things for you, and I think that that's one of the biggest takeaways I can take away from my gymnastics career, and that is also why, at the point that I did, I was an independent contractor working on big projects for some of my clients. When the this state is the chief data officer opened up and I just I don't quite know why, but I just thought it was really interesting and I wanted to see if I would apply, if I would get through the first interviews, but somehow I ended up getting this job and I'm super happy about it.
Speaker 1:This is a fantastic journey and this is so interesting because this is a topic that comes up a lot when you talk about data and especially when you go into data leadership roles that you kind of need that what you call pipi long stockings kind of approach to life. Right, I don't know how to do it, but I will. I will learn it, and I think you need that, because nothing is written in stone yet, especially not in data, since it's still a young profession. So we need people like you. Just jump into it and make the best out of it. And what is also really important is and we talked about this in the podcast a couple of times before is that there is a need to get other disciplines involved in data. It experience from other disciplines, like your experience on engineering, is really important to make data and the data profession better.
Speaker 2:Yeah, I totally agree. It comes into the topic of diversity. For example, you need diversity when you're working with data, when you're planning your data journey and everything that you want to do with data, because most of the time, a single person or a single mindset isn't the most capable of seeing all the potentials.
Speaker 1:Not just all the potentials, but also all the hurdles, and this is something that we're going to talk a bit more about later on when we talk about your approach to diversity and inclusion. But before that, let's talk a bit more about your role as a chief data officer. A chief data officer is still a young role and it's an evolving role in the data space. That have been, and there are a lot of different roles and approaches, how to organize your data organization, and I think a couple years back if you think 10 years back we had a chiefs mobile application officer, right, someone responsible for mobile applications. We don't need that role anymore. Now we're talking about chief analytics officer, we talk about chief algorithm officer, we talk about classic roles like the chief information officer or the chief technology officer that certainly have taken the game when it comes to data. But why is the role of chief data officer so important and why do you use and get well lost?
Speaker 2:I think the role of chief data officer is super important because you need someone who can both see the bigger picture of like the entire operation, but also how data affects everyone in different ways.
Speaker 2:In my role as the chief data officer the city of Reykjavik, I am responsible for forming the data policy for the city and making sure that it aligns with other important policy policies of the city, such as the digital policy, the service policy, the green plan, the equal rights policy and many, many more, and I'm also responsible for creating the vision statement for my department, defining our goals and determining the steps on how to achieve our goals. I am in charge of defining the roles and responsibilities of my department's employees and so much more. I mean, it's a pretty big role and a big responsibility to take on, but I think that when you have good people behind you and with you for example, the politicians are supportive they are also supportive of our digital transformation journey that my department, which is the Department of Service and Innovation, is driving, and then also with my colleagues, and we need to just understand that it's a shared effort into making the digital transformation efforts happen, but also that we present reliable and applicable data at every point in time.
Speaker 1:I really like that, because we are talking about a holistic approach to data and you can't just narrow it down on just the technology problems and you can't just narrow it down on just the business value that you are intending to produce. But there's much more than that, and I think you mentioned that a couple times. There is a certain social responsibility that comes with managing that data and the way you manage it. You mentioned digitalization, and this is kind of for me to understand, because the question arises quite often what is the difference between a chief data officer and a chief digital officer? Do you have any points on that?
Speaker 2:Yeah, I think that with the increasing demand for digital services and faster and improved information delivery, people become more and more aware of the importance of data. Having said that, people often don't understand what we mean when we talk about data, data quality, data structure, data accessibility and so on, and it's a kind of a different area or a different skill to do digital transformation efforts, and data therefore often becomes an afterthought when it comes to designing, developing and implementing software or new digital services, and people don't often realize how important it is that the data collection is thought through and intentional, that we know in advance what it is that we would like to know about this particular service, that we ask the correct questions in the beginning of the development process, like in the beginning of the design, when we're talking to our users, for example, and using the service design processes. That we ask the correct questions like what questions would we like to be able to answer regarding this service? What data needs to be collected in order to answer those questions? What data is currently being collected?
Speaker 2:What external data sources do we need to integrate to enrich the data sets for this particular service? What other services might need to leverage the data generated within this service and so on. We also have to know how we can access the data within our systems, that it's made clear who is the owner of the data, and especially when it comes to third-passed eventers and so on. And there is kind of the difference between, I believe, the data officer and the digital officer, because we both have the same end goal, but the approach is different and we both need to come together collectively to make the best services applicable to our users at every time, and I think, especially now, with the fast development in generative AI, for example, and how easy it is for individuals to access and use this technology, that people gain clearer and better understanding on what data is, how it can be used both to enhance your life, but also that people become aware of the dangers of feeding AI software, for example, with sensitive personal data, pictures of themselves or others, and so on.
Speaker 1:Yeah, now you are into a really important part of what I think is the role of this chief data officer, and it is also to have a role on being an advocate for data, not just in your department or organization but beyond, and you have done a lot of work to be present in the public debate in Iceland, writing articles, being on the radio show and much more. How interested are people in the topic data?
Speaker 2:I think they're becoming increasingly more and more interested in the topic of data, especially with the increasing demand for digital services. People expect today that they can just access the information they need at the time they need, from the comfort of their own home, for example. And this is the reason for why I have been sort of campaigning and trying to create the awareness of the importance of data, but also like what is the difference between data and information? When does data become valuable information or sensitive information, and how and when it can be used? Because I think, with the increasing knowledge of the potentials of data but the importance of data collection, and that we ask the correct questions in the beginning, we also ensure the data quality as a unit.
Speaker 1:Yeah, definitely agree, and I don't want to go into the entire topic of data quality, but the earlier in the life cycle you can ensure data quality to better. First, let's talk a bit about your setup and what initiatives you have started in the city of Reykjavik and we talked a bit about that already at the beginning and you mentioned a couple of those and I just want to dive a bit into those. I think we talked about data stories. Maybe you can explain what those are.
Speaker 2:Our main goal is that we want to increase data literacy, and we also want to promote the idea that data is a valuable societal asset, where we all have this shared responsibility to ensure data quality so that data can be used to provide, for example, better services and to ensure equality and inclusion and, initially, make people's lives better.
Speaker 2:Our goal is to make data accessible and comprehensible to all, and everyone should be able to access reliable, applicable and correct data at the time they need in order to make data driven decisions.
Speaker 2:In that sense, we are building, for example, the data buffet you just talked about. The DataBuffet is an open data visualization platform and an open data portal where we are trying to make as much of the SIFT-states that easily accessible and usable, because we believe that access to a wide variety of correct and reliable data will act as an abler for innovation in societal services. It benefits the educational system, the innovation community, employees of the SIFT, politicians and the job markets and individuals. We are also constantly hearing from more and more parties, within the job market, for example, that have been making use of our products in ways that we didn't even think that they could be used. We heard, for example, from a primary school teacher who was super excited about the DataBuffet because they saw this on teaching opportunity to teach kids to read information based on graphs and how it could be described in text, for example.
Speaker 1:It is a fantastic initiative and I think there are two things that come together here. This is, a making data available for everyone and B also making it available in a way that can read it, understand it and use it as a valuable asset. Then we are right into the data stories. There has been some discussions, also in this podcast, about well, if we talk about data literacy in general and in society, we also need to ensure that we can actually provide educational material and provide data in a way that it is understandable and it is readable, can use it. A bit of the discussion was that if we have a data literacy program set up in, like an organization or a company, that will definitely also have a benefit for society at large, but it needs to happen sometimes from a governmental or city setting. That's exactly what you are doing. Maybe you can talk a bit more about what data stories are and what you're thinking was behind it.
Speaker 2:We are the data service units. We are working on data policy, which also has a chapter on digital accessibility when it comes to data and visualization, where we believe that, as I have said before, that everyone has an equal right to access information and make data-driven decisions, both in their profession but also for their own lives. Psychological studies have shown that around 80% of verbal communication gets disordered on the way from the center to the receiver, and our brains receive around 90% of information through our senses, but it's also said that the brain receives more information in one day than a supercomputer does in a year, because it receives both internal and external messages which affect all of our senses, sometimes at the same time. It's also said that the brain receives more information in one day than a supercomputer does in a year. Sound is only affecting one of our senses. The other senses, besides hearing, are seeing, tasting, smelling and touching. So the more senses we can enlighten while presenting information, we're more likely to receive the message that we want to come across when we receive it like we intend to, and that is why we are so focused on how we present data through data visualization, where we use most appropriate color schemes suitable for colorblind people.
Speaker 2:We present data through simple and informing texts in the form of data stories and supported by graphs which can be read by a screen reader, for example for the visible impaired or people who can't read, but then making sure that the screen reader reads it in the same way as we would do those of us who can actually see.
Speaker 2:And this is all evident in the data buffet. And when we talk about data stories, we are trying to visually, verbally and then also like use sound to bring our message across. We are making sure that our text is written in a simple manner, because we don't always sound like you're more smart if you use complex words. It only means that people are less likely to understand what you're trying to say. And by simplifying our wording, by simplifying our data visualization and bringing it all together in a data story where people can access information about the city whether it is just because they are interested in knowing how many people are at the swimming pools at this time, or if they want to know something about the financial status of the city we're trying to make the data as understandable and less likely to be misinterpreted as possible, and that's how we use data stories, for example.
Speaker 1:It brings us into the middle of our main topic the innovators' potential of data for diversity and inclusion, and these are fantastic examples of making data available for everyone. Before we dive a bit deeper into this, I just wanted to get your definition on innovation and what value creation means to you, especially in this context.
Speaker 2:Yeah, for me, innovation is taking something that has been done in a specific way for many years or decades and doing it in a different way, where we reduce waste through the process. So, whether it is by being more efficient, we reduce time waste or we get a clearer understanding of what we're doing and why, while we also provide, through that, provide extra value to our employees and or our citizens. So, like simplifying processes, simplifying wording, simplifying how we present our information and data, gaining clearer and more common understanding of what we're doing, asking the questions why are we doing things this way? And not be afraid to try different things to resolve newer or existing problems.
Speaker 1:I really liked that because it really fits well with what we already talked about and how you used that innovative potential of data for diversity and inclusion. I want to not dive deep into it, but just get a sense of what your technical setup is on a high level.
Speaker 2:Yeah, our infrastructure is set up in the Asia Cloud, but the technical setup for the city is kind of a hybrid setup environment where some of our systems are on-prem and all their services are in the cloud, but all new software development is happening within the cloud environment. We are building our data pipelines in our Azure environment. We are using an open source software where we use Python as our main development language for our pipelines and then for developing our data models we use R.
Speaker 1:Probably a lot of people have that question and are in kind of the same situation as you are on the open source part. Possibilities are large with open source but there comes with a certain risk. How do you manage that? How do you use open source and why did you make the decision of going open source?
Speaker 2:Mostly because the purchasing process and purchasing rules within the city are really strict and you have to go through a lot of different hurdles in order to buy like just if you want to buy a software and it often takes such a long time that it's almost that the software is out of date when you actually get the acceptance of buying it. So, but if you go open source I'm not saying that it's the best way to go every time, but in this case we were much quicker to be able to start developing the data pipelines, which were highly needed but not present at the time we could also more adjust it to the way that, to our architecture and the way that we wanted to use the pipelines, and also we have, then, more access to, like, the community of people who are also using the same open source software, so we can ask questions and seek guidance without maybe having to go through like a contractor or pay big amounts for it.
Speaker 1:Yeah, I think these are the main points that I also see that there's a time issue and there's a cost issue connected to not going open source, and if you can balance those with the inherent risks that you have with open source I mean they are there then I think you are not on the right track. But I think you need to have an open eye and know what you're doing, and it sounds like you are on a really good track.
Speaker 2:Yeah, I hope so. At least we are aware of the dangers as well as the comfort of going on this route.
Speaker 1:There are two more things you already mentioned those, but I do like to talk a bit more about those and one of those was the data visualization library, where you tried to make information more accessible, more readable for everyone, and you talked about. You created your own component. Can you tell us a bit more about how those components are set up and why you couldn't find anything on the box and decided to build your own component?
Speaker 2:Yeah, we mostly went into developing our own component codes for graphs because we couldn't quite find anything out of the box that met the digital accessibility requirements. We also wanted to ensure a consistent appearance across multiple platforms when it comes to presenting the data of the city of Reykjavik, and also that it was applicable for the screen reader to be able to read through our graphs in the same way as we do when we can actually see the graph, and we couldn't quite find anything out of the box that like checked all of our boxes. We also have that design platform called Hanna, and there we could implement our components into our design platform and making it more accessible to both developers but also employees within the city who want to display graphs.
Speaker 1:Great. Thank you so much, and I thought I would too. And the other thing that I wondered about and you said in one of your interviews that language is really important and you try to incorporate as much as possible of Icelandic language. Don't have anything in English. Really, why do you think that is important?
Speaker 2:Language is super important, especially the terminology we use Terminality we use today within our workplace. There's often a different meaning behind the same term depending on who it is that is using the term and that creates some briefs in understanding, and we also need to make sure that the terms used in services directly pointed at our citizens are the same that we use between each other within our workplace, whether it is the service provider that is using the term, the service receiver or our digital systems who enable the service. It also is important in regards to the data collection how we structure the data and then how we can utilize the data to do our analysis and to get the correct picture of our services, who we are serving, how we are doing and yeah, things like that.
Speaker 1:Exactly, and the discussion is not just an Icelandic one. I think we have the same discussion in Norway and Germany or that matter, because a lot of the lingo that is used in data is English and there is always a pros and cons translating that lingo to your mother tongue. It's more inclusive, definitely, to have that in the language that you speak, but it could also change the meaning of the terms, so it has to be done with care.
Speaker 2:Yeah, I totally agree it has to be done with care, but also we have to know why it's important. It's not only important in clarity when we're providing services or talking directly to our citizens, but also when it comes to making use of our data, and in that sense, we're developing business glossary because, also within our field, experts working in technical fields and specialists in various fields, as well as our service users, all have different needs for data and they utilize it in various ways and when different terms are being used for the same data tree, which creates increased complexity in information flow and data utilization for the city, and it also increases the risk of bad data or that we give worse answers or make bad decisions.
Speaker 1:It's really warming for my data governance heart to talk about A passionate speech about data glossary. Really enjoyed that Last question from my side because I think this is a bit more high level. But how can we ensure that and we talked a bit about the inherent risk of data as well how can we ensure that the affected data has some people's life and tree is a positive one? How do we ensure that we have the right quality of data in place, ensure that we don't include biases, or at least be aware of those biases, and work with fairness in the work with data?
Speaker 2:That's a good question. I think, especially now, with the continuously increasing development in online services and the increasing demand for digital accessibility, everyone needs to understand the importance of correct and high quality data. I feel like, especially within the public sector, that data collection hasn't been deliberate, in the sense that there are constant changes in how data is collected, which data is being collected and why. Oftentimes we don't even know why the terminology changes and changes are being made, frequently directly within our databases, without the thought of the possible effect it might have further on in the process or directly on our services. By that, we also lose the historical context and it affects the quality of our data. In addition to that, I don't think that anywhere within the public sector data has been collected with that in mind, that we might be training AI models based on our data, and I'm not sure that people realize how much data you need to train these models and how extremely important it is that the data is reliable, without gaps, that our data sets are not presenting in any biases. But it's also hard because historically, our societies have been biased. If we look at these axes, for example, and if we are not careful, we will introduce these biases to our models and even might do it without realizing it. So we all need to be aware of the imposter's update and the value it can bring to our everyday lives, while, at the same time, think of it as a shared asset where we are all responsible for assuring the data quality and that we ask the correct questions from the beginning.
Speaker 2:What is the most important for the user to know regarding the service? Which question do we need to be able to answer? What's the most important data for our operation? Which questions would we like to be able to answer regarding that? How is the data being collected? Why is the data being collected? How is the data being used? Who can access the data, and so on.
Speaker 2:And when working in a targeted manner towards data utilization, the possibilities are endless. We can, for example, assess the social status of people within marginalized groups. We can set a light on the status of gender and our marginalized groups within the city, which then allows us to better monitor the impact of certain actions within the safety system on these groups. We can bring attention to whether certain services meet the needs of specific groups Equally to others. For example, we make better and more informed decisions and take counteractions earlier, and we can also, for example, evaluate investment, investment needs and likely return on investment for the city and make more accurate financial plans, which makes the use of public funding even more efficient. And all of this should have the goal and should result in making the lives of our citizens better.
Speaker 1:What a fantastic answer. Thank you so much, and I think just to highlight this, since we talked about AI on purpose, but you said it quite clearly large language models have a huge potential for value creation, but they can also become an exponential accelerator for problems within your data, and I think this is important to keep in mind. Thank you for a fantastic conversation. At the end of it, do you have any key takeaways, or even a call to action, you want to share with us?
Speaker 2:You need to think about how you present your data and data visualization. Don't expect everyone to understand what you're trying to say, and don't expect people to be an expert in the field that they need to get more familiar with or need to be able to make decisions, regards, or something like that. We always need to make sure that simple is better.
Speaker 1:I love that. Thank you so much.
