 Hello, and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer of Data Diversity. I'd like to thank you for joining the most recent webinar on the Data Diversity Monthly Series, Elevating Enterprise Data Literacy with Dr. Wendy Lynch. This series is held the first Thursday of every month. And today, Wendy will be joined by Veronica Wilski, Content and Engagement Director at the Data Lodge, Collette Lung, Data Governance Program Manager at the Northern Alberta Institute of Technology and by Christine Haskell to discuss the literacy experience interviews from the trenches. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. If you'd like to chat with us with each other, we certainly encourage you to do so. And just to note, the Zoom defaults to send just the panelists, but you may absolutely switch that to chat with each other. For questions, we will be collecting them by the Q&A section. And to find the chat and the Q&A panels, let me click those icons in the bottom of your screen to activate those features. And as always, we will send a follow-up email within two business days, containing links to the slides, the recording of this session, and any additional information requested throughout the webinar. Now let me introduce to you our guest speakers. Veronica is one of the world's first certified Data Literacy Program leads through her work at NTAGRS, where she created and implemented an Enterprise Data Literacy Program. Last month, Veronica joined the Data Lodge as the Content and Engagement Director. Her work at the Data Lodge supports and empowers data literacy professionals to build and scale programs at their own organizations. Klet is the Data Governance Program Manager at the Northern Alberta Institute of Technology. With experience in government, financial institution and industry, she specializes in building data governance programs from scratch in multidisciplinary contexts to help organizations maximize using data intentionally and to set the foundation for data-driven processes such as analytics, business intelligence, and AI and machine learning. Klet is passionate about advocate of data ethics, privacy by design, inclusion, and innovation. And Christine Haskell, the Principal Consultant at CHC. So she helps clients improve performance by interviewing relationships with results to increase leadership and data fluency. And let me introduce to you the speaker for our series, Dr. Wendy Lynch. Wendy is the founder of analytic-translator.com and Lynch Consulting. For over 35 years, she has converted complex analytics into business value. At heart, she is a sense-maker and translator, a consultant to numerous Fortune 100 companies. Her current work focuses on the application of big data solutions and human capital management. In 2022, she was awarded the Bill Whitmer Leadership Award for her sustained contributions to the science of corporate health. As a research scientist working in the business world, Wendy has learned to straddle commercial and academic goals, translating analytic results into market success. With through this experience, she has created her new book, Become an Analytic Translator, and an online course. You shall check that out. And with that, I'll get the floor to Wendy to get this panel discussion started. Hello and welcome. Thank you so much, Shannon. Glad to be here wrapping up 2023. And for those of you who have been here before, welcome back for those who are just joining us. Welcome and welcome to Collette, Christine, and Veronica for joining us today. I'm going to start with a little reflection back to where we started since we're ending up the year this month. And you may recall that we started at the beginning of last year after Shannon and Tony and her colleagues had done some focus groups. And we took those focus group interviews and tried to condense them and we revealed what they said in February. And what we have done since then is talk about these big topics that were on the minds of individuals who work in corporations thinking about data literacy. And so at a high level, just for a couple minutes, I thought I would review that and remind us where we started and set the stage for today. So one of the biggest messages that we got out of the focus groups is it is an important issue. It is on the minds of so many leaders right now and it's critical that we get our arms around it and figure out what to do with it. There's also questions about where it belongs. Is this part of governance? Is this within individual departments? Is this something that belongs in corporate training or is it something separate? There's a question about who needed to be literate. Is it everyone? Is it people at certain levels? Is it people who have different jobs? Where is it critical and what is the location of these people and how to get it to them? Then, and we addressed this question on several of our webinars, is how literate do people have to be? Are we trying to get people to expertise levels of analytics? Are we trying to get them to where they understand just the basics of what's available? Are we asking them to figure out how to use it and as they say, argue with data? Then there's a couple of questions that I wanted to remind us what the answers were and that is what is the goal of data literacy and almost universally what they said was we want data to create business value. We also want to leverage data so that we are making a change in how decisions are made so that they are informed and data driven. Then we also want to have a transformation in how people see data. So the goal is a great big one and we're going to be asking our panelists today to comment on some of the things that they've seen in the past year that give them encouragement and that we're breakthroughs toward these types of goals. Then we also had questions like what gets in the way and at the very basic level people said there is a big burden of cost and time. They also said you know training everyone across the organization is a huge undertaking and we're not quite sure exactly how to do that. They asked whether the implementation needs to be one where it's you know one and done or is it over the years you keep on doing it and then is it something that we're always learning or does it ever end? Is there a beginning and an end to it? And also today our panelists are going to talk about things that this year got in the way for them and what they learned from it. So we're going to start today with an initial question that I asked these three experts to ponder and that is if data literacy had a mascot what should it be and why? And I am going to start with Collette first and so Collette what should the mascot be for data literacy and why do you think so? Hi Wendy thank you so much I actually I really love this question because well speaking of literacy I really love metaphors and so I'm always trying to think of new ones that speak well and work for people. For me the one that kind of came to mind when I was thinking about this was actually a bumblebee and there's a bunch of different reasons for this. So I actually have a background in linguistics and one of the things we learned when we took courses is bumblebees are actually one of the creatures that are closest to having what we would define as language. So I thought there was kind of a nice parallel there but definitely there's like this cool idea to me about how bumblebees have to turn pollen into honey the same way that we would look at how do we take data and turn that into something useful. There's different types of bees that have different types of roles so the same way you know we've been talking about some of the things you mentioned with literacy having you know what level does every person need to have in terms of data literacy and then I think there's also just this cool aspect of you know bees have a buzz and I love that idea of data literacy having kind of a buzz that kind of grows and that spreads you know pulling around or data around and the practices on how that's done. Yeah so that's kind of the one that came to mind for me. That is awesome I really like that and now of course now I want to go read about their language ability because I did not know that about bumblebees. All right so what about you Veronica if there was a mascot for data literacy what would you say? So I don't know if this was cheating or not but I said that the mascot would be a school of fish so maybe not just one mascot but many together but it might be a nod to the fact that I was born and raised in Washington state but I just think of a school of fish as it's everyone everyone is there together. I know one of the questions you asked is you know does this ever end? Is everyone included in data literacy as some of the main questions that came out of your forum which are great questions and you know you think of a school of fish and it fits well because it doesn't end they don't stop swimming but they're working together they're going where they need to go obviously they're very flexible and adaptive they can change you know course as needed to avoid the predators. I didn't quite meet the correlation with that in the workplace so I don't know that there are predators but there are dangers you avoid by working together especially using data literacy and then together they form something bigger I know some fish can create the form of a larger fish or a larger creature so just the thought of people swimming together was very powerful for me but also I love bees too so I was like oh Colette yeah no I I love that and the keeping swimming makes me think of Dory just keep swimming and I think many of us who are working in this field we have that feeling that they have to just keep swimming and keep it going yeah Dory needed her school for sure yes she did she did she's all by herself so we're steamed now we have a school of fish and a bumblebee what else have we got as a master I almost went with bumblebee but I went with crows not because of the counting crow band although that was part of it but that crows can count and they they recognize numbers and they've used computer screens to play numerical matching games but they're also high in emotional and subjective intelligence and they can play rules games and detect emotional states of other crows and I think that the more we get into the data space and we'll get into this with our as we get deeper into the discussion I'm sure but you know the the irony of data is that human skills are are just as or more important than technical skills as we understand you know literacy you know the analytical judgment and flexibility and EQ and those things are are really critical and crows are adept at those skills is great all right so we've got fish and birds and insects so that is awesome I will I will say that my mascot was going to be a little seedling which is why I had that initial idle page because we really really really need to nurture these things and help them grow so we had plants and birds and insects and fish so the next question that I wanted to ask these experts is about things that went well this year since we're looking back I want to hear a specific breakthrough that they had and I and I gave them a lot of leeway for you in the audience that this didn't have to be a particular event or a particular assessment or something measurable it could have been acceptance or a shift in demeanor or anything so I want to start with Veronica what what specific breakthrough did you have this year and what were the implications of that yes thank you this is a wonderful question and a good chance to reflect I will give a little bit of context I ran a data literacy program at Integris which is a semiconductor manufacturing company global presence over 10,000 employees just to kind of set the stage for what what we were doing and what we were working with so I think one of the biggest breakthroughs this past year in 2023 was that the awareness piece so you know data literacy has an element of change management and really thinking about people's mindsets along with you know the language they're speaking and the skills they're using but the mindset I think can be the most challenging to to address in mass so the thing that really grew is the awareness of really what data literacy is and how it could be relevant to people especially in their work so as far as measuring I'll kind of give like a couple little anecdotes of things that happened but we we did run some data and analytics awards where people could submit their own projects or submit a teammate who exemplified a few different data literate characteristics one of the stories that really struck me was there were people who were evaluating a project that had gotten the green light like prior there are a lot of I don't know a lot of scientists at Integris and people who are trying and who are innovating so they had this idea for a project people stepped in and they're like all right let's reevaluate this project and there was some discrepancy because there was not a shared language for that team and the broader team so they were talking about the same project but the way that the metrics were defined and then proven like hey I think this is a viable project it's worth pushing forward did not align with the vocabulary and the metrics that were being used to evaluate so once they kind of settled that discrepancy figured out how to make those metrics aligned it was discovered that the project was not viable and it should be you know cut and not moved forward because it would not produce revenue and and that is what happened you know it kind of seems like a sad tale like oh their project was cut but in the end it was a success because they established that before investing people's time and the resources to that so that's just an example of where the general population of Integris really understood what data literacy is and the implications it has on them but more so the company overall so that was really really great to see and yeah just in general people could give examples of good data literacy around them and I think that that's the best way to make that tangible to people initially is that they can see those successes and understand how it touches everybody wow so I'm curious what was the award I mean what what did they what did they quote unquote win oh that's it well so we had four categories um there was data I'm gonna not say these word for word because my memory is not that great but data-driven team um there's one around sharing a language and making sure that was set with people which is where this one fell under um trying to think I can't remember the others but the awards um a lot of it was recognition within the company and being able to be leveraged and shown and then also just some fun fun swag things of course oh great great so you got you got your uh your literacy hat or your uh your literacy jacket yes yeah well I'm a stickers person lots of stickers okay there we go literacy stickers I like that all right so Christine um tell us about a specific breakthrough that you had um in this past year and what the implications were um one was in more of a relational culture so I've done more programmatic things like what Veronica was talking about where you can go more top down this one was more of a bottom up thing where um I landed different case studies in different groups so the top down structure wasn't going to work and so people were kind of they live in their particulars and so they were uh living in sort of their their specific uh kind of pain models there was a an architect who was dealing with um a spend issue in an external uh data warehouse problem and uh so I framed a case study around that and used the roles that were coming together to help solve that problem as sort of general data literacy archetypes well how was the engineer coming together how was the program manager coming together how was the studio how how are these people coming together to kind of solve that problem and what were their needs and what were their what were the skills being employed and what were the relevant job aids and how would this kind of fashion itself into a toolkit and so coming up with that case study and landing that toolkit in that group helped shift their mindset around how does literacy play a role in these uh job classes and and bringing that to different job functions around the company so I landed another one in HR another one in product management to land concepts around experimentation and project management and and process improvement in HR with a similar type of approach that's great so in those case studies were they used more broadly besides the groups that you use them with yeah then I brought the case studies end up going yeah yeah I brought them into marketing and they were up-leveled in using marketing materials the company um had a had low had a product called blueprint which was one of its sort of flagship kind of marketing materials in a really beautiful document and they put a lot of that material in the blueprint document and and other kind of marketing materials that they use great so um yeah I have a couple of other questions but I'm going to wait until we hear from Colette and then we can kind of open this up more broadly so Colette tell me about a specific breakthrough that you had this year um and what you learned from it yeah um can I just say this is this has also been really interesting for me I love hearing these stories um I also maybe want to give just a little bit of context to Nate where I'm currently working um so Nate is a polytechnic in northern Alberta in Canada um and right now uh the program that I lead the data governance program focuses very much on business data uh so data from the business and also with instructors and the reason I highlight this is because um this institute really has quite a large variety of comfort levels that people have with data depending on the nature of their job or what kind of instructor they are and all of those kinds of things um I have always taken the the that mantra you know that um you need to tell someone something seven times for them to remember it and I always try and remember that when I'm trying to do literacy initiatives um and that usually means that I also try and look at multi-pronged approaches which is what I have traditionally done in other institutes I've worked at so you know things like um you know luncheon learns taking time to have coffees newsletters all of those kinds of things and so I kind of felt like you know oh I know I know how this trajectory is going to go and and what's going to happen um but then there was actually kind of two moments that were kind of really interesting breakthroughs that happened for me this year uh the first was and this kind of ties a little bit to where I was when I was thinking about mascots but um really paying attention to the use of the language you're using and what kind of metaphors are resonating with people when you're talking about things um for me in particular this happened when um data quality processes started to come up um so we launched a data issues process that would allow people to bring data quality issues to us so we could start doing you know rucos analysis and have a discussion about them and I think that it was just kind of a bit of a hard concept for some people to wrap their heads around like what what is a data quality issue and so then when I started reframing these kind of with a little bit more emphasis on something a little bit more fun as like data therapy sessions like just come to me and tell me all of your problems and let's start there and have a discussion and figure out how to talk about this in a consistent way that really resonated for people and that was really cool you know it's such a great in to kind of start improving uh like data literacy in general the second thing that I want to mention really quickly also is um this year of course um chat gpt was a huge theme and ai was a huge theme um and this was kind of a really interesting in for me because I started getting to um I started being invited to spaces where people were having discussions about ai like literally just someone would book an hour where people anyone in the institute could come and sit and be like hey I'm worried about these things these things are on my mind I don't understand this what are other people going through and those were really amazing conversations to be able to sit in on and it really made me realize the validity of having that kind of safe space to start some of those literacy conversations in and it was also really validating space because I started to realize like oh these newsletters that I'm putting out into the void are actually being read by people and it was really interesting to hear um you know comments from people in the business or from instructors based off of uh either kind of materials we've been putting out so it's kind of a nice affirming moment so I guess one of my breakthroughs was don't give up even when it feels like maybe no one's listening someone probably is so yeah well those are great ones so one that you found out that people were paying attention even if it felt like you were sending things out to the ether and you wondered whether it was actually getting seen and then I really like the data therapy idea because um I guess it could be considered a therapy because you're helping them and listening to them but it also could be a gripe session because we all have our frustrations when they so how much of it was sharing oh my god this is overwhelming versus that these things really aren't working well at all so was it more gripe or more empathy do you think or both and maybe that and maybe this is just like a very personal breakthrough for me this year but I think that was a realization that I really came to is like I think that's a very poignant question because I think you need to give space for the gripe in order to find where like that more like salient points are so then you can bring that out and start bringing that language in that discussion and highlighting some of the concepts issues and why this is happening um yeah and and it feels like all three of you so whether it's building awareness and inviting people to contribute so the people figured out more on their own and they got an award it wasn't like it was a huge award but they got recognition for getting involved in those things and then the case studies by having people work together and understand the relevance as a team when it's multiple stakeholders coming together and then this safe space I guess I'm curious how much uh language um had played a part in each of these we've had a few sessions where we've talked about whether or not we should be calling it literacy you know because it's so off-putting to be the opposite and be illiterate and so and you've addressed this a little bit Colette with the data therapy but Veronica how much do you use different language and terminology about literacy do you specifically go with literacy or do you use different words yeah so that's a I mean a good question and definitely yes you're correct that it can be off-putting um I think optimistically I think that's also just based on the fact that industry slash world right wide data literacy does not have one definition I'm currently at a conference in Boston and you know the presenters that presented they each had a few different definitions up on the screen as they spoke about data literacy so from my viewpoint um defining data literacy you know the ability to read write and communicate with data in context it's a work and a life skill and just like with learning a new language literacy is kind of that foundational level that everyone needs to have to be on the same playing field to be on the same level so you start with literacy and then as you grow you move to fluency and then as you move past fluency you grow into mastery so it's not meant to be punitive and it's not meant to say to anyone your data illiterate because everyone has data literacy skills whether they are aware of it or not but kind of opening up that realization that oh I am a data person I work with data you know when I you know whatever it is drive my car do my budget figure out what groceries I need to buy so not meant to be an insult but to have let's get everybody on the same page so that we can you know raise I think I heard someone use the analogy of like raising the floor together with everyone so everyone has the same chance to keep growing got it got it what about you Christine language but I agree I agree I I think the Valerie's definition Gartner's definition I think that's um the the standard definition of literacy and I think how Valerie tackles the topic of you know not everyone needs to be fluent right I mean you know not everyone at this group needs to function at the uh analyst level of the New York Times right and right and that's okay to you know you know we don't need to to navigate a food label we don't need to um you know operate at a NASA level but but I think what what Colette the point that Colette was making I think is critical and one thing I didn't mention in terms of doing these case studies what I did before the case study I kind of I I enter every system that I come in like a corporate anthropologist and I I reflect the language that's being used back I mean we all come in I think uh for doing a good job we all come in looking at these organizations like different little villages right and what language are they using what tools do they prize what are they clutching to what are their sacred cows how how are we going to get them to pivot um just a little bit toward you know uh using this new language or using this new tool in some in some new way um and and that's that's the goal is that it is it is to get them it is to get the the village to embrace um a broader um a broader use of of of the tool yeah yeah yeah it's uh I find it fascinating that our breakthroughs here are people oriented it's not it's all it's all human skills right you guys aren't saying well we improved our um our literacy metric 7 by 12 percent you know I mean it's not no we're not hearing those kinds of it's all it's all going to be about analytical judgment flexibility emotional intelligence creative evaluation intellectual curiosity bias detection um ability to delegate tasks well like it's it's not going to be about uh the more sophisticated visualization technology yeah it's not going to be about how to use the the better um you know the better app it's going to be about better uh bias detection of AI output it's going to be about being able to challenge the data that you've been given to be able to understand you know where did it come from how is it being defined how is it calculated how you know do we agree on the definition right right those are all rationalizations and so it yeah it's a it's a level of understanding um that's very different from the assessments that give you you know a multiple choice to see whether you got it right or wrong so so Colette um what about you besides the data therapy which I I love so much what other language modifications have you noticed and and used or are you also an anthropologist yeah can I just say I I love that phrase like I wrote it down corporate anthropologist yes 100% I think that's such a great attitude to have towards it um I think um yeah and there's some interesting themes that I feel like have kind of surfaced in some of this conversation that I think are worth highlighting and that kind of tie in with that kind of language idea but I really think there's um this idea of like consistency and I mean consistency not just in the language that I'm using when I talk with people but also in offering spaces for people to come and bring that inquisitive nature and um by having that kind of as a recurring thing that people can trust will be there for them to come to like whether or not like I really noticed those sessions worked really really well for people where anyone could come and show up and bring and ask questions like that was such a great way to build that kind of community feel around data um and I think um yeah so kind of tying into language for sure and I think I love that idea of reflecting language back but also that other aspect of it as well which is almost I think what you were picking up on there like Wendy with that like community feel almost it's not it's not about the the tool or whatever it's this is real people skills right so yeah right right it is and it's a shift in mindset um which is exactly what Veronica started us with so we're going to move from the breakthrough to another set of questions um this next one is sort of the flip side um can you share a specific setback that you experienced this year that detracted from literacy efforts and what you learned for it and I'll start with you this time Christine um what described us a setback that was meaningful and and it taught you something well I think one of the most challenging setbacks that I've had to deal with was you know the going into Tableau and Salesforce at a time when Tableau was going through a five year integration into Salesforce and then the culmination of a challenging market and an integration and then that the layoff but the the the climate at the time was dealing with a shifting um the shifting sponsorship that was going on through that entire year so um uh you know I had kind of set up my executive sponsor you know and sort of the classic change management you know approach and um it just no one no one was staying still you know I would have you know I would get my my sponsorship secure and then five of them would leave and then another five would leave and and so um that's when I shifted my approach from a more traditional kind of governance um committee to clusters of of influence and using the case studies as a way to secure kind of smaller groups of influence and um more stable kind of um more you know groups of states groups of stability that would last longer and that worked um and then they were and then they went through their rounds of um through their rounds of layoffs but uh so that was interesting you know um but that was uh you know it's just an interesting climate lack of resources and lack of common skills and staff for operationalization it was just an interesting time well and probably lack of focus when you're going through um downsizing and mergers yeah very difficult to stay focused but I I think what you're saying is is at the beginning you pointed out that you had some breakthroughs because you went bottom up instead of top down yeah and that was a necessary adjustment because the top kept disappearing so hard to be top down when the top is gone um and so you were going bottom up um yeah for a reason and finding and and I like what you said uh clusters of stability when you have something that's relatively static that you can hang on to then you have a platform to to get it done mm-hmm yeah and so it's actually a wonderful example of how you can be in this chaotic kind of situation and there's always change not always that big but there's always some kind of change and so you were able to have some some wind um even though you were yeah I mean I I I always show these kinds of examples with my grad students so it's kind of like going underwater and letting the the waves kind of go there's always calm somewhere sometimes it's above the waves sometimes it's below and in this case it was below and um yeah I made a ton of traction for a while you know with the bottom up approach um and um but it wasn't to be but but you know it was a good it was a good experience for that but but that was my challenge there yeah yeah big big big challenges with the big C yes so uh so call it tell us about a setback that you experienced um that you had to adjust to or learn from yeah for sure actually um listening to Christine talk was kind of like oh wow this is so uh parallel I think um for us this year one of the biggest setbacks we had was um just constant turnovers in in staff um and so it'd be very difficult because it would be like particularly if it was someone in leadership you finally kind of made headway it seems like everyone's kind of on the same page and then now there's a whole change up and it becomes difficult not just because you've lost the traction that you've had with um you know however that configuration of group of people used to be but it also becomes difficult because um you kind of have to like start like you can't like you have to start over from where you were before you have to do an assessment see where they're at kind of figure out where their new goals are at you need to give them space for them to figure that out too um and I think uh yeah like I like what what Christine was saying like really resonated for me like I think one of the things I really had to realize this year and really um healthy institute even realize is like you know there's a lot of this that is it's it's flow and sometimes it's going to flow faster than other times and that's just kind of the nature of the work um I think there was another kind of setback that is one that I continue to have to um figure out strategies around but uh and I know this is just echoing things we kind of said before but really you know um it can be very interesting to me when I see urgency around a um priority particularly if if that priority is somehow data driven and then I'll come in and I'll say well we need to be thinking about these aspects of data if this is really going to be a data driven project and then I'll be told there's no time so that it becomes very interesting like oh well there's there's obviously a gap in literacy somewhere in in where that decision is being made right and um right yeah and and figuring out how um how to communicate and how to influence um becomes a really interesting um strategy um and and one that you kind of have to be it's good to be taking pulse checks I guess with with the the areas you're working with so yeah right right so it's a lot of moving parts at all times and I like your point that sometimes they move quickly and sometimes they move slowly and so you had a great breakthrough in the middle of all of that but in the meantime no wonder you were wondering whether any of it was resonating when you're losing the various stakeholders at different times yeah you you sum that up perfectly yes yeah so um Veronica tell us um a setback that you experienced um I appreciate yeah both Christine and Colette mentioning those things because those also resonated and then I also you know Colette you talked about maybe things move more slowly or faster at certain times and for my mascot I was like should I pick a tortoise and I was like no because it's not always slow but like I was thinking slow and steady and you know I think the challenge word for me came down to timing like entering projects like you mentioned like where how do we enter them at the right time so that data literacy and encouraging data literacy skills or something that is then just embedded as a part of the process um and then still once again you know we had successes with the awareness piece but also I would list that under the challenges as well because people got it but then when it came time for projects especially bigger initiatives even dealing with data um they you know it's it's hard to see data literacy skills as not a time expenditure but as a time saver because once you're all on the same page and talking about things the same way and understanding where you're going with the project really it saves you time and you're all just going or should I say swimming swimming in the same direction and you know not expending effort on things that don't contribute to that so yeah I definitely like what you said Christine about smaller stable groups yeah I think the challenge is how do you create that on a large scale so you can move you know slightly more quickly right yeah it's uh it's interesting because the three of you identified breakthroughs that were people oriented and shifts in thinking or shifts in participation or shifts in awareness and yet the the the setbacks are organizational I mean they may involve people also but really what you're identifying Veronica is it would be great if it was part of a the organizational fabric it would be great if it was part of processes so that it wasn't this one-off thing that you guys are all pushing forward well that was the grand challenge yeah I think that was the grand challenge maybe for all of us was that it wasn't part of the um the sort of corporate like the corporate goals right right and so yeah tell me any of the three of you um have you seen it be a corporate goal anywhere I mean have you seen it embraced at the at the highest level um I mean other than it at a company that is actually a data literacy company but as a company where it is a set of skills that you want people um what what ways have you seen organizations make it more part of the fabric anybody yeah I'm I'm happy to jump in with that one in my role now I work with um over 60 data literacy leads at companies throughout the world um and actually you know this has been a topic for us this week is you know what what drives that success and as Christine mentioned it's very much having that you know having it embedded in the corporate goals and in job descriptions um and there are a few of the you know those that partner with us who have seen this success I mean people are winning awards for their data culture and advancing these types of things and it is very much that they have that support um for more than one person so you aren't working in you know there are pockets but they're being brought together very purposefully um by the leadership and being supported in that yeah great all that or Christine other comments about how you make it part of the fabric um I saw Christine just unmuted go ahead first Christine um I think there's a tremendous amount of opportunity in co-golling HR to embrace digital upskilling as part of their mandate I don't think that organizational culture is different than a data culture goal it's not a different mandate and yet I think a lot of a lot of HR departments perceive that vocabulary to be discreet and distinct and a different goal because it's different language um and and I think that those things can work in tandem and and could be co-gold um and that uh that data teams could be um they should still be separate I don't think that that HR departments should in any way dictate what data teams do but data teams could certainly influence HR departments in terms of what skills need to be cultivated and infuse HR mechanisms with um uh data uh examples and behaviors and language for their commitments for their performance mechanisms for uh for all sorts of HR tools um that could make them more data forward than they are today yeah yeah no that is a really interesting that data culture isn't different from corporate culture it's it's embedded in the same thing yeah yeah I got it I'm so excited by that I totally agree I think um it's what it's one thing I'm hoping with the current program that I'm growing uh we'll get to that point as well um but uh I've worked in organizations where actually just the same way that when you're on boarded to the organization you have to do your security training and you know your um time sheet training and all of that stuff but actually having a specific data training module in there as well is I think a really great way of just doing a level set right from the get go um and uh I I love that technique I I try and bring that into all of my data governance programs so I think that ties in really nicely with what Christina was kind of saying so yeah yeah oh that's great all right so the last question and we'll just go through that um relatively quickly I don't want to rush you so so please answer completely I just won't meander quite as much on my end um and then we will have a few time a time for a few questions if anyone in the audience has a burning question please put that in there um so tell us a trend that you see in literacy that you saw this year specifically that makes you hopeful about the future and I'll go ahead and start with you Colette. Yeah for sure um I was very excited by this question I think I'm just a naturally optimistic person by nature so I was like yeah let's be hopeful um but I think uh and this has tied also with again you know Nate has been engaging with a lot of uh AI ML kind of investigation work in the past year um and so this kind of ties in with that trend which is like you know as I've seen people talk a lot about AI and it's been forefronts especially at Nate in a lot of our conversations I've also seen this trend where people really want to be intentional about it and that's what makes me really excited I see people coming saying you know I want to have discussions about this I want to know that we're doing this the right way um how do we do that and we're kind of at this really cool moment um where we can kind of shape how we want that to move forward and by that kind of exercise it's like naturally bringing people into a higher level of literacy uh I saw this also kind of surface out a lot so Nate has these we have a few days a year where people different people from the business can present on topics from the area and anyone in the organization can go and learn about these topics and it's a really great way to kind of encourage professional development um in 2022 I presented and I actually had a small section in my presentation about AI um and and and how you can think more about even data in your everyday life and how it affects the things you do and you know there was interest but it wasn't anywhere near as interested as what I saw in 2023 when I came back and gave another presentation um where people have really started to come with these really interesting questions uh that are really great opportunities to open up ways that we can increase data literacy in the organization I found um and more broadly than that I've seen this come to the institute not just from the business but also like from our students and from lay people and so I think that's a really interesting trend to be watching happen and and it does make me really excited so yeah yeah so part of what you're excited about is people bringing bringing their interest to you so rather than you sending out okay here's another update here's how you look at quality here's what you do you have people coming to you and saying god I'm really interested in AI or I'm really irritated by such and such system so um by engaging with you that makes you hopeful yes definitely and I think also because yeah I could have seen a scenario where it would be like oh AI is here I don't really need to think more intentionally about that but the fact that people want to have that intention is I think yeah yeah very hopeful so great great so Veronica what are you hopeful about from trends that you saw this year yes um I loved everything that Colette said and I feel like hopefully everyone can hear me nodding I feel like as you Christine and Colette talk I'm like uh-huh like so just I am nodding along over here um I think kind of along the same lines as bringing you know like bringing interest and learning more I think something that made me hopeful was people also bringing their solutions or things that they found that were helpful to them and you know that kind of it both goes internally like an integrist when people would share things that were helpful or share their successes and help I mean that we had groups you know kind of conferring with each other outside of our norm to help each other out with these things but then also having an external community of other data literacy professionals when I started the program about two years ago if I had not had some external support as well I cannot imagine you know having gone as quickly or even just feeling motivated because it is a little you know sometimes like some jobs it's a little bit lonely where you're like come on everybody so having an internal community and an external community that is growing I know we've kind of mentioned a few times that it's it's a growing industry it's a growing trend of you know topic of conversation especially with AI and AI literacy so just having that support was you know instrumental and and a key yeah so the internal and external community and you're right I can imagine that if you're sort of the sole leader of this initiative to have a set of people who can relate and empathize even externally has to be really powerful yeah so uh Christine one last comment here on what trend in literacy did you see this year that makes you hopeful um well echo everything that's been said I think the importance of a community is essential because the loneliness of a single change agent doesn't really set you up for success but the I'll speak on a macro level because I don't think that's been hit yet I think that the data science and literacy act of 2023 um the voluntary program at the Department of Education through the educational entities um that's sort of a Republican Democrat coming together to support law for data literacy I think is that's significant um I think an increase in awareness and education for educational institutions and online platforms and organizations offering courses and training programs and informatics programs popping up across the country is huge I mean I teach at the Washington State and University of Washington and um you know I love teaching and um you know having those courses and I you know I'm writing getting a book coming out in January to deal with um uh data awareness issues and project and change management uh in the importance of that you know that those things are are in demand I think is a is a huge sign of the of the need for these kinds of materials and and um in interest in this type of topic so those are those are heartening but to but to have this kind of stuff coming in law um I think is that that's even more interesting to me yeah so you're focusing a lot on momentum and institutional momentum yeah all the way to political momentum and so all of you were were sort of being momentum of some kind or inclusion more broadly whether that be um the community around you the people who are coming to you rather than being told and then this institutional momentum well I I thank you guys so much for this and uh Shannon I know we have a couple of questions so I want to make sure we have five minutes to let our experts answer what the audience wants to know absolutely thank you all so much this is a great discussion so uh just diving in here I want to get in as many questions as possible so this is specifically for Colette you know what could data training look like uh in an onboarding program um yeah that's a great question um I would say okay so part of this is going to depend on what kind of organization you're at you're going to have to figure out what is that base level that everyone at this organization kind of needs to be able to um be on the same page to borrow a metaphor here um I think like in the past things that that we have done are definitely just even explaining some very basic um concepts what and tying that into whatever services are coming out of our programs that are tied to data so if we're thinking about like you know data definitions what does that mean why do we care about those why are those important um by the way we also have a data catalog where we're keeping those things and you should be aware of that like kind of tying that into like those those kinds of real things um yeah and then the other thing that we we would do is that we um we would also have like specific training that would be okay well we had one that was optional for like you're really into this and you want to do like the next level up so here it is um and we would also have trainings that would be specific for people that were coming into roles that were tied to stewardship or um or ownership or trustee ship whatever custodian worth uh are you using in your organization um that would then be specific to those roles as well so I hope that helps kind of give some context to what some of that training could look like um yeah I love it anybody want to add to that yeah okay sorry I was trying to unmute do we still have time yeah we've got we've got about three minutes left yep absolutely okay yeah I think that's awesome to kind of look internally and see like you said what what people are interested in using um but then also you know identifying you know we kind of talked about the mindset of people and you know using the same language but then you know the skills that people need to be aware of um finding that solid set of skills that provide that that benchmark but then like you said too you know giving them room to grow in the area or you know in the kind of language that they need to know or the the analytics that they need to learn but I think one of the keys that I found was making it very approachable because a lot of times people come in and if they see a bunch of skills that are foreign to them it can be a little bit shocking so kind of trickling those in and making it really approachable perfect I just add something really quick to that I love that so much I agree so much I think like one of the guiding principles I always try and do when I'm thinking about training is like is this fun and like I know that can get like hokey but like it is really important like to kind of bring so it has to be totally approachable that way I so strongly agree yeah yeah and I even like made it I'm not going to sing it but a parody of like a a song and saying it about data literacy and it was so it was I'll sing it for you later okay I love that okay so you know there's a question about here in here about do you have an outline of data training module and if you have anything like that feel free to send it to me um and I'll include that in the follow-up email um I do want to slip this one last question and we've got less than two minutes here um with twenty four around the corner any steps that can still be taken this month to start or even enhance a data literacy program in the new year and I leave that open to anyone yeah so I always think that actually even asking the kind of questions that were asked in this in this panel like to yourself and thinking about you know your work in your organization is always a really good step that helps you take stock of like what is working where are areas that maybe there's gaps that I haven't thought about um and I think I always think of December as like uh and this is partly because I'm in post-secondary so December's always like you know the wind down before the next semester but it's such a good reflection period and that piece of reflection is really important for figuring out what you want to do next and so that'd be kind of where my immediate thoughts went perfect oh well that is bringing us to the top of the hour here you all this has been such a great discussion Wendy thank you so much for facilitating panelists thank you Christine Collette Veronica thank you so much for joining us today really appreciate it it's a lot of great great information that I know even I'm gonna take away I learned a lot to myself so thank you so much and just again a reminder to everybody I will send a follow-up email by end of day Monday for this webinar with links to the slides and links to the recording so thank you everybody I hope you all have a great day thanks for having me thanks everyone yeah thank you for the invite thank you bye