 Thank you Helena and I do want to say a big round of applause as well to the previous speakers. I thought they had a great session and I love that I get to continue in this idea of enabling people with the right tools in this case with data knowledge and data skills. Thank you. Welcome everyone. My name is... We're just going to remind our audience that they can browse into the platform to send you the questions through the chat and that you are the last keynote or the last talk before lunch, the lunch break. So you're going to have that challenge, especially after that fantastic talk we had previous but we're looking forward to listening to you Eva. So all yours. Thank you. Yes please do put your questions in the questions box. So yes my name is Eva Murray as Helena already mentioned. I'm a senior evangelist with Snowflake based in London, responsible for the MEA region and my background is data visualization, data analysis and community building. And I'm really really passionate about bringing people together around data and helping them build their data skills. And something I also care greatly about is helping organizations to get business value from their technology and also get their people excited about data and about building a data-driven organization and a true data culture. Now let's dive right in. If we want to make data literacy the foundation of our enterprise data culture, you might be wondering well why do we need to do that? Why is that important? So I want to turn this question around. Can we afford not to make data literacy the foundation? And what are the risks of low levels of data literacy among your people? Helena mentioned a couple of statistics around people not being confident in their own skills when they make decisions. So what if your people don't know how to work with data and what if they're maybe even afraid of numbers? I think that's actually quite common that people say oh I'm not a numbers person please you do that. And what does that mean for those people and for the work they're doing but also for the company that you're working in and for your customers that you're serving? So there are a number of risks that are associated with those incorrect decisions being made. Sorry with lacking levels of data literacy and one being making incorrect decisions because if we have people who lack the understanding of how to interpret data how to analyze it and how to draw conclusions from it it's much harder for them to then really work with their data and with information they're presented with and to come to the right conclusion and to make a decision that's correct. So in that way there's going to be biases and assumptions that find their way into the decision making process but that we should probably avoid you know we as a whole. So how can you ensure that decisions are made based on data and one of the really important things to remember here is that data literacy is important at every level. I think we as practitioners as people working with data we can sometimes get caught up in this belief that very senior people who have done their jobs for many many years and are very confident in their field and in their part of the business that they understand data they know what to do with it they know how to interpret it and how to work with it but quite often that's not the case. It might be fine for the part that they're responsible for but they haven't necessarily had training in statistics and analysis in computer science and those kind of subjects and if they had maybe it was many years ago that they really truly had the the peak of their knowledge when it came to data literacy. So while we might think that they're very confident it's just as important to help them build their data literacy as it is for everyone else in the business. So we need to ensure that data literacy exists at every level of the organization so that everyone can make better decisions no matter whether they're big important decisions or smaller everyday tasks that we have to decide on. Another risk is that people lack understanding so often in a situation where we don't understand something people are actually afraid to ask questions and the less data literate they are the more they should actually ask a question they should ask about this data and they should ask about the insights we're presenting to them until they are truly understanding what's going on but of course it's understandable that we as humans we don't want to show weakness we don't want somebody to think we don't understand so if we can't understand it and we're afraid to ask questions it's just a disaster waiting to happen. So if there is a lack of data literacy and we might not know how much the person next to us actually knows how much they've learned at university or afterwards we can't rely on people understanding insights correctly and that is a problem and it's not just about what somebody understands but also how they communicate information. So when there's a lack of data literacy it impacts how people communicate it and how they understand it so both sides suffer the ones who need information and the ones who share information the one person that sends out information and insights maybe shares a dashboard with the rest of the team they might struggle to communicate information clearly if they're not very confident in working with data and the ones that are looking at this dashboard and are trying to make sense of the numbers and the information presented to them to make decisions they might struggle to understand what's going on and how they should use that information for their job and for their project and then thirdly another big risk is that data is treated incorrectly so when somebody doesn't know how to work with data they might create incorrect calculations and treat data incorrectly if they don't know the difference between a median and a mean well that can lead to big problems right and also if they don't know what the right way is to visualize data they can also lead to a problem and I want to use that as an example because I imagine a lot of you have worked with data and charts and been presented with dashboards and reports so a chart that many people understand is a bar chart or a column chart it starts at zero and you see the bars and you can compare the length to see what the performance is for example sales of the product many of us also regularly look at line charts and the current pandemic that we all still find ourselves in I think I see seen us look at a lot of charts and the line charts show us over time how many COVID cases there are for different countries so we can look at them and most of us probably understand them but there are types of charts that are much harder to understand but that people use because and I kid you not they look cool they look great they look fancy and that is also for me in this category of treating data incorrectly because the way we present the data also has something to do with it one example is a box in whisker plot and a box in whisker plot is often used in scientific papers and I've you seen it used in business environments and I've never ever been in a situation where I thought this is the best chart type to use in this situation because it's never explained it's never annotated people use it because they can create it and they think it looks good but it leaves the other side the ones that are trying to get information from this data it leaves them struggling to understand what's actually going on they don't know how to interpret it they don't know what to do with it so at best they just ignore the information at worst they misunderstand it and make some wrong conclusions so it's important that analysts and anyone else who works with data to produce outputs like reports dashboards and analyses to support decision making that they're trained in how to do their work just like in any other profession there are so many tools out there today that make it quite easy to work with data to build dashboards and reports and to share them in the organization and that's great because it makes data much more accessible for people it gives them a chance to interact with data and use it for their work but still it's important that what we put out there is accurate it's correct it's reliable so we need to treat the data correctly and we need to ensure that those working with data and creating the reports know what they're doing quite simply so we need a scientific approach we need to treat the data right and people need to understand sign statistical methods so having talked about these challenges that probably at least some of them resonate with you what is a possible solution how can we tackle this problem of data literacy and actually turn it around the first solution step in my opinion is to focus on building a data culture and that data culture needs to be focused on enablement and professional development so building the right skills with any organization among your people so they can do great work and this focus on enablement and on data literacy allows you to build that data culture that is strong and that supports your organization so you're essentially building them both at the same time you drive education programs around data and you have people teach each other for example if they've been in the company for a long time they have a lot of business knowledge but they're also really good at working with data they can share that expertise they can share their experience and help others gain the skills they need and while you're building people's skills in this way you're also helping to make this emerging data culture visible because suddenly people are seeing things happen they see training courses they see maybe one-on-one sessions presentations there's much more happening around data and if people are able to share it then you're starting to see a ground swell of engagement around data that really helps you make data part of people's jobs which is important for building that data culture now your data culture is going to change and evolve over time so it shouldn't be the end goal to have you know this data culture in a box it's really that journey that you're taking when you're gradually having people increase their use of data for decision making and data just becomes part of every process they engage in now a second recommendation for solving the challenge around data literacy is to also include non-technical staff as I call them in this whole process so make the whole data culture inclusive for business people even if they don't have a technical or data background because they can contribute their business knowledge and their experience and different perspectives now I'm going to give you an example I moved to London a little over two years ago and when I arrived I needed a mobile phone contract so I went to the store and I sat down with the sales rep and they took me through the options and I decided on a plan and of course they had to ask me some questions about personal details where do I live what's my date of birth my account details all those kind of things those were important and of course I had to answer those questions so I can get started but to complete the contract he also had to go through really long long form of other questions or other fields of data that he needed to provide to close this process and he didn't want me to have to sit there for another half hour answering all these questions that are not really important for setting up a contract so what he did instead is he just entered dummy values so he could proceed on the screen so maybe random letters or space or punctuation and I sat there thinking the poor person at the other end who has to clean this data every time a new customer is added so that the analysis can still be done correctly and I just wish that those people would actually talk to each other and that's why it's important to include people who are actually doing the processes in the business so if the data analyst and the data engineer who are on the other end of the process of you know a new customer being on boarded were to talk to the sales rep in the store and actually ask them questions about why is all this poor data in the database why are they putting these strange values in and if the sales rep could tell them it's because otherwise the customer experience suffers and the customer isn't going to be happy to sit there for so long then they could find a solution together so maybe instead of sitting there and asking all these and you know asking me all these questions and populating the fields correctly which takes a long time they just do the bare minimum complete the contract and then send me the customer an email afterwards that says hi either would you be happy to share more data for a five pound voucher or a 20% discount on your next bill so put the effort on the customer instead reward me for it but get good quality data and that's why I think it's really important like I said get those people together have the business people involved even if they're not dealing with data they probably are but might not consider themselves data people or be part of the analytics team but get their input on what's working and what isn't so you can find better solutions together and have more perspectives on what's going on in the organization and then the third step is to apply a framework and I have brought a simple framework that I like to take people through to figure out how to establish this data culture and data literacy so let's go through three simple steps when you start embedding these new ideas how do you want to go about it first step is to plan and prepare and it's really important to figure out where you are today and that is a very honest and detailed look at what's going on today what do you have this could be tools systems programs the people their skills the processes where do you want to be when what does success look like and what's to get in between so if you want to have a strong data culture that's quite vague what does a strong data culture look like what does using data for decision making look like for you and for your organization does it mean everyone uses data for decisions and also are they empowered to make those decisions or do they still have to give them to their manager to make for example and what are the timeframes you're working with what are maybe some budget constraints you're working in what skills and people do you have internally and are you hiring expert experts externally or do you want to build up your people internally and grow their talent and their skills then we move to step number two and that's to apply and execute so in this phase you would take your plan and you know your your goal where you want to go and find activities that support this goal some of you might know that for well over four years i co-hosted the social data project make of a monday and our goal was to help people become better at visualizing data and analyzing data and what we did as an activity was it's kind of in the name make of a monday every week we would publish a data set individualization and the challenge for the community was that they would take the same data and create an improved data visualization that tells the story in a better way so what worked really well there when it came to the activity is everyone was self-directed it was a free project they were not our employees they could participate if they wanted to everything was voluntary they could participate if they wanted to and they did because they wanted to build their skills and it in terms of the format of the project we had a very repeatable format it was scheduled it was predictable and it was actually quite simple every monday they could participate but they didn't have to do it every week they could come every two weeks every three weeks whatever worked for them but they would know that if they miss one week because they're busy at work or busy at home they could just join again next week so knowing that it happens on a monday was something that really gave us a chance to get a really big community going because it was just so simple so what i recommend if you want to establish some activities internally to build your data literacy among your people is to create a system that is very simple and easy for people to understand for example if you want a weekly activity and i do want to warn you it is quite a commitment you can start with something simple like office hours let's say you use a specific bi tool internally and you want your people to learn how to use it better or to maybe just have a chance to ask some questions to someone who is um yeah has some expertise in this tool maybe you have three different people who are really good users of this tool and they know a lot well between the three of them they could rotate so one does this week one does the next one does the third week and just for one hour in that week ideally always on the same day they are available via slack or teams phone maybe in the office if you are working in the office and people can come to them and ask their questions get their answers and move on so that way you're facilitating this learning process is not a sharing process very quickly easily and simply and an hour of someone's time every three weeks is probably not too much to ask you can also think of other activities that are less frequent maybe it's every fourth night or once a month you have lunch and lunch sessions once a quarter you have an internal showcase maybe once every six months you have an internal conference all those kind of activities that bring people together around data but they can share what they've built what they've created what they've done but also they can learn from others is a great way to engage people and to make them feel like they're making an impact and they're part of something bigger so once you've planned what you're going to do and where you are today and you've figured out the activities you would like to put in place it's time to then actually make them happen but also really importantly monitor what's going on and measure the success of what's happening so how are the outcomes and the outputs of data and your organization improving because people's data literacy levels are increasing and how do you define success and how do you measure it and I recommend finding a good balance between some of the more qualitative measures and some of the more quantitative ones so with this being said we have looked at our risks and we have looked at some solutions for addressing them what can you actually achieve well it's about equipping your people with tools and skills but it's not just about delivering training you're really giving them the skills to do that job to do their job well and be successful and you're taking away the fear of data they might have you're enabling them to get better at their job and to drive growth and innovation and if you can drive that within one person you can drive it in the company as a whole and you're helping people gain confidence and independence when they make decisions and I think that's something really empowering and really beautiful to have in your organization that people are more confident in the things they're working with and companies that are training their people they find that people gain skills it makes their jobs more satisfying it helps with employee retention and as a result also better decisions are being made because people just know more about what they're doing now if we go back to our make of a Monday example people learned by practicing regularly coming back week after week to build these visualizations and also they got feedback they got feedback from their peers and they got feedback from us as the hosts of the project it also meant that they could see every single week because we did this all via Twitter what everyone else created so they found a lot of ideas they found hundreds of visualizations all from the same dataset hundreds of different ways of telling a data story and that gave them a lot of inspiration and they gained the skills and what they did with those skills is they didn't just use them for making Monday they applied them to their own jobs and a lot of people actually found that they got promotions because they got so much better at visualizing data maybe they became team leaders maybe they made their company actually stand out a bit more because suddenly there was this person becoming an expert in data visualization and they also became unknown in the community and built their personal brands and achievements around using technology and using data to tell stories something else you can achieve is that you can find data champions internally that means identifying the leaders that are inside your organization and letting them teach and guide others so you might have experts in specific tools or statisticians who are really really interested in helping others learn and sharing their expertise with them and what you then as a company and as leaders need to do is to support them in building the leadership skills because they have the technical expertise already and they have that hunger to make an impact and help others they might not have been receiving any training on how to actually lead others and how to teach others and that's something where you can really empower and encourage them so they can pass on all this great knowledge and those people are really critical because they will drive adoption so for example adoption of software tools but also new processes and changes and they will help you drive that change to become a more data driven organization and to help drive the data culture being established and companies that nurture these data champions they find they're more able to build really effective data and evidence communities internally develop stronger data cultures and they find it easier to navigate change and one example is St. Joseph's University in Philadelphia I've worked with them and they have students that are studying analytics courses, masters in analytics for example and the more senior students who've gone through the course but who've also acquired skills in certain technologies for example Chablow and others they act as mentors and teachers to the junior students so while they're teaching they're developing their teaching leadership skills but also they're helping junior students pick up new skills that they need for the program and these senior students have actually established a comprehensive training program including office hours including make up a Monday sessions and other things as well and you'll be able to achieve the delivery of better insights because as you have more people being data literate and being involved in the process more data is being used as inputs you have more ideas coming together you have more perspectives around the table and more diverse ideas from all these different people the technical business people the more experienced the more junior people so there's less reliance on gut feeling which is great but also every decision can then be traced back to specific data points so you can assess afterwards well was this the right decision and hindsight and if it wasn't you can actually figure out why you came to a wrong decision and then fix that process and the organizations that involve employees from across the company in these processes through you know specific groups and teams they combine the expertise around data and the business expertise and experience as well and then finally you also achieve some improved outcomes because data literate team members help improve overall outcomes for example creating better products for your customers better services improved services but also new products that weren't even there before because maybe they found something in the data that indicated to them well hey our customers are actually asking for something we're not offering yet and speaking of customers data literacy really helps us all become better understanding what the customers want what their needs are and their preferences and then delivering better internal products as well that enable us to really figure out what is going on and the companies that focus on using data literacy to improve these outcomes what they achieve is better analytics and reporting to help communicate information because the people that are creating these reports and dashboards and that share their findings they understand what it takes to really communicate data information and make it accessible to the different audiences so what are three things that you should do next you've probably noticed I love doing lists of threes and I think they're just fairly catchy and easy to follow so let's look at three steps you can take next the first one as I mentioned is assess where you are today and be really honest and it can be a bit painful because maybe things are not as great as you thought they were but it's good to know where you're starting from so that you can figure out how to improve what it takes you know what the next steps are etc and then find those champions because doing it all by yourself is going to be a lot of work and just quite difficult unless it's your whole job it's going to be near impossible to get all of this going just as one person so identify who the people are that are already passionate about data that maybe are already loving to teach others and share their knowledge and share their experience but also we have built a good network internally with the business and you know with different parts of the organization and then test and implement so when I say test what I recommend is with those activities that I've mentioned that you can use to get people involved and to get people empowered and enable around data test them out if it is a weekly office hour session do four of them do office hours for one month and see how they go ask the people who've attended and ask the people who post the office hours for their feedback how you can improve them what they need um what they maybe don't need or what they don't want so you can improve it before you then implement but also remind yourself that it's not about perfection it's not about nailing it the first time it should be something that evolves and that grows and these champions will help you because every one of these people will bring their own unique style and approach in their way of doing things so it doesn't all have to be perfect right from day one and that's why it's so nice to test things out implement what works discard the other stuff and then also work on new ideas because things will grow and other people will probably want to get involved as well so with that I'm at the end of my presentation and we have time for questions from the audience thank you so much Eva it was fantastic it's not easy to do it before the break the lunch break or the breakfast break or the dinner break whatever you are in the world in your case I have to say that if you're looking at Eva's background you see all these medals we have to tell them the truth Eva she she is or she was at triathlete now she I think she prefers cooking and just doing the bike training but she's a fantastic cook and a fantastic athlete as you can see all these medals from her past triathlons many triathlons right Eva is this correct that is correct yeah I did um I did a few triathlons I actually did a race on Saturday my first race in two years because of COVID so I'm now focusing more on running because it's nice and simple it requires less things than that's for sure that's for sure it's a tiring job anyway well congratulations on that and if you want to follow Eva and has nothing to do with data but she has apparently a very nice website I just discovered Trimi Greens right for cooking and baking is that that one I've actually I've actually stopped that one I have tried my data so it's trimydata.com I did have tried my greens for cooking but I I just ran out of time because there was too many things too but I do I do talk about cooking anyway so that's lovely we all we can all relate to that but let's talk about data and they ask you if you could extend a bit more on hashtag makeover Monday the project give us a bit more details because it could be used also as a tool and it's also a book so could you develop a bit more on that yes absolutely so the make of a Monday project emerged from something so Andy Kregel started it and he just did it like as his own practice he said I want to become better at using Tableau how can I do that well I'm just going to create you find some data on the internet and the visualization and then do a makeover and that's how he practiced and then it evolved so eventually in 2016 him and Andy from Tableau asked the community to participate it became really popular and I took over in 2017 so Andy and I ran this project together for the next four so years and really enjoyed growing the community and we our mission was to improve the way we visualize and analyze data one chart at a time and what I loved was that we always had new people joining and we had existing members helping us help others because we did this as a hobby so there's only so many hours in the day but when you have a community and this implies two communities in organizations as well when you have a community of people who are really passionate about something for example data and teaching others it makes such a difference because if somebody has a small question it doesn't need to be directed to me if they just want to know a very quick answer to a calculation probably anyone could answer that so it's really about removing this reliance on the leaders the official leaders with the title and saying hey everyone here can contribute something everyone here can help you out and it means that people automatically go into these roles of being teachers and what I've noticed especially through make of one day if you're the person teaching suddenly you have to assess whether you really understood what you're talking about because if you're trying to explain something to someone else you have to understand it first and that's the really fascinating thing for me there and we stopped the project this year because we just kind of ran out of steam with our day jobs but also we felt that it's time for someone else to come up with something new it was a long-running project the data sets are all still there so people can still engage can still use the data sets they're all freely available from the internet so we didn't use any confidential data it's various topics from politics economics environment animals sports health anything and it's just a great way for people to apply their spills and while we had most participants from the tableau community because as a tool it makes it very easy to share your data sorry say your visualizations we it wasn't exclusive to that we have people with power bi google data studio we had people from yellowfin micro strategy anyone can participate so if people are looking for sample data sets just go to makeoveroneday.co.uk and you can just do a bit of experimenting there fantastic Eva the power of the community is incredible and maybe i'm not maybe for sure covid has made us realize that the communities are even more important for all of us because we feel more isolated so we tend to gather find more support in any sense in the in these communities they're also asking regarding tools and references well it's a double question can we build data can we build data literacy oh can sorry can building data literacy be fun and are there any apps you can suggest so yeah i i definitely think it can be fun and it's really up to you to make it fun and that's why i love looking at quick wins because if you look at all the things you have to do or you want to do something will jump out to you and you'll be like oh i want to start with that why not start with the thing that's most fun and a food analogy is that i i used to always eat the thing i didn't like first i would have the best thing last and my dad said if you always eat the best thing on the plate first and you're always eating the best thing until the meal i finished and i thought that's actually really smart so while in the world of work there will be things you have to do that you're not necessarily always passionate about or really excited about make sure there's always a project or a way of doing it or an activity where like yeah that's really fun and for me doing make of a monday while it was extra work you know the afternoons in the evenings i just also loved seeing other people grow and engage so it could be that for you it's a specific topic topic maybe you are really really passionate about football so use football as a way to work on data literacy whether if it's for yourself well maybe it's football data sets it's analyzing results from games that help you figure out how to use a piece of software or how to figure out certain calculations and it's in the organization and people say we don't all want to do football well maybe you have maybe every week someone else gets to pick a topic and they provide a data set or something that you can all work on on a specific problem uh i think it's really you have the freedom i give you i give you permission to make it fun it shouldn't be just lecture style or you know all these statistical analysis which can seem very daunting i think if you can think of a way like oh i would really like to do this find a way to connect to data find a way to you know while you're having a really nice meal figure out well what what role does data play here and whether it's where you ordered your groceries and okay what does the supermarket know about me the supermarket will almost suggest certain types of chocolate or coffee to me well there's data in there and maybe that's a little research project you do to see how do they actually do that what are the algorithms behind suggestion engines so it's finding the interesting things in the everyday life connecting them to data and then using that to help you build your own knowledge and skills and in terms of apps um i guess you're talking about mobile phone app i haven't come across one yet doesn't mean it doesn't exist but there are quite a lot of initiatives out there around data literacy and if you just do a quick google search the first page will probably give you the top ones excellent well we run out of time Eva but i'm gonna leave you with this sentence of yours i love it i'm gonna say building data literacy is fun it's up to each one of us and as a suggestion eat the best beats on your plate first i always do so by the way just in case so uh eva marie thank you so much for joining us from my snowflake obviously it was lovely listening to you we hope to see you very soon otherwise next year until then i say goodbye to eva marie and uh thank you for coming to big things conference 20 bye bye