AI and the Future of Data with Dr. Julie Alig, The Data Diva

[00:00:00] Brian AI: Are you not sure how to feel about the way AI is suddenly everywhere? AI for Helpers and Changemakers is a show for people who want to do good work and help other people. Whether you’re already using AI tools and loving it, or you are pretty sure that ChatGPT is the first sign of our downfall, we want you to listen in and learn with us.

[00:00:22] Your host on this journey is Sharon Tewksbury Bloom. For 20 years, she’s worked with helpers and changemakers. She believes that we’re about to see the biggest changes in our work lives since the Internet went mainstream. We’re in this together. Join us as Sharon interviews people in different helping professions, navigate what these new technologies are doing to and for their work.

[00:00:44] Julie: Welcome, Julie. Thanks so much for coming on today. I would love it if you would just introduce yourself to our listeners It’s my pleasure to be here. Thank you for the invitation. my name is Dr. Julie Alig. I go by the moniker, The Data Diva. And, how have I come about, learning about AI and using it so much? I learned way back in graduate school when I was getting my doctorate, I learned a lot of, quantitative tools and machine learning and a lot of those are subsumed today into what is referred to as AI.

[00:01:27] And so I’ve been doing this for a while, but. The difference is the generative part of it. So the generative AI, that’s very new, and I’m right there with everyone else learning how to use Claude ChatGPT Gemini and others.

[00:01:49] Sharon: generative AI is really the new kid on the block. It’s the exciting thing that’s bringing in a lot of new possibilities, putting all of us trying to learn as much as we can. How is generative AI changing what’s possible right now with data and with your work?

[00:02:09] Julie: It’s the sort of thing that it’s such a change and such a transformation that I don’t even know how much of a change it’s going to end up having been, if that kind of makes sense. I heard, I can’t remember who said this, but it’s like the internet, sure. It’s like electricity. When electricity was discovered, people had no idea, they couldn’t even conceive of what would be possible. It was that profound a transformation. I think this is going to be on par with that. I also think we are very far. away from that being our reality. right now for where generative AI is, there’s still a lot of hallucinations. And you still cannot expect 100 percent veracity, or even sometimes 50 percent veracity from things, if you’re asking it to create subject matter or something like that. What it is really good at is, and what I’m finding myself personally and my clients are finding, is being able to automate those rotes. tasks that we do over and over that especially with, no code or low code, things like, like Zapier or make or something, those are really going to help. that’s one of the big ones. I think there aren’t enough people who really are aware. of all the possibilities going on with that. And again, going back, I think there’s a part of this that, that is the new toy, the shiny new object thing. But I’m also hearing a lot of people being intimidated by this because again, it’s math and it’s robots, right? a lot of these no code, low code solutions, I think once they become a little bit more broadly used, not just at the enterprise level or the Gen Zers or, and Millennials, I think people are really going to be surprised at the efficiency and productivity gains that they get.as far as me, there are are people out there, some of the more well known people at the cutting edge of research on AI, the generative AI, who are coming up with these examples of having in, sent the generative AI a data set and told it to analyze it and it’s come back. And I, it’ll come back, sure, I, I don’t trust it, farther than I can throw it and not even there.

[00:05:22] And I think people are, that is rather dangerous right now. So when I use it in my day to day work, I’ll use it. I use, I code. To do my analyses in Python and in R, and if I can’t remember the code for something, I’m able to say, I’m trying to do this, give me the code in R, and it usually gets it right. It doesn’t always. It doesn’t always get just plain old coding correct, much less interpreting what the output says. I don’t use it for that at all. Even though there are other people who are out there saying, Oh, it can analyze your data for you. It can not. It’ll be great once it can, but for right now, you still need to have that knowledge and understand what a p value is. Understand when you tell it to handle the missing values, in your data set. And it says, Oh, great. I just got rid of them all. Or I did. pairwise deletion. Why the heck did you do pairwise? There’s nothing paired in here. We’re not,that sort of thing. So some of the more detailed sort of things, you really need to know and you need to be checking. But, so as far as in my day to day work, helping me to also, craft proposals End for my clients for the kind of work that I do. I have done enough of them now after five years that I know what I want to say. I know what the important points are going to be and I am very good at using the prompts. to be able to come up with something much more quickly than I could if I were just working by myself. I think one thing to keep in mind is,

[00:07:22] and I hadn’t even realized this until a friend of mine mentioned it, was, a lot of people think that these generative AI models are like Google, where you want to put in the fewest amount of words and they’re 180 degrees. You want to be as rich and detail filled, providing as much context as you possibly can in order to get back something really useful. really getting used to being quite wordy in your prompts and quite directive, giving it examples, so zero shot or one shot or many shot prompting. those are going to be the key, for others as we all get used to this new tool. But those are the things that are really helping me the most right now.

[00:08:20] Sharon: Yeah, I think that you’re echoing something I’ve heard from other guests, which is that right now it feels like it’s really great as an assistant for you when you know how to do something and you can give it clear directions and you can check its work. that’s something that it can do well in terms of being able to speed up your own process or help you with some parts of it that are already pretty well dialed.

[00:08:47] Or even as a thought partner, one thing that I haven’t tried, but now I’m curious to try would be instead of giving it the data set and asking for it to analyze it, I’m curious if I could give it the data set and say, what are the types of analysis I could do on this data set? like what kinds of, questions could I answer or, so it’d be fun to cause I do the same sort of thing in my writing, where sometimes I’ll say, okay, this, these are some ideas I have.

[00:09:20] Could you offer me five different metaphors that I might be able to use to take this idea further,and use it as a thought partner. So it’d be interesting to get it to just. Remind you of Oh yeah, have you thought about presenting it? It’s this kind of graph, or have you thought about, looking at this?

[00:09:37] because I have actually, I have put a data set into chat GPT and said, can you tell me, some basic things about the data that I knew I could, check the answers because I was like, will it be able to pull this information out? It was accurate, but I was asking it very simple things in terms of like, how many people on this list are from Arkansas, or convert this to a table where it shows what state, how many people per state, things like that.

[00:10:08] So it could do some very basic things. and that is something that I hope will be possible in the future, is that for people who just want something basic, take my spreadsheet and turn it into a table that I can put in my report that shows the same information and a slightly different visual. It will make the barrier to entry for that small amount of use of data better, so more people feel more comfortable with it.

[00:10:33] But right now we do have to be able to check the work on everything.

[00:10:39] Julie: Everything. Yeah, I hope we’re there, too. I really do. And yeah, the same thing, I tell people, treat it like a summer intern. They’re really raring to go. They want to please you. They do not want to admit they’re making mistakes. And you have to check everything. But they’re very nice and they’re very, yes, exactly. But, but just with that, though. Just with what you and I have talked about, the possibilities, the next year, I just, I can’t even imagine what’s coming down the pike for us. So that’s why I remain optimistic, cautiously optimistic, because we haven’t even touched on all the ethics and the bias around all

[00:11:27] of this. but cautiously optimistic, with where this is headed and how it can help us in our day to day.

[00:11:37] Sharon: Yeah. I think the huge amount of variety of ways that could be used in the future. One thing that’s exciting, and I guess I’ll save this and then pivot it to maybe a final question for you, which is I have been really enjoying learning about ways that people are combining technologies right now.

[00:11:56] They’re calling it multi modal. So for instance, a,robot math tutor who’s able to have the student hold up what they wrote on the paper and they can actually, Take in that image and know that the student wrote two plus two equals five and then know that’s not correct and be able to, prompt back.

[00:12:21] So being able to combine multiple technologies, Is something that just infinitely increases what these tools are capable of. I’m curious What’s something that you’re following closely or really loving learning about right now that’s on the edge of, what you’ve been hearing about that you want to learn more about?

[00:12:42] Julie: Definitely the multimodal is on my radar. I was at a, at conference last month down at MIT about AI. and it was all over. It was all over the place. So it’s an area I have not been paying a whole lot of attention to prior to that. now I’m thinking more. And it’s just, you think of these as a language model. And so you think language, written. But you’re not thinking about visual, oral, all those others. And it’s just. All of a sudden, it’s like you’re walking into a completely new room in your house. it’s I had no idea this was here. This is cool. I always wanted a room like this. so multimodal is definitely one of the things that I’m keeping my eyes on. And then what I was talking about earlier, the low code, no code solutions, there’s a lot more of those that are coming.coming across my radar, I think that if we’re going to be using these tools in anything beyond something like write my 500 word essay or, something like that, I think those are going to be one of the ways for people to really be able to reap some serious benefits. And then I’m always keeping my eye on what’s going on with the data analysis. It’s just not there yet. It’s just not. but I think it will be. So I’m very eager to get some, some good evidence of that.

[00:14:29] Sharon: I heard someone lamenting the fact that they had found that the tools were only as intelligent as a human three year old. And I was like, wait, I’ve met three year olds. Some of them are very intelligent. that seems like we should be thinking, Wow, they’re already as intelligent as a three year old and we’re just getting started.

[00:14:50] Julie: No, I’m with you on that.

[00:14:52] Sharon: This is your host Sharon Tewkesbury bloom. And I just want to pause briefly to say that we really appreciate that you’ve been listening to AI for helpers and change makers. I am supporting organizations who are trying to figure out how to utilize AI. Through my work at bloom facilitation. So, if you are interested in how to bring new practices into your workplace, I hope you’ll reach out to me through bloom facilitation.com. Now for more. With my conversation with Julie. 

[00:15:25] many listeners, feel like AI is brand new. They’re racing to catch up. They feel like they just heard about these tools. but I’m a history major by original discipline. And so I love Figuring out, how did this evolve? Where did this come from? What were the steps that came before this? And so I’ve also learned a bit about the different waves of technology advancement in this area. And like you said, machine learning is one of those. so can you give us maybe a little bit of background about what you had studied. 

[00:15:59] Sure, it’s interesting you mentioned you were a history major. I was a French literature and history double major, and look at where we are now, doing all of this techie stuff. my head is in numbers all day, every day, and I love it, machine learning has been around for a while. it’s a way of telling machines what to do. similar to something like computer programming. If you think back to the early days of computer programming, you would, have punch cards that would get fed in and whatever you told it to do was output. Machine learning was a step further it was more descriptive, more prescriptive to the machine about what you wanted it to do. And so the models were. What I got in when I was in my doctoral studies was with the quantitative analysis part of it, which is heavily influenced by statistics and econometrics.

[00:17:04] Julie: So yeah, not everyone’s going to have any idea what that is. It’s basically using computers, using machines to help find patterns. in a very high level way what machine learning is about a part, under the umbrella of artificial intelligence, that is one very well defined field or subdiscipline that feeds right into everything else that’s going on today with those generative AI. Because what is generative AI? It’s actually generating. So traditional machine learning, typical machine learning is, it’s not generating anything based on what I tell it to do. the generative part of it. is like we see with chat GPT, with Claude, with some of these, the language models, right? It’s actually generating paragraphs, output, conversations, that sort of thing.

[00:18:16] So you can get into that much more fluid back and forth. That’s not typical machine learning. how does it generate all this? It goes back to prediction. these large language models hoovered up all the text from the internet, running equations and algorithms to figure out what words were most likely to be associated with other words. And so once you know that, then you can start predicting what the next word is going to be and So it’s using that high level kind of thinking of like machine learning But it’s putting it to use with predicting words and the there’s more to it But that’s basically how they’re tied together.

[00:19:10] They build on top of the other.

[00:19:14] Sharon: that was really eyeopening to me. I just completed this MIT executive education course around AI, and I didn’t realize that so much of this is statistics. So much of it is based on probabilities and on being able to notice and learn from patterns, then recognize patterns, anticipate patterns. I was not very good at statistics, I’ll admit.

[00:19:41] I was really good at algebra. I was really good at other kinds of maths. Statistics was the bane of my existence in high school. to know that these tools I’m excited about are based in statistics is amazing.

[00:19:54] Julie: Don’t give up hope. My first time with statistics in grad school was nothing to write home about. it wasn’t, absolutely. It’s a different way of thinking. Sure, it’s math. I’m good at algebra. This is just a different way of thinking, just like calculus is a different way, and you got to wrap your brain around that, and it doesn’t always happen neatly within the confines of one semester but I would encourage you, or anyone else out there who’s curious, keep at it, and you’ll be surprised by how you’re able to grasp things.

[00:20:34] Thank you. Much more than you thought. That’s what I tell my students, too. And it works. It works. They do get it. But, yeah, it’s all probability. These generative AI, large language models, it’s all probability. And that’s wonderful, but that’s also something that we need to keep in mind is a downside. And it can be dangerous.

[00:20:56] Sharon: Because the same sorts of things that, The same caveats that I have to talk about when I do forecasting and those sorts of things for my clients and, in my own work, I only have the past data, the history, to base it on. And the same thing with these large language models. They’re not necessarily built. or good yet at conceptualizing something that hasn’t happened. And so they also can fall into the trap of almost seeming like they have blinders on because it’s simply the data that was fed into them. They’re not able to think outside of that, at least not yet. And that’s where, humans, we really have a leg up on the machines, And I think for something relatable for a lot of people in terms of how these tools and algorithms were originally brought into many people’s lives is something like Netflix and how they recommend movies to you and how, based on your own viewing history and maybe what you spend a lot of time looking at in the previews, they start to recommend other movies.

[00:22:17] And, I do feel like I fight with the machine sometimes where I’m like, Don’t put me in a box. that’s not the only kind of thing I contain multitudes. I like sports documentaries and I like cheesy romances and I like, Nazi Germany history movies, because that was my, studies growing up.

[00:22:35] So I’m sure Netflix is very confused by me, but it’s also a great example of how, the machines and computers are trying to make sense based on the data they already have. And they’re trying to notice patterns and predict things, but they can only work with the data they already have. They don’t know that I’ve gone through some metamorphosis and I’m suddenly interested in something brand new that I’ve never shown any inkling towards before.

[00:23:05] exactly. They’re advanced pattern recognition, trend recognition tools, if we think about it that way. Think about on your YouTube Timeline and every so often you’ll get something that pops up and says, do you want something completely different? And that’s exactly so YouTube, at least this is what I’m assuming. They’re trying to be able to pull us out of that. What might be a trap almost not a trap, a dead end, maybe. Maybe. Yeah, I joke that my dad has reached the end of YouTube on his subspecialties because he has some very narrow interests that I think he has watched every single thing 

[00:23:50] Julie: Yeah. 

[00:23:50] Sharon: in that. If it’s about earth moving machines and it’s been recorded by a bearded white man, he has watched it. So,and he knows because I make fun of him about that, but All right.

[00:24:03] So you are the data Diva. At some point, I’d love to hear if you gave yourself that title or if someone else gave you that title. And then I’d also love to hear how data, what you think people who don’t study data all the time need to know about data to, live in this world today with how it’s being showing up in everything we’re doing.

[00:24:28] Julie: Mhmm. I’ll take that first question first. my husband came up with it and he came up with it years ago. back when, I was in one of my full time jobs. And, when I went out on my own, doing my own consulting, my analytics consulting, at some point I just jokingly said that in a networking group and people really liked it.

[00:24:53] And I was like, Oh, I guess my husband was right. so it comes from my husband, my

[00:24:59] better half, I call him, and what do people need to know about data?um,one research study recently found that 93 percent of Americans had some sort of math anxiety. And at least in the business circles, the business ecosystems where I am right now, a lot of times data equates to dollars and cents. Equates to something that my accountant does for me, or my bookkeeper, or my CFO. They handle that so that I don’t need to look at it. And, my question back usually is something like, You realize that mountain of data has gold in it. And your accountant or your CFO, they might be looking in certain places, right?

[00:26:04] For certain things so that they are able to create your profit and loss sheet, right? Your quarterly reviews. There’s a whole lot more in that mountain of data that if you had a different tool, like some statistics, you be able to mine that for gold and diamonds and emeralds. And I’ll usually say something like that. And then I’ll give a couple of examples about because a lot of people think my business doesn’t have any data. And I have clients that all they had was their financials, I was able to find underperforming profit, profitable, offerings, I was able to help them to untangle, what was going on with customer retention and why having their sales team just continually bringing in more new customers wasn’t moving any needles. And so with the data and the kinds of tools that I use, these more statistical tools, I’m able to say why these things are happening. And once you know why they’re happening, then you can start fixing them. And ultimately, businesses want to stay in business. They want to be profitable. They want to be sustainable.

[00:27:35] And many businesses, business owners, want to be able to have a legacy. And If you’re just drowning in red ink and you don’t know what’s actually making money, what’s actually driving your customers to your competitors or to just leave, which marketing channel is actually working, you’re never going to be able to achieve that.

[00:28:02] Wow.

[00:28:07] Sharon: fighting the good fight on that. it made me think of this business that I know about, where I actually had one of my first jobs ever. It’s a locally owned, family owned retail business. They just celebrated 50 years in business. The daughter was able to take over ownership from her father.

[00:28:30] So she’s now looking to the future of the business, and she cares a lot about sustainability. And so not only is she interested in continuing to run a profitable business, but because they do have this stable foundation, she’s now thinking, how could we do even more for our community? How could we do more for the environment or have a lesser impact on the environment?

[00:28:58] And I’m curious beyond just the numbers and cents, what other ways might there be gold in those data hills for someone like that who’s looking at that sort of, what do they call it? Triple bottom line business want to do good for customers, the environment and their business.

[00:29:19] Julie: I’ll go back to what I say a lot of times to the skeptics who say, my bank balance is fine. My employees are paid. I don’t have any fines from the IRS. What do I need to worry about? And yeah, you don’t need to worry about anything. if that’s your goal, then you’re right. You’re good. If you want to actually do something and you want to, be a good citizen in your community, you want to be a good steward of the environment, anything like that, you’re not going to be able to if you don’t have a solid foundation of what your business is doing wrong, what you can improve on in those. it’s all well and good to sponsor,Arbor Day or something. Is that really making a difference? And if your business isn’t making a profit, are you going to be able to, sponsor any of that? If your employees aren’t engaged, healthy, both mentally and physically, fulfilled with their jobs. Are they going to be willing to, even if you pay them for the day, right?

[00:30:48] Yeah, and maybe part of what I was thinking is. If you don’t know, for instance, your energy usage as a business and how that’s also related to your costs, like how much are you paying for heating and cooling and, that’s often a great place to start with something like improving your impact on the environment because there could be that win of this could lower costs as well as be able to use less energy.

[00:31:19] Sharon: But those are data. those are sources of data of being able to track what are your energy uses? How does that relate to your ability to run your business? Are you staying open later hours because you want to run a promotion, but then does that cost you more in energy or, other things?

[00:31:36] So it’s just interesting all the layers that I can see of potential data inputs to that.

[00:31:43] Julie: Yeah. And if you’ve also got a solid aim in mind, it makes it easier to convince people to track your data. So no one likes tracking.no one likes tracking anything. I don’t like tracking, did I brush my teeth this morning? Did I brush it after lunch? Anything like that. And I see so many in our space out there that almost like admonishing. business owners and business professionals. Like you need to be tracking this. You need to be tracking that. Coming up with these checklists, these laundry lists of things to, to track. And I’m like, yeah, sure. We can track everything from here to the moon, but does it make a difference? And is it going to really, move the needle on the things that our business needs to? If instead, like what you said, Sharon, We have a commitment to reducing our energy usage, then boy, someone is going to be on top of that, figuring out what it is on a day by day basis. today when we’re recording this, I’m in the Northeast and it’s very hot, unseasonably hot. Energy usage is way high. What if I’m a business owner? If I’m a manufacturer, what am I going to do to offset? all this extra energy that I needed to expend or buy today. I have no idea, again, if I’m not tracking. So it’s a nice way also to really make it clear to the members of your team. Like this isn’t just putting numbers in boxes. We’re going to do something with this and you’ve got a role to play. Yeah.

[00:33:40] Sharon: three possible barriers or hesitations people might have that there’s math anxiety that keeps people from digging into their numbers. Perhaps they might be skeptical about the benefits they’ll get from actually collecting tracking and using the data and then tracking itself can be a chore.

[00:34:02] is there anything else that you’ve seen? that kind of keeps people from diving into their data and getting the most benefit out of it.

[00:34:13] Julie: People don’t draw enough pictures.and by that um,what’s the lingua franca of business in the world, not just the United States, when we think about numbers and data. It’s Microsoft Excel, Google Sheets, something like that, right? And what is that? It’s columns and columns of numbersand rows and rows of numbers. And if you don’t put the format in then it’s all those ugly, number signs in there that some people have no idea how to get rid of. And it’s just, it’s overwhelming. even me, I don’t know about you, but even to me, some of these are like, I just feel like I’m being drowned in numbers. And I can’t blame anyone else who’s, doesn’t have fun, like me, looking at numbers. I don’t blame them for not being all gung ho about digging in and what have you. The one thing that, I use with my clients, I use with myself, I use with my students is draw a picture, make a graph.doing that, the human brain, just the way that the human brain is hardwired, it understands and perceives visual stimuli much more quickly and in a lot of ways much more completely than other types, than, looking at numbers on a spreadsheet, or even hearing things. and so sometimes, most of the time, the first thing I do is start making, drawing pictures.

[00:36:01] I make bar charts, scatter plots, and line graphs. Those are the three that research has shown the human brain is, best at perceiving with a very high degree of accuracy. And so bar charts are not sexy. They’re not necessarily fun, but if you’ve got like several hundred thousand rows of data and we’ve all been there, you can use a bar chart and it will start cutting through all the noise and it’ll start showing you, Oh, that’s what’s going on. And then you can go from there or making a line chart or something. honestly, I really do think making an Excel makes it easy enough, to simply insert and it’s got, a couple of point and clicks and it’ll make them. It’s a completely separate topic if they’re actually necessarily any good, the defaults that are in there, and that sort of thing. But at least it’s a way to cut into the data and start to see what the heck is going on here.

[00:37:12] Sharon: Yeah. I have two follow up points on that. One is also, sometimes it’s not until you try to make that picture That it requires you to really ask yourself, what question am I trying to answer with this data? Cause you are asked like, what do you want to see on those bars? What is your X axis? What is your Y axis?

[00:37:39] What do you want to see compared to what? and I know I recently was working with AmeriCorps programs on recruitment. And one thing we often ask them is, where are you getting people from? Where do people find out about you? That’s important to know, but a lot of people have stopped at that level.

[00:37:59] So they’ll just have maybe a pie chart that shows, okay, we got this many through Indeed, this many from a referral from a member, this many from a job fair, et cetera. But then I’ve asked them, okay, that’s one piece of data, but is that really what you want to know? Or can we go deeper and say, Which of those referral sources actually led to members who didn’t just apply but actually became members and even better yet, if we have the data, which of those actually were successful as members and made it through to the end.

[00:38:38] oftentimes, No one’s ever gone through that data with them and said, we could go deeper on this. We could find out some really interesting answers to our questions. Not just the first default, which is where did the applications come from? 

[00:38:54] yeah, exactly what you were talking about. Lead sources and conversion rates. That kind of pipeline analysis is something that I do for some of my clients, and it’s eye opening, absolutely eye opening. When we look at, the sources that might have the largest volume. are not necessarily the ones where we get the stellar whatever kind of customer or something. Yep. And it’s very easy to show that then with a, some sort of visual. Yep.

[00:39:35] Excellent. Yeah. I also had done a training back in the day called making friends with metrics. Okay. Because, as you’ve found also, people do have a lot of anxiety about numbers, and I work with a lot of groups through universities, through grant funding, etc., and they’re required to track data, and they’re required to report it to a funder or to a higher up, but they’re not using it for themselves.

[00:40:00] They’re afraid of it. And trying to help them

[00:40:04] be empowered to actually say, this is your data, you can use it. to help you feel more confident in decision making or to understand where to put your resources on time or to advocate for more funding or whatever it is that you need. and one example I show because it’s often easy for people to picture is that I used to run the litter collection program, adopt a street, adopt an avenue, and so we would do a very rudimentary type of.

[00:40:37] data collection, which is that we’d have a little slip of paper on the bucket that the volunteers took out with them to pick up trash. And they would just record how many volunteers for how many hours picked up how much, how many bags of trash. So it wasn’t fancy. We weren’t using any tech. It was just a pencil and a little piece of paper, but then I could input that into my spreadsheet.

[00:41:03] And then we did actually have a fancy GIS map. And we were able to start overlaying the trash, volume data over the map of the city and we could start to see where’s the higher concentration of litter. In the city, geographically. And then that led us to be able to make better decisions about if we have a big cleanup day, like on earth day, are we going to send people to the most popular park?

[00:41:36] That’s also the one that never needs any trash picked up because everybody loves that park and they pick up their trash and keep it clean to begin with. Or are we going to send them, to maybe a part of the neighborhood that has. It’s, a lot of busy commercial area with dumpsters that get flown open and, everything gets brought by the wind down to one low point.

[00:41:58] And so all of the trash gets collected in that area. so you start to see those patterns that show up and it’s just so visual because it’s literally litter and it’s in your city. 

[00:42:10] it helps people understand, Oh yeah, that would be really nice to be able to know, like, where’s the litter and then we can start to investigate why is it always showing up in the same places and that metaphor is the same for business or same for people’s operations of like, Where are the things showing up all the time and why are they always showing up? 

[00:42:31] Julie: Exactly. 

[00:42:32] Sharon: thank you so much for Lending some of your insights and wisdom and knowledge with us. How can people learn more about your work and maybe even contact you if they have their own data that needs a diva to take a look at it.

[00:42:49] Julie: You got it. I have a website, www dot j analytics, all one word.com. If you go there, you can book a call with me. You can download, I’ve got a couple of, Free resources there to help professionals and business owners try to untangle and make some sense of their data. I hang out over on LinkedIn, so definitely come over there, find me, connect with me. Would love to meet you over there. and I host live streams. On Friday afternoons where I talk about something to do with using data and AI in business. So it’s usually three on Eastern time. So noon Pacific, early Saturday morning, in Australia, New Zealand. I’ve got a few people over there who watch, so I would love to see anyone over there. And then, pretty much.

[00:44:00] Sharon: Awesome. Yes, I’ve been able to tune into some of those lives and they’re very insightful, so I’d highly recommend them. You can watch them live, or I believe they also show up if you can see the recording afterwards. Do you know if that’s true?

[00:44:15] Julie: You can, you can see the recording over on YouTube channel

[00:44:19] at nhdatadiva. There you go.

[00:44:23] Brian AI: Awesome. Thank you for joining us on this episode of AI for Helpers and Changemakers. For the show notes and more information about working with Sharon, visit bloomfacilitation. com. If you have a suggestion for who we should interview, email us at hello at bloomfacilitation. com. And finally, please share this episode with someone you think would find it interesting.

[00:44:45] Word of mouth is our best marketing.