Everyday AI Podcast – An AI and ChatGPT Podcast - EP 268: AI’s Data-Driven Decision Paradox

Episode Date: May 9, 2024

AI + all your data = good move or bad move? Sure, AI can do data crunching wonders when we pair the two together. But, what challenges and conflicts should we be aware of before doing so? Aakash Indurkhya, Head of Product at Virtualitics, joins us to discuss AI and data analysis.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Aakash questions on AI and dataRelated Episodes:Ep 145: NVIDIA Leader Talks GenAI + Data: Unlocking new ways to interact with our worldEp 142: AI is Changing Data Analysis: Insider TipsUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Generative AI Technology and Data Visualization 2. Role of AI in Data Analysis3. Statistics Paradox and Data Collection Challenges4. Importance of Hands-On Experience and CollaborationTimestamps:01:30 Daily AI news04:30 About Aakash and Virtualitics08:51 Stress of data utilization despite its value.10:39 Data layers may disagree, causing decision paralysis.15:33 Generative AI aids human-driven data analytics.17:38 RAG concept crucial for tracing data decisions.24:06 Beginning data analysis requires tools, exploration, openness.26:43 Future of jobs with technology, data and humans.29:21 Communicate with experts for effective problem-solving.Keywords:data in the age of generative AI, large language models, OpenAI model spec, Google DeepMind AlphaFold 3, protein behavior prediction, Zapier Central, AI bots without coding, Virtualisix, data-driven decisions, industry-specific decision intelligence, data abundance paradox, ChatGPT technology, changes in data collection, Apple, Google, maintenance data for HVAC systems, human role in data analysis, industry-specific expertise, generative AI in data visualization, RAG technology, conversational interfaces in generative AI, sensitive data handling, GPT language models, Virtualitix AI platform, decision intelligence apps, data-driven decision paradox, decision congestion, common language in data analysis, data quality, decision socializSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the all-in-one creative AI studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. We are in the age of data.

We have so much data, almost too much. But what are we doing with it, right? Especially now that we have generative AI and large language models that can help us do infinitely more than we could in years past without it. So how do we still have all of this data, but so few people are using it in their decision making? It's actually something I think about a lot, but today maybe I'll get some answers and hopefully you will too. So what's going on, y'all? My name's Jordan Wilson. I'm the host of Everyday AI.

We are your guide to learning and leveraging generative AI with our daily podcast, newsletter, and our live stream. So thank you for joining us. If you're listening on the podcast, make sure, as always, check out your show notes. There's going to be a lot of links to check out with more helpful information as well as a recap of today's show in our daily. newsletter. And if you are joining us on the live stream, like Juan, coming in from Chicago, like myself, thank you. Get your questions in. So before we get into that topic, and I'm extremely excited to talk about this kind of not a new intersection, but a very exciting intersection of so much data and what we can do with it with generative AI. But before we do,

we're going to start as we do every single day with the AI news. All right. Got a lot going on today. So OpenAI has kind of told us how chat GPT works. So OpenAI has released a proposed framework called Model Spec to shape how AI tools respond in the future. The framework includes principles and rules that aim to assist developers and end users like us, what they say to benefit humanity and to reflect well on OpenAI. The company hopes to gather public input and incorporate that feedback to ensure responsible development of their AI models. So AI tools, behaving badly has become a common issue, as we all know, highlighting the need for responsible development. OpenAI's model spec framework aims to guide the behavior of AI tools with principles

such as assisting developers and end users and complying with applicable laws. So we'll obviously have a link to that in today's newsletter if you want to check it out. It's actually a pretty light read. So don't think it's crazy, crazy advance or anything. All right. Speaking of our newsletter, we snuck this in in our newsletter, didn't get to it on the podcast. yesterday because it hadn't come out, but worth talking about Google DeepMind has unveiled the latest version of its artificial intelligence, Alpha Fold, which can predict how proteins behave and interact with other molecules. This is huge. So the breakthrough has the potential to advance research in fields such as medicine, agriculture, bio, everything. So the new version,

Alpha Fold 3 can predict how proteins interact with each other with other molecules and has an accuracy of reportedly between 62% to 76%. Scientists can use Alpha Fold 3 to design molecules and antibodies for medical treatment, potentially saving time and accelerating research. Also, this new AI may contribute to understanding complex biological systems, such as photosynthesis and plants as an example. But if nothing else, here we are now where generative AI is definitely already discovering new drugs.

But with Alpha Phode 3, yeah, I think it's going to expedite that process a lot. Last but not least, kind of a small one, but I think very relevant for our audience. Zapier has released Zapier Central in AI workspace. So Zapier, known for obviously business automation services, has launched Zapier Central in AI workspace that allows customers to create AI bots using natural language. So Zapier Central allows for the creation of AI bots without coding, making it easier for customers to integrate automated services. So pretty cool.

They just released kind of a demo video, a couple of hours. ago. It does have this kind of chat GPT like interface where you can chat with Zapier. And it does just kind of create little agents that can do your work. Isn't that what we all want? Yeah, that's what I want. All right. So there's, as always, there's going to be more on those stories and more AI news and just what's happening in the world of AI in our newsletter. So make sure to go to your everyday AI.com. All right. But today we are talking about data, right? So there's literally so much data, too much data. I can't make sense of it all. So a lot of us use AI to help make sense of it.

And, you know, there's pros and cons to that. So I'm not going to talk about this by myself. Very excited. So welcome on to today's show. Let's go ahead. There we go. We have him there. All right.

So we have Akash and Durka, who is the head of product at virtualistics. Akash, thank you so much for joining us. Yeah. Thanks, Jordan. Great to be here. I think I got like a C plus on the pronunciation of your name. there. But anyways, Akash, tell us a little bit about what you do in your role at VirtualLytics. Yeah, yeah. So I'm the head of product. So really helping to navigate kind of the future

direction of the product, making sure that we're constantly creating new value for our customers. Also, you know, a lot of the product leadership kind of figuring out how we're going to, you know, embed generative AI and other other new AI technology into our platform. And, you know, as a company, we really focus on using AI to change the way that people approach analytics and get past, you know, data-driven decisions to impact. Give us the super high overview. You know, what's the average, you know, client or customer? What are they using virtual analytics for?

Yeah. So I'll give a quick example. We've done a lot of, you know, work with the maintenance operations world, right? So talking about maintenance of like physical assets. you know, aircraft, facilities, things like that. And so a really common problem for maintenance and repair operations is they might have predictive maintenance that tells them when something's going to break, right? But that's not really a decision, right? The decision is, okay, what do you do about it,

right? And to be able to answer that question, you need to be able to know when it's going to break, but also do you have the parts, the people, any special equipment to do that maintenance job. And so that's like an example of a problem that we solve, right? So we do a lot of work with maintenance operators, but also their analysts and our platform in general kind of extends to developers and even executives, right? So really industry-specific decision intelligence, if you will. So Akash, let's start just at the end. Let's fast forward.

Let's get to the point here. So, you know, there is this time now. I think we're in this sweet spot here in the, you know, age of the internet where we have so much data and it's so easy, right? And we have generative AI, which is actually pretty good at, you know, making sense of all that data. So, you know, can you talk a little bit about what this paradox even is, you know, it's you have so much data.

People want data, but are they using it? Right. Yeah. Now, I think it's something that ultimately is really relatable. I'll give an example at the end. end, but over the last decade or really 15, 20 years or so now, there's been a huge proliferation in data collection and also data analytics, right? That's both things have become easily accessible to every business out there. The problem is that even though 80% of people out

there want to make data-driven decisions, 70% of people are also saying that analytics and the data itself is the reason they can't make a decision, right? So that's a kind of what I call the data-driven decision paradox. And that decision paralysis is something that we honestly experience in our own life, right? You know, we were chatting before and I kind of mentioned like, you know, if you had to go buy a toothbrush, you know, 15, 20 years ago, you would just go to the store and buy a toothbrush, right? Nowadays, if you need to do something that's simple, a lot of us will end up going on Amazon, looking at reviews, checking if some of the reviews are fake, really getting into it, but also kind of getting in our own

So you can kind of imagine that if buying a toothbrush has become that complicated and you can get into this data-driven decision paradox, just imagine making an actually complicated business decision, right, with a lot on the line. It just really ups the ante. And I think with all of that, a lot of people have become kind of overwhelmed or just really unhappy working with data at this point. You know, I'm laughing because I feel personally called out by that. Yeah, I've literally spent hours, you know, literally scraping data from review sites, running semantic analysis on a purchase that's like $50, right? So why do you think it is, right? It's no, you know, data has been the new gold for, you know, a while, right?

Or whatever you want to say, the new oil. So why do people have all this data and why can they not actually do anything with it? Is it too complicated? Is there not the right systems? I mean, what are some of those actual reasons? You know, I even think myself, right? Like I'm a big data person. I probably use the data once or twice a week, but I have to be very intentional about it and go in.

And maybe it's a manual process, even still using generative AI. So why this big paradox, why the big drop off between. that, you know, 80% of people wanting to use it and only 70 or 70% of them not doing it. Yeah, yeah. I mean, a couple of answers there. So one, why do we have all this data? I mean, just we started collecting it and we never stopped. And I don't know that we should, right?

I think it, you know, we actually can put it to good use. That being said, 97% of data that's collected out there just goes completely untouched. Right. So, so like, even though we have it, we're definitely not getting our full bang for the buck. as for why I think people run into this whole paralysis situation. It's really interesting, right? So I think that there's a few key reasons. One is that nowadays when we make, we want to use data to make decisions.

The problem is it's not just like one data set or one column. And it's like, yeah, just pick the thing furthest to the top right. And that's it, right? It's the best. It's not that simple anymore. Data is really complex. And honestly, you have to fold in different layers of the data. And one of the biggest problem is that the layers of data might start to disagree with each other, right?

You know, one, you've got to be able to figure out how to join that data together. That's part of the battle, right? But ultimately, that's pretty doable. You just have to get through it. The problem is, even after you do that, what if dataset A tells you that your decision is right, but D-Set B says there couldn't be anything worse than the decision you're about to make? What do you do then, right? Classic decision paralysis.

I think the other thing, you know, beyond the data quality and kind of the conflicts between different layers of data, there's also a whole other layer to it, kind of like the human layer of it, right? Which is, first you've got to get to the point where you feel comfortable making the decision, right? You have the transparency around it. You can explain it to other people. You could show them the data. But then you have to socialize it, right? It's very rare that you have a single, you know, we're not all the president, right? We can't just say, this is how it's going to be and then it just happens, right?

we have to kind of socialize our decisions, get other people bought in on it. And while that's necessary, it also sometimes invites what I call decision congestion, where it's like, that's where the bureaucracy comes into play. It slows things down, right? Too many cooks in the kitchen. So there's a few different layers to how it happens. And I think it's created like a big gap where there's all these expectations to be able to use data to do amazing things. And some cases we're doing that.

But I think in the majority of cases, we're nowhere in. near what we should be able to do with it. And I think like some of the things I highlighted are play a big reason and why. Explain that decision congestion. Let's go into that a little bit more because I can kind of imagine how that plays out because I personally feel that way a lot. I'm sure a lot of people do. But explain specifically what that decision congestion is and how, you know,

whether they're business owners, marketers, data scientists, how they can work through that. Oh, absolutely. Yeah. I think it happens for a few different reasons, but basically at its core, it's like, I think it's often called bureaucracy in other situations. But, you know, you may see that there's a clear idea for what needs to happen. And you can explain it. You have transparency. You've got the data on your side.

But when you present it to other people, because that data, you know, even the whole concept of making a data-driven decision is really new for people. They're more based on relying on their hunches, what they're used to doing. hey, it's the status quo, why break it if it's not, you know, why fix it if it's not broken? Getting past that mentality. And often it's because the people that are kind of pushing back on it, they don't have visibility into the data, right? And I think one of the things that we've really been exploring a lot as a company is, you know, you've got kind of these different personalities.

You've got analysts. You've got developers, right, that think about things really differently. And then you've got executives, which is like polar opposite of a developer in a lot of ways, right? And so you actually need an experience that you need a common language across all of those, right? Otherwise, you're just going to talk straight past other people. As a data scientist for, you know, 10 years, right? I think what can often happen is you just talk straight past an executive or they talk straight past you. And it's because you're not talking in each other's language.

So having that common language and having an experience that fits the mold for each of those personas, but then is easily translated to the world of the other person you're talking to, right? So they actually get it in their language and in their world. I think that's a big, you know, that hasn't really existed in the market. And so that's, you know, one of the ways we're trying to approach like solving this problem. You know, one thing when I specifically think about data and decision making is I think people underestimate how powerful a large language model can be. I think that there were all these narratives maybe very early on, right? Like when companies were still, you know, blanket banning generative AI

in large language models and when people would, you know, share screenshots and say, oh, you know, chat GPT doesn't know what, you know, four plus four is, right? Yet today, if you know how to use generative AI in large language models correctly, they can save you, you know, even for myself, 90% of time, right? Like I can have a spreadsheet with 100,000 data points. I used to spend hours on that. Now I can get that decision in seconds. So how can the average person maybe who's not like yourself, you know,

with a decade or more of data science experience, how can they still find that sweet spot, right, of using a large language model, using it responsibly and not going into that, you know, decision congestion or, you know, being able to actually use data and in large language models. Yeah, I mean, I think there's, you know, on an individual basis, right, for just someone that wants to be able to do more data analytics, more data science,

and leverage generative AI. I think one thing that's key across all generative AI is the conversational interface has been really powerful because there's kind of check-ins, very intermittent check-ins that are very regular check-ins that we're not getting too far off-base, Right. And the user is still the pilot and the generative AI is kind of there as a co-pilot to help produce options for the next step. And then the human can kind of say, okay, I want to go more this way. And I think generative AI is also kind of going through another wave with, you know, rag technology that allows it to be a little bit more clear where it's getting its information from. So I think all of those need to kind of also translate into the data analytics. you know, AI for analytics world, right? Where it can't just be full automation from, you know, one end of here's a question all the way to, okay, we solved your problem, right?

There needs to be check-in. So I would say as that technology kind of comes to market, it's going to help a lot of people and allow them to do it in a responsible way, which I think especially when you're making important business decisions is like so critical, right? So yeah. Yeah. And those critical business decisions, I think, you know, you mentioned rag there, which I think is

extremely important because then I think that could, in theory, improve, you know, the outputs, right? So for those that don't know, retrieval augmented generation is rag. So Akash, can you talk a little bit more about how rag may or may not? I mean, I think it will help, right? But how can RAG when we start bringing in our own data, how can that help maybe close that original gap that we talked about, right? Of all these people wanting to use data but not using it, right?

How can Rang help in that? Yeah, I mean, I think it's really the concept of RG, right, being able to pin a path forward to something you've already seen, right, tying it down to some sort of ground truth, if you will, right? that whole concept is super critical in data science because you have to have like, you know, breadcrumbs along your path, right? We always refer to the path from data to impact has to be, you know, with breadcrumbs for each mini decision that went along the way. And so RAG is just kind of that concept of you can always kind of refer back to some source data and kind of cite it, right? I think when you're doing analytics or making a decision, you need, you know, one, yeah,

RAG for generative AI will help, but you need that whole concept of being able to trace back your decision through the analytics, through the visualization, back to the source data, right? Or even any of the transformations or AI you apply along the way. So I think that that concept of being able to cite it, it has that, you know, kind of scientific method about it that really gives people more confidence as they're going through that analytic journey. Yeah. And I think even, you know, because I know that there's some people who haven't even, you know, touched their toe with, with rag. But the way I like to describe it is, you know, essentially think of it as a layer of your own company's data that is between you and a model to,

you know, ensure, not insure, but, you know, improve the likelihood that your data is being used in a responsible way and you can trust the outputs. So one thing, actually have two very similar questions here from Brian and Cecilia. So I'll go with Cecilia since there's actually two questions here. So asking what are the best tools for data-driven communications and asset management? Kind of specific, but we'll see if we can tackle that one. And then the second one, or maybe we just do the best tools in general. That's also kind of what Brian was asking here for data analysis.

this, but then also, are there AI-based texts or communications driven by data that provide alerts? Yeah, okay. So this is tricky for me because I'm going to have to plug my own company. Yeah. I think the platform we're working on, VirtualLytics AI platform and the decision intelligence apps that live inside of it are really built to address like everything, you know, that was mentioned in the questions, being able to improve your communications, really having that common language across your different personas and then also being able actually able to get into the weeds of the analytics, right? And apply, you know, maybe not generative AI methods every time, but even just predictive models, you know, traditional quote unquote AI, right,

to better your analytics. So, you know, we have AI kind of had a few different layers, but I think really the focus is still on helping the humans through their analytics. Yeah, no. And, you know, I'm curious. So, yeah, we'll definitely have a link to virtual analytics in the newsletter for those of you that are interested in learning a bit more. Maybe Akash for, you know, your standard tools, right? Large language models, because I'm sure a lot of people, you know, especially our listeners use those and they're trying to, you know, figure out, okay, well, number one, we should always say don't, don't upload confidential, sensitive, proportionation, right? That's a big one. Did that blanket disclaimer out of the way. But do you have any best advice for people who do want to bring, you know, kind of public or non-confidential data into large language models?

Is that a good idea and how can people best use those large language models? Yeah. So, I mean, one, again, like you said, you definitely don't want to bring in confidential data, right? That's almost a whole separate conversation. where I find that it can be very helpful in, and this is something that we're also doing, is I do think you can ask, you know, GPT and these other large language models about methods, right?

You can kind of inform it, here's what I'd like to be able to do, right? But I'm not a data scientist. How do I get to that point, right? And it's actually pretty helpful at being able to suggest ideas for how to get there. You might still have to do some of the legwork, right? or as, you know, the technology is maturing in the market, the LLMs themselves will be baked into all of these analytics and AI platforms, right? Which is, that's really the future that I'm envisioning.

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Speaking of the future, right? How, you know, how can, you know, company, and I'm sure this is, you know, a big part of what you're working on at virtualitics, but how can, you know, business owners, decision makers, how can they start to close that gap, right? And the reason I am, you know, talking over and over and trying to hammer this point home about this like sweet spot of data is because, you know, big companies are making it harder to collect data. You know, we've seen iOS updates from Apple making it harder. You know, Google has been talking about getting rid of cookies now for four years, right? And they say, oh, this year's the year. But, you know, we have

this sweet spot of having so much data and we have this, you know, extremely powerful technology in generative AI in large language models, how can we actually take advantage of this time and not have that huge gap? Yeah, I would say honestly just getting started, right? Because it's, it's all about turning those unknown unknowns into at least known unknowns so you can start improving it. I think one of the biggest problems in general is people just don't know what's in their data and you're not going to know what's in it until you just start getting hands on with it. So you need the right tools, but you also need people that are going to feel comfortable to look into it. And I think there's always this pressure of, well, there's data, so you better do something good with it.

Having a little bit more openness about, okay, maybe all of this data isn't that good. And even though you might have a terabyte of it, it might be better off to learn what's wrong with it and go collect another. terabyte, right? Because you can do that faster now than you could have before. But if you don't start that journey and start discovering how broken it may be, you're never going to get past that first step. And there's all sorts of tools out there that can help with that. Give me, give us an example of, you know, a couple, because I'm sure what you said there, probably just resonated with a lot of people, right? Because we all spent, you know, hours staring at an Excel sheet or a Google sheet. Oh, yeah. I find out like, oh, this is garbage. So I've seen

this before, I'll kind of go back to that maintenance example, right, for maintenance operations. You know, suppose that you've got maintenance data about, you know, different assets. Let's talk about HVAC systems, right, in large buildings. Maybe you've got a bunch of different HVAC systems across a series of buildings. And some of that, you have the data on whether they're breaking, you've got sensor information. And then separate from that, you've got your technician data, right? Who's going to different sites and doing maintenance? in completing which jobs. Imagine having both of those data sets

and wanting to put them to good use, and then you discover you've got no ability to track which asset they were going to each time. So you know that there was maintenance going on, but you don't know on which assets or you can't tie it to the sensor data. That's a great example of where it's just, you're missing a link.

And if you don't start going down that road to figure out how to link your data, you're not going to discover it and you're not going to fix it, right? I've literally like walked around campuses with people and just said, look, the thing you need to do is just take out a notebook, write down the asset ID here, and then go back to your desk and figure out which asset ID that lined up to, right? So sometimes making the fixes, it's like you have to get a little bit literal and like put your finger on it a bit, but it is solvable. You just have to get started to discover it.

And you know, it's interesting, Akash, and I love that you gave that example very illustrative of literally, you know, you could be a data scientist. And maybe, you know, we always talk about, okay, what do the future of jobs look like, right? Like if a large language model can pretty accurately crunch, you know, millions of data points in a couple of minutes that it used to take days, what does the future of jobs look like for those people? And, you know, maybe that example of what you just said is a good one is having that human piece and going out there, literally in looking with your eyes and writing things down in the notebook and starting to connect data points and tell stories. So is that the future, right? Because a lot of people think like,

okay, you know, with coding and data analysis and, you know, I don't know, bookkeeping and in all of these more manual jobs specifically around data, people say, okay, well, these are going to be large language models. How do we think that, you know, the role of humans is going to change and helping us maybe close this data paradox. Yeah, I mean, one, I think, you know, analytics in general is about to get much more industry specific or it's going to need to be because the value proposition for analytics in general only really makes sense when you're talking about within the confines or the context of a specific industry.

I think how humans can help that is focus on the real world constraints that the data is not going I know about, right? Discover the issues in your data, but then also really try to understand the information that that's not actually going to show up. I sometimes call it like the physics of the data, right? There's the data, which is just what you collect. And then there's the physics behind it of like what constraints were there that aren't documented anywhere, but there's a reason why the data looks the way it does, right? So, you know, become experts in your industry. That's the part that humans need to provide. How can, you know, you make it sound so easy, right?

But you're obviously an expert in this field. How can people better do that? How can they better understand, right? Because I do think, you know, people who are going to excel in this new age of generative AI, they have to think about their roles differently. I think you just gave a fantastic example. How can people do that? How can they, you know, kind of take a step out maybe from, you know,

metaphorically or literally, how can they step away from the data and start to better understand the physics of it? How can people do that? Yeah, it's a good question. I don't know that there's a single bulletproof answer. I would say, you know, especially as the data and the generative AI aspects sort itself out more, just start communicating more, you know, with subject matter experts, whatever industry or in, you know, someone at your company is going to know in depth how your manufacturing process works. How does distribution work? Go and have those conversations, right? Increase the communication about those things because the guy that's, you know, the guy or gal running your data science, if they don't know that, they're never going to come to you with good solutions to those,

those questions or those business problems. So, I mean, maybe it's a cop-out answer, but, you know, just getting out there and communicating, uh, and, and, and, you know, just, uh, taking an interest in it, you know. It's, it sounds crazy, but something that might be just the advice some people need to hear is like, get out and communicate because I think in, you know, in today's world, sometimes, you know, that, that gets lost. It's a lost art form. Uh, I think we have one more question here before we wrap up today's interview.

So Douglas asking, uh, do you have recommendations for using AI to visualize findings? asking, is it just Python-based reporting, or are there other approaches? Yeah, so this question is very much at the core of virtualetics technology. So I got to, again, plug it. You know, that's where we really got started is using AI to guide what to even look at in your data, to surface where are those interesting intersections. You know, you can use Python and other, you know, programming languages are to do some of this. but it's kind of a different way of thinking about AI, right?

Typically, people think of AI as more like predictive, right? And they really go for it from that. This is kind of flipping it and saying, AI will help you discover what to look at rather than looking at stuff so you can make a predictive model. So I think programming languages are good if you actually know how to write code and that's the barrier that most people get stuck in, right? So, yeah.

So we've talked about a lot here. So, you know, we've, we've gone over how the role of data analysts might change about how not all data is good data. We've talked about RAG and how large language models can actually help you just look at data differently. But, you know, as we as we wrap here, Akash, what is maybe your best takeaway or your best piece of advice for maybe people either who are working in data or maybe lead. who are having to make decisions on it. How can they start to close kind of that gap that we talked about earlier between wanting to do something with the data and actually doing something with it? Right.

I think, one, just be really honest with yourself about data quality and issues that are coming up when you have to start integrating with data. Two would be, you know, if you want to get past this data-driven decision paradox, you're going to need tools that assist you with that. So invest in your tools. And I don't think kind of the traditional B.I approach is really getting it done. So you kind of need a different set of tools, right? BI is great for reporting, seeing what maybe isn't working or is working.

But if you actually need to get into the art of solving a problem, you need different tools. And then third is really start to practice decision socialization, but it has to fit in that sweet spot between being able to communicate about it and congestion, right? So those are the three things, right? If you want to get past this whole paradox, you have to invest in all three of them. Jeez, my fingers literally hurt because I'm typing so many notes in so many takeaways. Because I think you just helped, I think a lot of people out there better tackle.

This huge problem of having so much data and having great AI, but not being able to do anything with it. So thank you so much, Akash, for joining the Everyday AI show. We very much appreciate your time and your insights. Yeah, thanks, Jordan. All right. And as a reminder, everyone, like I said, so much great information. This is one, whether you're here listening on the live stream or on the podcast,

make sure to check out your show notes or just go to your EverydayAI.com. We recap every single day's conversation by me, a human. I go and write this. And I'm excited to give you. more insight. So make sure you check that out and make sure you join us tomorrow and every day for more everyday AI. Thanks y'all. Meet Firefly AI assistant. Now live in Adobe Firefly, the Allman One Creative AI Studio. Just describe what you want to create in your own words and the assistant handles the rest, orchestrating multi-step workflows across Adobe Creative Cloud apps, including

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