Ep 268: AI’s Data-Driven Decision Paradox

Navigating AI's Data-Driven Decision Paradox in Today's Business Landscape

Modern businesses are entwined with AI and data analytics, creating a shift in everyday decision-making practice. The increasing adoption of generative AI and advancements in large language models are shaping the future of data-driven decisions. In the age of digital revolution, businesses are grappling with vast data availability and the paradox that accompanies it.

The Emergence of Sophisticated AI Tools

The AI landscape has seen significant developments with organizations like OpenAI and Google DeepMind. The unveiling of frameworks aimed at guiding AI tools' behavior and predicting more complex attributes like protein behavior hints at the vast potential of AI applications in unprecedented fields.

Data Analytics and Decision-Making

Today’s businesses are increasingly leveraging AI to streamline their analytics and decision-making processes. While AI in analytics changes the way data is approached, the true challenge lies in making effective use of the available data. Although businesses are privy to an abundance of data, the actual use of this for decision making is hindered by complexity and lack of skill.

Tapping into Industry-Specific Decision Intelligence

Industry-specific decision intelligence can significantly enhance maintenance operations. For instance, the use of maintenance data for complex infrastructure, such as HVAC systems, demonstrates the necessity to bridge missing data links to ensure efficiency.

The Human Role in Future Data Analysis

While AI continues to revolutionize data analysis, the role of humans remains critical. Industry-specific expertise is necessary for understanding the "physics of the data," highlighting the demand for increased inter-industry communication and collaboration for effective data utilization.

Exploring Generative AI Technologies in Data Visualization

Generative AI technologies are making waves in data visualization. Models like the RAG technology can provide clarity on information sources in generative AI, which is essential in data analytics.

Conversational interfaces in generative AI are critical for user-friendly data interaction, with AI acting as the assistant producing options for the user. This relationship reinforces the importance of maintaining responsible use of AI in decision-making to ensure data reliability and integrity.

Addressing the Data-driven Decision Paradox

Despite the availability of vast amounts of data, businesses often face decision paralysis due to data complexity and the human element in decision-making. This phenomenon is often referred to as the "data-driven decision paradox," which can be exacerbated by decision congestion when there is resistence to data-driven choices due to a reliance on hunches or lack of clarity in the data.

An effective way to navigate this paradox lies in the honest appraisal of data quality, investment in auxiliary tools beyond traditional BI, and the practice of decision socialization. Large language models can help molt the complex data layers and assist in swift decision-making for businesses.


Navigating the data-driven decision paradox through improved communication, increased openness, and adopting advanced tools are the key to unlocking the potential of AI for businesses. The growing alignment of AI and business operations promises a sea of opportunities for businesses to leverage data and enhance decision-making processes. Pushing past the decision congestion and capitalizing on the data revolution can create a competitive edge in this new age of AI.

Topics Covered in This Episode

1. Generative AI Technology and Data Visualization
2. Role of AI in Data Analysis
3. Statistics Paradox and Data Collection Challenges
4. Importance of Hands-On Experience and Collaboration

Podcast Transcript

Jordan Wilson [00:00:16]:
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 is 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 livestream. So thank you for joining us.

Jordan Wilson [00:01:00]:
If you're listening on the podcast, make sure, as always, check out your show notes. There's gonna 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 gonna start as we do every single day with the AI news. Alright? Got a lot going on today. So, OpenAI has kind of told us how chat gpt works.

Jordan Wilson [00:01:42]:
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 wanna check it out. It's actually a pretty light read. So don't don't think it's, you you know, crazy crazy advance or anything.

Jordan Wilson [00:02:33]:
Alright. 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, AlphaFold, 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 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 treatments, potentially saving time and accelerating research.

Jordan Wilson [00:03:19]:
Also, this new AI may contribute to understanding complex biological systems, such as photosynthesis in plants as an example. But if nothing else, here we are now where generative AI is definitely already discovering new drugs. But with AlphaFold 3, yeah. It's I think it's gonna 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, an 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.

Jordan Wilson [00:04:02]:
So, pretty cool. They just released kind of a demo video a couple 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. Alright. So, there's as always, there's gonna 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 everydayai.com.

Jordan Wilson [00:04:28]:
Alright. 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 gonna talk about this by myself. Very excited. So welcome on to today's show.

Jordan Wilson [00:04:47]:
Let's go ahead. There we go. We have him there. Alright. So we have Akash Ndurga, who is the head of product at Virtualisix. Akash, thank you so much for joining us.

Aakash Indurkhya [00:04:59]:
Yeah. Thanks, Jordan. Great to be here.

Jordan Wilson [00:05:01]:
I think 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 Virtualytics.

Aakash Indurkhya [00:05:10]:
Yeah. Yeah. So, I'm the head of product. So really helping to navigate kind of the 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 gonna, 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 and get past, you know, data driven decisions to impact.

Jordan Wilson [00:05:44]:
Give us give us the super high overview. You know, what's the average, you know, client or customer? What are they using, Virtual Analytics for?

Aakash Indurkhya [00:05:52]:
Yeah. So I'll I'll give a quick example. We've done a lot of, you know, work with the 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 repair maintenance and repair operations is they might have predictive maintenance that tells them when something's gonna 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, you know, when it's gonna 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.

Aakash Indurkhya [00:06:38]:
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.

Jordan Wilson [00:06:53]:
So, Akash, let's let's start just at the end. Let's let's fast forward. Let's get to the 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?

Aakash Indurkhya [00:07:22]:
Right. Yeah. No. I think it's something that ultimately is is really relatable. I'll give an example at the end. But, you know, 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 that 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 the a decision.

Aakash Indurkhya [00:07:55]:
Right? So that's kind of what I call the data driven decision paradox. And that decision paralysis is something that we've honestly experienced in our own life. Right? You know, we were chatting before and and I kinda 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 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 head. 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 it just really ups the ante. And and I think with all of that, a lot of people have have become kind of overwhelmed or just really unhappy working with data at this point.

Jordan Wilson [00:08:51]:
You know, I've I'm laughing because I I feel personally called out by that. So, yeah, I've 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 it's it's it's no, you know, data has been the new gold for, you know, a while. Right? Or whatever you wanna say, the 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?

Aakash Indurkhya [00:09:52]:
Yeah. Yeah. I mean, couple couple answers there. So one, why why do we have all this data? I mean, just we we started collecting it and we never stopped. And 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.

Aakash Indurkhya [00:10:14]:
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 wanna use data to make decisions. The problem is it's not just, like, one dataset 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.

Aakash Indurkhya [00:10:39]:
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 gotta be able to figure out how to join that data together. That's that's part of the battle. Right? But, ultimately, that's 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 dataset 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 the conflicts between different layers of data, There's also a whole, other layer to it.

Aakash Indurkhya [00:11:23]:
Kinda like the human layer of it. Right? Which is, first, you gotta 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 gonna be, and then it just happens.

Aakash Indurkhya [00:11:42]:
Right? We have to kinda 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. In some cases, we're doing that. But I think in the majority of cases, we're nowhere near what we should be able to do with it. And and I think, like, some of the things I highlighted are play a big reason in why.

Jordan Wilson [00:12:19]:
Explain explain that decision congestion. Let's 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.

Aakash Indurkhya [00:12:41]:
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 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 you know, even the whole concept of making a data driven decision is really new for people.

Aakash Indurkhya [00:13:08]:
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 why fix it if it's not broken? Getting past that mentality. And often, it's because the 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 you've got kind of these different personas. You've got analysts. You've got developers, right, that that think about things really differently.

Aakash Indurkhya [00:13:35]:
And then you've got executives, which is like polar opposite of of a developer in a lot of ways. Right? And so you actually need an experience that you you need a common language across all of those. Right? Otherwise, you're just gonna 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 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, you know, 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.

Jordan Wilson [00:14:23]:
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. Right? I think that there were all these narratives maybe very early on. Right? Like, when companies were still, you know, blanket banning generative AI and large language models and and when people would, you know, share screenshots and say, oh, you know, ChatGPT doesn't know what, you know, 4+4 is. Right? Yet today, if you know how to use generative AI and large language models correctly, they can save you, you know, even for myself, 90% of time. Right? Like, I can have a spreadsheet with a 100000 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 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 in, in large language models.

Aakash Indurkhya [00:15:33]:
Yeah. I 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, or very regular check ins that we're not getting too far off base. Right? And and the user is still the pilot, and the generative AI is kinda there as a copilot to help produce options for the next step, and then the human can kind of say, okay. I wanna go more this way. And I think generative AI is also kinda going through another wave with, you know, a 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.

Aakash Indurkhya [00:16:38]:
Right? There needs to be check-in. So, I would say as that technology kinda comes to market, I it's gonna 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.

Jordan Wilson [00:16:55]:
Yeah. And 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 if for for those that know, don't know, retrieval augmented generation is 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, you know, start bringing in our own data, how can that help maybe, you know, close that original gap that we talked about, right, of all these people wanting to use data but not using it. Right? How can Rank help in that?

Aakash Indurkhya [00:17:38]:
Yeah. I mean, I think it's it's really the concept of Rag. 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, bread crumbs along your path. Right? For we we always refer to the the path from data to impact has to be, you know, with bread crumbs for each mini decision that went along the way. And so RAG is just kinda that concept of you can always kinda refer back to some source data and and kind of cite it. Right? I think when you're doing analytics or making a decision, you need, you know, 1, 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.

Aakash Indurkhya [00:18:37]:
So I I think that that concept of being able to to cite it, it has that, you know, kinda scientific method about it that really gives people more, you know, confidence as they're going through that analytic journey.

Jordan Wilson [00:18:51]:
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 a layer of your own company's data that is between you and a model to, you know Right. You know, ensure not ensure, but, you know, improve the likelihood that your data is being used, in a responsible way and you can trust the outputs. So so one thing, actually, I have 2 very similar questions here from, from Brian and Cecilia. So I'll go with Cecilia since there's actually 2 questions here. So asking, what are the best tools for data driven communications and asset management? Kinda specific, but we'll see if we can, tackle that one. And then the second one or maybe we just do, you know, the best tools in general.

Jordan Wilson [00:19:35]:
That's also kinda what Brian was asking here for data analysis. But then also, what are are there AI based text or communications driven by data that provide alerts?

Aakash Indurkhya [00:19:48]:
Yeah. Okay. So this is this is tricky for me because I I'm gonna have to plug my own company. Yeah. Right? I I think the platform we're working on, Virtualitix AI platform, and the decision intelligence apps that live inside of it are really built to address, like, everything, you know, that 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 ill actually able to get into the weeds of the analytics. Right? And and apply, you know, maybe not generative AI methods every time, but even just predictive models, you know, tradition traditional quote, unquote AI, right, to to better your your analytics. So, you know, we have AI kind of at a few different layers, but I think, really, the focus is still on helping the humans through through their analytics.

Jordan Wilson [00:20:38]:
Yeah. No. And and and, you know, I'm curious. So, yeah, we'll 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 your standard tools. Right? Large language models. I'm because I'm sure a lot of people, you know, especially our our listeners, use those and they're trying to, you know, figure out, okay. Well, number 1, we should always say don't don't upload confidential sensitive Yeah.

Jordan Wilson [00:21:03]:
Information. Right?

Aakash Indurkhya [00:21:04]:
That's a big one.

Jordan Wilson [00:21:05]:
Get that blanket disclaimer, out of the way. But, you know, do you have any best, you know, best advice for people who do want to bring, you know, kind of public or nonconfidential data into large language models. Is that a good idea, and how can people best use those large language models?

Aakash Indurkhya [00:21:24]:
Yeah. So, I mean, 1, again, like you said, you definitely don't wanna bring in confidential data. Right? That's 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 it's actually pretty helpful at being able to suggest ideas for how to get there.

Aakash Indurkhya [00:21:59]:
You might still have to do some of the legwork. Right? Or as, you know, the 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 the the future that I'm envisioning.

Jordan Wilson [00:22:58]:
Speaking speaking of the future. Right? How you know, how can, you know, comp and I'm sure this is, you know, a big part of what you're working on at VirtualYtics. 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.

Jordan Wilson [00:23:46]:
You know, Google has been talking about getting rid of cookies now for 4 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 and large language models, how can we actually take advantage of this time and and not have that huge gap?

Aakash Indurkhya [00:24:06]:
Yeah. I would I would say, honestly, just getting started. Right? Because it 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. Right? So you need the right tools, but you also need people that are gonna, you know, feel comfortable to look into it. And I 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, you know, learn what's wrong with it and go collect another terabyte.

Aakash Indurkhya [00:24:52]:
Right? Because you can do that faster now than you could've before. But if you don't start that journey and start discovering how broken it may be, you're never gonna get past that first step. Right? So and there's all sorts of tools out there that can help with that.

Jordan Wilson [00:25:06]:
Give me give us an example of, you 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. To find out, like, oh, this is garbage.

Aakash Indurkhya [00:25:20]:
So I I've seen this before. I'll kinda go back to that that maintenance example, right, for maintenance operations. You know, suppose that you've got maintenance data about you know, different assets. May let's talk about HVAC systems, right, in in large buildings. Maybe you've got a bunch of different HVAC systems across a series of buildings, and some of the you 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 and completing which jobs? Imagine having both of those datasets 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.

Aakash Indurkhya [00:26:02]:
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 gonna discover it and you're not gonna 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 that lined up to. Right? So sometimes making the fixes, it it's like you have to get a little bit literal and and, like, put your finger on it a bit, but it it is solvable. You just have to to get started to discover it.

Jordan Wilson [00:26:43]:
And, you know, it's 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 and and maybe, you know, we always talk about, okay, what do the future of jobs look like? Right? Like, if a 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 of jobs look like for those people? And and, you know, maybe that example of what you just said is a good one, is is is having that human peace and going out there literally and looking with your eyes and writing things down in the notebook and starting to connect, data points and tell stories. So is is that the future? Right? Because a lot of people think like, oh, okay. You know, with coding and data analysis and, you know, I don't know, bookkeeping and and and all of these more manual jobs, specifically around data, people say, okay. Well, these are gonna be large language models. How do we think that, you know, the the the role of humans is going to change in helping us maybe close, this this data paradox.

Aakash Indurkhya [00:27:47]:
Yeah. I mean, 1, I 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 the specific industry. I think how humans can help that is focus on the real world constraints that the data is not gonna know about. Right? Discover the issues in your data, but then also really try to understand the information that that's not actually gonna show up. It's I 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 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? Mhmm. So, you know, become experts in your industry.

Aakash Indurkhya [00:28:40]:
That's the part that humans need to to provide.

Jordan Wilson [00:28:43]:
How can you know, you make it sound so easy. Right? But you 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 you know, you know, metaphorically or lit or literally, how can they step away from, the data and and start to better understand the physics of it? How can how can people do that?

Aakash Indurkhya [00:29:21]:
Yeah. It's a it's a good question. I don't know that there's, like, a single bulletproof answer. I I would say, you know, especially as the data and the generative AI aspect sort itself out more, just start communicating more, you know, with with subject matter experts, whatever industry you're in. You know, someone at your company is gonna 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 that's you know, the guy or gal running your your data science, if they don't know that, they're never gonna 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, and and, you know, just, taking an interest in it.

Aakash Indurkhya [00:30:14]:
You know?

Jordan Wilson [00:30:15]:
It's it it 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 that gets lost. It's a lost art form. And I think we have one more question here before we wrap up today's interview. So Doug was asking, do you have recommendations for using AI to visualize findings? Asking is it just Python based reporting, or are there other approaches?

Aakash Indurkhya [00:30:43]:
Yeah. So this question is very much at the core of technology. So I gotta, again, plug it. You know, that's 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 and other, you know, programming languages are to 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 and they really go for it from that.

Aakash Indurkhya [00:31:17]:
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 and and that's the barrier that most people get stuck in. Right? So yep.

Jordan Wilson [00:31:36]:
So we've we've talked about a lot here. So, you know, we've we've gone over how the role of of data analyst might change, about how not all data is 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 leaders 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?

Aakash Indurkhya [00:32:17]:
Right. I think, 1, just be really honest with yourself about data quality and issues that are coming up when you have to start integrating with data. 2 would be, you know, if if you wanna get past this data driven decision paradox, you're gonna need tools that assist you with that. So invest in your tools, and I don't think kind of the the traditional BI approach is really getting it done. So you kind of need a different set of tools. Right? BI is great for reporting, seeing what 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 3rd 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.

Aakash Indurkhya [00:33:07]:
Right? So those are the three things. Right? If you if you wanna get past this whole paradox, you have to invest in in all three of them.

Jordan Wilson [00:33:15]:
Jeez. My my fingers literally hurt because I'm typing so many notes and so many takeaways, because I think you just, helped, I think, a lot of people out there, better tackle, this 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.

Aakash Indurkhya [00:33:41]:
Yeah. Thanks, Jordan.

Jordan Wilson [00:33:42]:
Alright. 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 insights. So make sure you check that out and make sure you join us tomorrow and every day for more everyday AI. Thanks, y'all.

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