Ep 142: AI is Changing Data Analysis: Insider Tips

On today’s episode of the Everyday AI Show, the transformational impact of generative AI on data analysis was explored, shedding light on the shifting landscape of business intelligence. Amidst ongoing debates about the potential of AI to replace traditional tools, the conversation emphasized the significant role of generative AI in accelerating the process of answering critical business questions, ultimately propelling organizations towards a more data-driven future.

The discussion centered around the integration of generative AI in data analysis, particularly highlighting the use of AI to generate SQL queries and its potential to streamline the analytical process. Pioneering advancements in AI technology were underscored, signaling a future where reliance on AI to write SQL is envisioned, while the role of data analysts pivots towards documentation oversight and unfaltering quality assurance.

Furthermore, the conversation evoked insights into the evolving nature of company workflows and the trajectory of AI from being an experiment to an indispensable asset. The notion of AI becoming increasingly central in organizational operations was deliberated, highlighting its transformative potential to enhance productivity and efficiency.

Delving into the pressing concerns of data security and confidentiality, the podcast addressed the implication of leveraging generative AI models from cloud providers, emphasizing the imperative need for guarantees and stringent measures to safeguard data integrity. Such discussions resonate deeply with decision-makers, underscoring the strategic considerations surrounding AI adoption and its implications on data governance within enterprise environments.

The potential impact of advanced AI models, such as GPT-5 and GPT-6, on data analysis systems was a focal point of the discourse, eliciting a glimpse into the future of AI-infused business intelligence. The impressive capabilities exhibited by advanced data analysis mode in ChatGPT were hailed, showcasing its potential to handle large datasets and facilitate interactive, conversational engagement with data.

Furthermore, the nuanced shift in the role of data teams and the expanding accessibility of internal data through generative AI initiatives were explored, shedding light on the democratization of data-driven insights and the broader organizational implications.

Of paramount importance was the narrative threading through the discussion – the need to dispel misconceptions and harness the true potential of large language models for data analysis. The nuanced understanding of AI’s capabilities in transforming data analysis, coupled with a meticulous approach to confidentiality and compliance, emerged as crucial considerations for businesses poised to embrace generative AI models.

In conclusion, the episode encapsulated a vision of AI as a catalyst for revolutionizing data analysis, propelling organizations into an era defined by heightened data-driven insights and expanded accessibility to internal data. As business leaders navigate the evolving landscape of business intelligence, the insights and considerations shared underscored the pivotal role of generative AI in reshaping the future of data analysis, heralding a new dawn for business intelligence and decision-making.

Topics Covered in This Episode

1. The Future of AI in Data Analysis
2. Use of Generative AI in Data Analysis
3. Concerns and Considerations Around Generative AI
4. Accelerating Business User Questions and Data Analysis

Podcast Transcript

Jordan Wilson [00:00:19]:

How is AI going to change data analysis? I love data. I love spreadsheets, but I also love AI. So today’s conversation should be a fun one. I’m excited. Are we still gonna be using spreadsheets and databases and dashboards in a couple of years, or Is AI just gonna take care of all of that for us, and we’re just making sure that it’s doing its job? I’m not sure. That’s why it brings smart people on the show. So welcome today to Everyday AI. My name is Jordan Wilson.

Jordan Wilson [00:00:48]:

I’m your host, and Everyday AI is it’s for you. It’s for all of us, actually, but It’s a daily livestream podcast, free daily newsletter, helping us all learn and leverage generative AI. So if you haven’t already, make sure you go to your everyday a I dot Sign up for that free daily newsletter because it’s awesome. I’m biased because I write it. Yes. I actually write it. A human writes a newsletter. I know.

Daily AI news

Jordan Wilson [00:01:11]:

Crazy concept. Alright. But before we talk about, AI and how it’s changing data analysis, Let’s first go over, like we do every single day, the AI news. Alright. So first, pretty soon, AI won’t even need us according to, a recent report. So, singularity is less than 8 years away according to a scientist who’s an expert in the field. Singularity is the moment when artificial intelligence surpasses human control, and this report is coming out of popular mechanics. So this is Ben Gortzel, who is the CEO of SingularityNET.

Jordan Wilson [00:01:49]:

And he recently said that artificial general intelligence and singularity It’s 3 to 8 years away. Alright. So, apparently, everyday AI might be run by AI. Next piece of news. So Gen AI is kind of coming to our banks. Well so JPMorgan is collaborating right now with US regulators to implement some generative AI models to ensure proper risk management and control measures. So this new g, JPMorgan, Gen AI model, if approved, would be used for their help desk service that helps solve problems and also Track earnings summaries for all the companies that JPMorgan tracks. Alright.

Jordan Wilson [00:02:31]:

Last but not least, Bill Gates says we’re all getting personal assistance. Alright. Bill, does that mean you’re paying for mine, paying for everyone’s? I don’t know. So billionaire Microsoft cofounder Bill Gates predicts that AI powered agents will revolutionize the way that we work with technology Carry out tasks, and it’ll be in the next 5 years. So Bill Gates recently said in a very new report about 30:30 minutes old, said that they will utterly change the way we live, in quotes. And he said, we’ll all have them even if we don’t work in an office. Right. I think we, assume these personal AI agents are just gonna be for those, you know, people who are on the computer all day, but, hey.

Jordan Wilson [00:03:13]:

According to Bill Gates, We’re all gonna have 1. Just following us around in the home. That sounds fun. Alright. Also, if y’all didn’t know, GPTs, Custom GPTs from OpenAI. They should be out in the wild. If you haven’t seen them yet, go hit refresh. Let us let us know.

About Zain and Vanna AI

Jordan Wilson [00:03:29]:

Alright. We’re not here to talk about all the AI news that’s happening On this beautiful Friday morning, we’re here to talk about how AI is changing data analysts. Data analysis. Sorry. Data analysis. So, I’m excited for our guest today because I love data. I love AI. So Let’s let’s bring on to the show, and please help me welcome.

Jordan Wilson [00:03:53]:

Here we go. Got him. Zain. Zain Hoda, the CTO of Vanta AI. Zain, thank you for joining the show. I appreciate it.

Zain Hoda [00:03:59]:

Hi, Jordan. Thanks for having me.

Jordan Wilson [00:04:01]:

Yeah. Absolutely. Hey. And, everyone, if you’re joining us live, what questions do you have about data? You know? All those numbers, all those databases floating around. Zain’s gonna help us figure it all out. But but, Zain, tell us, tell us a little bit about what you do at Vanta AI.

Zain Hoda [00:04:16]:

Sure. Some context may be helpful. So I used to work in finance, and then I I actually ran a data company, that I sold to, a larger, data analytics company. And so I think, You know, what we what we generally saw when we were delivering data was that we, Like, if you’re delivering data to business users, you’re typically delivering that data in the context of dashboards. So you’re making, like, a fixed dashboard to answer, like, a limited set of questions. And if any sort of business user wants to dig deeper, They need to get an analyst involved in order to query that that database because, really, only the data analyst is gonna understand, like, oh, where is this Data stored. How does that, data join together? And so what we saw was that, actually, generative AI can can solve a lot of this this problem because particularly if you have SQL databases, you can just, use the information that you have about, the database and ask questions and and have it generate SQL. And then we have systems to then go and execute the SQL, generate charts, and things like that.

Zain Hoda [00:05:36]:

And so that’s kind of like the genesis for for Venet AI. And so, like, how do we help data analysts Answer these questions from business users, like, much faster. And then for, you know, the simpler questions that that business users have, like, when they have when they’re in the dashboard and And they wanna go slightly deeper, they could potentially use the AAS system themselves.

How AI is changing data analysis

Jordan Wilson [00:05:59]:

So so let’s start at the end, Zain, let’s start at the end, and we’ll circle our way around. How are how are you seeing already how AI is is changing data analysis? I’ve I’ve seen it myself, but I wanna hear from you first. Like, from from your vantage point, how is it already changing?

Zain Hoda [00:06:19]:

Absolutely. So I think, like, the the time to, to insight is getting shorter and shorter. So, you know, from when you have a question or when you have a hypothesis to actually getting an answer, I think AI is substantially accelerating that process. And then on the, on the job side, so if you’re a data analyst using, AI right now, generative AI as part of your workflow. I think what you’ll quickly realize is that documentation becomes Much more important as part of your job. And I I think if you you think about, like, you know, if you are onboarding a data analyst And you’re training them on, like, you know, where where the data lives, like, how it’s collected, how it joins together, what fields to use, what fields not to use, like, How this company functions, what’s important, things like that. All that now if if you document that in a text format, That is all information that you can pass on to the AI and have the AI do a lot of that that heavy lifting for you.

Jordan Wilson [00:07:28]:

You know, it’s it’s interesting here, and, you know, I’m I’m I’m actually just gonna jump straight into Jay’s question because this is where my mind was going to. So, Jay, thank you for the question. And if you do have questions for Zain, make sure to get them in. So Jay asking, I assume no more spreadsheets needed. You just feed AI, the data, and ask questions you want answered, and the AI will derive unseen insights. Is that where we’re headed toward? Are we headed toward, Maybe a dashboard less, spreadsheet less future of data?

Zain Hoda [00:07:59]:

I think that that’s one possibility. And so right now with, Check GPT’s, like, advanced data analysis. You can already do some of that. You can actually upload, CSVs or or Excel files And have it begin doing analysis for you. I think that is, a definite direction that we’re going. At Vantaa AI, we’re we’re actually not not doing exactly that because what we’re doing is, like, we are just accelerating the process from Question to end answer. And, like, it’s the data data analyst and business users’ jobs to, come up with the questions and to come up with, like, the the actual insight. But that yeah.

Zain Hoda [00:08:48]:

You know? As we see, you know, GPT 5 come out, GPT 6 come out, I think, this, questioner is probably right that more and more of that will shift, directly into the into the AI system.

Jordan Wilson [00:09:03]:

Yeah. Zayno, I’m I’m happy you brought up, ADA. So if you’re if you’re new to data Or maybe you’re newer to, you know, even ChatGPT. So, advanced data analysis is actually a fantastic mode, inside of ChatGPT. I think, early on, OpenAI and ChatGPT kinda got a bad rap because it’s not good with numbers by default. Right? It’s not good at math. So people kinda wrote it off. But The advanced data analysis mode, inside of ChatGPT is actually extremely impressive.

Jordan Wilson [00:09:36]:

I’ve I’ve upload spreadsheets with Tens of thousands of data points that would normally take me you know, I’m not a professional data person, but it would normally take me many hours To make make sense or make use of this data, and and and to be able to have a conversation with it. It it really I think, Zayden, I’d like your your take on It seems like it’s it’s also bringing data to more people that maybe think they either didn’t need to, you know, analyze data before or maybe they think they didn’t need it for their role. Is that something you see kind of for the future is so many more people are gonna start using and leveraging data that didn’t before because of and The accessibility.

Zain Hoda [00:10:16]:

Absolutely. I think that’s actually one of the major reasons why people come to us because, You know, typically, what we’ll see is, like, at large organizations, you know, the business users have tons and tons of questions, And the way they get them answered is they need to get a data analyst involved. And typically, you know, they’re gonna be asking them either via Slack or, like, you know, if, if it’s Like, a structured team. They’ll put in, like, a Jira ticket. And, like, in the ticket, you’ll be like, oh, can you, like, answer this question? Then, like, eventually, weeks later, some data analyst, like, picks up the ticket, writes the SQL, runs it, you know, and then sends back the results. But then The results actually, like, prompt, like, potentially 5 additional follow-up questions. And so now you have to begin that process all over again where you need to, like, submit additional tickets. And for the data analysis team, they’re just, like, inundated with with all these tickets.

Zain Hoda [00:11:11]:

And so and, you know, they’re not really providing that much value add as as part of this process. They’re literally mechanically taking in questions and then, you know, writing SQL queries and and getting the results back. And so that is the perfect place for AI to accelerate that process. Because if you can ask a question and just get, like, the answer in, like, a couple of seconds And then have additional follow-up questions. It just allows you to dig deeper and deeper into the data and just basically make organizations a lot more more data driven. And we’ve seen typically, you know, a lot of a lot of organizations have already done the heavy lifting of, like, the data collection and getting a lot of that data into data warehouses, into databases. And so now that you have all this data in the database, A lot of that is just, like, sitting there. And and really the the key question is how do you unlock that for, like, really anybody in the organization?

Impact on data teams in companies

Jordan Wilson [00:12:08]:

Yeah. Are we gonna see something as an example? Because I know all companies work differently and it depends on the size, but it seems like to me sometimes in, you know, medium Organizations, let’s say. You you know, you have your data team. They’re doing great work. They have their dashboards. And it almost seems like that data Kind of lives with that team. You’re right. And and you almost have to go to them or they have to run you some reports or maybe if you’re, you You know, a highly technical person in a in in in a different apartment, department, you might be able to make sense of those dashboards.

Jordan Wilson [00:12:41]:

But how how is even the role of those types of people going to change as generative AI becomes, more commonplace. Are we gonna see, like, as an example, the majority of companies have a in house large language model that you can log in to an internal system and just being like, hey. What’s going on with With my, you know, my sales, with with my KPIs, and are are they just gonna be having conversations with in house data, and then those data teams are just making Sure they work.

Zain Hoda [00:13:12]:

Yeah. I think that’s that’s a potential direction. I think one of the things that we’re seeing a lot of is that, There’s a lot of skepticism around using OpenAI because, like, a lot of, companies, like, don’t wanna risk leakage of their confidential information, through OpenAI to, other parties. And so, you know, as you as you said in your talk, you know, JPMorgan, for example, is is building up their own large language model. And, you know, it has some potential, but generally from what we’ve seen, GPT 4 is still the best at, being able to do data related tasks, in particular, be able to generate, like, accurate SQL queries for for databases. And so I think until some of these open source models like, you know, get a little bit better at at, some of these tasks, I think for now, what we’ll we’ll probably see is organizations, use OpenAI in one way or another. So what we’re seeing for, like, for organizations that have particularly strict confidentiality requirements, They’re sort of okay with using OpenAI through Azure because Microsoft via Azure is providing some SLAs and guarantees that, You know, any information that you provided, won’t be used to train the foundational model so that you don’t have that that risk of of leakage.

LLMs and data misconceptions

Jordan Wilson [00:14:47]:

Yeah. It’s it is interesting. Right? Because I think Maybe it’s it’s a common misconception or it depends on, you know, your level of of of technical expertise, but It seems, at least to me, there’s a divide on on OpenAI and ChatGPT and even advanced data analysis. I think people either think of it and they think, oh, that’s, you know, that’s to write a blog post. Right? Or it’s someone like yourself. You’re saying, oh, no. Like, that’s That’s the future. People should be using this.

Jordan Wilson [00:15:21]:

Why do you think there’s so much misconception, around, you know, such a powerful tool like GPT 4, OpenAI and and advanced ad analysis even. Why do you think that maybe more people aren’t even leveraging it?

Zain Hoda [00:15:36]:

I I think a lot of it is just exposure. So I think people haven’t seen the capabilities, And and that really hasn’t, like, propagated. So, you know, what people typically do is, like, they go to ChatGPT and they use it for, like, and Text based stuff. So they use it, you know, for blog posts. But, actually, you know, there’s a lot of nuance in blog posts. Like, you people have different writing styles. Like, in fact, I would actually say that, that Large language models are probably better at doing data analysis than they are at, you know, generating copy and things like that.

Jordan Wilson [00:16:17]:

Okay. Okay. We’ve got we’ve got our 1st hot take of the Friday morning. I like that because I think people always assume, Right? That, oh, ChatGPT is not good at math. It’s not good at computing. So they think they they kind of couple that in With data analysis, whereas it is kind of almost a different thing altogether. You know? People always try to stump, oh, You know, ChatGPT can’t handle this math problem, so it probably can’t analyze data. It’s like, yeah.

Jordan Wilson [00:16:47]:

It can. Right? Like, it’s it’s it’s 2 different things.

Zain Hoda [00:16:51]:

Yeah. I I think in particular, if it’s connected to other systems.

Jordan Wilson [00:16:55]:

Yep.

Zain Hoda [00:16:55]:

So if if what you’re doing is you’re you’re using it to generate SQL queries. Now the the the one of the biggest things, has to do with hallucination. So, you know, if hallucination is your biggest issue and what you’re doing is generating SQL, there’s actually ways to then verify that, Okay. Is the SQL output correct? Is it formatted correctly? And then does it run? And then once you get the results, like, do the results, like, make sense? It’s a lot easier to to kind of verify that than to ask, ChatGPT about facts Because, like, facts need some some sort of, like, external references. But in the case of data analysis and generating SQL queries, The facts live in the database, and so all it has to do is generate the correct SQL to access it from the database.

Jordan Wilson [00:17:53]:

You you know, and just just hey, everyone. Just in case you’re listening, we’re dropping some buzzwords. Don’t worry. If you’re here in SQL, that’s a structured query language. It’s kinda like a standard language for database creation, manipulation. Right? I’m like, I’m pretty sure I’ve got that right, but I wanna explain that as well because also, A great a great question here, from Tanya. So Tanya asking, what does the future look like for SQL engineers? Like, Yeah. Zain is is I mean, we kinda touch on the future, but what does that look like specifically for SQL engineers?

Zain Hoda [00:18:25]:

Absolutely. I think that, I think people will still need to learn SQL just in the same way that you learn, like, long division, for example. But The the question is how much of that are you gonna be manually writing in the future? And I think we’re gonna see less and less SQL in particular manually written. And I think that that role changes a little bit in terms of instead of, like, writing SQL, I think the thing that you’re gonna be doing is writing documentation, Getting really good at documenting everything so that you can get AI to write your SQL for you.

Jordan Wilson [00:19:04]:

Yeah. And I’m even wondering. Right? So many and it’s it’s hard, Zain, too. It’s it’s hard to think, Like, with how quickly things change, it’s hard to think, you know, multiple years in the future. But, I mean, is there specifically, when we’re talking about data analysts, Is there a point where mainly data analysts in the near future are just overseeing AI models Exclusively. Right? And maybe they’re not even writing the documentation. Maybe they’re just write making sure the Gen AI models are writing the documentation correctly and that they’re running all the models correctly. Like, is that an actual thing? Because it it seems like data is very finite, zeros and ones, bytes and bits.

Jordan Wilson [00:19:48]:

Right? Or Are there always going to be the the need for human intervention in the actual, analysis process and not just the Over overarching, making sure that all of the AI systems are doing it correctly.

Zain Hoda [00:20:04]:

Yeah. That’s a great question. And I think a lot of that It’s gonna come down to, like organizations are very, like, dynamic. They change over time. You know? Companies buy other companies. They’re like integrating different systems. You know, you were using a particular data collection method that changed or broke somehow. So there’s a lot of this knowledge that, gets built up inside of an organization.

Zain Hoda [00:20:30]:

And I think, like, as part of the data analyst role is going to be Keeping on top of that and basically making sure that that information is then transferred into, the AI system so that it understands what’s going on in the larger business. And so I think it’s gonna be even more important for data analysts to Be kind of that bridge. So, like, be in touch with with company leadership. Follow what’s actually going on inside the company, and, basically, like, document. So document, document, document, like, all of this stuff that that is happening at the organization.

Enterprise LLMs and data safety

Jordan Wilson [00:21:12]:

Yeah. And I’m I’m curious about this one because, You know, I know people who are in data fields, and it seemed like even up until, you you know, a month or 2 ago, it seemed like so many of them weren’t even Using generative AI at the time, and I know a lot of it because when we talk about data, data is gold. Right? Data is everything, and you have protect it. You you have to be secure with your data. You, but I guess now that there’s these, kind of more enterprise, models coming out or enterprise, systems. You know? ChatGPT enterprise is is rolling out. Microsoft 365 Copilot. You know, you mentioned as, Azure.

Jordan Wilson [00:21:56]:

Is, like, is there still gonna be that point Where companies or data teams are still gonna be like, oh, no. We can’t use these models. It’s it’s our data. Like, It won’t will that wall ever, I guess, crumble down?

Zain Hoda [00:22:11]:

Yeah. I think once people realize how much more efficient you can be, I think We’ll start to see some of these, barriers come down. In particular, I think where where we’re at in the cycle is that, AI is largely being used as, like, an experiment or a toy. But I think very quickly, we’re gonna start To see it become like an integral part of company workflows. And the way that that’s manifesting itself where we see it is that we see a lot of companies doing hackathons. And so, like, there a lot of companies are doing these hackathons where it’s like, oh, there’s like A bunch of AI. How can AI, like, help us and and integrate into our workflow? And so, You know, the vast majority of the the winners of these hackathons end end up being something related to Data in particular because I think that’s one of the easiest ways to, for an organization to just become a lot more efficient.

Jordan Wilson [00:23:17]:

Yeah. And it you know, as as we, you know, move move forward in this, right, because I I I literally just had this conversation with someone the other day. They they said, yeah. We have all of our data, you know, already in, you know, Microsoft’s cloud or Google cloud or whatever. Specifically, when it comes to data, if companies are already storing it, Right. In a cloud, I guess from a from your perspective, you’re the data expert. I’m definitely not. What’s the difference then? What’s the difference, you you know, if if let’s say if they’re using, you know, Amazon’s cloud for so using, You know, if if if their data’s already in the cloud, why wouldn’t they just use that same cloud providers? Generative AI, is there is there a difference between something already being in in the cloud versus tapping into the generative AI model that they may, that the same company may be offering.

Zain Hoda [00:24:11]:

I think that, yeah, that’s an excellent point because You’re right that a lot of data that used to be stored in house, is going more and more into the cloud. And if you’re already trusting the cloud provider, Then what’s what’s the difference, between trusting the cloud provider and trusting an AI system in the cloud? I think that’s that’s Very accurate. I think the one thing I would say about that is that there’s still an apprehension about, like, about leakage. So it’s like the the fundamental question is, like, is the data that you give it being used to train the system, The same system that will be used for a different user. And I think, like, you know, we’re gonna see tighter guarantees around that because as long as we can guarantee that, like, you know, when you give data to the AI that it doesn’t, It will never end up in the hands of another another user of that same system. If you can isolate them, much better, I think that’ll drive additional enterprise adoption.

Jordan Wilson [00:25:13]:

Yeah. It’s it is something it’s, for me, just head scratching that that people are always so, so open and willing to to you’d, to use cloud services or to, you know, send big documents. But when it comes to, You know, using a generative AI model specifically with data, it just seemed to me that there was always some some sort of hesitation that didn’t quite add up. It’s like, alright. Well, If you’re gonna do it, anywhere else, but it seems like maybe the big, maybe in the data analyst world, Zain, is what you’re saying is it’s it’s more of the, How they do it. Like, how they use their their datasets, how they train it. That’s necessarily what they don’t want the generative AI models to and Quote, unquote, learn or or use, and it’s not necessarily the data itself. Is that right?

Zain Hoda [00:26:04]:

Yeah. I think that’s that’s part of it. I think the other thing that’s That’s happening is, like regulatory. So as an example, like, if you are in health care And, like, you’re uploading, like, potentially confidential patient information. Now the question is, is that even allowed? And I think, like, We haven’t even answered that question and, you know, probably beginning with, like, the recent executive order, and additional regulation that comes down the pipe. Like, we’ll probably see a lot more clarification around Potentially, like, what’s allowed, how you’re allowed to use use that, particularly as it relates to, like, these regulated or heavily regulated industries like health care And potentially finance and things like that.

Jordan Wilson [00:26:49]:

Yeah. And it’s that that that is an interesting point. And, it’s it’s Why don’t you bring up those 2 industries? Because I feel those 2 industries before Gen AI, right, traditional AI, I mean, and Financial and health are are 2 of the the sectors that have been using kind of, like, quote, unquote, old school AI and machine learning for the longest. You know? It’s been for many decades. You know, Zain, I wanna transition to this. What’s maybe one thing that other people in your field, that maybe they’re getting wrong When they’re thinking about data, data analysis and and AI, maybe everyone’s saying, oh, this is the direction. What’s What’s one thing that maybe you’re seeing people get wrong or maybe 1 opinion that you have that some of your colleagues might not really agree with when it comes to data analysis and AI?

GPT-4 is best for data analysis

Zain Hoda [00:27:37]:

Yeah. So I think the the biggest one there is that GPT 4 is still far and away the best, foundational model to use for data And so if you’re gonna be using AI right now with data analysis, you pretty much have to be using GPT 4. If you’re trying to train up or fine tune one of the existing, open source models, then I think you you’re gonna see that you’re gonna fall a little bit short potentially. And, you know, it costs potentially like 100 of 1,000 of dollars to or Potentially 1,000,000 of dollars to train a model from scratch. Mhmm. So I think what we’re gonna see at the at the end of this is, like, Probably a number of companies will will try to train their own large language models and then realize that, actually, you know, GPT 4 and then, like, whatever, Like succeeds it. GPD 5, like, a year from now potentially is gonna be way smarter than the one that the LLM that they have right now. So they’re always gonna be trying to play catch catch up.

Zain Hoda [00:28:44]:

So I think that’ll be an interesting interesting thing to to keep an eye on.

Jordan Wilson [00:28:48]:

You bring up such a good point because, You know? And, I mean, we just got GPT 4 turbo, you know, in the last, like, 48 hours. I do think that, you know, smaller companies, medium companies think that they can build yeah. They’ll they’ll build their own model. But, yeah, like you said, sometimes, I think if you fill out the form on OpenAI’s website, I think you have to click, and you have to click like you understand that it could be a 2 to $3,000,000 investment To build your own model. So you’ll I I guess moving forward, Zain, maybe there’s a small business owner or someone that works in a medium, you know, size company on a data team. What what kind of, parting pieces of advice, can can you have for them as we, you You know, as we wrap up today’s episode, because we’ve covered a lot. But maybe what’s what’s that takeaway, that at at least when where where you see Data analysis heading in the world of AI. What do people need to get right? What is that that one direction that they should be focusing on?

Zain’s final takeaway

Zain Hoda [00:29:52]:

Yeah. I think the the main thing is, like, think about all of your hidden assumptions. Like, when you’re doing analysis, think about, like, You know, if some if business users are asking questions like, oh, what was, like, you know, our our daily active users, like, over the last week? Just think about, like, all the assumptions that that go into that. Like, what what is the actual formula? Like, are there any restrictions? Like, are you excluding certain things? Like, I think being able to, like, articulate that and document that is going to be like a key skill, over the next, You know, probably a year or so.

Jordan Wilson [00:30:29]:

Yeah. Absolutely. It’s, the next year or so is going to be a wild ride, but at least I think we’re all a little more secure now in our data. Thanks thanks to you, Zain. So, Zain, thank you so much for coming on the show. We really appreciate having you on.

Zain Hoda [00:30:46]:

Thank you, Jordan, for having me.

Jordan Wilson [00:30:47]:

Hey. As a reminder, we covered a lot. There’s a lot more. We always break down each and every interview in much more detail. So make sure, If you haven’t already, go sign up for the newsletter. Go to your everyday AI.com. Sign up for that free daily newsletter. We’re gonna have a lot more From Zain, a little more information too about Vanta AI.

Jordan Wilson [00:31:05]:

I know there’s a couple question or 2 we didn’t get to it, so make sure we’ll get that in the newsletter. So we hope to see you for that, and we hope to see you back and For more everyday AI. Thanks, y’all.

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Ep 142: AI is Changing Data Analysis: Insider Tips