Dialoguing with Your Data: UNCORKED with ROB DARLING

What if the most significant breakthroughs in your business, relationships, and life were sitting right in front of you, hidden under mountains of data, waiting to be discovered? In this episode of the UNCOMMODiFiED Podcast, Tim Windsor dives into an UNCORK conversation with Rob Darling, Founder & CEO of RunQL—an expert in decoding data and unleashing bold insights through the power of language and AI.

Why Listen? 
If you’re tired of sifting through endless data without seeing accurate, actionable results, this episode is your wake-up call. Rob breaks down why speed without trust is a disaster waiting to happen and how most of us ask all the wrong questions—whether we’re talking to data, AI, or each other. You’ll learn why data won’t just surrender its truths and why you must interrogate, decode, and translate it into decisive actions.

What You’ll Learn:

How to Ask the Right Questions: Discover how the language you use with data (and people) can transform confusion into clarity. Speed vs. Trust: Learn why fast insights mean nothing if unreliable—and how to strike the perfect balance. Practical Strategies: Rob shares actionable techniques to 'talk to your data' and get it to talk back, whether through SQL, AI, or everyday conversations. From Insight to Action: Turn data into decisions that drive your business and personal life forward.

This conversation isn't just about data—it's about decoding life’s hidden messages and turning noise into a powerful narrative. 

If you’re ready to stop being a passive bystander in your own story and start being the bold interrogator of your data, hit play now and let’s UNCORK the truth together!

Tim Windsor
the UNCOMMODiFiED Podcast – Host & Guide
tim@uncommodified.com
https://uncommodified.com/
 
PRODUCERS: Kris MacQueen & Alyne Gagne
MUSIC BY: https://themacqueens.ca/

Transcript

PLEASE NOTE: UNCOMMODiFiED Podcast episode transcriptions are raw text files and have not been proofed or edited. They are what they are … Happy Reading.

© UNCOMMODiFiED & TIM WINDSOR

[00:00:00] Raw and real data never lies, but it also doesn't just surrender its truth either. It just sits there waiting for somebody to be bold enough to interrogate it, decode it, and translate it into something useful and powerful. Hey, my friends, welcome back to the Uncommodified Podcast. I'm Tim Windsor and my guest on the show today for another Uncorked conversation, my favorite kind of conversation is Rob Darling.

Rob, welcome to the show.

Hey, thank you, Tim.

Awesome. This is gonna be great. Now, quick intro about Rob. So, Rob is obsessed with how we talk to data. Ah, but here's the kicker and how data talks back. This is going to be an interesting conversation. Now a bit about Rob. As a founder and CEO of RunQL, he's on a mission to help businesses adopt AI to speed up data insights putting [00:01:00] their business at risk, which is a delicate balance because speed is useless if you can't trust the data insights you're receiving.

He's built and sold SaaS companies, led, enterprise data initiatives, and knows that great questions lead to great insights, whether you're conversing with SQL or the database, AI, or human to human. And when he's not building companies and doing all this data stuff, he'd rather be playing sports than just them, which says a lot about who he is.

Now, this is an uncork conversation, Rob, and I did pre warn you. So what are you going to uncork tonight?

I have actually I have the bottle here.

let me just grab it.

What's the bottle? 

I did an ode to, uh, to Canadian producers.

at you.

So this is sort of ledge. Uh, it is a Canadian whiskey and syrup from Montreal.

Nice.

, And for me, it is one of my favorite whiskeys that has maple [00:02:00] syrup in it. There's a lot of great ones like tap three, five, seven, a lot of great brands out there, but this one, I think hits the perfect mix between maple syrup and, and whiskey.

Now that's awesome. You know, that's interesting because, I've actually never, uh, had anything with maple in it, so I'll have to try that one. Now, I hate to say, in the midst of the time we're doing this, you're going Canadian, and I'm going with brothers bond. And it is an American bourbon. So I'm gonna go American bourbon.

I'm gonna go, I'm gonna go brave tonight. And I'm going American bourbon.

Well, we got to drink one for our friendly neighbors south and and one for for us, right?

Yeah, absolutely. And the other part of all that is, you know, if you think about it, we've gotta understand that I gotta go to work to theUS next. So, you know, I still got lots of friends in the U. S., so I'm not ready to totally give up on that thing yet.

Yeah. Well, you know, , I was thinking about this the other day. There's a lot of animosity right now, right? But [00:03:00] it's, mainly based around one individual. It's it's not the whole country. 

no, 

no, it's not. And we have to probably just, uh, cooler heads, cooler heads need to prevail, I suppose, at the end of the day. that's at least, I think, the way we ought to look at it. So, listen, cheers to you, my friend. Thank you very much for joining me on the show.

Yes. Cheers.

That's 

a good drink. That's a good drink. Okay, Rob. Here's let's kick it off this way. So let's get into this with this big question. So I'm going to ask a really big question and then we'll figure out how we're going to tease it out. So my big question is how does language how does? Query. How does translation unlock bold insights within big data world you live in?

And, you know, this idea that you, you've talked to me about. So I want to really understand. So how do we talk to data, which, by the way, sounds like we could need some kind of help. But how do we talk to data and [00:04:00] how and can Data talk back to us. That is a fascinating framework. So that's the big question.

How does this all work? Get us into the world of Rob. Right.

You know, I think the, the spot to start is human to human communication, right? Um, so when you are, and I are talking, we're translating each other. We're trying to understand the questions that we're asking, what the person is really asking about, and then trying to translate that and then spit out an answer that we think is.

You know, where Tim is trying to go, you know, databases. are no different. You're using SQL to ask a question to get an answer from the data. And there's a translation there. I've got a problem. It's a human problem. I need to somehow understand the data behind that [00:05:00] problem. And I need to use some sort of translation mechanism in between for us as humans.

That's our brain, our brain is doing the translation, even in English to English, right? , and then in AI, as we've all seen with, chat GPT and other tools, there's a translation layer that happens there. You know, that if you ask a question a certain way, you're going to get a better result than if you ask it another way.

, and so a lot of it comes down to how we comprehend. a question and, , how the system can comprehend what kind of answer to give out.

Interesting. So, I like this framework of thinking about data like a language. That is a very interesting way of thinking about it because I don't think it's the way I typically think of data. don't think of it as a language, and I suppose if I'm using this sort of analogy, if I understood it more like a language, I would understand that translation, obviously, is important, but then how I [00:06:00] speak to it.

and how I interact with it becomes important because there is a nuance to that experience that's happening. And so how did you find yourself in this world? I you've had a very successful career, you know, and you found yourself in this world and now you've got this new you're moving into, you know, run QL, which is, which is interesting.

what's your journey here and why is this so important for you and for all of us to consider?

Yeah. , you know, my journey started, uh, actually, if you really want to go back, my, my journey started with, , visually impaired students. Yeah. Working for the ministry of education and, uh, it's all related though. So if you're working with visually impaired students um, you to help them break down a problem. Because they don't have the visual cues to know how to do up a zipper, for instance. So you got to break down that those tasks and figure out a way to [00:07:00] communicate those tasks in a way that they can understand, interpret it, and then do the action. Um, where with, with other human children who can see, you can just say, watch me do it. And they can do it, right? But when somebody can't see you got to break it down and communicate it to them, step by step to get them to do that action. Um, I went back to school for computer science, you know, the human stuff was fantastic, but I got a little bored. So I went back to school for computer science and long story short, ended up in a role, as director of enterprise architecture for manual life Canada, where, they had this failing data project.

That had been going on for two years, they were trying to bring in business intelligence. It wasn't working. There was all sorts of issues. And so finally they tapped me on the shoulder and I got that implemented in four months. So a project that had been struggling for two years, I got in place in, in four months.[00:08:00] 

And so that was my first kind of, introduction to the data problems that organizations have. I'd done a lot of software engineering at that point. Server systems and, and security and that kind of thing, but not a ton on the data side. And then from there, everything I did afterwards had to do with data.

So every startup I did. had huge amounts of data that we'd have to go through to figure out, what was in the data? You know, what were the behaviors that were happening? How are these users using the app? Do we need to change things? what are the patterns that we're seeing here in this data?

Excuse me. , So, so it just kind of evolved. It continued to evolve where most of my time in all my SaaS startups was spent in data, , understanding, but, but in data for a reason, understanding people's behaviors in the apps, understanding [00:09:00] patterns in the world. , And, and that kind of. You know, grew into me building a data team, , at another company and, uh, and then eventually seeing this problem, that we're solving today.

we're going to get into that problem because that's an interesting thing. I want to go back to, you know, just this idea that, you know, you and I chatted about like this idea that great questions lead to great insights, whatever we're talking to, which is interesting to me because I have this sort of general bent.

I actually believe that questions are, human superpower. We don't think about them that way often, but in a very profound yet basic ways, the way I think of questions is questions have this innate power within them to do two things. So on one level they propel or cause to happen a conversation or an interaction.

Typically when you ask a question, most people feel some obligation to respond. So there's, there's a propulsion of [00:10:00] conversation, a generation of conversation that happens when you ask a question. The other thing that I think a lot of people don't think about is that not only does it propel a conversation.

It actually predicts it in the sense that, uh, you know, if I asked you a question, you know, you love to play sports. So if I said to you, he say, Rob, like, what's your favorite sport? If you could only play one. your favorite sport to play?

Yeah. Yeah. That's an easy one, right? So I would say hockey.

Right. So, the, the reality is, is whether people understand or not, my question propelled Cause to Happen and actually predicted your answer, meaning the reason you and I aren't talking about the geopolitical situation in Russian Ukraine right now is because I asked you a question about what sport you would play.

So, in fact, our question, even in its rudimentary fashion, propelled, caused to happen, but it also predicted or bounded, created a box in which your answer typically would happen. So, to me, this is the superpower, the simple superpower of questions. I guess my question to you is, does the querying of a [00:11:00] database have the same inherent superpower in it?

The questions we ask, does it both propel and does it predict, at some level, the conversation that we're going to have with the data?

yeah. A hundred percent. I, I love the way you framed this. , A hundred percent. Because , let's look at it another way, if you're ignorant of the other things that are going on in your example, other parts of the world, right? Um, if you're ignorant of the other data that you have, then your questions are always going to be bound by the data and information that you have.

Right. But it's actually the question that propels other. Data to be brought in. 

So if you and I are talking about sport and we have no knowledge of Ukraine, um, we might start talking about sports in other countries. And then we start talking about Ukraine and then we're like, Oh, [00:12:00] I wonder what that data shows.

And then all of a sudden we discover and explore more about. Ukraine and what's going on there. Right. , But I think that all on the surface, the question is the important part, but there's an underlying piece and that is just curiosity,

Hmm.

right? You to be curious and you have to ask more questions. I was a judge last night for the University of Waterloo.

, Data science club, and they had 350 students, , participate, which is amazing. They were engaged and last night they were presenting their findings. And the amazing thing was that these teams were able to look at the data and deliver insights based on that data. In a two week time period.

And they had never seen that data before. And if I think about the insights that they gave, like they were giving good enough insights for people who have [00:13:00] been in that space for a year or more, right? Because they were curious that the best ones were so curious. They didn't just come with insights. They came with more questions.

They said, they said, I looked at the data and I had this question. And so then I dug into the data and then that made me have this question. And then I looked into that and that made me have this question and then. I discovered this thing and here's what I discovered. , and so it's, it's the ability to be curious and to ask questions and cascade that open the doors to, to other areas that we might not have thought of originally.

That's, that's interesting because, you know, we're so focused, you know, when, when you think about it in our minds, when, if I say to somebody, like, Q and, we immediately say A, which is answer. And what you seem to be suggesting is, is there, it isn't just question and then answer, it's question and then actually.

[00:14:00] A curious second question and, and that maybe is the power to drill down and, and rake that data. Whether we're doing that in a human way, in our own human brain, or whether we doing that in complex systems that are doing that at multiple times faster than we can do it. Those subsidiary questions almost rake into the data in a different way.

yeah, totally. If you think of, um, is it the three Ys? Is that what it's, I think it's the three Ys, right? So if I ask you, Tim, like what's your favorite color and you say

Blue. It's

blue and I say, why is blue your favorite color?

because, because I always, I look good in blue

Oh, and why do you think you look good in blue?

because my wife tells me so, Rob.

Right? So I learned a lot more, like a lot more information there

than 

than just the simple, question and answer. Um, so now I know that like [00:15:00] Tim likes blue because he looks good in blue, his theory behind why he looks good in blue is actually cause his wife says that he looks good in blue,

That's right. Because my general theory of life is, is if my wife is happy, I'm relatively happy to

Rob. 

wife, happy life, 

Yeah. Yeah. 

As they say, as they say. So, I mean, you go on this big journey, you've had multiple companies, you've created them. And now, now you've got this new adventure. So what is the run QL?

So explain a little bit about what this is, what your team does. And yeah. Why another expression of what Rob wants to be? Because you, you've had lots of expressions along the way and you probably could have settled into one of them. So why another expression in this idea of RunQL? And what is your team doing today and what are you doing today that is exciting you the most?

And what is, what do you see as the benefit or the beneficial outcome for the people that you're partnering with?

Yeah. [00:16:00] Hey, that's a lot of questions.

know, I know. I got a lot of questions, man.

I like to say I have more questions than answers. When I'm on a podcast, I try to limit my questions because I end up , interviewing the podcaster,

enough. Fair You're not going to get very wise if you do that, Rob. Ha, ha, ha. Uh,

So, um, let's start by the first thing that, you know, what, what drives me, what propels me to solve this problem, and, and solve these problems in general. So I'm a big believer in, we'll call it servanthood. Okay. I'm a big believer in finding how I can provide service to others. That propels them forward.

and, and so what is the best way given my skills and abilities, , to do that? , so that's number one. I always look through that lens. , number two is I, I saw this problem and I went out and interviewed a bunch of, data analysts, data leaders, business leaders, , [00:17:00] and I listened to their problems and saw this pattern in those problems that they described.

 And that kind of repels me because at that point I'm invested, right? Like when I hear enough people have a problem, I want to solve it for them. It's, it's very human driven. I'm very human driven from that perspective. and so the way I like to think about it is I'm trying to maximize my service.

to others for the greatest value that I can provide to them. , so that's number one. That's, that's where we start. Number two is, , if I remember the order of the questions. You know, this problem in particular, data analysts have, have been underserved in organizations and, data leaders, there's a change in expectations from data teams.

so it used to be, you can push them to a BI dashboard and say, you go find the data. I've put it on a BI dashboard. You go find it. Well, with it, it just [00:18:00] like Google changed people's expectations for apps, chat and chat GPT have changed people's expectation and texting has changed people's, , expectations for getting data insights.

, and there's a lot of problems there. It's just a bottleneck and it's. Slow and it's painful for everybody involved, both sides, the business and the data team. , And so that, that really intrigues me. how do we solve that? How do we help businesses move faster? How do we help them make better decisions?

, how do we help them get technology out of the way? And I think, you know, the greatest technology, , innovations is when there's less of an interface. Um, and so people are used to kind of this chat interface. So how do we remove technology from it? it remind me of your other questions, but I, I think I hit the first

No, yeah, you did. I asked, I did ask a lot of questions So, I, I overloaded you with [00:19:00] questions. So, that's interesting. So, you, so you see this problem. You want to solve it. You put your team to work on it. So, here, here's a question. I mean, Again, I, I don't know a lot about this world, I know a little bit about this world, and obviously my listeners come from lots of different worlds, but, but this problem that we're talking about is, is that I've got data that I need, to extrapolate some information from, and in the old way maybe of thinking, I say, I need a business intelligence tool, give me a dashboard, I need to see the following data, put it here, and now I gotta get somebody who can write this complex way of extrapolating that data out, and now I, can see it.

Your world and your idea is moving into a different direction here somehow. It's, it's moving away from a traditional way of how, I would get that data out or how I would get a programmer to get that data out for me. Your vision and the way that you're looking at now starts to approach this in a different way.

How's that different way of approach now look for you?

Yeah. [00:20:00] So let me start by saying for 30 years, you know, we've been promised that dashboards would democratize data access. , and then here we are 30 years later with Um, and it becomes, you know, if you're a small business owner, it's not as bad, but if you're, you know, in a larger business, you now have hundreds of these dashboards. And how, how do you find the dashboard that has your information? Like sometimes this one dashboard has. The data, but it's, missing one piece and there's another dashboard that's almost exactly the same and it has another piece. And, uh, how do you find them? So that's one problem as they get bigger.

The other problem is, is businesses just move faster now. And so a lot of times when you get a request for data, you know, you need, you need it today. But next week, it's probably not valuable, , or may not be valuable, right? So how do you enable data [00:21:00] teams to answer those questions faster? And then there's going to be more questions and then you're going to find out what's valuable.

And then maybe you build a dashboard because it's consistent, right? , but there's all these ad hoc requests and exploratory work that happens, , in between there. so how we're handling it differently. Is when we started with a data analyst to allow them to just speed up the time that it takes them to write queries, to manage the queries, uh, think about it, you know, for your audience, like documents, I've got a bunch of documents.

How do I write those documents faster? How do I find my existing documents faster so that I can reuse them? Uh, data analysts don't have that. They don't have those tools. They've never been developed for them. the piece to that is how do we leverage AI in a, in a way that doesn't put the business at risk?

So, I don't know if your listeners may not know this, but, , because there's a lot of [00:22:00] hype around AI and AI is really good at tech summarization and, and creative, you know, wording around things, but what it's not really good at is. Deterministic responses to structured data. So deterministic means if I ask a question, I always get the same response that happens in the same way, and I can be confident that the response I got is correct. AIs or AI LLMs are probabilistic, meaning that there's some randomness in there. , and so the average accuracy rate for For generating queries to get data out of a database is 16. 7%.

Oh my gosh.

So, you know, if you're a business owner or CEO, you're not saying, Hey, everybody throw your data into an LLM and ask it to give you answers.

Because, you know, that most of the time the answer is [00:23:00] going to be wrong. you can use some special things called graph rag that kind of help ground the data in truth. Kind of like your example of we're talking about this context window, like hockey. We're talking about hockey. So then Ukraine doesn't come into the conversation.

, you can ground an LLM with that truth and say. All the questions coming in here are about hockey and that helps it increase the accuracy rate to 53 percent on average, that's still like flipping a coin. So what we're doing is saying, Hey, business user, you ask a question. If there's an existing query.

To the database that a data analyst on your team has already written, and it's been, , deemed as the source of truth for that particular question, we're going to with that. So now you, as that business user, see that, [00:24:00] oh, , you know, Rob Darling wrote this query. He's a data analyst on our team. He knows about this stuff.

I can trust this data, but where we are using AI is to augment that data analyst. So if I ask a question as a business user and, , the answer didn't exist, the query didn't exist, it actually gets created for the data analyst. The data analyst gets a. SQL query, which is, uh, a language used to talk to databases.

It gets an SQL, they get an SQL query that they can then just review and modify. So they didn't have to write it from scratch. And when they click save, it's added to the dictionary of answers. So the next time a business user asks a question, it's already in the database. And they can get the answer that's, that's one way as it grows, we're taking it even further where we're grounding these questions for some organizations who are willing to take a little bit of a [00:25:00] risk or allowing the AI to edit the query, , and respond to the business user without the data analyst, but that's, we're allowing the business to make those choices so the business can control that.

,

So again, so let me put this in my world of my my little head So what you're describing to me if my company partnered with you and your services I have a bunch of data that I collect, you know, let's say for my, in my world, you know, I do leadership development, I do leadership assessments, I do organizational health assessments.

Okay, so let's, let me put this into my world. So let me think it through so I can try to get my head around this. So I've got. Years of data in my system about leadership assessments of executives. I've got organizational health assessment data. I got all of this stuff. I might have year over year comparative data, of different organizations or the same organization, but I got a bunch of data currently right now.

I, [00:26:00] I hire a programmer and my programmer. Um. I tell the, I want to see the data in this way. And so he's written a series of queries that query that information and bring it out to me in a static report. So I get a report of the data the way I want to see it. But there are times where I get the data out and it's very hard to compare that data.

I get data about this year, you know, the survey was done in this year and then we did it two years later. And my crossing that data and understanding what's happening isn't always easy. So in this case, I have to then go back to my program and say, look, I really need a better way of getting this data to talk to one another.

I got this. I actually want to do something I've never done. I want to do a broad based study of all this data. And I want to understand what are the primary weaknesses of all the leaders I've ever done assessments on. Okay, so, in the world I live in now, that's [00:27:00] clunky, it's hard, I gotta go, I gotta get my guy, and eventually my guy, he retires, can't find him.

In the new world, the world, and the run world, I actually know almost like I'm interacting with a I I query. I asked the question of my system and say, What does this look like? How does this data compared to this data? And there's no query that's written.

So the system then says, Hey, I'm going to create. I'm going to translate What you said, Tim, into the technical language of database of S Q L language, and I'm going to write for you, the proper syntax of the query that's needed to be made to what you want, and it's going to happen And depending on how I want to interface, I might have the choice to say, once you do that, please send it to somebody who actually knows what they're doing and let them verify that and, and say, yep, [00:28:00] that's good and go boom. But once that happens, that query that I made in an independent way is now available to anybody in my organization.

But if I'm a, little more risky, I might say I'll forego the data analysts vetting over here. I'm beginning to trust this thing. Let's just, fly with that. And now it becomes a set query that can be used in multiple ways, but because I'm doing that, it's iterative for what I want.

But then in somebody else, when my, my partners ask a different question tomorrow, it's now creating that and putting it in the repository. Am I. tracking.

Yeah, you are. , for sure. I think the important part there of the last statement is once we have all the queries and we know that the queries are correct that you've been using, much easier to ground everything in those queries in order to ensure the accuracy of future queries.

Yeah. That's fascinating because again, I'm, I run a very [00:29:00] small business really like, like it's not a big business, but we run into this, problem right now. We now have a bunch of data and we want to compare it in different ways, but it's clunky.

 It's really hard. And frankly, it's. Very expensive. And it's difficult. And to be honest, even at that, I don't always trust it. So I find myself going back and doing a lot of, frankly, you know, like, uh, caveman verification, where I'm literally taking, I'm exporting the CSV file out of the data, and I'm now sorting that, CSV file and I'm, and I'm actually hard comparing it across to this extrapolated data.

And I'm checking it in eight or 10 places because I got to validate, do I actually think this whole thing is correct?

And you just described what a data analyst does. You're,

you are, a hidden data analyst,

but that is exactly what a good data analyst does, right? They, they look at the data and they say, okay, this looks pretty good, but I need to [00:30:00] just double check some things, right? And they go and look at the raw data and they say, does this align with what I've output in this report? Am I missing something? Is there some nuance? Because often there is a nuance, , that you're missing, right?

Yeah. It's a really fascinating thing. And again, if you're listening in, I mean, this conversation is for a subset of my listeners. You know, I've got, listeners in a bunch of different countries and they listen for lots of different reasons. And for those of you who listen to the Uncommodified Podcast, you know, it's a bit of all that and uh, You know, a little bit of a kettle of something.

We don't know what's going to happen next. So this conversation isn't maybe for everybody, but at the same time, I would say that there's a general application to anybody listening, forget if you're into big data or business or not. There is a, there's a part about this, which is again, the questions you ask.

of the data that you are analyzing either in your mind or, you know, in society or even a conversation with a friend, the questions you ask are critically important and the [00:31:00] assumptions and biases you bring to those questions are critically important to create that conversation either with another human or a system.

So you know, You know, the big macro thing is questions are critical because they, they drive everything. So regardless of whether you're applying this to your big data business or to your, your big data life and the conversation you had with a friend, those principles are really true across the board.

But for those of you are, you know, who listen and are looking at this from a larger data set perspective in your business. You know, I would challenge you to ask yourself, like, what, does the approach that Rob's talking about, what would it do for your business? Because there is a fundamental difference in the way that you're approaching this, Rob.

There's something fundamentally different and quite fascinating and a little bit magical about it in the sense that You're going to create a whole different world and accessibility to people that is not available today in the traditional [00:32:00] ways that we look at these things.

Yeah, a hundred percent. I think, , I'm sure a lot of your listeners are, , you know, have needed data for their job, you know, wherever they work, , and have had the pain of having to wait for the data team to respond. With data, and so if they can ask it in a natural language way. And still know that they get a trusted insight that's based on, foundation or based on a data analyst response, then they can be confident in their decision that they need to make.

but that is the real balance. There's a balance between speed and trust. There were lots of solutions out there that, uh, took natural language and translated it to SQL for you, , just for the business user. Nobody else was in the loop ever, wasn't really grounded in anything. and those have all failed because they missed the most important factor and that is trust in the insights, trust in how you [00:33:00] got to the insights.

And LLMs are a black box, so you really need to ground them in, in truth.

You know, and it's interesting. I mean, I think about even yesterday I had a meeting with a client of mine and working with. Sort of some newly formed an executive group and a director group and having a conversation with them and leading them into sort of, you know, what does it look like to come together in a new season?

And they, they're on the back end of a large enterprise, you know, ERP changeover and, of course, now, You know, they're coming from a very antiquated system historically, very solid system, but very antiquated and very difficult to get programmers who could support that anymore. And then, you know, they made this decision, which was a massive decision, you know, to move in a different way.

And now they have a whole different, ERP system and enterprise software, and it's running their whole business. Of course, now they've got a series of dashboards and they have a team that's creating these And even yesterday in the conversation, you know, there [00:34:00] was this about, you know, one of the business leaders saying, I'd really like to know this.

And then of course, Oh, you chew, Oh, we're going to have to get in the docket to get that. Put in the dashboard, 

you know, like, Oh, man, is it not in the, Oh, it's not in the current dashboard. Oh no. And so I'm thinking about this conversation yesterday. And what I'm hearing you say, if this company was employing the discipline and product and the team that you are working.

What happens at this point is that person says, You know what? I'm going to ask the query. I'm going to query system and I'm going to ask what I want. And it's going to create a query. It's going to verify it or get it verified. And then I am actually going to be part of the creation of something.

And then it's going to feed back something. And because there's systems and checks and balances, I'm going to be able to say, You know what? That data is relatively or rather or really trustworthy. And then that query is available to anyone else and we can [00:35:00] pack on and sort of build this thing.

But now I don't have to stack up behind the last 40 requests.

exactly.

that's the brilliant. piece of this because even yesterday, this came up in a conversation and you could see everybody. Oh yeah, that'd be great to understand that. And then you feel like the deflation in the room.

It's like, Oh no, have to, Oh, we're going to have to get that another dashboard.

Cause it, cause it takes a lot of teams, , at least a week, maybe four weeks, and sometimes even longer to create one of those dashboards, right? It's a, it's a long process. And, the sad part is, is organizations end up with hundreds of them and people forget about them. And then the, this leader that you're, know, this example, uh, you know, a month or two later, they'll come back and they'll say.

They'll ask the data team, where's that dashboard or I need this data, but I need it with this filter and that, that dashboard doesn't support that. [00:36:00] So it's just like, it

I forgot. I forgot. I need this too. I need it. factored by this. Oh no.

And if we just realize, if we just accept the fact that people are going to have more questions based on every response.

And we, we actually build the systems to, to accommodate that then they can get the answer they need. Right.

Yeah. Well, and I go back to something you said earlier, Rob, which is very interesting. I think it's connecting for me right now. You talked about the brilliancy of these students that you just went to judge. And you talked about the, you know, some of the most journeys was they asked a question, they got some insight, but it drove them to ask another question and another question, another question.

The challenge in this, the older way of looking at this is you ask the question, the data team has to build this thing. Actually, that second question. Is almost a punishment to the data team because now they got to go back and do it again. So maybe you don't ask the second question because you go, man, I [00:37:00] really want to ask it another question, but man, I feel like I just, you know, I just probably burnt all my, my money with these people already.

But what you're saying to me is, is that architecture and system that you guys are building and the way you're moving, it's actually more of the mind of that student where I get to ask the data a question, then intuitively it builds this query. And then I actually, when it comes back to me.

it should ask me to ask another question and I can drill down again and create another layer, but I get to do that through my own interaction and curiosity and curiosity to want something different doesn't become this sort of. conundrum for the data team, where they keep saying that, that Tim Windsor, can he stop asking questions and trying to get me to screw around with the dashboard?

This new system is saying, Tim, ask me another question. Ask me again, and we can rebuild it. That's where I feel like I can hear this going, which I think is [00:38:00] just wonderfully brilliant because at the end of the day, that means I get to be as smart and curious as those students.

hundred percent. I think, , exactly to your point. Imagine you've built up a catalog of all these queries. And so now, you know, in an organization, everybody who asked this question, the next questions they had were this. And you can actually show that to the user. Now it's, it's much like Amazon shopping, right?

Like if you look at a product on Amazon, they say customers who bought this product, like looked at these five products. Well, we can do the same thing now because we've captured all those queries. , Tim asks a question, we can now say, Hey, Tim, people who asked this question before in your organization, ask these five questions next.

You don't have to go down that path, but you, you can at least leverage that path, , to, feed your curiosity.

Wow, that, Rob, it's a brilliant conversation and I [00:39:00] really, really appreciate it. And so here's a question. Let's say somebody is listening to this conversation and they've, they've got a lot of big data. And they want to ask a bunch more questions of it. And they want to free themselves somehow from the old way of having to be architects of all these things in the background.

And they want to find this new way of brailling and touching that information. how do they hunt you and your team down?

Yeah. A great question. So they can find us at runql. com. if any of them actually go to runql. com slash. Meet. html, it will automatically open up my calendar and they can book a meeting with me to talk about anything they want related to data.

Awesome. You know what? Listen, if you want to ask better questions of your big data, if you want, uh, better intuitive answers that become in nature, that allow you to control the exploration and become even more curious without feeling like [00:40:00] you're burdening everybody around you, You know what?

that's a great URL that you ought to visit. So, Rob, listen, really appreciate it. Let, let me take this conversation in a totally different direction for a second. You know, my podcast is this idea of uncommodification. You know, one of the reasons when you and I chatted, why I was so interested in having you on the show is, you know, you are clearly A very uncommodified individual, the way you want to look at the world, the way you want to look at data, the you want to serve, the business problems that people have, and understanding the information they have, and leaning those insights.

I'm interested to know, so, uh, when you walk in a room and you're, bringing in that room the thing that you know only Rob can bring, that unique expression of Rob that has some kind of unique and positive benefit maybe to others around you, what's Rob doing?

Yeah. So when I walk into that room, the thing that I bring is, , I understand change. I understand how to change systems. [00:41:00] Uh, I grew up around change, long story short, that's the short of it. And, and so I learned how to adapt. I learned how to, , help others adapt. And so, uh, when I come into that room, I really understand what the motivations of people are, what their desires for change are.

And, uh, and how I might be in service. Of those desires to change. And it turns out that for a lot of organizations, it usually comes down to data because data is what drives the decisions in order to make the change.

Awesome. I love that. I think it's a really, wonderful, unique expression of who you are. , you know, you and I have, you chatted only one time before chatting now, and you know, I, I have to admit, Rob, I'm just super inspired by this conversation and the one we had before. It makes me want to understand what data do I have available to me, whether it's enterprise data in my business or whether, frankly, it's just the human data I get every [00:42:00] day in the exchange that I have with people, and I want to ask better questions of it.

I want to ask better questions that are not necessarily a question that's already drawn a conclusion, and I'm trying to ask a question to get people to tell me what I want to hear but to actually ask questions of data and of others in such a curious way that I actually can become alarmed or surprised by the answer and the learning that comes from it.

That, to me, is part of the journey of being human. And we're talking about a I were talking about data, but actually Going back to the where we started. It's all about that human exchange between information and the processing of that for a better outcome, a bigger understanding of the worlds we live in, whether that's a business world, whether that's a community world, whether that's the global world we live in.

So again, if Rob and his team could help you in a way, please connect with them. I'm interested to know if, as you listened in, if you found something of interest in this conversation, either for yourself or your business, do me a favor, [00:43:00] email me at uncommodified. com.

And let me know how you're processing this, how you're uncorking this idea in your life and how you're asking better questions of data and how you're letting data speak to you. Rob, thanks so much for joining me. Cheers. thank you.

Yeah. Cheers, Tim. Thank you.