Optimizing Data Processes with Ash Tiwari

This is Making Better Decisions. I’m your host, Ryan Sullivan. Decisions are where rubber meets the road for organizations. Each week, we’ll be learning from people who are on the front lines of turning raw data into better outcomes for their organizations. This show is sponsored by Canopy Analytic, helping companies make better decisions using data.

Ryan: Hey everybody. Welcome to another episode of the Making Better Decisions podcast. Today’s guest has over 15 years of consulting experience, culminating with a focus on financial modeling and data analytics in the tax space. He was an early investor in training, He’s Uh, an experience using modern BI tools and was, in my opinion, a contributor to giving KPMG an early mover advantage with some of that stuff.

Ryan: Please welcome Managing Director at KPMG US, Ash Tiwari. Hey, Ash.

Ash: Thank you. Nice to, [00:01:00] nice to have me in on the call. Thank you.

Ryan: Yeah, I’m super excited to, uh, to dig in with you. So I want to start the podcast off with the same question that we’ve been kicking all of them off with. What is one thing you wish more people knew about using data to make better decisions?

Ash: Yeah, I think, uh, that’s, that’s a very good springboard question, uh, um, right. Uh, I think, uh, I have actually two things, uh, one for, uh, business people and one for data people. Because in today’s world, we know that we work in hybrid teams, right? Business people and data people work together to solve the business problems and, and, uh, work efficiently.

Ash: So I think for data people, I think the most important thing which I feel is that the context matters. Understanding the bigger picture, the overall business objective, the problem we are trying to solve is very much important. [00:02:00] And sometimes it gets neglected because we as data people get overindulged on our technology side and forget the business aspect of it.

Ash: So that’s on the business, on the data people side. Similarly, for business people, I think, uh, the important thing is, uh, that business people need to understand that data is usually not readily available. It is scattered across our organization. We need to bring in from different places, different sources, different file formats, et cetera.

Ash: So it, it, it is a joint responsibility of, uh, and the technology teams and the data teams to basically work together to curate the data, which is really, uh, really useful for today’s BI requirements.

Ryan: Yeah. Wow. There’s, um, I think five podcasts were the places that I could go at that. I think, you know, the, the obvious [00:03:00] place to start. Is by saying how much I agree with that team mentality. Um, you know, obviously the tech people do need to know about business. Otherwise you tell them to build stuff and it doesn’t make any sense when it comes out the end.

Ryan: And the business people also need to have a basic understanding of, of kind of tech and data paradigms, right? Otherwise, you know, they’re not going to know which are the right questions to ask and things like that. Um, and so. You know, but there really is this, I talk to a lot of clients or honestly, even, even many times folks that were in kind of the prospect and sales journey with where they’re trying to figure out like, you know, Hey, do we want to work together?

Ryan: Do we need a consultant at all? And the statement that I always make is we’ll never understand. Your business as well as you do. But on the other hand, you’ll probably never understand this tech as well as I do. And what that makes is a [00:04:00] really good team. That’s why I think, you know, Hey, here are the requirements, throw them over the wall.

Ryan: That, that sometimes goes awry. It’s like, let’s stay a team. Let’s stay working together. One of the things that I want to dig in on next with you is That data is scattered and not connected problem. Uh, so some people call this siloing, um, technical people might call this integration or ETL. Um, but I do definitely have the same experience with you where there’s a lot of people who say, Oh, well, we have the data for that.

Ryan: And it’s like, okay, sure. But it doesn’t talk to the other three systems that have the other chunks that we need. So. What has your experience been trying to solve that problem?

Ash: Yeah. Yeah. I think, um, very valid point. I think I work in the area of, uh, uh, tax compliance and consulting, uh, [00:05:00] and, and work as a part of KPMG’s tax ignition practice. Um, In this space, we deal with a lot of data, especially in the compliance side. We need to have data coming from different sources, like whether it is stack softwares or ERP systems.

Ash: Sometimes, uh, tax departments have their own set of work papers, which they maintain manually using Excel and other kind of, kind of desktop based

technologies. So it, it comes from different places, uh, and in different formats. Ahem. A lot of time joining them together seamlessly to prepare it for compliance process becomes a big, big task in itself.

Ash: And I think my experience in this space has been that I think we as a, as a data people, we try to, Advise the business people to get the data in the right format as much as possible so that we can reduce the [00:06:00] downstream data processing and data transformation requirements. Uh, I think that’s where the discussion with business and the technology teams makes a lot of sense.

Ash: And they help us in giving the data in a better format, which reduces the transformation requirement. Obviously then. Once we have the best possible data in whatever format, then we need to basically leverage the technologies like desktop based low code, no code technologies or some other technologies to clean up that data and get it prepped for, uh, for, for compliance processes, etc.

Ash: And that’s where Key things like data volume, data quality, and those things come into picture. The scalability of various automation we build becomes an important aspect there. Because if you are dealing with, let’s say 20 entities, trial balanced data versus thousand entities, trial balanced data, [00:07:00] then we need to look for different types of, uh, data transmission solution, which can handle those varying scale of data.

Ash: So. I think my experience is that, uh, carefully looking at the process and the sources and then appropriately choosing the optimal combination of technology to process the data helped us in long, long run.

Ryan: Yeah, yeah, absolutely. And I mean, I think that you, even though you were talking about tax, you really kind of accurately assessed the reality that most businesses interact with, which is that the cleaning, shaping, connecting. Work of getting ready for the actual analysis in my experience, almost always been more than three quarters of the work.[00:08:00]

Ryan: So like, you know, somebody asks a question, right? I started my career. I was kind of doing things the old way, right? Like do a bunch of exports into Excel and then manually. do all the stuff necessary to get it into that place. And then, okay, cool. Then I spend the last, you know, five to 10 percent of the time doing the actual analysis.

Ryan: Like all of it was collection and prep and all that stuff. And To me, the thing that got me very excited about using some of these modern tools, whether it’s, you know, Power Query or Power Pivot or, uh, you know, Alterix or, you

know, like any of these things that are out there to be able to help with pulling that information together in an automated and repeatable format is that it, it, it kind of removed a lot of that pain.

Ryan: And I think One of the things that’s been very exciting about getting to know you is I think you, you accurately assess that that was the case. So can you tell me a [00:09:00] little bit more about that kind of early adoption journey? Like how did you see and recognize the impact that some of these tools could have?

Ryan: And then, you know, how impactful have they been for you all?

Ash: Yeah, definitely. Definitely. I think I’ve been in the area of kind of, uh, financial modeling, process transformation, automation for the last 15 years. And I think. I still remember those days when we used to kind of purely work within just an Excel, using VBA coding, et cetera, to kind of build our models to, to support the kind of better decision making, et cetera.

Ash: But I think over a period of time, as, as we realized that we are dealing with Surge of volume of data and a lot of volume of data is coming through and we need to clean it up. One thing which we’ve also realized that within, especially the adoption of technology was little slow in finance, tax and [00:10:00] accounting industry.

Ash: So we were a little bit behind, but I think We realized and we realized that we have almost 60 spending 60 70 percent of our time in preparing data. So that’s where we figured it out that this is something which is even more important than than actually the modeling or the visualization aspect. So we started investing in Uh, data transformation technologies, especially like Alteryx, Power Query, because those are desktop based technologies and give citizen developers a really good kind of tool to handle the challenges at their hand, which is kind of dealing with the data in a shorter span of time, preparing it, uh, for, for their processes.

Ash: So, um, uh, we have been early adopters of Flywire. These technologies have developed a lot of best practices over a period of time, uh, use them in different combinations and, and also learned how to best utilize them in different scenarios. So it’s been a great experience so far. [00:11:00] Uh, um, and I think, uh, we’ve been able to kind of transform many processes, 100 plus 200, I think 150 plus Um, projects, which we have done in the last seven, eight years, where we have utilized these technologies heavily and in automating our processes and making our engagement teams much more efficient.

Ryan: which is, you know, incredible. Like if I think one of the things from kind of a non technical perspective, that’s sometimes been difficult to communicate the value proposition of to folks is to say, well, Hey, like, like you said, right? Like you’re actually spending the majority of your time that you think is going towards analysis and insights and improving your business on just cobbling stuff together.

Ryan: So. Imagine if you were able to double the amount, the quality, the speed of the insights that you have [00:12:00] about your business. Like how much would you spend on that? And it’s like, Oh, okay. You know, like it really changes the paradigm of the way that people look at the value of, of analytics. If, if you’re able to streamline or increase the efficiency of a lot of that stuff. When you were answering the last question, you talked about how some of these, you know, kind of desktop ETL data automation tools, um, are much more approachable for, you know, citizen developers, people who would not be the type who are like, in a traditional IT role or, you know, kind of code writers. Um, but there are people that understand the concepts of data, but haven’t had a tool set that’s approachable with a lot of these low and no code tools that are available.

Ryan: I was wondering, can you talk to me a little bit about how much of a revolution that has been with this kind of bigger army of people that can now interact with this stuff?

Ash: Yeah, definitely. Yeah. [00:13:00] I think one of the, one of the greatest thing which low code, no code technology has done is, you know, The inclusion of these citizen developers and democratizing the data analytics for normal people, uh, who are usually part of business, as you said, right? So they are, they, they know the context more than the data people, but didn’t have the tool, right tool, uh, or the entry barrier was too big.

Ash: So these low code, no code tools reduce the entry barrier so they can get into it and now understand the perspective of the data people. Obviously, data people have. They’ve got more experience, faster way of working and utilizing these tools, but that gave them an ability to work or collaborate more effectively with data people.

Ash: So,

Ryan: Yeah.

Ash: this, this has definitely increased the involvement of more number of people and participation. And as overall, the data and data analytics [00:14:00]

and BI industry has benefited from this. If you look at, look at some of the other kind of related trends in this space, they’re also complementing to this. So, another trend which I see is that, apart from low code, no code technologies, there is another shift of moving to cloud.

Ash: Now, we all know that there’s these low code, no code tools. Thrive in cloud environment because the developers of these tools can release new versions, scale them quickly using cloud technology. So in a way they are helping each other. And then in last one year, you might’ve heard about that. Gen AI, right?

Ash: Gen AI is also making things easier for both parties, both business and data analytics. Uh, it, it, it is basically increasing the comfort level of business people in interacting with the, uh, with the data tools through natural language processing [00:15:00] and various other things. Or you can simply use Gen AI to write Power Query codes or Python codes or VBA codes to do more than what business people were used to doing it.

Ash: And, but on the other side, if you look at, uh, if you look at the impact of Gen AI on data people, they can also be more efficient in collaboration. Their communication is going to improve. They are going to have a better understanding about the business context as well. So you can see that all these technologies are not only Basically complementing each other, but also converging the path of overall growth as well.

Ash: So, and, and, and the lines, lines between the business and the data people is getting blurred with the passage of every day.

Ryan: What an incredible answer. Um, you know, I think when I think back on kind of the, the old days before a lot of this stuff [00:16:00] came out, like the modern data tools, the gen AI, all this stuff that we’ve been talking about, there is this like huge chasm in between it and the business and. You know, I, I, I understand how this happens, right? Like, you know, like computers came out and everyone’s like, Oh, well, we should be making use of computers. And then like the people that really liked that take it and run with it. And everybody else is just kind of like gradually learning how those things are going to impact their job.

Ryan: The same as kind of modern data tools and gen AI and all of these tools, all of these technical revolutions. There’s obviously like this group of people that start with it. Um, But if I, if I kind of use like, uh, a technical analogy, right, we had the business that was mostly like hacking stuff out in Excel and they’re doing everything manually and there was no next step towards the things that it

was using, which was all, you know, [00:17:00] lots of code, everything lives on a server, all of these kinds of like big knowledge and.

Ryan: You know, experience hurdles that take to get to a place of competent competence and IT. There was no middle ground. And just like you mentioned, I’ve started seeing this kind of push towards the middle, where someone who started in Excel might be have a really easy journey going into something like PowerPivot or Power BI.

Ryan: And once they have that, they may have a really, really easy step into something like, you know, some of the Azure solutions. And it doesn’t, you know, I’m just using Microsoft’s tools as an example, but like the story is the same. Like a lot of these low code, no code tools are designed to have a very intuitive interface and they, they allow for a grow up story.

Ryan: Both for people to grow their skills, but also for a given report or a solution to start off as something grassroots and Excel, and then be gradually over time [00:18:00] as it makes financial sense turned into something bigger and better. And that chasm has really gotten destroyed. Now, The other, you know, you, you opened the AI can of worms.

Ryan: So obviously we know that there are all sorts of tools that are out there, chat, GPT, um, you know, lots of people are incorporating some sort of large language model into their existing tools. Like you had mentioned, kind of co pilot in, in Power BI, you know, write Power Query code or things like that. What are some of the places where you’ve You know, in particular, you know, for, for tax and for business that you’re seeing people, you know, get some sort of benefit out of using generative AI.

Ash: yeah, that, that, that’s a good question. I guess, uh, I think we all know that Gen AI is more, Uh, geared towards kind of, uh, text-based kind of responses, right? Like as, as with [00:19:00] the launch of, uh, chat GPT-4 0.0. Uh, they’re going to take it to multimodal level, where, which means apart from text, they are going to include audio, uh, uh, audio, visual and videos as well.

Ash: But we all know that it is. It is more on the tech side, while when we deal with data, that’s slightly different than dealing with text. But I still feel that there is a good application for Gen AI as a general technology in data analytics and BI space. And the great example is, you mentioned about Microsoft Stack, right?

Ash: The great example is MS Fabric. Right? So, MS Fabric is an evolved version of low code, no code solution, which is Power BI, including Power

Query, Power Pivot, and everything together. And now, it is moved into cloud inside the Azure environment, [00:20:00] packaged as a SaaS solution, and the co pilot is going to be included in it as a Gen AI, uh, kind of component in it.

Ash: So, you can see that Gen AI is not directly helping with data tools, but it is making the use of data tools easier and easier. Also, some of the data people who were kind of not liking the low code, no code tool and they used to love doing the coding and getting their hands dirty, for them, Gen AI can still do with a lot of coding support as well.

Ash: Right? So Gen AI can write Python codes, VBA code, or any other language, SQL queries as well, Power BI, sorry, Power Query, M language queries as well. So, I think In a way, it is going to complement in a great manner.

Ryan: Yeah. Yeah. I think of it, you’re absolutely right about the text based stuff. And I’ll, I’ll circle back [00:21:00] to that if we get a chance in a second, but you, you’ve talked about it a couple of times as this kind of like great translation engine that kind of like expands and speeds up somebody’s ability to do something.

Ryan: You know, I saw a post the other day of, you know, a lady that was like, Uh, you know, uh, a copywriter, right? She like writes stuff and she’s like, you know, I’ve been like a little bit resistant to using Gen AI to write the type of copy that I do. Cause you know, my value proposition is that, you know, it’s human written and that it’s, you know, in theory better.

Ryan: Um, But she said, one of the things that I’ve always wanted to do is to kind of spin up different platforms for the hosting of this content. She’s like, I’m not a big tech person, but I wanted to see if I could use Gen AI to learn how to spin up a website and do all the backend stuff that I don’t really know how to do.

Ryan: And she said that within a couple hours, she was able to do that. And. I [00:22:00] see the same thing like you mentioned with a lot of this stuff. So it gives somebody the ability who may have been right on the edge of, you know, uh, technical piece to, to maybe grasp that, to maybe be able to get into that place that they wouldn’t have been able to get before.

Ryan: And same thing, you know, you mentioned kind of the translation perspective of the technical side, still needing to understand some of the business concepts, the ability to kind of ask questions and get like really

distilled, really high quality answers about. Pretty specific subject matters is, has really been phenomenal.

Ryan: You know, one of the things that. I think is very important, like as a data person to mention when we have a conversation about AI as a whole is that, you know, there are all sorts of these other artificial intelligence and kind of like machine learning algorithms that are actually really applicable to data, right?

Ryan: And so [00:23:00] there are all of these different models and I’m, I’m, I’m Obviously, Gen AI and the text based stuff, LLMs, are the, you know, the cool kid on the block right now. Everybody’s talking about it. Everybody’s aware of it. You know, I wonder if, you know, you had any thoughts about some of the other types of machine learning stuff that’s out there and available for data people.

Ash: I think it’s coming to the forefront slowly and steadily. Um, I think with Chad, GPT, and some of the other competing products from Google and Meta and others, I think the big companies have focused more on kind of large scale, large language models, which is more general AI. However, I think over a period of time, I think the focus will start to shift because industry specific need for small language models is also going to come up in the future.

Ash: [00:24:00] So I think more and more efforts will be put in these spaces and you will see us. SLMs will start getting developed for various industries like supply chain, retail, or, or marine, or healthcare, or, or finance and accounting and tax, etc. Especially like tax softwares, right? They, they have, they have their own world.

Ash: A lot of companies kind of handle their tax processes and operations through tax software. So Thomson routers and SSEs and all those. Companies will be investing in this space and will have their own GenAI, SLM, and applications to harness the power of their own data, which is kind of lying in their systems.

Ryan: Yeah. Yeah. I, um, I’m excited for the future. I know it makes a lot of people nervous, but I’m excited for it. I think, you [00:25:00] know, there’s going to be really cool stuff like the, the four Oh, um, from GPT that you mentioned when it came out, I, you know, I have the app and everything like that. And so you can turn on the voice and I did the conversation and it’s, You know, I, I realized that it’s, it’s, it’s not actually like technically, it’s not actually that different, right.

Ryan: They just translate the things that I say into text and then run the text through the engine and then the text that comes back, they just have a voice read it, but I think it talks a little bit about the availability factor, you know, just literally just. Having a conversation with my phone was mind blowing.

Ryan: Like I had, like, I was talking to it about all sorts of stuff, like technical stuff, you know, like personal, you know, like whatever. And it was like, it was actually really crazy. You know, I was like asking it questions about like, Hey, what should I do with my business here? Things like that. And it like gave me real ideas that were actually helpful, you know?

Ryan: And I think that. [00:26:00] You know, you bring up a really good point with what you said of, as the market continues to mature, we are going to see more of these things, which are unbelievably technical, be made available to the market in this kind of low code, no code fashion, right? So, like, under the hood of a large language model is unfathomable.

Ryan: Amounts of data run through a very complicated neural net transformer

Ash: Hmm. Hmm. Hmm. Hmm.

Ryan: somebody like You or me could not build something like this, right? Like we don’t have the computing research, like all of these things, right? So they’re, they’re very, very, you know, technical. I look at some of the other stuff that’s out there, like some of like these more specified, like whether it’s industry specific or whether it’s like a specific machine learning algorithm or, or [00:27:00] things like that.

Ryan: Like, you know, it, it could be, you know, I, Obviously I do like a lot of categorization with data, slicing and dicing. And I think of like the random forest algorithm, things like that, just kind of like random forest as a service. You know, I just, I think you really hit the nail on the head that we are going to start seeing a lot of really, really cool tools that May have already existed, but we’re not approachable, right?

Ryan: You need tons of education, tons of technical background, possibly even more computing resources than you have. We’re going to start to be able to see those things become available. And it’s really, really

Ash: Yeah, I think one of the point I will, I will add there is that another thing which will happen apart from like industry specific solutions is the use of Gen AI for the general purpose AI is going to kind of impact the entire strata of the [00:28:00] society. Right? Whether it is a college student,

l from business side, or whether it is a data side, everyone will have a digital companion to talk to and take advice from.

Ash: So you can see that earlier a lot of information, a lot of knowledge was kind of bagged in various kind of knowledge management portals is now going to be abundantly available to all these different types of users. Right? So, not only the abundance of data, but how easily you can access it on a, on a, on a kind of, uh, on a just click of buttons or, or tip of your fingers.

Ash: That is going to change a lot of things. Yeah.

Ryan: Yeah. You know, I think of the, the trend in that regard, right? So it was funny. I remember my parents making comments about me, like they’d see me use a phone or use a computer or [00:29:00] an iPad or something like that and just be like totally blown away. Not just at like the things that I could make it do, but like how quickly I could do it, how intuitively I understand it.

Ryan: And, you know, I don’t have kids yet, but there are, you know, you know, are plenty of kids in my family and like, I watch them as well. And not only is it just like, Hey, this, you know, technical device that they learn how to use, but like, It is blatantly apparent like that there’s just they have immediate access as like six year olds to like, what are the trends in Japan, you know, like I didn’t, I didn’t know, you know, like there’s this like such an ease of being able to consume so much information from so many diverse locations so fast.

Ash: Agreed. Yeah. It’s amazing.

Ryan: And I. I view some of these technologies and like where they’re at right now. And I understand how they work and where they [00:30:00] came from. So they don’t really scare me. But I always ask myself the question, right? Like sooner or later, I’ll be an older guy in my rocking chair, looking at the world scared or, you know, not under, yeah.

Ryan: How did we get here, man? This is crazy. You know? So I, I always try to think about like, what is it that’s going to come out? That’s going to like totally blow my mind. Right. And like seeing some of this stuff, like talking to a phone or some of these other things, like we’re getting real close. And I think the pace that Of that is also increasing.

Ryan: I really want to tap in to the fact that you’ve had so much experience. I mean, I understand that it’s, it’s been focused mostly on financial modeling and tax and all of that stuff, but you’ve had the opportunity over 15 plus years to work with a lot of different companies.

Ryan: And one of the things that I personally like a lot about, um, Finance is that it’s a really, really precise window [00:31:00] into the, the inner workings of a company. You can really look at the financial statements of a company and see through a lot. Um, it’s pretty hard to make those give a, uh, you know, a better or worse picture of the business than it actually is.

Ryan: So having, having had all of this experience and getting to see so many companies worked so many projects, I was wondering if you could, you know, offer a couple pieces of advice. Like somebody has been listening to this and they’re like, Hey, low code, no code sounds really exciting. Hey, this gen AI stuff’s really exciting.

Ryan: Um, Maybe some of the team stuff that you already mentioned. If you had just a couple of suggestions that you could give somebody that would make their, you know, decisions better, what would they be? Yeah. Yeah.

Ash: advice. Um, I think for kind of, um, for entry level people or, or even at a later stages as well, uh, I think the biggest piece [00:32:00] of advice for data people is to learn to learn. I think learning itself is an, I feel is an, is an, is an art and a little bit of science as well in it.

Ash: A lot of times we, we. We get busy with our day to day schedule and kind of don’t have time to kind of Get get comfortable with the new technology, which is coming up I think the best way to do that is basically have a dedicated time on On everyday basis or weekly basis and make sure we don’t miss it.

Ash: I think the discipline is something which Which is more important. We need to be curious. We need to be motivated. We need to be diligent and make sure that we are making a kind of a consistent effort in upskilling and learning new technologies. Because if we don’t do that, then we become comfortable within our [00:33:00] own comfort zone and then get stuck with one technology.

Ash: While the technology space is constantly changing. Uh, regarding the change in pace, I think I remember one of the amazing quote, which, which, which is my favorite anecdote is, is from Justin Trudeau. Justin Trudeau, he, he went to World Economic Forum in 2018 and in Davos, and he said that, think about it, the pace of change has never been this fast.

Ash: And yet, It will never be this slow again. That itself, it’s a, it’s a, it’s a profound statement in itself. It gives us an understanding of how things are changing and how we need to be ready for all this change. Uh, the change is

something which we need to kind of embrace and adapt to it, and then make best use of it.

Ash: And I think learning is the most [00:34:00] key part of it. When we continuously learn and keep on evolving, then no matter of any amount of change, which is coming our way, we are able to handle it. So that’s one piece of advice for all the data people. And I think second thing is, I think for, for all the senior level people who usually work at a higher level.

Ash: Um, I think a lot of times we deal with the problem of. Centralization and democratization, right? A lot of times that’s, that’s a big problem in, in various organizations. Some people, some organizations have a culture where they prefer centralization so that the best practices are maintained, uh, in a stringent manner.

Ash: However, in some organizations, you can see that, uh, decentralization is very much promoted so that we can include more and more business people into this development. I feel that there is a fine balance between these two, and we should never lose that. [00:35:00] lose track of if we are able to maintain that balance or not, right?

Ash: Because both things are equally important. Best practices, uh, we can have a center of excellence kind of a team which can guide the best practices throughout the organization, but we should never limit the involvement of, uh, business people in trying out new things because their business context understanding is much better than the data people.

Ash: So they can definitely come up with new ideas and, and kind of help us move, uh, move forward in a, in a more, uh, effective manner. So two, two pieces of advice. Yeah.

Ryan: yeah, both awesome. Uh, we’ve definitely touched a couple times on the podcast so far about that, you know, tightrope walk between how much we centralize, how much we democratize and kind of like, obviously like being 100 percent at either end of the [00:36:00] spectrum is probably not good. for almost anybody.

Ryan: Um, the thing that I really liked you mentioned there is that where you fall on that spectrum is going to really, really depend a lot on your organization. There’s no kind of like one right answer. Um, one of the podcasts where I was talking with, uh, Jimmy Holmes, he talked about really finding the people. in the organization. Like it’s obviously all about the people, the people that are asking the questions, the people that have interest and technical expertise and all

of those things and just finding a way to get them involved, finding a way to get them into a place where they can make the right level of contribution.

Ryan: And that’s really, you know, how to make that decision. The other one, the, the lifelong learning. You know, I really like that. As soon as you [00:37:00] said that, it caused me to think back to something else that you had said earlier about, you know, you had said about 60, I said about 75 percent of the time was put into,

Ash: Yeah. Data preparation.

Ryan: preparing and doing.

Ryan: Yeah. And so just think about it, right? Like if the two of us had just kind of kept doing what we were doing, the way we were doing it, it would be taking, you know, one and a half to two and a half times as long to get anything done that we were trying to get done. Um, you know, so if, if I think about that, it’s like, Oh, well if I donate 10 percent of my time and then I get 60 percent of my time back as a return for that, that’s a really good return on investment.

Ryan: Like that’s a really good argument. For what you’re talking about from a financial perspective.

Ash: Yeah. Yeah. Totally, totally agree. Yep.

Ryan: So one thing that I love to do to try to close up is give everybody an opportunity to get to know Ash a little bit better. So tell me a little [00:38:00] bit about, uh, your background, uh, hobbies. I see you got medals all over the place there. Like, tell me a little bit about, um, Ash.

Ash: Yeah, yeah, definitely. Um, from, from, from professional perspective, I’ve been in this field for 15 years and I’ve, I think the, the interesting thing which I’ve seen is that I’ve gone through kind of work with different industries, uh, worked in IT industry, then moved to kind of management consulting, then moved into tax.

Ash: So my domain has been changing consistently. So, I think that was a bit challenging, kind of, because I had to go out of my comfort zone and learn kind of new business domain related things. As I mentioned, the context matters and that kind of contributes to your success. But that also gives you, you know, Confidence that if things are not that difficult when you go outside your comfort zone, [00:39:00] if you have done it once, you can do it again and again.

Ash: So that is a good learning from, from professional perspective. But I think apart from my, my role as a, as a kind of a solution architect within KPMG tax ignition practice, outside work, I love running, hiking, traveling. I’m a little bit of a history buff. I like reading about history, going to museums, and my travel plans are kind of around these activities.

Ash: So I definitely have a 12 year old daughter as well. She also loves to travel. and doing extracurricular activities. I usually get involved with her, but I think, um, I have plans that maybe, I think in coming, coming years, we’ll, we’ll go and travel around the world as well and learn about different kind of cultures and their history and how, how they have [00:40:00] shaped the current, uh, current societies, which in which they are living in.

Ryan: Yeah, I love that. It’s also, it’s so awesome when, you know, you find things that you love that you get to share with your family, like sharing hobbies with your loved ones is just kind of like, to me, the best part of life, you know, everyone’s doing stuff that they love and doing it together. And that’s, uh, that’s awesome.

Ryan: You know, what else is funny that really strikes me is, you know, from a professional perspective, I kind of assess you to be like a very forward looking future oriented person. Um, so it’s, it’s really interesting, you know, that like when you, when you’re on your own time, you’re like, Hey, looking to the past and learning about history, um, is very cool.

Ryan: So you got your kind of your eyes going in both directions. I love

Ash: yeah, yeah. Sometime my wife kind of asks me, why are you so much interested about museums and history? And I, I feel kind of, it’s pretty funny. And I do mention that a lot of times we can learn a lot from history. I think humans have a [00:41:00] tendency of forgetting history and making the same mistake again and again.

Ash: Whether you see it in long times, Kind of a span, uh, period, or if, even if you look at in the, in the shorter time span period as well, the history repeats itself. I think learning that and appreciating that, it gives, gives me a lot of kick. And yeah, I guess, uh, that also gives me another kind of reason for traveling, which I love a lot.

Ash: All these medals, which you just saw that those are for kind of my running, uh, events I’ve done. Kind of half marathons, full marathons, so love doing that and now that my daughter is also doing it, so I enjoy doing that with her.

Ryan: that’s, yeah, that’s even more fun. Yeah, I can’t, I, I really can’t agree more. I, I, I have to be honest as a, as a younger guy, I, um, history was not really my thing, like there was just other stuff that I was more interested in. Um, and [00:42:00] it hasn’t been, you know, over time, my friends that knew a lot about history, we’d be like talking about some sort of, you know, current event or what do we think is going to happen in the future, whatever, and their perspective, like almost every time I was like. Oh, where’d that come from? And they were like, Oh, it’s the same thing that’s happened like 15 times

Ash: yeah, exactly.

Ryan: And it kind of just won me over where I was like, we can learn a lot, you know, by. By taking a look at what’s happened, you know, obviously, you know, the pace of change and all that stuff is, is true, but there are governing dynamics.

Ryan: There are things that do stay the same. Like some of the rules don’t change. Um, so that’s, that’s very, very cool. I love it. Now, if, um, if anybody wants to reach out and connect with you, what’s the best place to do that?

Ash: probably yes. I guess they can search me on, on LinkedIn with my full name, Ashutosh Tiwari, KPMG. I’m sure they will be able to find me. I think that’s the best way. They can send me a message and I can respond. Yeah.

Ryan: [00:43:00] Yeah. Yeah. Yeah. That’s been great. So Ash getting the opportunity to hear from him. You know, 15 plus years of experience, as you mentioned, across like many different domains with many different market segments has just been such a treat. I can’t thank you enough for donating your time to come on and, and talk with all the listeners, talk with me and for all the listeners on the podcast.

Ryan: I really, really appreciate it.

Ash: Yeah. Yeah. Thank you very much for having me on the, on the, on the podcast. And it’s a pleasure talking to you. You’ve been doing a great job in this space. Thank you very much.

Ryan: Thank you. I really appreciate that. I also want to make sure to thank the audience. If you learned something today, laughed, had a good time, um, please tell somebody else about the podcast or give us a rating, hopefully a good one. Um, thank you again, Ash, really appreciate you. this has been another exciting episode of the Making Better Decisions podcast.

Ryan: Thanks for

Ash: [00:44:00] Yeah. Totally enjoyed it. Thank you, Ryan.

That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live a special thank you to our sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together and make better decisions. Thank you so much for listening. We’ll catch you next week.