Building Trust Through Data in Finance with Divyasom Malhan

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 is a highly experienced and accomplished leader with over a decade and a half of experience in leveraging data to drive business growth. Has experience in both the finance and consulting industries, has used his data finance and technology expertise to create a proven track record of large scale problem solving.

Please welcome Senior Director of Analytics for Global Finance at Schneider Electric, Divya Malhan. How’s it

Divyasom: Hey, Ryan. Thank you. Thank you [00:01:00] so much for the, for the brilliant introduction. Thank you.

Ryan: Yeah, yeah. We like to gas everybody up, get you nice and excited before we get

Divyasom: Yeah, I wish everybody had a hype squad like you, man.

Ryan: Thanks. I love it. I love it. Okay. So I want to get right in and ask you the same kickoff question that we ask everybody. What is the one thing you wish more people knew about using data to make better decisions?

Divyasom: uh, uh, that’s a good one to start off with and what I, what I think, uh, people. Need to focus a little bit more on is not to get too bogged down with having the perfect data. See, at the end of the day, it is about making decisions, right? We need data for decisions and depending on what you want to do, you’re never going to have 100 percent or a complete data set that you need to make the [00:02:00] perfect decision, right?

So What we need to know is how do we make it good enough? Cause, uh, when we are in, in the today’s business environment, things need to move really, really fast, right? And there’s a lot of time where we see that how quickly you react to things is better than making the perfect decisions, right? So that’s, in that sense of innovation, uh, doesn’t need to slow down for the perfect data.

And that’s what I think people need to keep in mind.

Ryan: Yeah. I love that. You know, when I, when I first started my career, um, I was doing financial performance and analysis at FPNA for all of those finance pros in the know. Um, and that was kind of one of the things that I was really struck by is there’s, there’s this concept of total [00:03:00] optimization that took me a little time to learn.

So when I started, it’s kind of like, all right, well, I’m focused on this piece of the puzzle. How can I make this the most optimal possible? But it’s like, if. The amount of time, effort, and energy that it takes me to optimize this is less than the return that I get. Well, then I should just not do that. And so it’s, as you mentioned, kind of really thinking about the bigger picture.

And if you’re in a landscape where speed is much more valuable than You know, one unit of speed is more valuable than an incremental unit of accuracy. Well, that’s a good decision. And that’s not something that you hear very often in the data world, right? Like there’s this assumption that every piece of data needs to be a hundred percent accurate, right?

So it’s really thinking about the business goals and, and the trade off with all of that stuff. You know, I think that one of the things that a lot of organizations interact with is this idea of [00:04:00] figuring out. Which of those things are important? So what advice would you give to an organization to try and figure out, okay, well, what is the right amount of effort that we should put into this to make sure that it’s as accurate as it needs to be, but is also getting out the door quickly enough?

Divyasom: Yeah. So again, uh, I think what we as data professional and what organizations focus on already, uh, and should continue to focus on is data quality, right? Uh, and data quality depends on your use case. The same data may be a bad quality data for a particular use case, and it may be a good quality data for another use case, right?

Uh, for looking at, you know, uh, a business performance, we look at highly aggregated numbers, right? It doesn’t matter if. A couple of records in there, uh, and you [00:05:00] know, you’ve been in FP& A, the level, if the errors are below the level of materiality, it doesn’t matter. The same use case for the same data set.

If you’re using the same data set to send out letters to your, uh, to your customers to get back the money. If you do not have the right records there, even there’s a minor difference in the balances, the same data then becomes the bad quality data, right? So what needs to be focused on is making sure that organizations have the right quality of data for the use case that they are aiming for.

And for each one of them, there would be at some point diminishing returns in getting the perfect data set.

Ryan: Yeah. I, I absolutely love that. And those were also, I w I would argue, kind of perfect examples for it, you know? Um, so one of the things [00:06:00] that, really interesting about getting to do the podcast as I get to talk to people with a lot of different types of backgrounds. And, you know, obviously the common thread is that everybody’s kind of, you know, a data professional believes in the value of data has, you know, certain technical aptitude to be able to create or understand analytics.

I also love digging into the things that everybody is different on. So one of the things that I think. You really bring to the table, um, you know, is that kind of consulting and, and financial background. So what would you say are some of the unique aspects of generating, you know, financial reporting and what are some of the challenges and the, the things that are different about financial reporting versus any other type of reporting?

Divyasom: Oh, how it is different. I’d have to probably give it a thought, but some of the things that I, I think are absolutely essential in financial [00:07:00] reporting is trust. Somebody who is, uh, who’s looking at your financial report reports, whether it is within inside the organization for your, uh, performance metrics or outside the organization for your reporting.

Trust is is the central pillar. So anything that you say in a financial report needs to be backed by, uh, by an auditable, uh, record of whatever you’re saying. Uh, that is one. The second thing, uh, I think which is really, really essential is to make it really easy for people to understand, right? Uh, as we. You know, if we delve into different types of reporting for different people, uh, it can end up being very technical and niche to a particular subject area. And especially when we talk about financial reporting. [00:08:00] Numbers or financial reports. They’re used by all types of people, right? Uh, outside the finance organization within a company, within an organization as well.

I’ll give you an example. Uh, I think a few years ago, maybe like seven, eight years ago, uh, I went into a meeting with a business VP and his entire staff, and we were discussing financial performance. And I started talking about, okay, here’s the forecast. This is how far we are from our target. And as I read the room, I could feel like there’s like something missed, like, you know, suddenly everybody was really quiet. So, uh, I just asked like, Hey, do you understand what I’m talking about? Cause I’m, I’m right now talking about forecast and [00:09:00] target, and these are two different things. And then we came to a point where everybody was like, we don’t really understand this. And I went to that meeting thinking about these are business people.

They must be talking about these numbers every single day. So I was going into that meeting with my own bias that, oh, Everything that I talk about in finance is going to be understandable by every person. So I think keeping it simple, uh, in finance to, uh, understanding that it is going to be viewed by people who do not understand finance is, is important.

So going back, I think, I think Trust is really important in financial reporting. Auditability is really important. And the third thing is making it simple and understanding that people beyond the finance world are also going to look at it.

Ryan: Yeah, I, I, I love all of those answers. [00:10:00] One of the things that I think is, is kind of interesting is like the intersection of those last two things that we’ve talked about. So obviously within the finance space, particularly like when generating financials for a publicly traded company or any company that really has material investment in it, there.

are, you know, demands for high fidelity and for, you know, supporting documentation. Like you talked about, there may even be, you know, in the case of publicly traded companies, you know, socks compliance to people, you know, there’s all of this additional work and kind of red tape and governance and compliance stuff.

How do you balance the requirements of all of that with the speed and the need to change quickly that you’ve talked about?

Divyasom: Yeah. I think we’re, comes down to, uh, building trust is basically, uh, you know, okay. Okay. Let, let me start over [00:11:00] on this one. Speed, speed is important when it comes down to innovation, right? Business decisions, keeping, uh, But when it comes down to financial reporting, I would primarily categorize it into two ways, right?

One that you do as part of your performance management, and the second one where you’re doing it for statutory purposes. statutory purpose is, you know, you have external auditors working with you on those reports that you’re publishing for your external stakeholders. It’s a standardized process. On the, uh, performance management or your internal financial reporting that you, uh, must be doing that we do to different business leaders, et cetera.

Uh, the speed is key. And again, we come down [00:12:00] to making things highly standardized.

Ryan: Hmm.

Divyasom: To give you an example from, uh, from my own work that we’ve done in the past couple of years now, we were at a point where a new standard report, it would take us somewhere between two to three. months to build it and then distribute it across the organization to, uh, to the point where we are now doing the same reports in almost two weeks or so.

And, and this shortening of the time period has been because of how we have standardized the whole process, how we have put in a lot of effort at the back end in the quality of data that is being generated. and how we have basically developed this big financial data warehouse that we [00:13:00] can rely upon to quickly generate new insights.

So I think it eventually comes down to overall data maturity of an organization. to be able to leverage and harness that data quickly.

Ryan: Yeah. You know, from. I’ve had the opportunity to work with, you know, small and large financial institutions, you know, funds, you know, a lot of different areas of, of finance, whether it’s, you know, uh, a business whose primary business is not financed, but obviously everyone has to do finance, or it’s an organization that does some type of financial work as their bread and butter.

And one of the things that I have found about finance is that. It’s one of the areas where almost everybody has the data mindset because you kind of have to, right? It’s a it’s a very numerically based field and most of what [00:14:00] we’re doing We’re talking about, you know records and transactions and things like that like the bread and butter of finance really is data and One of the other things that that you find is obviously, you know when it comes down to dollars Things matter a lot, you know?

So like that demand of, of very high fidelity and accuracy and can we trace and explain everything has kind of put us in a situation where, you know, like the lifeblood of finance is the manually generated Excel document, right? For better or for worse, right? There are some places where people have, as you talked about, like moved into, you know, areas of higher data maturity and are starting to use things like A more complete data warehouse.

And, you know, but even still then it’s kind of like, all right, well, I’m going to dump that. You know, out of, you know, some, whatever system I have it into and do an Excel doc, and then I’m going to like tick and tie out to somebody, you know, like there’s a lot of that going on. So I understand where [00:15:00] that comes from.

I don’t think it’s a bad thing. I think it’s a good thing that everybody wants to make sure the financial numbers are accurate. That’s a really, really good thing. But. If there’s somebody out there who says, Hey, you know, this process that I’m working on, it feels repetitive. Like we know that it needs to be standardized and we know that it needs to be explainable, but that doesn’t mean mean that it needs to be manual.

I’d love to improve this process. What recommendations do you have for people in finance functions to try to improve that data maturity, but to do it safely so that it’s not going to cause a data integrity issue?

Divyasom: Uh, so I think before I get to that, I firmly believe that the way we do finance now is very different from how we were doing 10 years ago and how we will be doing it 10 years from now is going to be very different. There was, uh, a time where we needed a lot of people to be processing transactions, uh, [00:16:00] You know, generating records, doing all of these Excel files.

And the way that has transformed is there’s been a lot of automation at different points in this whole cycle of recording transactions to recording, uh, those transactions, then getting the data out of whatever ERP systems or applications you’re doing to a central point, and then to start and analyze that data, right?

Uh, so. Those who still feel that, you know, they need to do this somehow in an Excel like environment, see inherent risk in there, which is that you It is easy to lose track of who edited what. To say it simply, who edited [00:17:00] what. There was a point of time where we used to, early on in my career, rely a lot on sending Excel files with financial numbers.

Right? Uh, And over the past four or five years, we’ve realized that we still leverage Excel, but we leverage Excel for more on the fly simulation, analytics, what if kind of scenarios, you’re, you know, all of that stuff. But when it comes down to really disseminating information, we’ve started to move away from Excel a long time ago.

And I think that is where people need to differentiate. Different tools have different purposes. Uh, and for purely analytical purposes where you have to set up an enterprise level analytical system where people can log [00:18:00] in and consume information and then take business decisions. You probably need to look at something which is Governed inside the organization so that people cannot edit the numbers in there.

People cannot put in their own information in there at the layer where information is being consumed. Because once I. Or you as a user, you log into your system and you can say, I know these numbers that I am looking at. If I go back to the origin of these numbers in whatever ERP or replication system, I’m going to find the same numbers.

That’s where your trust comes in, which we were talking about previously. That, that’s really, really important. Uh, I’ve been in meetings where we started discussing business performance and the conversation completely shifted [00:19:00] somewhere else to, are these numbers even correct? And I think in order to avoid that, You need to be careful how we are managing the data throughout its life cycle, right?

Who’s touching it, who is able to create new records, who is able to edit those records, etc., right? So I think that’s an important point that finance professionals need to understand.

Ryan: yeah, I, I, I think I, again, like kind of understand where some of that, you know, the movement towards like, Hey, like every single question has a model in Excel and there’s a person that’s, that owns that model and is responsible for that. I kind of get it. Cause then it’s like, all right, well, that employee provides the additional level of support and comfort for the model.

Like we can just go ask that person, but you’re, you’re absolutely right. Like at a, at a certain Level of technological [00:20:00] advancement that we’ve just seen happen in the marketplace. Um, you know, there are new tools and new automations like Excel is still an incredible tool. I still use it all the time and I recommend it for a lot of situations, just not all of the situations.

And so I thought you brought up a really great point by saying, you know, If you’re not at a place where you are making use of some of these additional tools, you know, it’s an opportunity to differentiate yourself from other competition and to say, Hey, well, you know, if we’re doing the same process to generate the same report and it’s highly standardized, can I automate some portion of that with the proper documentation?

And now it’s faster and it’s more trustworthy because there’s a higher degree of governance. So

Divyasom: Can I give you an, can I give you an example of that?

Ryan: of course, yeah, that’d be great.

Divyasom: when I started with Schneider, I, I started as a, uh, as a financial analyst and I inherited a process where on fifth day of the [00:21:00] month, I had to send out, uh, financial performance reports for the top line. Numbers. So the process that I inherited was that I would get an Excel file.

Basically it was table, orders and sales. I’ll have to go through the whole process of, doing my analysis. Then the next thing that I would do is build charts out of it. Then I’ll copy, I’ll paste it in PowerPoint and I’ll send it out. It would typically take two days to do this entire process. And then.

After doing it for, I think around four or five months, I, I said, okay, how can I automate it? And the way I did it is I wrote a custom SQL, use that to bring in data into Tableau, and then build, uh, build a dashboard out of it, [00:22:00] which was similar to what we had in, in PowerPoint, which we used to send out, and then Just published it on a server.

And from the next month onwards, all I would need to do is just refresh the data. So instead of sending it two days after we had the numbers, I was able to share the results within five minutes of us having finalized the numbers.

Ryan: Wow.

Divyasom: So. And I think that was something that, that was appreciated a lot by everybody, uh, both inside finance and on the business side as well.

Ryan: Yeah. I, since we’re sharing, you kind of spawned a couple of memories for me. So this was, this was actually before I was a consultant. Um, you know, this particular story, but, um, I worked straight out of college. I worked for a telecom company [00:23:00] and that’s where I was. I was initially on their FP& A team and I, there were some cool stories of automation there, but you know, I’m talking about generating financial numbers.

So, When I was on the FP& A team, they had one job and I had basically automated that kind of in the exact same fashion as you. I had done it for a little while and I was like, man, there’s got to be a better way. Um, which I think is kind of a trait of people with the data gene, just that like constant thought, like there’s got to be a better way.

So, you know, naturally I, I spent. You know, hours coding to avoid doing hours of work, but anyway, so I built this automation and kind of like it automated my whole job. And so they were like, all right, well, like, what do we do with this kid? He just like automated his job. He’s like fresh out of school. We just hired him.

Like, so there were like these two other people that were doing. BI and data work under the finance umbrella. And they were like, yeah, just like, go send them up with them. Like, we’ll, we’ll figure out something smart for them to do. So I went up with these guys and we start figuring everything out. And so the process that I [00:24:00] had, you know, originally targeted on was like our ability to close our books at the end of the month.

So there were like all of these processes of, okay, well we have to like go through, generate a bill run, all of those bills get generated as a process of all of that. Like the data gets captured for all of that stuff, and then all of those numbers get input into the system, they get validated, and then they get, you know, aggregated and loaded into the ledgers.

And then bang, boom. Cool. Here we go. Now we can say how much money officially we made them on. And so this process, same thing took like a handful of the business days. And it was like a big and involved process. And so I remember talking to them and I was like, okay, so where do they get all the information from the bill runs for?

Right. And they were like, Oh, those are over in these tables. And I was like, well, why don’t we just use those tables to close the books? Right. Instead of waiting to generate a bunch of bills.

Divyasom: You keep going back, backwards and backwards. And it’s like, okay, let me now automate next step and the next step. And you started [00:25:00] integrating the whole process end to end.

Ryan: Yeah, exactly. And just kind of like inching a little bit further back up in the process and sprinkling in like little automations and queries and what have you along the way. Um, and, and you’re absolutely right. So we got to a place like, obviously like closing the books is very, very important for, you know, the finance function in a large company.

It’s basically everybody saying like, all right, the numbers are right. So we had to go through a handful of months before we were process of like watching this new process run in parallel with the old process. Is it tied out? Have we made any mistakes? But you know, once we got through that whole thing, we were able to take our business process from saying like the books are closed, you know, five or six business days after the last business day of the month to saying the morning after the last business day of the month, here are your numbers and they were trustworthy and signed off is really, really cool what the possibilities are.

Divyasom: I want to ask you a question because you, you do talk to a lot of people, right? [00:26:00] And, uh, we’re talking about these two areas, finance and data, right? Finance, some people consider it to be like, you know, very, there, there are certain roots, there are certain fundamentals, you cannot change them, right? It’s a, it’s a more, Stable, static environment in finance and data is this ever changing, rapidly evolving environment with new tools, technologies, new ways of thinking.

Have you seen that when these two combine, is it like something beautiful comes out all the time, or do you always like, Oh, this is the explosion because the reaction’s happening here?

Ryan: Yeah, that’s, that’s a fun question. I like that. So I think it’s really, it comes down to kind of the, the right tool for the right job. And while the [00:27:00] tools that we can use in finance and in anything else from a data perspective, you’re right, are rapidly evolving. I think the optimization parameters change much more slowly if not stay stable.

And like this, this need for a very high degree of fidelity and accuracy within finance is not something that I imagine changing super fast. So, you know, obviously could I build an entire house with a hammer? Maybe, but it’d take me a really long time, you know, I wouldn’t want to do it, you know, so I think the biggest thing that I always liked looking for, particularly In, in finance and in looking for optimizations and automations is anything clearly defined that happens more than once.

And the thing that’s really cool about finance is as you talked about, there is such a high degree of thought and preparation and standardization that goes into [00:28:00] things that many times the logic is much more clearly defined in finance than it may be in other areas of the business. So like another area of the business, right?

Like whether something goes in a category A or B, maybe. An employee’s judgment call, right? Whereas, you know, within the books of a publicly traded company, it’s like, no, no, no, they’re like legal standards. This goes here. So I actually think that that plays to the advantage of someone who’s trying to find new ways to innovate within finance.

We can use that rigor and that structure. To be the backbone of automation. So the more clearly defined the requirements are, the easier time a computer has following them without needing human intervention. So. When it comes to, you know, doing things like certain categorizations or, you know, certain processes, if they are the same every time and they’re repetitive and they follow clean rules yet absolutely leads to those like beautiful, Hey, we cut this time, you know, [00:29:00] drastically down, you know, like it is an amazing result.

However. On the other side of finance, I think that some of the most critical decisions. About how a business’s future is going to go happen in the finance department. Yeah. Can we afford this? Should we make X investment? What is our expectation of how the market will respond to X, Y, or Z? These are decisions that, you know, I want to try.

To be a part of informing as best as I can, but really do come down to, to like human beings opining and using experience and using all the data at their fingertips. And so like, there are definitely spaces where I think like that restraint and standardization and human elements still belongs.

Divyasom: Yeah. I agree. I, and the reason why I asked you this question is I come across both type of people, right? I, I [00:30:00] have worked with people. Uh, I remember many, many years ago when I started my career, I was working for, uh, an accountant who had fortunately started using computers, but the thing was, I was given a template to build a balance sheet. And if I would change anything, move a font, change the width of a column, it was like, it was like I would sent right back to do it exactly in the way I was given. It was so rigid, the environment was so rigid, I could not change anything. And on the flip side, I’ve also seen, uh, leaders who come in and say, okay, give me, give me.

a new way of doing it? What’s the other way of looking at the same information? Can we do it differently? Can we find a more automated way of doing it? Or can we find [00:31:00] an easier way of of doing it or explaining this problem? So I think that’s why I asked that question, just to see, you know, if you have also had Both kind of experiences.

Ryan: Yeah, I think, you know, one of the other things that you mentioned when I was asking a little bit about, you know, socks and high fidelity and how to balance that with speed, you had talked about there being a big difference between finance reports that are like going out to the, the SEC and like traders and all that stuff, and then reports that maybe we’re using internally to describe the financial performance of the company or some financial, you know, modeling that we have for the future, whatever the case may be.

And, you know, on the compliance side and like external reporting side, I tend to say, okay, like that’s the place for the rigidity. You know, that like, it just, it is what it is. There’s government regulations. We want clarity. We want simplicity, all of that stuff. The internal place, that’s where you start, at least in my [00:32:00] opinion, to have a lot of opportunities for fun.

And. I personally have found that, as I mentioned, finance professionals kind of naturally have to have that data gene. And many times because of the rigor that’s required for external reporting, and because that’s just kind of a standard of finance, that tends to kind of permeate the culture. And so when you can show them like fun new ways of looking at data that drive into insights and start looking at them in different ways.

It really can electrify a team. So for example, you know, like we’re talking about like a standard, you know, you, you generate some financial reporting, right? Your standard financial reports. So your balance sheet, your, um, profit and loss. One of the most common things as, as you know, but for the listeners that people will do is they’ll start looking at like ratios between some of these numbers.

So, you know, maybe I look at current assets versus current liabilities and I’m trying to get a handle on like, Hey, okay, well like, what’s my ability to pay my bills? [00:33:00] You know, and so this is actually a really interesting number. What if I generated a line chart on like a transaction daily basis that looks at the volatility of that?

Like, can I see, is that trending upwards and then spiking downwards? And, you know, there’s all sorts of fun stuff. Or another thing that I really like doing is combining some of the financial information that you have with other areas of the business. So obviously, you know, dollars are. A huge part of every part of the business.

But when you start combining, you know, looking at marketing and sales and then looking at some of, you know, more traditional finance functions, like looking at finance, you know, from a larger perspective, including, you know, uh, accounts payable, accounts receivable, some of that stuff. Okay, cool. Now we can build.

Really, really cool reporting on, you know, cash conversion cycle and, you know, cost of customer acquisition and comparing that you can start building all of these like really, really [00:34:00] impactful high level reports that come directly from that financial information that are still like extremely impactful.

You can be extremely agile and fast with them.

Divyasom: that is true. And the, the more that the more type of data we bring in and we put it in a one place and We start these, we start looking at the cross section of different types of data points. We suddenly start seeing patterns emerge, right? And, and I, I do believe that in a lot of, you know, organizations now you have these teams which are specifically being set to go out and do these kind of explorations, right?

To, okay, come up with a hypothesis, this. get the data, see if that hypothesis is true or not, and what does it mean, uh, right for the [00:35:00] organization, what kind of decisions can be taken based on that. Also, at the same time, you know, something that people should keep in mind, do keep in mind, is correlation doesn’t necessarily mean causation, right, uh, so that is, that is also an important part, uh, Another thing, uh, about the finance, uh, organization and people who work in finance, deal with data day in, day out.

But I think something that is more prominent in finance, uh, organizations is the attention to detail. So people are more detail oriented, uh, And we want to be more precise, uh, when we’re dealing with, uh, with numbers. So I think that is one area which, uh, which is quite unique to, to finance [00:36:00] world.

Ryan: Yeah, yeah, absolutely. And I think that that’s a good fit. Now, one of the things, if I can circle back just a little bit that you had mentioned, as we were talking about certain ways that an organization, right, you kind of thrown down the gauntlet and said, you know, this is a way For finance functions or companies to differentiate themselves is to kind of make some of these investments.

And one of the things that you had mentioned was a data warehouse, which is obviously something that, you know, I know quite a bit about and that we’ve talked a lot on the podcast about in different, um, levels of depth and from different perspectives, but from a financial perspective, are some recommendations that you could provide to people?

Because in general. A finance function may understand the concept of like, okay, cool. We’re going to like go build a database. It’ll have some stuff in it. I’m just going here instead of some other system. What recommendation can you make for finance leaders to like really understand the value of what it is that they’re investing in when they [00:37:00] try to build something like a data warehouse and how should they go about it?

Divyasom: Uh, two things, two things, uh, there that, that I would say to keep it simple. There are like so many things, but. To keep it simple, the first thing is think of, think of the entire life cycle of data, not just at one point, not at the point of creation, not at the time of, you know, managing it or consuming it.

Think of it across the entire life cycle. And for that, I would recommend the organization to choose one benchmark for data maturity, uh, do a baseline assessment, and then work towards, okay, what are some of the things that we can do to improve on this cycle of being more data centric, being more mature with how we manage, handle, uh, our data.

That is one thing. The, uh, The second thing that I would say is lot of the problems [00:38:00] can be overcome if we focus on ensuring that the data quality at the time we are capturing it. Is correct. Let me give you an ex, uh, give you an example. Let’s say, uh, uh, let’s say, uh, you know that there’s a company out there, GE Healthcare or, or Philip Healthcare, let’s say Philip Healthcare. You have 200 offices and anybody can go in and create a record. Somebody would say, Phillips Healthcare, somebody can write it P Edge or somebody can write Phillips, hc. Philips edge care, et cetera. And then eventually down the road, if you have to do a simple thing as how much business did we do with this, with this customer is going to become really, really difficult, right?

So that’s, that’s a, [00:39:00] That’s what I would say. And maybe I’m biased here because I feel like, you know, uh, in the eventual consumption of data in the analytics function, we see a lot of challenges arrive because of not having good data in the first place. Right. And that is where we started also our conversation today.

You know, what, what do you say about, you know, having the right data for a particular decision? So I think having that, that broad perspective of data maturity across the life cycle of data is really, really important, and then. Having a secondary focus in implementing quality at each step of the way is, is going to be the two things which I think are really, really important to begin with on a journey, but as you go along, there are like. Each part of the journey, there are so many things which, which can be improved.

Ryan: [00:40:00] Uh, talk about full circle. What is, I, what is slam dunk? I don’t think we can improve on an ending for that. So what I’d love to do is I want to, I want to pivot and talk a little bit about you. I like giving everybody a chance to get to know who’s, you know, on the other side of the mic and get a little chance to know you a little bit.

So tell me a little bit about you. What do you like to do outside of work? You know, what’s, what’s your life like? What makes you tick?

Divyasom: Sorry, I, I, you cut off for me a little bit.

Ryan: Sure. No, no problem. We’ll try that again. So basically I love to make sure that everybody gets to know the person on the other side of the mic a little bit. So tell us a little bit about what you like to do outside of work. What makes you happy, hobbies, fun, anything like that. Tell us a little bit so we can get a picture into who

Divyasom: Oh, uh, sure. Uh, what makes me happy is, uh, learning something new. So, uh, A few days ago, I think two weeks ago, me and my son, he’s 11 year old, we [00:41:00] played our first game of golf ever. It was my first game of golf, his first game of golf as well, and we totally enjoyed it. And ever since then, I’ve started learning a little bit more about the game, you know, how do you play it and getting into it.

Uh, so, At any given point of time, uh, I’m always looking forward to learning something new. Um, I’m also very interested in, uh, you know, investing and just in general learning about different businesses. Uh, and also I think we share, uh, another passion, which is flying. So, you know, I’ve been, uh, been learning, uh, To fly, uh, working towards my private pilot license for a couple of years now.

Ryan: Yeah. Yeah. A lot of, a lot of shared passions. Um, I will tell you is, so I grew up golfing. I took a long time off and then maybe a year and a half, two years ago, I started [00:42:00] getting back into golfing. Um, it’s a cruel game full of disappointment that I just can’t put down. I love it too much. Golf is a humbling, humbling game.

I’ll tell you that. Now, in case anybody wants to reach out to you or wants to connect with you, what’s the best way to get in touch?

Divyasom: the best way to get in touch with me is, uh, through LinkedIn. Um, I typically respond back to people, uh, Within like a day or two after I receive a message, uh, I’m not always able to help, but I do my best, uh, based on, you know, whatever I can, uh, I love, uh, I love connecting with, uh, uh, with people. The people, especially in the early career or mid career phase, uh, because I think I’ve, I’ve gone through that journey and, and I love to share and I love to help if I [00:43:00] can.

Uh, also since I, uh, you know, I’ve, I’ve moved many countries. I was, uh, born and raised in India and then I moved to Canada. I lived there for, uh, seven, eight years and then I moved to US. Uh, so I’ve also seen how moving countries. can impact your life and your career. And I’ve also, uh, volunteered with a couple of organizations who work with new immigrants to help them settle in a new country.

So the best way to reach me is, is on LinkedIn.

Ryan: That’s, that’s awesome. I love it. And I love hearing about the volunteer work. That’s, that’s something that I’m a big believer in as well, trying to give back where it makes sense. Actually to that end, uh, this past weekend I rode in the pan mass challenge, uh, which is 186 mile bike ride across most of Massachusetts to fundraise, um, for the Dana Farber Cancer Institute.

So we’ll put a link to donate. To [00:44:00] that and Divya, if you want to follow suit, if you want to send us a link to any charity that you want, we’ll be happy to throw that up. But if you’re interested in, in sponsoring, volunteering, donating anything, uh, 100 percent of the money goes to the Dana Farber Cancer Institute.

So check out that link in the show notes. I got to thank you so much. You were such an incredible guest. In fact, I think you have the distinct honor of being the first guest who has asked me a question, which I absolutely loved. It was such a good one as well. I got, you know, got me super excited talking about all sorts of random, uh, financial metrics.

So thank you so much for sharing your expertise and your time with us today. It was absolutely incredible. Thank you so much for coming

Divyasom: Yeah. Thank you for having me on the podcast, Ryan. It was a pleasure talking to you. Uh, you made it far more, uh, far easier than I thought it would be.

Ryan: I’m

Divyasom: It’s, it’s a good conversation. Uh, a lot of. People who have tried it may know that, you know, as soon as you, you’re the best [00:45:00] speaker in the world, but as soon as the camera turns on, you get tongue tied.

Ryan: Yeah, it’s all an act. I’m right there with you, man. It’s just cause I’ve gotten some reps, but you know, to all of the listeners, especially if you’ve made it this far, I want to thank you guys so much for listening. If you learn something today or you laughed or you liked something, please make sure to tell a friend or like subscribe, write us a review, hopefully a good one.

All that stuff helps keep the podcast moving. So we’d really appreciate it. If you can do that, Divya, thank you again, once more. And this has been another exciting episode of the making better decisions podcast. Thanks for listening.

Outro: 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 [00:46:00] it together and make better decisions. Thank you so much for listening. We’ll catch you next week.