Episode 12: 5 steps to make a CFO’s office data intelligent

Shilpam Ahuja Shilpam Ahuja

AVP, Invoice to Cash Practice

Genpact

Madhurima Gupta Madhurima Gupta

Senior Product Marketing Manager

HighRadius

Available on

Synopsis:

In this episode, join Shilpam Ahuja, Assistant Vice President, Genpact as she discusses the 5 steps for CFO offices to become data intelligent and how businesses can tap into analytics and modern technology to be data-driven.

Transcript:

Madhurima Gupta:
Hi, welcome to the Mid-Market CFO Circle, a podcast by HighRadius, where we talk to industry experts and bring forward the challenges that mid-market CFOs face and how you can leverage emerging technology to solve them. Today for this podcast we have with us an exceptional speaker who has been working in the O2C domain for 15 plus years. So let’s welcome Shiplam to the podcast. Hi Shilpam. How are you doing?

Shilpam Ahuja:
Hi. Thanks. I’m doing great.

Madhurima Gupta:
Great to have you with us. So I’m gonna take a couple of minutes and explain what an incredible person we have with us today to talk about the steps that mid-market CFOs can plan to make their offices data intelligent. So Shilpam is an O2C expert who has 15 years of extensive experience in end-to-end design-to-implementation of Digital Innovation and Transformation programs. She’s right now working as AVP and is part of the O2C consulting team for Genpact’s global clients. And she’s been partnering on order-to-cash finance transformation journeys for multiple clients, including the design and development of multiple concept solutions across accounts receivable. So thank you so much for taking time today, Shilpam. And I’d not want to, uh, you know, waste any more time and I wanna dive right into what makes it possible for emerging enterprises, um, and how should these emerging enterprises make sure that whatever are the misconceptions that finance leaders have today that they should be watching out for.

Shilpam Ahuja:
Thank you, Madhurima. First of all, thanks for the great introduction. It’s my pleasure to participate in this and share my insights. I think the common stereotype in the office of finance that we see is that there’s a fearful, cautious, uh, CFO office reluctant to take risks. But I think rather than being risk-conscious, it’s more of, if we actually go ahead and, you know, do transformation, then do we get the commensurate payout? So with the momentum that we see in digital technologies, right, rather than viewing it as a risk, it’s more of how do we get rid of the process challenges or the painful processes. Then I feel that the focus for the office of finances is no more, how did I perform or the business perform last year, but it’s, how, where are we going to go in three years from now? Right. And I think, it’s more of how do we foresee the results. So it’s no more how we performed, you know, in the previous years. And it’s more of prediction and how can we take the corrective actions. And another misconception that I see is actually, you know, these days in the oil of transformation, which is data. So it’s, I think it’s no more being data-driven, but it’s more of being data-informed, right? So the dynamic is changing to a great extent. And the misconception that we have in the data governance strategy is like, we need to have a centralized repository for data consistency. But I think it’s more of a consistent than an ongoing effort that is required and bad quality data can lead to detrimental consequences. So it’s very important to be data-informed. And to actually harness the data to fuel growth. So I think these are the common misconceptions that we see based on the interactions that we have recently had with our clients.

Madhurima Gupta:
Now, what is, according to you the biggest or most common challenge for CFO offices that are embarking on their journeys to become more data-driven?

Shilpam Ahuja:
That’s a very interesting question. I think it’s plenty. So actually you see that, uh, you know, data is basically the heart of any transformation exercise. We all know that right. We actually look up to the data teams to provide us with high-quality data, but I think it’s easier said than done, right? So apart from the technical issues, there are other human challenges like cumbersome human challenges, I would say in terms of communication, in terms of collaboration, in terms of internal alignment. I feel it’s very important to democratize the data culture, uh, to have a, you know, or to improve the analytics coverage and to deepen insights. And the biggest challenge, you know, apart from this that we see recently, right, um, is that there are fragmented systems. There are different business units and all of them have their own legacy systems, different ERPs are there. So data exists in silos. And because of that, there are so many issues in terms of unlocking the data. So what I mean is like, there is no one single system of truth, right? So you have to actually spend a lot of time in reconciling the data. There are a lot of data quality issues that are there, lots of manual efforts to do that work. And another thing that culminates out of it is that there are challenges in data modeling because of that, right. Because there are, you know, different versions of truth and they’re dispersed across the systems. So because of that, data modeling becomes a big challenge. So I think we need to break these silos from a data standpoint to actually have an effective transformation exercise. And because of all these challenges, it becomes very difficult to have digital transformation or to embrace digitization at the right point in time. Because there’s a lot of dependencies, which is there on finance departments to consolidate the data, um, and reconcile, and then to report the data.

Madhurima Gupta:
And, let’s see if a CFO’s office wants to get their data domain right for their organization. So what would you say would be, let’s say the top five steps or the first five steps that a CFO should take to have an effective data governance strategy.

Shilpam Ahuja:
This is a tough one and an interesting one, I should say. But I think to get the data domain right, is quintessential right. And according to how we have done the transformation recently and what I have seen, I think it starts from, um, you know, from people, then a process is followed and finally, it ends with technology. But each of these components needs to be build over one another to have an effective data governing strategy. Right. And I feel that for any initiative to be successful, it’s very important to have a leadership buying or the key stakeholders buying. I think that’s very important for having the data governance strategy to be impactful. And you can only get the leadership buying if you tell what would happen if anything goes wrong.
Right. So to build the right business case. Now, if you, for instance, you know, if you have your AP contacts within the master data, correct, obviously your collections efficiency will go up. Right. But if that data is incorrect, then no matter you build any tool, you will not be calling the right contacts. So, just an example from the AR domain that I wanted to, you know, portray, but I think it’s very important to showcase what could go wrong if the data is not, you know, uh, well maintained or it’s not enriched. Then I feel that it’s very important to delineate the ownership, um, the roles and responsibilities to the right stakeholders to actually lead any initiative. And, um, so when you have, you know, when you go ahead with this data enrichment or data governance initiative, then I feel that there should be a data governance team.
There should be the members like the tactical owners who are responsible for updating the data. Then there are data policymakers, there are data users. So it’s very important to demarcate what are roles and responsibilities that are required to be taken care of and how the ownership is going to be driven by the team. Then I think for anything to be successful measurability is very important. So everybody would be interested that, you know, how are we performing based on the key KPIs that are there. So it actually helps you to have the right checkpoint to see that, uh, whether the best policies are in fact, or it’s just in theory. Right? So, it’s very important to, you know, put together what are the key metrics that we are going to observe. A small example is, for instance, if you are taking care of an update, say just the banking updates in customer master data. Now, how effective has been your cash application after that? Right. If you have maintained your maker details, you have maintained your banking details. Were you able to apply the cash well? What was the difference before and what is, uh, where have you reached after the data enrichment later on? So figuring out the key KPIs is very important. And that should be properly, you know, delineated, um, from the KPI standpoint to measure and see where the corrective action is required. And finally, I feel that it should be an ongoing practice and not a short-term project, definitely because, this is something that needs to be done consistently.

Madhurima Gupta:
And when a CFO’s office is able to put all of these things together to get their offices data-driven and have the right data domain strategies in place, how can they expect it to change the bottom for their business?

Shilpam Ahuja:
Okay. So I think, you know, to make, uh, application of data is very important, uh, to actually make better decisions. Every strategic decision I think, depends on data collection, analysis, and insights, right? So if you are not able to maintain the data well then a lot of things can go wrong. Not even your process implement or sorry, process transformation would be great, but also your digital transformation will lack the right fuel. So I come from an AR domain, right? So, I think that you know, there are certain, metrics that a CFO organization should track, in order to have faster revenue growth. I feel, for instance, the day sales outstanding is very important, right, to be tracked because, it actually helps in evaluating, your operational effectiveness because it’s directly related with collections. Now, faster collections means better cash flows, and that money you can allocate to your high purpose projects or purposes. Right. So I think that’s very important to track. For businesses to be successful. It is very important to track key risk accounts. Now, what is the value that those accounts have in the overall business? I think that can be tracked based on the aging analysis or the past payment behavior of that respective end customer. Then I feel, uh, collections efficiency index is very important and that impacts the bottom line. Right? So, it’s very important to track CEI because it helps you to understand what is the outstanding money or how successful the business is in collecting the outstanding money. And it also creates visibility into if your collection policies need any changes, an effective CEI index means that you have stronger credit policies and you have, uh, you know, an effective collections management system, whereas a declining CEI means that your cash flow is reducing and it’s impacting your bottom line. And I feel that, uh, finally I feel it’s very important to track the periodic customer reviews because it helps you to be on the top of your risk accounts of the end customers and reduces any chances of bad debt that can happen. Right? So especially for small and midsize companies, right, where you are at working on multiple projects, there is so much pressure at one point in time. The above metrics not only provide I think, visibility or transparency, but it also helps for faster revenue growth.

Madhurima Gupta:
And what about the latest trends in terms of implementing high-end technology platforms? And let’s say RPA to automate or streamline such processes, uh, what role can analytics play in such scenarios?

Shilpam Ahuja:
So I think, see, um, robotic process automation or machine learning or NLP, NLG right? All these digital technologies help a business to get rid of their non-value-added processes right. Now, analytics leverages all these technologies and they help improve cause analysis. It helps you to predict the results and foresee the results. It actually helps to uncover any problems. Oh, sorry. Uncover the solutions for any problems that the business is facing. Right. So, and also what we are seeing lately is that there’s a lot of, you know, inclination towards real-time data or real-time analytics, right. Wherein we want to mitigate the risk and react to any of the problems instantaneously, right? So it actually helps us to give a better sense of risk and react to the changes immediately now from an analytics standpoint. We are seeing that there are three types of analytics these days that that is coming up in most of the SaaS platforms, right?
One is, um, your descriptive analytics, which actually helps to understand what happened in the past. Then there is predictive analytics that actually tells us what could happen. And then there is prescriptive analytics that tells us what should happen in the future. So, um, I think analytics helps in taking better decisions. It helps in better enablement of the key strategic initiatives. It helps in better customer relationships. And, um, I just wanted to highlight one very interesting conversation. I was just, you know, thinking about it that we had, like, so there was the CXO for one of the companies, with whom we were actually discussing the digital transformation within the AR domain. And he mentioned a very good quote “I’m interested, uh, you know, to understand the analytics capabilities, but I don’t wanna spend money in data analytics. I wanted to figure out what are the various, uh, alternatives that you can tell me that help to make money from analytics capabilities.” So I think the culture where we are growing is data-hungry. And I feel that the best idea which is facilitated by best data is where we all want to invest.

Madhurima Gupta:
So that’s really interesting. You said that this customer of yours that you were talking to did not want to spend money, but he wanted to get benefits out of data. Did I get that right?

Shilpam Ahuja:
That’s right. So actually he mentioned, you know, that I don’t want to, you know, spend money on data analytics, but I want to use it to make money, to figure out a few alternatives that analytics can give me and make money out of it.

Madhurima Gupta:
What did you reply to that?

Shilpam Ahuja:
So it was like very interesting. We said, yeah, definitely. So these are the options that you can see to foresee the results within prediction, and this is what you need to do corrective actions, right? So it gives you a lot of, um, you know, it gives visibility to a lot of problems that you are seeing in your business and you know, that what needs to be corrected at the right time. But another thing that we’ve already discussed is that we need stakeholder buying for it, right. So everybody should be there as a team and work collaboratively. There should be communication. There should be an internal alignment that has to be collaboration to actually get to those results.

Madhurima Gupta:
Absolutely. So carrying on our discussion on technology. What do you think CFOs should opt for after RPA and technology platform implementation? What should be the next approach that CFOs should be taking for improving their business metrics?

Shilpam Ahuja:
So honestly, where we are actually right now doing the digital transformations for a lot of our clients, right, we see that there’s still at a nascent stage or the early stages of digital transformation in the way that would actually give efficiencies or value or insights within the business. So I think the promising areas after this is I think digital, um, I think data visualization to a great extent by giving the end-users the access to the real-time data and improve the organizational performance. I think that’s a very promising area. And another one that I feel is advanced analytics that we spoke about, but it’s not just for better decision making, but it’s also to help operations to uncover growth opportunities, right. As we just spoke about. And then I think that there is a shift from, uh, you know, it’s from a kind of a wave one automation to a wave to automation.
So what I mean by that is like, you know, most of the companies, they started with the first level of automation with RPA, and now there’s a lot of movement towards a second level of automation to have machine learning, NLPs, SaaS platforms. Right. So I think for this, it’s very important to identify the right use cases to do the transformations in the high-end technology area. Then finally I think the operating model is very important, right? So re-imagining the operating model with new capabilities is key. And I think that’s a very promising area as well. So we all are, you know, focusing our efforts to reduce manual tasks, which are there within the operations. And now the new operating model, I think, starts with leaner core, uh, tight data practices, new data management policies, and enhanced automation

Madhurima Gupta:
With this, we come to the completion of this episode. So thank you so much for having this really important conversation with me. Before we end this particular podcast, I wanted to, you know, get your parting thoughts for, you know, CFOs that want to make their CFOs office or their teams, their, you know, their solutions data intelligent.

Shilpam Ahuja:
Right. So I think, for any transformation, it’s a trilingual approach. There has to be a combination of process, policy, data, or you can say three DS it’s domain digital and data. Data is the fuel to get the domain and the digital transformation, right. So I think there should be a lot of effort to enrich the data in order to have the best end-to-end transformation.

Madhurima Gupta:
Great. Thank you so much for your time, Shilpam. I think this was really very interesting and will make for an amazing discussion for people to learn from. So I hope to host you again soon, but till then, thank you so much.

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