Why CFOs Need to Think Like Data Scientists

Megan - 00:00:25: Today, my guest is Mohammed Wasim. Mohammed has always been fascinated by how data can reveal stories and drive decisions. This curiosity has guided his career as a senior data insights analyst at Molson Coors Beverage Company, where he turns complex data into actionable insights that help steer business strategy. Mohammed's journey began with a master of science in data science from the Illinois Institute of Technology, where he honed his skills in data visualization, statistical analysis, and predictive analytics. In his current role with the Global Internal Audit Team, he dives deep into data from SAP, integrates it into Databricks, and performs thorough analysis and visualization. His goal is to help the team understand key risk indicators and align business strategies with the company's broader goals. The recognition of his leadership through the Clinton E. Stryker Award during his master's has been a highlight of his career. This award, given for his contributions to leadership service, fuels his dedication to mentoring others. He is passionate about helping international students and aspiring data professionals navigate their career paths and succeed in the U.S.. Sharing his knowledge and experiences is one of the most rewarding aspects of his work. Whether it's through data analysis or mentoring, his journey is driven by a belief in the power of insights to create meaningful change. Whether it's through data analysis or mentoring, his journey is driven by a belief in the power of insights to create meaningful change. Mohammed, thank you so much for being my guest on today's episode of CFO Weekly.

Mohammed - 00:02:29: Thank you, Megan. Thank you for having me here. And thanks for the invitation.

Megan - 00:02:33: Today, we're going to be talking about data and how data can reveal stories and drive decisions that create meaningful change. And I love this topic and I'm looking forward to learning more about it and you. So let's get started. Just so that we know a little bit about you, can you just start by giving us a brief overview of your career to date?

Mohammed - 00:02:53: So I started my career with a bachelor's in computer science back in India. So I graduated in 2019 and I worked for a supply chain company focused on data analytics role. There I developed my data analysis skills and data science and predictive modeling and statistical analysis skills. In 2021, I came to the U.S., to pursue my career in data science as an international student. And across the years, I worked with multiple companies like small companies, larger companies. I interned with multiple companies. One was automotive companies. Two were marketing companies. And currently, now I'm working with the largest beverage manufacturing company. So my career revolves around adapting to a new environment, navigating the challenges that came along, finding the right role in the competitive job market. I think I pursued data science at Illinois Institute of Technology Chicago, where I had a strong foundation of data science and data analytics skills. So today I am here as, you know, like a senior data insights analyst at the Molson Coors, working in finance focused on, you know, like a risk analytics role.

Megan - 00:04:02: And your career has taken you through diverse roles, as you mentioned, in data analysis and finance across multiple industries. So what was it that sparked your interest in combining data science with finance? And how has that shaped your approach to business strategy?

Mohammed - 00:04:17: My interest in, you know, like intersection of data science and finance was part by the realization of financial decisions that are often made, like, you know, based on patterns, trends within the hidden large datasets. So during my academic journey, when I was in grad school, I often, you know, like work with a lot of like financial datasets. And I literally saw how like predictive modeling, like statistical analysis could transform financial risk management, forecasting and like multiple financial strategies. I think this has led me to, you know, like focus on finance way data analytics, you know, literally helps you to like uncover the inefficiency, like optimize the financial strategies. So in my current role, this mindset allows me to like, bridge the gap between the financial reporting and data-driven decision making.

Megan - 00:05:06: And as you mentioned today, your role is a senior data insights analyst and finance and risk analytics at Molson Coors. So can you maybe walk us through a recent project where you use data analytics to help steer business strategy?

Mohammed - 00:05:19: So one of the like, recent project I worked was analyzing the vendor payment terms. The company had multiple vendors whom they negotiate on like different, different terms. The main goal was to identify the vendors who had, you know, like requested a reduction in payment terms. Payment terms have been set up in multiple variances like net 30, net 90, net 60. So I had to assess whether, you know, like whether these changes posed any kind of like financial risk to the company or if any payment terms have increased from net 30 to 60 or 60 to 90. And what was the reason? So to achieve this, like the foremost thing was like understanding the business, basically how the business works and like who are the data owners, where is the data stored. So typically in Molson, a lot of the like payment data that is coming that is from SAP. So I made sure to, you know, like meet the business owner and get the like historical payment data of the vendor data from SAP. And like once we got the data from SAP, I use specifically Databricks to integrate, you know, like a large datasets in the data sets are like huge and millions of rows. So Databricks, you know, allows you to like large scale processing. So once it was the data was pre-processed and cleaned and removed some like null values or like columns, which are not necessary for the business, I was able to, you know, like connect a Tableau Dashboard and build a continuous like monitoring dashboard of vendor transactions over a span of, I think, like three years. So I think we were able to like identify the vendors, you know, who are able to request shorter payment terms or longer payment terms, which literally, you know, like help the procurement team to renegotiate the payment terms. And also like definitely reduce some more potential financial risk. It helped the company with improved cash flow management and enhance kind of like a vendor negotiation.

Megan - 00:07:01: Yeah, that's amazing. There's so much power to be had in being able to harness data. How do you see the integration of advanced analytics and financial decision-making evolving over the next three to five years, especially with things like AI coming into play?

Mohammed - 00:07:19: Yeah, that's a good question. I think like most of the companies have already started, you know, like utilize AI kind of thing. I think in the next five years, I think we can see a deeper integration of like advanced analytics into financial decision making. And some of the trends might include, you know, like AI-powered forecasting, you know, like definitely organizations will start focusing more using on like machine learning towards the finance side to like maybe to predict revenue, maybe to like check like how the employers are spending, like how the employees are spending and some of the financial risk with the higher accuracy. I think I believe traditional budgeting methods will give way to like a dynamic one with AI-driven financial planning. And one more is like real-time risk analysis. Instead of just relying on quarterly reports or like weekly reports, I think finance team will monitor risk in real time using like continuous monitoring dashboard and like how to track specific key risk indicators or key performance indicators. I think AI will also will help you in streamlining some repetitive financial tasks such as like invoice processing or fraud detection. I think finance team will mostly rely on like insights from predictive analytics rather than just kind of a gut instinct. I think majority of the CFOs will play a strategic role leveraging, you know, data for like more financial decision making.

Megan - 00:08:37: And in your current role, you mentioned working with data from SAP and integrating it into Databricks. So can you elaborate on how these tools have transformed the way that you approach financial analysis and what benefits they bring?

Mohammed - 00:08:53: In the Molson, you know, like SAP is a backbone of our data storage. So it is primarily built for, you know, kind of like transactional processing. So I had to make sure that, you know, like understand the SAP terms. Those are written in specific German terms and those are like clearly visible. So I had to make sure that, you know, I get the knowledge of like what the data is telling, where the data is actually stored, what are the SAP common tables I have to use for my analysis. So before, you know, like integrating those into like Databricks, financial teams had, you know, like offer, they were using Excel to, you know, like just extract the data and kind of reading to slow on a manual process. Once you have like million rows of data, it gets difficult with Excel. So with Databricks, I'll be able to like process large datasets efficiently. Instead of dealing with the fragmented reports, I can load and analyze like millions of transactions in real time. And like I can definitely like once the data is loaded, I can perform like exploratory data analysis to check what the data is telling. If there is any missing data, if there is any inconsistencies in the data, that you can perform statistical analysis, machine learning and analyze the trends and the patterns that would be, you know, like impossible to detect a traditional finance tools. And also like I think by integrating Databricks with Tableau, I can create a real-time dashboards. And, you know, that helps definitely the leadership that made to make the data driven decision. Specifically with respect to my project, it has been helpful for vice president and senior managers in my team to, you know, attract the real-time like key risk indicators and to make better decisions.

Megan - 00:10:21: I've used Tableau. It's a pretty awesome tool. I'm just curious because I am illiterate here, but what is Databricks? Is it like a platform? Does it tie into SAP?

Mohammed - 00:10:31: So basically, various companies use different, different, multiple platforms. But Molson directly refers on Databricks. So majority of IT team is focusing on Databricks. And so we have a Databricks as cloud storage. So I think it sets apart from, as I mentioned, to process the large datasets efficiently. And it has everything. I'm starting to analyze the data to perform the predictive analytics. So I think that's where it sets apart from different companies. And companies more prefer to use Databricks. And it also lets you connect with multiple platforms. As we were talking about Tableau, Tableau has a direct option. Once you have generated it from Databricks, you can easily connect it to the real-time data. You can process the data in the back end, and it will reflect on your Tableau dashboard. So that makes the company officials for easy pre-processing of the data and like to understand what the data is telling.

Megan - 00:11:23: And given your deep expertise in data visualization and predictive analysis, what advice would you give to CFOs who are just beginning to leverage data? How can they start?

Mohammed - 00:11:34: So, yeah, from my perspective, what I have seen is starting with a descriptive analytics, you know, like to understand, begin by, you know, like begin small, begin by leveraging dashboards to visualize the key financial metrics. Understanding the historical trends is the first step before, you know, diving into predictive analytics. What we did previously, what the data is telling, how can we make a better decision by using the historical data? And also like investing in the right tools. There are a lot of various data analytics to data science tool. So understanding like what tools needs to be like invested and Molson specifically, you know, like Databricks, Power BI, Tableau. So which enhances for like a better auditing process, like financial reporting process. And also like rather than like focusing on the whole, I think understanding like identifying one or two immediate use cases, such as like I worked on, you know, like employee expense optimization. So those are like some use cases where you can like literally use the data for like, you know, showcase the value of the data-driven decision-making making, will better help in the next or future part. And also like build a data driven culture. Like a lot of our teams have seen, they are more into the financial auditing. But you need to understand, you know, like to rely on the data rather than gut instincts. Providing training among the employees to make sure to understand like what the technology side has and what the data science and data analytics side has and train them to improve data literacy. And then eventually scale once the basics are in place. I think you can expand it to more into predictive analytics to enhance strategic financial planning.

Megan - 00:13:08: Yeah, I love that point about making sure that everybody's literate in data. You don't necessarily have to be a data scientist, but I do think the training in how to use data is very important. And data-driven insights are crucial for making informed business decisions. How do you ensure that the insights that you're providing align with the broader strategic goals of the company, particularly when you're working cross-functionally?

Mohammed - 00:13:33: Being a data person, you know, like you have to be like things strategically to you. The work you're doing, you know, like making sure that ensuring that your alignment starts with the company's priorities. So before my analysis, I know, I make sure to ask some questions for myself, like what business problem are we solving? Who will use this data to make decisions? And like, how does this insights drive the business value? So like whenever I work with the cross-functional team, I make sure to, you know, like schedule meetings with them, like meet and customize reporting for different audiences. The vice president of the CFO might give a different metrics and they want a different reporting. But your manager might need a different, you know, things like some executives need high-level insights while analysts may, you know, they just need the raw data or like any kind of thing. And also like instead of just presenting numbers, you should know, like understanding, highlight what action should be taken. What do you see from the data? Like use a clear, actionable recommendations. And I think one more thing would be like a regular check-ins. Make sure like when you're doing the cross-collaboration, check-in with the team, check-in with the data owner, check-in with the business owners to ensure that, you know, like whatever the insights I have provided remain relevant to their business need.

Megan - 00:14:40: Great advice. And you've been recognized with the Clinton E. Striker Award for your leadership contribution. So how has mentoring others, especially international students and aspiring data professionals, influenced your perspective on leadership?

Mohammed - 00:14:55: So I think mentoring has fundamentally shaped my leadership philosophy. I believe that, you know, like a true leadership is about just, you know, kind of like empowering others rather than just driving results. So through mentoring a lot of like international students and aspiring data professionals, I have learned, you know, like empathy is essential. Many students who come from different cultures and different countries, you know, often face challenges beyond technical skills, visa struggles and like cultural adaptation, job market uncertainty. So I think recognizing these struggles had made me like more empathetic leader, ensuring that I approach the business, finance and, you know, like with the challenges, you know, like human first mindset, you have to get into the core of the problem. Then you will be able to like solve any kind of problem or solutions. So, and also it teaches you like reinforces learning. Whenever I mentor some students, whenever I mentor any kind of data professionals, I make sure to use a common term and a simple language, you know, like which has literally helped me, you know, to explain some of the like financial and data concepts to non-experts, which has made me like kind of a better communicator. And also I think believe that like growth comes from paying it forward. Many of the best professionals I've met have credited their success to a mentor who believed in them, which has literally, you know, like motivated me to like build an ecosystem where mentorship is a norm or like not an exception, both in the data field and, you know, like the corporate finance functions.

Megan - 00:16:17: Yeah, I think mentoring is so, so important. A lot of the CFOs that I speak to on this podcast credit a mentor for, you know, where they've ended up. So I applaud that.

Mohammed - 00:16:29: Yeah, I completely agree with you.

Megan - 00:16:30: And this next question is interesting to me, but as someone who has worked in both large corporations and smaller companies, what are the differences that you've noticed in the way that finance and data analytics are integrated into business operations?

Mohammed - 00:16:44: Yeah, there is, you know, like a different approach to like a significant different approach between the larger and smaller companies. Like when I started working for Molson Coors, I realized that, you know, there is a structured and a process driven culture. Finance and the data analytics teams, they have like standardized work close with strict governance. You have to follow those specific approaches. And larger enterprises, I think, like invest in some enterprise grade analytic platforms. We have SAP, we have Databricks, we have ACL Analytics for auditing and a lot of different, different technical tools. And, you know, like which makes the work easier and, you know, kind of have dedicated teams for, you know, like financial modeling or risk management or auditing. And also one thing I've noticed, there is like a longer decision cycles due to like, you know, like multiple layers of approval, getting buying charges or like new analytics initiatives. It takes times in like a larger organization. But when it, you know, comes to like smaller companies where I work, they rely on, you know, like finance and analytics teams, but have like more faster implementation of the insights. And what I've seen is there is a less automation and more hands-on majority of the like small companies don't have like more to invest in the enterprise software. So they manually extract and like manipulate the data making some kind of process like improvement critical. And like in the smaller organization, I have seen like finance and data teams like work closely with the executives, which definitely, you know, like translate some insights to take up some actions quickly. There are like both advantages and disadvantages of working in like both bigger organization and smaller organization. It's just about, you know, like how you believe, how you are able to like add value to the business.

Megan - 00:18:22: Yeah, I'm just curious, do you have any advice for smaller organizations with less resources on maybe how they can compete or keep up with large enterprises when it comes to data analytics?

Mohammed - 00:18:34: So I think firstly, it depends on what value you want to derive from your business. So I think like definitely once you have that like at a point that, okay, you want to get in there, so we want to build this. So then like investing in the right skill set, hiring people who bring, you know, the value driven. So hire those people who literally, you know, like help you make your business decision, help you make you some like analysis or make dashboards or build models. So it's also like investing in the right software. I understand like this is a concern, but like bringing up those literally there are some non-negotiables which will literally help your business grow. So bringing some like enterprise software and, you know, like hiring the right people to the right team will definitely, you know, like sets them apart with more like data-driven decision-making making.

Megan - 00:19:16: Great advice. So last question, but looking ahead, what do you think the future is going to hold for financial professionals in terms of their reliance on data and technology? Sure, you know, it's not going away.

Mohammed - 00:19:30: Yes, like the recent changes, you know, like the technology has been evolving. And as you mentioned previously, now it's a market of like artificial intelligence. I think it will be like kind of a financial analyst or a financial like supervisor or anything like that. They will be like a data-driven strategist. I think the companies will more prioritize on predictive analytics and AI driven decision making, you know, like rather than just solely focusing on like historical reports, you can like do the real time financial forecasting, like anomaly detection or like cash flow optimization. And also, as I mentioned earlier, like automation will definitely, you know, take place. And, you know, like I think like recently I read about like Robotic Process Automation. I think that will definitely help the workflow to, you know, like powered by AI. And also like a lot of future thing, I think like integration of financial and operational data. You know, what I have seen working through my road is like you get to work with cross collaborative with multiple things, finance team, operations team, procurement, supply chain. I think like future financial offices will like break down silos, ensuring that, you know, financial metrics are linked to the real-time business value. And also the most like important part is like understanding the cybersecurity and the risk management responsibilities, you know, with financial data becoming more like increasingly digital. So I think cybersecurity, like how do you store the data, maybe data security or like compliance or the fraud prevention will become, you know, like the major part in coming years.

Megan - 00:21:00: Mohammed, thank you so much for being my guest today. You've provided some great insights.

Mohammed - 00:21:05: Thank you, Megan. I appreciate your time. And it was great talking to you.

Megan - 00:21:08: Thank you. And I really appreciated your time. And thanks for finding the time to be here with us today to share your experience and knowledge. And I wish you all the best. And to our listeners, please tune in next week. And until then, take care.