Self-Service Analytics: Transforming Finance Operations

Megan - 00:00:18: Today, my guest is Alyssa Shadinger. Alyssa is the CFO at Sisense, an embedded analytics company that empowers application builders with analytics to help accelerate innovation and turn data into a competitive edge. In this role, Alyssa manages FP&A, accounting, data analytics, deal desk, and procurement. Alyssa is passionate about creating a culture of data-driven decision-making and promoting a bias for action. Alyssa lives in the Philadelphia area with her husband. In her free time, she enjoys reading fiction, live music, and traveling. She invites you to connect with her on LinkedIn, where she shares thoughts about finance and leadership. Alyssa, thank you very much for joining me on today's episode of CFO Weekly.

Alyssa - 00:01:32: Thanks, Megan. I'm really excited to be here. Thanks for having me on.

Megan - 00:01:36: Sure. Today, our discussion focuses on transforming data into actionable insights, and I'm really excited to hear more about this topic and to learn from you, so let's jump right in.

Alyssa - 00:01:46: Great.

Megan - 00:01:47: First, and just that we have a little bit of an idea of who you are, can you just start by giving us a brief overview of your career to date?

Alyssa - 00:01:55: Yeah, absolutely. I started my career in public accounting. I did about two busy seasons with a regional firm in the New York City area. And then I pivoted over to Protiviti. And there I focused mostly on business process improvement and some Sarbanes-Oxley financial control work, but definitely got a much broader range of operational experience in that role. After doing that for about three years, I felt like I had spent all of this time going to school for accounting and getting my CPA. And I wanted to get back into something that was a little more technical in the accounting and finance world. And during that time at Protiviti, I also got some great data analytics experience and really realized how much I do just love working with numbers and also wanted to maybe explore some of the FP&A side of the world. So then I moved over to more deal advisory, M&A, and IPO readiness advisory with a small firm called the Pine Hill Group out of Philadelphia. I did that for a couple of years. And then I went and did the same thing at Deloitte because at the Pine Hill Group, I got experience with smaller, more SMBs, mid-market companies. And at Deloitte, of course, you're getting experience with Fortune 500 companies. So I did a very similar role at Deloitte, but just on a much larger scale. So altogether, I spent about seven, eight years in the consulting side of the house. And then about six years ago, I moved over into the technology space. So my first role post-consulting was with an infrastructure as a service provider called Linode. They were headquartered out of Philadelphia. They were operating, I believe. Maybe seven or eight data centers in five different countries when I joined. And by the time we exited to Akamai Technologies, we were operating 11 data centers in eight different countries. So I was there for about four years and really got bit by the startup bug, loved seeing all of that scale and growth and feeling like I was contributing to that. So after we were acquired by Akamai, I spent about six months helping with the integration of the finance and accounting teams. And then I found my current opportunity where I came on board to Sisense as the VP of Finance and got promoted into the CFO role earlier this year. But yeah, that's my background in a nutshell. I kind of took a zigzag path to the CFO role, but really grateful for all of those early experiences, getting a really well-rounded view of the CFO organization.

Megan - 00:04:28: And looking back, can you share a specific moment or experience that profoundly shaped your approach to transforming data into actionable insights?

Alyssa - 00:04:37: Yeah, I love this question because. Because I've had such a broad range of roles and responsibilities in the finance and accounting domain. I found that data literacy is super important in all of them. So I would say I cut my teeth at productivity with a couple of small data analytics projects. The one project that really sticks out to me was during my time at Deloitte. I was leading a finance transformation project for a really large healthcare client. And as the lead PMO, I was responsible for overseeing a large team that was tasked with optimizing the finance function and establishing a finance center of excellence. So this role required lots of collaboration with different departments. And our ultimate goal was to align the finance processes with the broader business objectives of the organization. One of the key pieces of that project was improving and automating the department's analytical processes to streamline financial reporting. So we were actually able to... Really enhanced the value that the finance department delivered to the organization by taking raw financial data. And transforming it into strategic insights. One example that I remember was we were able to improve the company's sales and supply chain forecasting through improving their data model and automating it within their financial system. So we helped them anticipate their financial outcomes more accurately. And as a result, they were able to make more informed decisions around investments in their manufacturing capabilities. So that experience was really transformational for me and reinforced the importance of using data, not just for reporting, but also for shaping company strategy. And I would just say overall, my time at Deloitte taught me how powerful data can be when you use it to inform decision making.

Megan - 00:06:30: And with so much data available these days, what's your advice for narrowing it down and not trying to boil the ocean? How do you get to what's really important when you're looking at data?

Alyssa - 00:06:43: Yeah, that's a great question. I think it's not surprising to me that I ended up having such a focus on data in my career because as a kid, like growing up, I really always enjoyed word problems and logic, like word problems when it came to math and logic problems, if you've ever done those and crossword puzzle books. But I really think it's about looking at the problem and figuring out what is really the crux of the issue and which metrics are really going to drive, drive your outcomes for this initiative. That way you can kind of filter out all of the noise. Let's say you're looking at your portfolio of all your customers and you want to figure out which customers may be at risk for churning. So there's going to be tons of data points. And at the end of the day, you want to really be able to figure out which of those data points have a statistically significant impact on the outcome that you're trying to solve for.

Megan - 00:07:38: So Alyssa, can you just share a little bit about what it is that Sisense does?

Alyssa - 00:07:44: Yeah, definitely. So at Sisense, our mission has always been to empower leaders by making data more accessible and actionable. Over the years, as business intelligence or BI technology has evolved, our approach to transforming data insights has progressed significantly. So initially, BI tools really focused on reporting and dashboard creation, which does provide valuable snapshots of business performance. But they require significant manual effort and technical expertise to maintain. And again, they're really just providing a point-in-time snapshot. So as BI technology advanced, we've leveraged automation, AI, and machine learning to shift from static reporting to more dynamic real-time insights. This shift has allowed leaders to not only access historical data, but also predict future trends and make their decision-making process more forward-looking and proactive. Also, the evolution of embedded analytics has been a game-changer. Rather than having to access separate BI platforms, leaders can now get context-relevant insights directly within the tools they already use daily. So think about your Salesforce or your HubSpot or your NetSuite or your SAP system. And this seamless integration has dramatically reduced the time from data access to action, and that helps companies drive faster and more impactful decision-making. Also with the rise of self-service BI, we've been able to move away from a centralized model where only data specialists could generate insights. And now we're empowering every team member to explore and analyze data on their own without having to rely on IT or BI. This democratization of data has been critical in enabling faster and more informed decisions at all levels of the organization. Ultimately, our focus has evolved from just providing data access to delivering tailored, predictive insights that enable leaders to take immediate, meaningful action, ensuring they stay ahead of today's fast-paced business environment. And helping companies build those data experiences into their products for their end users and all of their data applications.

Megan - 00:10:00: So given your diverse background in financial leadership and data-driven decision-making, is there a particular project or maybe a challenge that stands out to you as having been especially rewarding or transformative?

Alyssa - 00:10:15: Yes, definitely. I would say the most transformative experience to date, my career has been working on the sale of Linode to Akamai. I'm really grateful for that experience because having a front row seat to an exit that large is quite a learning experience. We ended up ultimately selling the company for $900 million. And I got to be a key member of the transaction team. So I was involved in deal positioning, financial due diligence, and negotiating the terms of the agreement. And that was significant not just because of the scale, but also because I needed to work with the rest of the leadership team to synthesize so much financial and operational data. And translate it into a compelling narrative that would really resonate with external stakeholders. What made the experience impactful as well was also the complexity involved. So, we were still tasked with managing the financial health of the company. Which had global operations on a day-to-day basis, while also trying to align our growth story with the strategic goals of potential acquirers. So that required a ton of analysis from breaking down our revenue trends by customer cohorts, regions, products. All the way to understanding which key metrics would influence the various buyer profiles and their interests. The ability to combine that finance perspective with strategic storytelling, I feel like is so critical to any CFO's success, but especially in an M&A transaction.

Megan - 00:11:51: And at Sisense, your goal is to empower leaders by streamlining data access and insight. So can you maybe elaborate on how your approach to transforming data into insights has evolved with advancements in BI and other technologies?

Alyssa - 00:12:08: Yeah, definitely. I love this question because at Sisense, our mission has always been to empower leaders by making data more accessible and actionable. So over the years, as business intelligence or BI technology has evolved, our approach to transforming data into insights has progress significantly. Initially, BI tools really focused on reporting and dashboard creation, which does provide valuable snapshots of your business performance, but it requires significant manual effort and it's typically just a point in time snapshot. So as BI technology advanced, we've leveraged automation, AI, and machine learning to shift from static reporting to more dynamic real-time insights. This shift allows leaders to not only access historical data, but also predict future trends, making their decision-making more forward-looking and proactive.

Megan - 00:13:06: And how is it that you balance the need for comprehensive data analysis with urgency of making timely decisions? And what strategies do you use to ensure that insights are both actionable and timely?

Alyssa - 00:13:20: I'm glad you brought this up because balancing speed and accuracy is a constant challenge. I'm sure any finance professional can relate to that. But it's really essential to driving growth and operational efficiency. I like to approach this balance by focusing on three key strategies, which are prioritization, automation, and then cross-functional collaboration. So first, by prioritizing the data that has the most immediate impact on business outcomes. That really just allows the team to focus on the types of questions that they're going to be asked most frequently, and then work backwards to understand the key data and metrics that are going to inform those decisions. So for my FP&A team, that's typically budget and ARR trends, cash flow, customer acquisition costs, things like that. And by establishing these priorities, we can move really quickly without being bogged down by unnecessary complexity when we get a question on the fly. Next, automation plays a crucial role for my team to deliver timely insights. So at Sisense, we've led several efforts to automate much of the financial reporting and month-end close processes. So this helps us save time. It also helps the team maintain a healthy work-life balance. But then it also ensures that the data we're working with is accurate and up to date and helps us, again, just make faster decisions without sacrificing reliability. Last, I would say cross-functional collaboration is essential. So I like to work really closely with our CRO, CMO, CPO, and the rest of the department leaders to ensure we're all aligned on what the most pressing issues are within the business. And then that helps us focus on making critical business decisions and improving the data program for the future rather than waiting around for perfect data and getting sucked into analysis paralysis. So I feel like that collaborative effort ensures that everyone in the org can access and act on relevant information. And again, just drive that faster decision-making.

Megan - 00:15:28: And you've had significant experience in BI, FP&A, and other accounting functions. So how do you integrate these different tools and functions to create a cohesive strategy for deriving actionable insights, especially during something like M&A that's very high stakes?

Alyssa - 00:15:46: I'm grateful that I've had. Some of that broad experience across FP&A, BI, and accounting, because it really showed me the need to integrate these functions to create a cohesive strategy, especially when you're working with something so crucial as an M&A transaction. Each function definitely plays a vital role, and the key to success lies in breaking down silos between those teams and just making sure that data is flowing between the departments. For instance, during a merger acquisition, FP&A would be responsible for modeling. Future performance, coming up with cash flow scenarios. And that requires really close alignment with the accounting department to ensure that the data feeding into these models is accurate and compliant with all the latest accounting standards. And then at the same time, you'd probably be using your BI tools and relying on your BI analysts to help extract those insights from larger data sets and some of the underlying data that will inform the model, such as your customer trend margins and things like that.

Megan - 00:16:51: And while we're on the topic of acquisitions, I'm going to skip ahead one question. So in your previous roles at Linode and Akamai Technologies, you were instrumental in high-packed acquisitions and integrations. Can you share a specific example where data-driven insights significantly influenced the outcome of a major business decision?

Alyssa - 00:17:12: Definitely. During my time at Linode, we were really focused on scaling up and driving business growth, both organically and through expanding our product offerings and co-location footprint. So one of the projects that I really enjoyed working on there was our expansion into the Asia Pacific region. I got the opportunity to work really closely with our BI analysts and our data center operations team to analyze existing customer data and preferences. So we were looking at things like where are the customers located, where are their end customers located, where have they expressed interest in having a new co-location center. And we looked at that data alongside operational data, such as the cost profiles of doing business in different cities within the region and data center penetration metrics for those various regions. Our ultimate goal was to determine which regions would promote the most growth from our existing users. As well as new customers who weren't familiar with our brand yet, and balancing that which regions would have the fastest payback period on our investment. At the end of the day, we ended up launching data centers in Tokyo, Mumbai, and Sydney, and those locations contributed to our growth story that ultimately led to the successful exit.

Megan - 00:18:33: And you've touched on artificial intelligence and machine learning a bit, but how do you see the role of these two pieces of technology evolving in the realm of data analytics and decision making?

Alyssa - 00:18:46: Yeah, I'm really excited for AI and machine learning. Two more recent experiences come to mind that I think kind of highlight how they're evolving this space. The first is we at Sisense, we're always trying to, I feel like dogfooding is the term, but we like to say drink our own champagne. So we were trying to figure out how can we make a chatbot? How can we use chatbot technology that we've already developed within our tool and leverage that internally to make a pretty much a chat assistant for our CEO so that, oh, I should probably mention Sisense is a global company. So our CEO and most of our R&D leadership team is in Israel. And most of the go-to-market and G&A team is in the US. So quite a bit, a 7 to 10-hour time difference. So our CEO really felt like it would be beneficial if he could have this CEO chatbot that would be able to look at some of our financial data. Also some other key metrics around just like customer cohorts and health of the business. And that way he would have access to those answers and not have to wait for someone in the finance team or the data analytics team to come online on East Coast hours. So we were able to use, in conjunction with our sales engineering team and our AI team, we were able to put that in place and create this chatbot. And we've had a lot of success with that. I mean, our CEO is happy. We're happy because we're not logging on to tons of detailed questions. He can now self-service those answers that are relying on our underlying database of financial information. And then another example I can think of from Sisense is we actually brought on a machine learning intern this summer, which was an amazing experience. She's actually going to come back and join us this fall in a part-time capacity while she finishes up her degree. But we had already developed a predictive model to help us analyze customer health scores and basically come up with what we're calling a churn risk score. And when using the internal resources we already had, we were able to get this model to about 75% prediction accuracy on previous data and about 40% accuracy on forward-looking data. And with just a couple months of part-time work from a machine learning specialist, we were able to improve that accuracy on the next version of our risk score to 98% on historical data. And we're in the process of implementing it for the future. So we don't quite know what the real outcome is going to be yet, but we're pretty confident we'll get like a 60 to 70% prediction accuracy on live data. So that's really exciting. And I just think we'll continue to see more and more of this in the finance world.

Megan - 00:21:39: Yeah, that's an amazing example. And I'm also excited to see where technology takes us in the next three to five years. And I know we touched a little bit on boiling the ocean when you're looking at data, but Sisense aims to simplify the process of gaining business insights. So what are some of the common challenges that you see organizations face when trying to streamline data access? And then how do you go about addressing these challenges?

Alyssa - 00:22:07: Yes, I feel like that can be really tough. Streamlining data access can be really tough, even with the right tools and intentions. So at Sisense, we aim to simplify the process by addressing a couple common obstacles. The first one we see a lot is data silos. So most companies have their data spread across multiple department systems, even different regions. And that can make it really difficult to get a comprehensive view of their business. At Sisense, the way we address this is by providing a platform that integrates data from various sources. So you can pull in data from your CRM, your financial database, or even a custom bill application into one single cohesive view. And then that enables organizations to break down those silos and ensure that data is accessible from one centralized location. Another challenge we see is just accessibility. So even if the data is available, not every team member has the skills or tools that they need to analyze it effectively. So traditionally, this would require a data specialist or maybe an IT team to generate reports. And of course, that slows down decision making and creates a bottleneck. So at Sisense, we've also prioritized self-service analytics. So we empower non-technical users to create their own dashboards and run their own analyses with intuitive tools. So Sisense will really guide those users on how it'll even help you create your data model. And this ensures that data is democratized across the organization and make sure those insights are available really quickly when people need them. And finally, I would say data accuracy and trust is another common issue we see. We run into this all the time between like finance, accounting, and BI. If users don't press the data or have a different definition of a metric, or they feel like the data is outdated, they're less likely to actually act on it. So we address this by providing real-time data processing and ensuring that data governance and quality control are built into the platform. And then also through our professional services resource and customer success managers, we help train our customers how to, you know, the human aspect of that as well, like how to make sure you're adopting the right policies and processes across the organization.

Megan - 00:24:32: And within the same vein of accessing data, we have to talk about data security and privacy. Both are critical concerns in today's digital landscape. So how do you ensure that the drive for actionable insights and things like self-service don't compromise data security? And what best practices do you follow to safeguard sensitive information?

Alyssa - 00:24:55: This is a great question because I feel like data security and privacy are such hot topics right now and absolutely critical. And especially as I think the demand for data is just going to continue growing and growing, we're going to continue needing to rely on these complex systems that have access to a lot of underlying data. So we do take a couple of proactive steps to make sure that our pursuit of data doesn't compromise our overall data security. And I will admit this is not my area of expertise, but a few things that come to mind from working with our CISO and just having to implement these programs within my teams. The first thing that comes to mind is data governance. That's really central in how we manage and protect data. So we do that by maintaining strict data access controls so that users only have access to the data that they need to do their jobs. This concept of role-based access really helps us make sure that sensitive information like, financial data or customer information is only accessed by authorized personnel. And then we also perform regular audits to validate the ongoing management and compliance of those processes. And then one other thing that comes to mind is that we require all of our employees to complete regular security and data privacy compliance training. So just making sure that all of our employees understand best practices around data security and also privacy requirements like GDPR is a top priority for us. And we feel that cultural focus on security and privacy just helps ensure that everyone is going to play a role in protecting information.

Megan - 00:26:37: And given your experience with various financial systems and compliance requirements, how do you ensure that your data transformation processes adhere to regulatory standards while still delivering impactful insights?

Alyssa - 00:26:51: This is an issue I've faced several times. And what I've found to be most successful is making sure that the data and accounting teams are very closely aligned and have a process in place to test and validate results before they publish a data product. I always want my accounting or FP&A leader to validate a financial metric that is probably being built out in a dashboard or automation by the data team for some other business unit to use. But I want FP&A or accounting to validate like, yes, when we're pulling the source of truth and also just the underlying formula that we're using, everyone's on the same page. And that extra step just gives me the peace of mind that the business is singing from a single sheet of music and that we don't run into any issues around more complex topics like revenue recognition.

Megan - 00:27:45: And how is it that you foster a data-driven culture within both your teams and also across the organization, just that you can ensure that insights are acted upon consistently rather than just left like sitting in a report on someone's desk?

Alyssa - 00:28:01: Yes, I would say building a culture around data is not an easy task. I would say one thing I found to be super helpful is making sure that my team know, it's okay to make a mistake as long as as we learn from it. And I think that goes a long way. It helps alleviate that analysis paralysis and give people the autonomy they need to get their jobs done. And also, you know, hopefully they're coming up with new and innovative ideas for the business because they're not afraid of making the wrong decision or not having all of the data. And then I would say it's also important to make sure that teams have the adequate opportunity for training so they can continue to enhance their data skill set or grow that skill set if it's something they're not well versed in yet.

Megan - 00:28:45: And along that line, are there online courses or books or something that you'd recommend or that your teams use that you've really liked? So much learning opportunity out there. Sometimes it's just finding the best one or getting a recommendation.

Alyssa - 00:29:01: Yeah, yeah. I would say I've done a few statistics and data analytics courses off of Coursera. I feel like there's a lot of great ones on there. I feel like there's a ton of free resources online as well for more getting into like fast metrics and trying to determine what metrics are really going to matter for your business. The one that I've used before I believe, it's the CFO.com. That's a really good source of information. Don't want to misspeak here, but I believe Insight Partners also publishes like a benchmarking report every year that has various financial metrics, like the investment community is focused on, especially for software companies.

Megan - 00:29:43: Yeah. Thank you for sharing those.

Alyssa - 00:29:45: So yeah, Data with Serena is another one that I follow on LinkedIn, and she's got a lot of great free courses on LinkedIn as well.

Megan - 00:29:53: Okay, Data with Serena?

Alyssa - 00:29:55: Yep.

Megan - 00:29:55: Okay, last question here, but looking ahead, what emerging trends or technologies do you think will make the most impact on the future as far as data transformation and getting to actionable insights?

Alyssa - 00:30:10: Yeah, I think we touched on it a little bit already, but I really think it is AI and machine learning that's going to continue to transform the landscape and finance. I think it's just so important to stay up to date on that technology and figure out how easy ways and easy wins that you can implement that into your day-to-day tasks and processes with your team and make sure that your team is staying up to date on all of those technologies. One thing that excites me that we're actually working on at Sisense, like I mentioned, we've already released beta version of our chatbot technology. But I think a lot of companies, especially in quite a few in like ERP and financial software space that are working on co-pilots. That I think will just help streamline how quickly finance and accounting teams can get things done over the next couple of years. So I actually saw a demo from a company and then we reached out to them to test it out. And they actually let us know that they were taking it off the market because it wasn't cost effective yet. But it was basically a tool where you could enter in your financial, like you can basically upload your three statement model and then ask questions, have it build out, like even build out a board deck for you with charts and graphs. And that's kind of the realm we're trying to get into. That's the path we're on at Sisense. So hopefully someday we'll be able to just do that with our own internal technology. But I think that's going to be really exciting. And I'm excited to live in that world in the future.

Megan - 00:31:43: Alyssa, thank you so much for being my guest today.

Alyssa - 00:31:45: Yeah, thank you so much for having me. This was so much fun. I really appreciate you having me on.

Megan - 00:31:50: Yeah, and I really appreciate you being here with us today to share your knowledge and your experience. And I wish you and Sisense all the best.

Alyssa - 00:31:58: Thank you.

Megan - 00:31:59: And to all of our listeners, please tune in next week and until then, take care.