We know that getting your data right involves deploying appropriate technology and having people use it correctly, but how does one influence the other? In the recent webinar, “Getting Your Data Right: How Architecture Facilitates Data Culture,” two members of Chick-fil-A’s (CFA) Financial Solutions Lab — Camille Felton, CTP, FPAC, Principal Team Leader and Matt Burton, Director within Financial Operations & Technology — shared valuable insights from their journey.
The spark of an idea“Business experts are uniquely positioned to know their processes inside and out because they're closest to the work,” said Felton. “Often they can get overwhelmed though, because they may be the person who has done the process since the process started and can get bogged down in some of the enormity of their tasks causing them to focus more on the road instead of the path forward.” They may think, there has to be a better way!
That becomes the spark that kicks off an engagement with us, the Financial Solutions Lab. They come to us looking for collaboration and efficiencies to do their work in more streamlined ways. Capacity constraints within our Finance teams triggered a need for a dedicated support arm to triage process improvements, leverage data, optimize with automation, and ultimate drive Financial Services towards the future of work. This led to the creation of the Financial Solutions Lab whose main aim is to help Financial Services gain bandwidth to support our Restaurants, our Owner/Operators and their teams.
Our role at Financial Services is to provide back-office support, such as processing payroll or consulting services. And specifically for this conversation, we help bring data insights and reporting to life so they might achieve, or help achieve, those objectives.
Catalyst 1: Viewing data as an assetIf the spark is there has to be a better way, then the first catalyst is ultimately viewing data as an asset. An example came from Felton’s experience of trying to make a cash forecast during her treasury days: “I had to take the AP data, connect the bank transactions on the backend, and understand what that outstanding liability was between checks and payments and transactions. I also had to layer in the budget, and as that changed and iterated, it was really difficult to make sure I had captured all of those things within a network of spreadsheets.” Finance seemed to be largely in the business of managing spreadsheets
It was this experience that sparked the conversations which led to getting treasury, and ultimately finance, on board with a centralized data repository. Recognizing the value of data meant investing time and resources and becoming an active player in shaping the outcome to ensure the proper controls were in place.
Join the FP&A Series to dive deeper into data on June 9 at 11:00 am ET for tactical sessions and real-life examples of creating a data-driven organization. Registration is complimentary. Learn more and register.The solution for CFA: Create a centralized data lake or data storage repository with a data catalog that notes every source of information, what the data type is, and the governance applied to that data set. Being able to see data sets across our organization unlocks those silos and allows us to connect to enterprise-wide information.
The outcome of the new architecture was immediate: Calculating our cash position freed up an entire FTE day — every time we wanted to calculate it. That led to more financial analysis and a wider lens on the business, which is what we were all hired to deliver in the first place.
Prior to creating the data lake, Felton was limited to storing available information on a spreadsheet. After the data lake was created, she was empowered with new capabilities: “I was able to take GPS coordinates and connect them to our finance data and create maps. We could figure out regional trends related to our finance information” cross-tabulated by store type. “Going beyond finance [data] can really help you unlock decisions and data points that you wouldn't have gotten otherwise.”
Culture change: finance can provide new services when they are no longer in the spreadsheet-aggregation business.
Catalyst 2: The coronavirus pandemicCOVID created an all-hands-on-deck opportunity. Everyone across financial services was asked to perform their processes at their highest skill level, as fast as possible, and with the most efficiency to get to those quicker insights — this was how we realized several process challenges that had been masked.
When we were in person, we could sit with individuals to get questions answered in a three-step process: business people to finance to data analysts. That in-person ability to connect with people had been covering up a lot of opportunity in our processes. Financial Services thought, and many still do today, that you had to have advanced skills in order to do analytics and interact with our data lake. They thought, ”I don't have any SQL skills” or “I don't know how to build Tableau visualizations,” so they were hesitant to interact with our data lake. We realized that in thinking about the future of work, we had to consider where we had started and continue that journey.
The increased demand for data and shortened work cycles got IT's attention to the point that we ended up dedicating a pod of resources to help specifically with Financial Services data. This would have been unheard of prior to the pandemic simply because we didn't necessarily have those skill sets in-house. This opened the door for others who hadn’t dabbled in a data lake environment to test their aptitude with new skills and technologies.
Culture change: new ways of work can create, well, new ways of work! Be open to changing your processes, and never assume a reversion to the prior method is best.
Catalyst 3: Transition to the cloud
During this period, CFA also migrated our enterprise resource planning (ERP) tool to the cloud. This presented a challenge, and also an opportunity. Moving the ERP to the cloud and having it feed the data lake (also in the cloud) would allow for a single source of truth accessible from anywhere; however, that data lake was optimized for storage rather than high-volume demands, operational reporting and analysis. Could this be an opportunity to raise our skill sets and add to the Financial Services toolbox?
Our solution? Build a Financial Services specific data mart that allows us to quickly process curated data extracted from our ERP and integrate with our existing data lake ecosystem. This solution opened the door to new tools we had not used at scale within CFA. One such business intelligence tool, ThoughtSpot, allowed finance to interact with our data to review and report on data in similar formats to what they were used to. This tool also allows us to visualize the data by default and quickly identify data patterns without advanced technical or data storytelling skills. The most exciting part is that it's getting our teams out of Excel, while giving them the freedom to work within the Excel guidelines they're familiar with.
Routine requests have been minimized by democratized data. Where once a business partner may have messaged FP&A to ask, “Can you give me that spreadsheet that tells me how much I have in my budget, what I've spent and what those variances are?” Those items are now self-service. And the specialized data analysts? They are happily spending more time on other value-add analysis and deep research projects.
We also emphasized reporting in this cloud ERP transition. From the beginning, we made a concerted effort to ensure reporting was front and center in this project. We analyzed the data usage within Financial Services and found that in the on-premise platform, the thousands of queries being run could be consolidated to five or six core data sets, so we optimized around those.
Culture change: the technology roadmap utilized in one part of the business has a dramatic impact on others. It is important for FP&A to be informed and contribute to the data developments around the organization.
Catalyst 4a: Bringing along the whole finance teamBringing others along is about connecting all that we’ve talked about to bring finance into the future. We have an organization inside of Chick-fil-A called COPA, or community of practicing analysts, whose goal is to educate analysts across the organization so they're aware of other use cases happening. COPA allows the team to share use cases across the company, which spreads specific learnings, and more importantly, the idea of what is possible along with the methodologies to deliver. This community identifies individuals within finance who are tech- or data-savvy as the first frontier of users. As we grow that first frontier, we also expand our reach into additional frontiers (“fast followers,” “late adopters,” etc.), and that’s what is going to get you and your finance teams to the next level.
Our change management plan also focuses on the need to apply multiple tools. In the past, the spreadsheet was the Swiss Army Knife tool that could be used for all purposes; now, we need tools that can flex beyond what Excel traditionally can do. The tools are becoming more specialized, so in order to optimize them, we often apply multiple tools that bring different aspects to the holistic solution. For example, aligning lease administration, ERP and cash data may require me to pull from the ERP directly and blend with data lake inputs paired via SQL Workbench or ThoughtSpot reporting.
Culture change: architecture creates the foundation for data and tools, and then you need multiple pathways that allow your team to adopt and adapt to the new capabilities. This will take the “there must be a better way” spark to the point of creating and activating a data culture.
Catalyst 4b: Bringing along hesitant business partnersA critical question raised by the audience for this webinar was: How do I get other groups to share their data with finance? How do we build that trust that we’re going to use the data appropriately? “You have to start with the why,” said Burton. It's important to articulate why we need that data. From a finance perspective, we use a lot of data that’s generated upstream in the business to understand our financial outcomes, so we are upfront about how we're going to use the data, why we're going use the data, and when it's going to stop being used, if appropriate.
We also have a business steward, who is responsible for understanding the business context of that data and sharing insights with the operational user. Every dataset has a technical owner too, so if there is a failure, or there's an issue from a technical standpoint, they know who to contact related to the dataset maintenance.
With Chick-fil-A being a private company, we have data that's business-sensitive. What we’ve done is to provide those owners or those business stakeholders with audit capabilities. For example, when the data sits in our data ecosystem, we can see who uses it and how often they use it. There's a continual review meant not to get anybody in trouble, but to show that business owner that we're taking their data governance very seriously.
In addition, we have steering committees and execution teams for our large initiatives made up of a cross-functional group of leaders. We do this to make sure that multiple leaders from multiple facets of Financial Services, Enterprise Analytics, IT or others speak to their needs.
“Leadership does a really good job of creating freedom within a framework,” said Burton. “Here's the framework we want to get to: What does Financial Services look like in two, five and ten years? Then giving people the capacity and freedom to innovate” and build what we need. We've been given the bandwidth to look ahead to the future, test some of those new technologies and iterate against our theories. “It’s going to look different at every company.”
Culture change: finance cannot stand apart from data for fear of losing control and stewardship. It needs to go in the opposite direction and bring that discipline to the rest of the enterprise.
Want to know more? Check out AFP Learn and listen to the entire webinar, “Getting Your Data Right: How Architecture Facilitates Data Culture."