4 surprising lessons our firm learned about CAS data on our way to a new model

Mention your data strategy at a party and plan on being met with blank stares and hurried attempts to redirect the conversation. On its face, data isn’t the most exciting topic.

But you know what? Even if I know better than to start talking about data at the next backyard barbecue, I get insanely excited about it. There are several reasons for this. For starters, our approach to data has completely transformed our business. There’s no way we would be where we are today without overhauling our approach to data.

Reason #2: Data always has a story to tell. Every time we dig into data about our clients or about our own client advisory services (CAS) practice, we learn something new. Sometimes it’s something big, other times it’s less earth-shattering, but it’s almost always useful in some way. Most importantly, it helps us and our clients make smarter decisions without having to rely solely on gut instinct.

First things first
“CAS data transformation.” What does that even mean? It does sound like the worst of buzzword bingo, I know. But it actually does have a very specific meaning for us, so let me explain. Anyone working in CAS knows that having enough data isn’t a problem. The problem is that you have too much of it, from both the client and firm sides, coming from a lot of different sources.

That was our situation. We knew all that data had the potential to be a gold mine for us and for clients. But we couldn’t see a path to wrangling it all to make sense of it.

So, we decided we needed a unified data infrastructure—one central place for collecting, storing, processing and analyzing data. Sounds great (if a little buzzword-y)! But it’s not like you just go out, buy a unified data infrastructure, install and flip a switch. It’s a process. And in that process, we learned a few things. Here are a few highlights.

Industry-focused practice, industry-focused data
Our CAS practice is built around our ideal client profile, which is segmented by industry. Nothing new there. Most CAS practices are organized that way, and an industry-first orientation is widely viewed as a best practice. But it may be slightly less obvious that your approach to client data should be industry-specific, too.

Of course there’s a lot of overlap between industries, but each industry has its own nuanced data sets that are more important than others. For some, it’s most important for them to know what their customer acquisition costs are. For others, it’s all about their supply chain data: How are they acquiring raw materials? Manufacturers may want to dig deep to find out how efficient their manufacturing processes are, or maybe they’re more focused on demand planning.

In just that handful of examples, we’re talking about a huge range of data types. So, when you start thinking about how to prioritize and organize client data, start with a clear understanding of their key drivers for decision making.

Specialize as you go
When developing our data strategy, we found that it worked best to start with an approach that can work with all clients. Once that was in place, we were able to begin specializing, developing industry-specific templates. From there, we could further customize each to fit client-specific circumstances. Along the way, it’s important to maintain consistency across all industries in tracking and measuring clients’ financial data. From there, you can layer in sales data. Then operational data. Then you slice it by industry and toolset to develop standardized approaches for individual industries.

For example, we have an e-commerce template that uses high-level KPIs for all e-commerce clients. Once we start working with a client, we integrate data sources that are specific to the client, like Shopify data that provides us with so much more information beyond raw sales data. Customization is where the real value lies, and we stuck to a consistent process to achieve it.

A picture is worth a thousand words. A clickable picture is worth even more.
At this point we’re all familiar with pretty visualizations of financial data. But what’s missing so often in these visualizations is the ability to understand what’s happening behind the data: What’s causing what we see in a visualization. If you design your data architecture in the right way, you can deliver visualizations that allow you to slice the data with more precision, focus and intentionality.

Here’s what I mean by that. Maybe you generate a visualization showing that revenues are really high. Great! But why is that? You should be able to click into the visualization to see what services were sold. Maybe you double-click even further to see what was driving those sales. That’s the next level of visualization that you should design for from the start.

You’re going to need a little help at first

From day one, we developed our CAS approach based on a foundation of data, and we built an in-house team to transform how we operate internally and how we deliver services externally. If you already have that kind of team and approach in place, you’re head of the game. Most firms don’t. So, you’ll probably need help from an outside team to create some consistency in data for your data pipeline. Once you’re up and running with a centralized data infrastructure, you shouldn’t need much help from them. But at first, you will. In the long run, it’s worth it.

If you don’t lead with data, someone else will
CAS offerings are on the way to becoming a commodity. If your practice isn’t delivering high level services at a competitive price, it will only become easier for clients to find another CAS practice that will. In that context, your practice’s ability to distinguish itself through its approach to data is a key competitive advantage. Just as important, it opens the door to deeper client relationships, which can help with client retention in a more competitive environment—and it’s more rewarding for everyone involved.

As the CAS market matures, this is the right moment to strengthen your practice’s approach to data through CAS data transformation. We’ve done it, and I know from experience that it’s worth it. A great place to get started is with CPA.com’s extensive CAS-focused resources. CPA.com has been leading the charge on CAS for years and has a wealth of resources available on all things CAS, including data.

About the author
Guest author Roman Villard is the Founder of Full Send, an accounting and data strategy firm serving growth companies. After working at four firms in various capacities, Roman started Full Send out of a desire to enhance client experiences through technology, better internal workflows, and a focus on excellent service.