An Underwhelming Vena Experience and Sprawling Spreadsheets
Before Datarails, ISG was already in the middle of an FP&A transition. They had implemented a software provider, Vena, but the experience fell short, and the rollout never fully delivered what the team needed.
“We originally started with Vena last year, and that did not go well.”
At the same time, one of the most complex areas of the business, variable compensation, was still being managed manually. With up to 75 employees on commission plans and 21 unique compensation structures, the process relied on multiple spreadsheets, constant updates, and manual distribution.
“Our variable compensation was just a straight-up Excel spreadsheet. If you had to review it, you would not enjoy seeing your payouts on that.”
Even small changes created ripple effects across files. Updating data meant reworking multiple spreadsheets, consolidating results, and distributing individual outputs. The process was slow, fragmented, and difficult to maintain.
There was also a broader need: leadership wanted a more intuitive, accessible way for employees to understand their performance and payouts.
“Our director of finance really wanted some sort of dashboard or portal so people could actually see and understand their numbers.”
ISG needed a solution that could replace both their FP&A tooling and fragmented compensation processes while improving the experience for employees.
From Spreadsheets to a Scalable Engine
After evaluating several tools, ISG chose Datarails for its simplicity and ease of use.
Datarails just seemed very simple and straightforward, especially the data mapper and how you get data into the system.
Unlike their experience with Vena, the team was able to quickly understand how to structure and connect their data without heavy technical overhead.
What started as an FP&A implementation quickly expanded into something more powerful. Tripp built a full variable compensation engine inside Datarails, centralizing transaction data, automating calculations, and generating outputs from a single source of truth.
“I created a whole sales variable compensation engine inside Datarails, and it’s been very useful for me.”
Rather than preparing data in spreadsheets before uploading, ISG now brings in raw data and handles transformations directly within the platform. This removes extra steps and reduces reliance on manual processes.
“Instead of building everything in Excel and then uploading it, I can upload the raw data to Datarails and do the transformation in the data mapper itself.”
The team also introduced dynamic dashboards for tracking attainment, automated calculations for metrics like gross profit, and connected reports for quick validation. When inputs change or updates are needed, the process is no longer manual.
If I need to rerun or update something, it’s literally two clicks instead of manually updating four or five different spreadsheets.
With everything centralized, ISG moved away from fragmented workflows and into a single, connected system.
Cutting Compensation Processing Time by 50%
The most immediate impact was speed. Processes that previously took days are now completed in a fraction of the time.
Running variable compensation used to take me about four or five days. Now it takes me about half that.
Beyond time savings, Datarails completely changed how compensation data is delivered and understood. What was once buried in dense spreadsheets is now accessible, visual, and easy to interpret through dashboards and reports.
“It’s very nice to have a report already linked to the data where I can quickly check, at a glance, that everything looks right.”
For Tripp, the impact went beyond efficiency. It also expanded his technical skillset and how he approaches working with data.
Datarails gave me a completely different angle of learning how to work with data and build things in a more structured way.
Datarails as a Foundation for AI
ISG is already exploring what comes next. With Datarails as a centralized data layer, the team is evaluating new ways to extend its value, especially with AI.
“We’re looking at building our own custom agents and having them pull data directly from Datarails instead of all these different systems.”
Instead of pulling from disconnected tools, future workflows could rely on a single, governed source of financial data, unlocking faster insights and more strategic decision-making.
