“Once we got Coalesce set up—with great support from their team—I began to see the benefits immediately. Now, when someone on my team makes a change, I can easily review it and understand its impact.”
Built on shaky ground
Challenges
Growing data complexity outpaced the capabilities of the team’s existing tools
Manual data cleanup tasks were time-consuming and inefficient
Analysts spent too much time on infrastructure instead of strategic analysis
Anastasia Koslova leads Revenue Operations at Alasco, and her team includes a business analyst, a go-to-market engineer, and a revenue operations manager. Their original focus was on supporting the GTM teams using data from the company’s CRM and ticketing system to answer questions about sales performance and revenue. Their responsibilities have evolved over time, and today also include analysis of product and finance data. “For example, when our Customer Success team does a quarterly business review (QBR), we provide them with product analytics so they can show customers the value they’re getting from our platform,” explains Koslova. “However, our main focus is really on identifying initiatives that will help drive company revenue.”
As the scope of the team’s responsibilities grew, so did the volume and complexity of the data they worked with. Koslova began to realize the limitations of their data stack as her Revenue Operations team was tasked with deeper analysis on more diverse data sources, along with faster turnaround times. While the team’s role had evolved in the organization, the tools and solutions they used to do their work had not.
Koslova recalls that one of her key pain points was the ongoing need for manual work, such as renaming table columns when trying to clean up raw data: “When I set up all the connections by scratch—not for the transform models, but just to clean the raw data—renaming all the columns was painful as it involved many manual steps for hundreds of columns. For example, HubSpot is one of our biggest data sources, but they have a naming convention where they put a ‘property_’ prefix in front of each property. I had to remove this manually each time.”
Her biggest challenge, Koslova says, was simply being able to do the work that was her core responsibility: “The main goal of a business analyst is not building data pipelines or creating reports, but rather reading those reports, analyzing dashboards, and finding insights to help the go-to-market teams.” With no data engineering team to help her manage the upkeep of the data infrastructure, she was forced to spend her time doing it herself rather than the more important work of helping the company identify and develop new revenue streams.
Architecting a new approach
Solution
Switched from Matillion to Fivetran to make ETL easier and more flexible when source data changed
Worked with BI consultants at Kemb to find a more user-friendly solution for onboarding and collaboration
Brought in Coalesce as a replacement data transformation solution
According to Koslova, the company had Snowflake in place when she first joined several years ago, but there was not a lot of data stored there and it was not being used much. “We did all our reporting based on raw data back then,” she says. “We were using Matillion as our ETL tool, but we didn’t do any transformations in it. Instead, we loaded all the raw data into our warehouse and built reporting based on that.” She decided to make the switch to Fivetran for data ingestion, she says, “which helped us to have more optimizations if something changed in the source data.” But she soon realized that the new data transformation tool she brought on to work alongside Fivetran—a popular open source option—was not the right choice going forward.
“At first it was sufficient when I was the only one using it,” she says. “But after our new business analyst started, I realized how challenging it was to onboard someone on all the code in our repository. Trying to show them what I’d done, where I’d done it, and how all the tables were connected was very difficult.” Koslova realized they needed a better solution that new team members could learn quickly and easily. One that enabled better collaboration and, as she puts it, transparency: “If I build something new, another person should be able to see exactly where in the system it’s joined—this was a capability we didn’t have.”
Koslova reached out to business intelligence consultant firm Kemb, which had provided her with guidance for other technology challenges in the past. “When I talked to our advisor there about my plans to onboard our new analyst and the challenges I foresaw, he mentioned that Coalesce might be the answer.” After learning more about Coalesce, she agreed, and quickly moved forward with making the switch. “I realized it made a lot of sense to implement it soon rather than waiting, knowing it would help us faster with future onboardings,” she says.
With Coalesce in place, Koslova and her analyst began to configure the new solution. “We set it up together so that we could see exactly—from our source data—where the data goes and how it is transformed,” she says. “We were able to immediately see the direct connection.”
Blueprint for future success
Results
Significant reduction in manual effort and time spent on data tasks, such as 15 minutes to rename hundreds of columns versus 2-3 hours
Smooth, efficient migration process thanks to reusable queries and streamlined setup
Saved 4 hours a week by reducing infrastructure maintenance to just 15 minutes
Greater autonomy for non-technical business users, who are empowered to navigate data pipelines, understand data flows, and build reports independently
Migrating all the projects from their previous solution into Coalesce was a smooth process, recalls Koslova. “We didn’t have to reinvest the same amount of time as we had with the previous solution. We already had the queries—we just had to click through the process to set up the models. It wasn’t nearly as time-consuming as before.”
Koslova notes that being able to use Fivetran to schedule the Coalesce jobs was another factor that made the migration relatively painless: “We started with the biggest model, HubSpot, which was the first pipeline we rebuilt using Coalesce.” Fivetran handled the data ingestion, and they used it to trigger their Coalesce transformation jobs. “Once we validated the outputs, we turned off the old transformation logic and let Coalesce take over that layer. From there, we migrated each connector and its associated transformations step by step.”
“Once we got Coalesce set up—with great support from their team—I began to see the benefits immediately,” Koslova says. “Now, when someone on my team makes a change, I can easily review it and understand its impact.”
Today, with Coalesce as a core part of the Alasco data stack, the tedious work that ate up a lot of her workday is a thing of the past. “With our previous solution, I would spend two or three hours renaming columns in HubSpot tables,” she says. “Something that I really appreciate about Coalesce is that I can bulk edit several columns or nodes at once. Using Coalesce, that same task took me only 15 minutes.” Setting up new sources and adding new tables is not something she needs to do often, Koslova notes, “but when I do, Coalesce is a huge time-saver.”
Koslova appreciates how easy Coalesce is for her new business analyst to use—and the independence it gives him to get his work done without needing constant support. “He can just click through the data pipelines, understand where the data is flowing, and try building a report,” she says. “And if he doesn’t know what a column means, he can look it up himself in Coalesce—he doesn’t have to ask me.”
In fact, Koslova hopes that her new analyst can eventually learn how to build his own data pipelines as he grows more comfortable with the platform. “Right now, we have a couple of models running on our HubSpot and finance data, but we’re not yet connecting multiple data sources to drive deeper analytics,” she says. “That’s an area where I think we could potentially uncover more insights.” Being able to offload some of these responsibilities frees up Koslova’s own time so she can focus on higher-level initiatives to move the business forward.
As for future plans, Koslova hopes to soon launch a project to begin capturing and storing historical versions of the company’s data for future analysis. “Right now, all our data in the warehouse reflects only the current state—there’s no versioning or history tracking,” she explains. “This makes it difficult, especially for the finance team, to track changes over time, like comparing last week to this week or last month to this month.” Koslova will leverage Coalesce to process data incrementally and then append each daily incremental data set onto a historical data set, giving Alasco new insight into these chronological changes.
Alasco has laid a solid foundation for growth with Coalesce as the cornerstone of its new data stack. Now Koslova’s team is poised to transform data into a blueprint for smarter decision-making and long-term success.