Emerge was experiencing rapid growth, necessitating a more robust analytics platform to manage and scale its operations effectively. The existing analytics tools, such as Periscope, were limited to embedded customer reporting and did not provide the necessary visibility into operational efficiency. The data team recognized the need for a more powerful solution to support the company's expansion and improve data-driven decision-making.

Wurl faced challenges with the cost and limitations of their existing BI tools, which were crucial for their operations in streaming content and data analytics. The company needed a more efficient solution to handle the growing demand for data visualizations and targeted advertising services. The existing BI tool was becoming unsustainable due to its monthly fees for external viewer access, which was a significant issue as Wurl's customer base expanded. Additionally, the internal process of creating and managing dashboards was cumbersome, requiring significant resources and time.

With significant growth over the last three years, TrovaTrip has relied heavily on understanding, analyzing, and responding to a significant flow of data inputs. Serving Creators with a seamless integration with Trip Operators and Travelers requires collecting and streamlining information across all three audiences. Teams at TrovaTrip wanted to spend less time creating and managing dashboards and more time improving the customer experience and driving the business forward with data which wasn’t possible with Microsoft Excel and Google Sheets alone. As TrovaTrip grew, so did its need for data insights and a more sophisticated analytics platform. TrovaTrip’s data team had to build the data models and write custom SQL to get the final data products. After which, they had to check the accuracy and ensure the data they were using was valid before distributing the necessary insights to make data-driven decisions. This can be a time-intensive process, but with the right tool can be done efficiently. TrovaTrip needed a tool that displayed the performance of sales funnels and conversion rates. When TrovaTrip onboarded new sales members, it was important to create efficient ramp-up goals. By building a process and scalable system, data teams could spend more time cultivating the insights that could help the organization grow.

Greenwich.HR aimed to create a user-friendly labor market dashboard to provide real-time hiring and pay data insights. Post-pandemic, the labor market dynamics changed drastically, necessitating a broader reach beyond existing clients. Traditional BI tools like Looker, Power BI, and Tableau were evaluated but found inadequate due to their inability to handle large-scale data sets and provide fast, efficient processing. The need for a partner to build an application capable of real-time data updates and extensive filtering was critical.

Slope Software faced challenges with their initial analytics solution, Looker, which added unnecessary complexity and slowed down workflows. Looker required all data to go through its proprietary modeling language, LookML, which strained resources and added time to processes. Slope needed a solution that would simplify analytics and make data more accessible for their actuarial clients. They ruled out other solutions like Power BI and Sisense due to lack of capabilities and high costs, respectively. The goal was to find a platform that could be layered on top of their cloud data warehouse to enhance data accessibility and usability for end users.

DoorDash faced significant challenges with dashboard management and scalability. The existing processes were not conducive to scaling analytics efforts smoothly due to performance and connectivity limitations posed by traditional BI tools. The cost of per-person licenses was high, limiting access and making it difficult to share dashboards and reports with a wider audience. This situation left much to be desired by both creators and consumers of the dashboards. In 2021, Andrew Huynh, Senior Business Intelligence Engineer, and his team at DoorDash began searching for a new BI platform to build a self-service analytics environment and democratize dashboard creation. They evaluated Sigma along with other vendors and ultimately chose Sigma because DoorDash lacked enough data engineers to model and prepare data for all end users, which was a requirement for other tools.

Prior to implementing Sigma, SIA’s Customer Success team faced significant challenges in creating and delivering reports. They relied on manually downloading data extracts and uploading them into Google Sheets, which was a slow and cumbersome process. The limitations of spreadsheets in handling large data volumes further compounded the issue. As a result, the team spent 50% of their time creating dashboards weekly. SIA initially adopted Tableau as their BI tool, but it required technical data analysts proficient in SQL to create and perform ad hoc requests. This dependency on SQL knowledge created data silos and slowed down the turnaround time for insights. Additionally, connecting Tableau to their Snowflake data cloud and managing licenses for 125 users proved costly and complex. The complexity of Tableau and its steep learning curve frustrated Ryan Scheuermann, SIA’s CTO, as only a few individuals could effectively use the tool. This led to difficulties in sharing dashboards internally and externally, requiring advanced SQL knowledge. The process was time-consuming, costly, and manpower-intensive, hindering SIA’s ability to efficiently generate insights.

After several years of record growth from wholesale, fashion label and retailer Veronica Beard set out to build a direct-to-consumer presence, both online and in boutique shops, and build stronger relationships with their most passionate customers. The volume of first-party data grows exponentially once you start directly facing the customer, making it critical to process and analyze data at scale to provide a better customer experience. With a clear mandate from the CMO, the company needed to build out the data infrastructure to surface insights that accelerate business outcomes. However, with a limited number of people able to read and write SQL, accessing and analyzing data was a challenge. Existing BI tools lacked the agility and flexibility needed, requiring knowledge of SQL or proprietary coding languages, which would have been a heavy lift for the engineering team and prevented business teams from finding answers quickly.