Vitality, a company focused on making buildings smart by analyzing IoT data for energy savings and risk mitigation, faced a significant challenge as it grew. The company's original plan involved building basic analytics into its platform. However, as the company expanded, customer demands for analytics quickly outpaced what Vitality could build in-house. The company realized it needed to purchase analytics capabilities to stay relevant to its customers. These capabilities needed to be seamlessly integrated into its proprietary platform, matching the look and feel of its software, and delivering an industry-leading user experience. Additionally, Vitality saw a huge potential for future growth by augmenting its powerful algorithms with industry-leading embedded analytics.

Profusion, a data science and marketing services company, was grappling with the challenge of slow and reactive reporting for its clients. The company wanted to transition its clients from relying on manual, Excel-based reporting to a more proactive, real-time optimization. A specific challenge was presented by a London creative agency, which required Profusion to develop a solution for reporting ticketing data to its client, an international live show production company. The agency had two requirements: an intuitive interface to communicate financial investment and return through different marketing channels, and the ability to query a single customer view of its customer and extract this data for use within its other business tools. The existing process was labor-intensive, with information only available sporadically or on the client’s request.

TLC’s approach to organizing high-quality data for analysis and distribution across the company and to clients was both labor-intensive and extremely time-consuming. Client data wasn’t being retained in any real meaningful fashion with data analysis and reporting was mostly done manually and managed in extremely complex Excel spreadsheets.On top of this, report delivery was done on a monthly basis by email and the sheer size of the spreadsheets was becoming problematic. Their data was triple the size of what any cloud-based system could handle and the need for superior reporting and analysis in a timely fashion to maximize fundraising was imperative.

Interfolio, a software service provider for higher education institutions, faced a significant challenge as its user base and data volume grew. The company needed a modern data strategy that could unify internal data, streamline reporting processes, and be flexible and scalable enough to serve as an embedded solution within the Interfolio platform. The primary challenge was selecting a BI platform and a cloud data platform that could handle multiple data sources, model complexity, and enable straightforward dashboard creation. The company had been using a competing BI vendor and an in-house solution for managing their consumer-based SaaS metrics and reporting, and for managing reporting data for quarterly business reviews. However, as data sets grew exponentially, these solutions were no longer performant or scalable.

MindMax, a company that partners with universities to increase enrollment, particularly among continuing education and adult learners, faced a significant challenge in scaling its customer base. The company's legacy analytics and BI solution required manual extraction of data from disparate sources, including Salesforce, Google Analytics, Facebook Ads, LinkedIn, and Marketing Automation systems. This process was time-consuming and inefficient, making it difficult to create meaningful reports and dashboards that incorporated data from all these different sources. The company's VP of Technology, Brian DiScipio, and Senior Business Analyst, Kiersten Warendorf, recognized the need for a cleaner, faster way to empower their customers with data-driven insights. They knew that the full automation of BI and ETL through the creation of a modern data pipeline and stack was crucial for the company's growth.