In the process of testing new hypotheses for the business and developing their product, Finiata was using project-specific data and facing a range of ad-hoc challenges. They had an issue on how to efficiently combine and represent sales and advertising data in flexible reports that could be used between departments for different purposes. In order to fulfill analytical tasks, Finiata needed reliable data warehouse, custom integrations and tracking tools. Designing data management processes required expertise such as analysts, developers and data architects that Finiata, when it was founded, did not have in-house. The company was in need of a flexible business intelligence solution to create data architecture for the business and support ad-hoc projects from a technical and analytical perspective.

• ✗ data Lake and Warehouse – missing warehouse
• ✗ ETLs (Extract, Transform, Load) – not existing ETLs practices, in order to take data from one system, transform it, and load it into the organization’s data warehouse
• ✗ data pipelines – lack of pipelines for automated transfer of data from one system to another
• ✗ customer integrations and tracking tools  – to better understand customer journeys and anonymized behavior analytics, the team needed a robust event tool.
• ✗  data quality – A best-of-breed technology approach coupled with the integration of acquisitions resulted in numerous data conventions and a need to manually replicate data across multiple databases, which contributed to the degradation of data quality.