Our customer’s growing business, recent acquisitions, and evolving data and analytics landscape (including cloud migration) required a change in how its staff leverages data and derives actionable insights. The existing data analytics setup had too many moving parts; the company’s strategy needed an overhaul. Moreover, expectations regarding data and analytics were higher than ever before.
Legacy system and data integration issues – Our customer sought a single data platform reservation system to consolidate its pet grooming, daycare, and hoteling functions. The in-house legacy CRM was limited to grooming operations and couldn’t scale; it also increased costs and deployment time any time a new feature was added. Also, data volume caused bottlenecks in the ETL (extract, transform, load) process, thereby delaying the availability of business reports.
Data quality and lack of trust – Redundancy in existing consumer data caused the retailer to have issues with effective product and services campaigning. It needed to uniquely identify its customers whose data could only be captured across multiple systems. Data cleansing was done manually in spreadsheets—a time-consuming, error-prone, and inconsistent process. Nothing was scalable, performance issues led to delays, and its users could not completely trust the data.
Lack of data governance and automation – Spreadsheet-based reports were manually created after ad hoc requests; collaboration occurred over shared drives. Versioning issues ensued and change tracking was difficult. Anyone having spreadsheet access could view sensitive enterprise information. Clear ownership and accountability didn’t exist, and the entire process was perpetually prone to errors.
High development costs and IT dependencies – Converging data from disparate sources took 16 analysts to create requisite user reports. Data scientists had to spend time getting the right data from the correct sources. An existing enterprise data warehouse (EDW) catered to these needs, but it created increased dependencies on the BI team and increased its workload. Our customer sought an alternative data access layer to complement the EDW.
In short, our customer needed a governed data provisioning solution that could accelerate time to value