A leading e-commerce company is struggling to manage its complex data environment due to the growing volume, variety, and velocity of incoming information. The organization collects and processes data from diverse sources and formats, including:
Fragmented and Inconsistent Data
Critical information is spread across disconnected databases, spreadsheets, and external platforms. This creates silos that make it difficult to locate trusted data, leading to inefficiencies in analytics and reporting.
Integration Failures Across Systems
Customer duplication, mismatched inventory counts, and errors in order processing occur frequently. These problems stem from incomplete or unreliable integrations between legacy ERP systems, cloud-based CRM tools, and proprietary analytics platforms.
Data Quality Issues
Absence of standardized data validation and governance practices results in inconsistent naming conventions, missing values, and outdated records. Reports generated from this environment are often inaccurate or contradictory, undermining executive trust in data-driven decision-making.
Slow Data Pipelines
ETL (Extract, Transform, Load) jobs are not optimized, causing hours or even days of delay in producing insights. Marketing teams cannot react quickly to customer behavior, and operations lose the ability to make timely adjustments to inventory and logistics.
Scalability Limitations
As the business expands into new regions and product lines, the current data infrastructure struggles to handle increasing demand. Storage costs rise steeply, query performance deteriorates, and system downtime becomes more frequent.
Opaque and Complex Architecture
Documentation is outdated or incomplete, leaving IT, data engineers, and business analysts with different understandings of how systems interact. This gap causes miscommunication, slows troubleshooting, and increases reliance on a few “knowledge holders” within the company.