DAS42

DAS42 Data Consultants is a data consultancy that provides services related to data engineering, analytics, and AI for commercial organisations. The company is identified by its trade name and is presented as a specialist advisor and implementer of data platforms and related systems. The organisation is described in connection with clients in media, entertainment, telecommunications and consumer-facing technology sectors and maintains a formal partnership designation with Snowflake.

Services and capabilities

• Data platform implementation: design and deployment of cloud data platforms, including configuration and migration to Snowflake.
• Data engineering: building data pipelines, ETL/ELT processes, and data integration between sources and analytical stores.
• Analytics and reporting: development of analytical models, dashboards, and reporting systems to deliver business metrics.
• Machine learning and AI enablement: development and deployment of predictive models and integration of model outputs into production systems.
• Data governance and quality: establishment of data catalogues, lineage, access controls, and validation processes.
• Automation and operationalisation: automation of data workflows, testing, CI/CD for data assets, and operational monitoring.

Industry focus

• Media and entertainment: delivery of analytics platforms and data engineering to support audience measurement, content analytics and advertising operations.
• Telecommunications: integration of customer and network data to support operational analytics, churn analysis and product reporting.
• Consumer-centric technology companies: implementation of data platforms to support product analytics, personalization models and customer intelligence.

Technology and delivery approach

• Platform-centred delivery: primary engagements revolve around cloud data platforms, with repeated reference to Snowflake as a core technology in implementations.
• Integration and pipelines: emphasis on building ETL/ELT pipelines and integrating disparate data sources into centralized analytical stores.
• Analytics and ML lifecycle: projects include development of analytical models and machine learning workflows, and integration of model outputs into business processes.
• Automation and operational practices: use of automation for data workflows, CI/CD practices for data code, and operational monitoring to maintain production systems.
• Data governance: implementation of governance controls, lineage tracking and quality checks as part of platform rollouts.