Data Warehousing

A robust Data Warehouse is the backbone of modern Analytics and Data Science. It’s not just storage space for massive amounts of data, it’s a sophisticated data management system providing many necessary capabilities:

  • ETL (Extract-Transform-Load) processes – pulling data from many sources, in many formats, and preparing it for Analytics and Data Science use on a daily basis requires powerful software that can scale with you whether you have thousands or millions of members
  • Data anomaly detection and remediation – missing data, corrupted data, duplicated data…these are common issues and if not detected and corrected can cause inaccurate and misleading results
  • Historical snapshotting – member-level trend analysis and point-in-time historical reporting is not possible with many changing data points unless daily snapshots of the data are taken and preserved…something not possible in most business systems
  • Performance optimization – Analytics and visualizations combining data from many sources with complex calculations can become so slow as to be almost unusable without advanced optimization of database structures, queries, filters, and report building