A Step-by-Step Guide to Data Integration Modernization

Ever since the early days of reporting, data warehousing, and analytics, there has been a need for data integration for many reasons. However, if you fast forward 30 years to today, you will see the traditional extract/transform/load (ETL) approach is insufficient to enable real-time predictive and prescriptive analytics. This checklist explores ideas for determining where traditional approaches to data integration are impeding modern analytics and will guide the reader in ways to modernize. The report focuses on seven key areas.

  1. One: Assess your data integration landscape
  2. Two: Identify opportunities for blending data access paradigms
  3. Three: Establish data integration service-level agreements (SLAs)
  4. Four: Blend ETL and CDC
  5. Five: Ensure that your data integration is scalable and flexible
  6. Six: Leverage data visualization and virtualization to simplify data sharing and exchange
  7. Seven: Embrace tools that help minimize complexity and coding using automation