One of the challenges facing enterprise architecture is maintaining consistency across the enterprise. This is challenging because data comes from disparate sources and systems representing different purposes, focuses, and objectives. On top of that, there needs to be more clarity regarding the terms Data Lake, Data Warehouse, Operational Data Store, etc. To overcome these challenges at an enterprise level, a framework is necessary to apply data uniformly, consistently, and meaningfully. The framework will transform data from multiple, disparate data sources into an operational data store, a consistent, homogeneous environment, as the single dependable source of truth for all reporting and analytics.

This session will present that framework and discuss the following:
• The challenges facing data analytics and reporting due to multiple disparate data sources
• An introduction to the various terms used today for data storage
• The purpose of an Operational Data Store and how it improves data usability
• The data zones for Raw, Refined, and Published data
• How data standards simplify and enhance data usability
• A method to provide primary and foreign keys across disparate data