Data Roadmap

A structured data warehouse serves as a centralized hub for accessing company data, offering standardized data, enhanced security controls, multidimensional analysis (MDA), accuracy, and ease of use. Emphasizing the Enterprise Data Warehouse (EDW) as the primary repository for conformed enterprise data ensures central intelligence and facilitates enterprise-wide computations. This approach is crucial for fostering meaningful data relationships and supporting data science applications throughout the enterprise.

Given the vast landscape of Enterprise Data spanning multiple applications and databases, the ability to comprehend both current business data and machine-generated data is pivotal. This capability serves as a bridge, connecting the business side with diverse formats of technical data, ranging from simple text, time, and numerical data to intricate digital formats.

Looking ahead, as we consider the evolving landscape of AI analytics, the EDW becomes even more critical. Future AI analytic abilities will be integrated into the EDW, allowing for advanced data insights, predictive analytics, and intelligent decision-making. This expansion will empower organizations to harness the full potential of their data by incorporating AI algorithms that can uncover hidden patterns, trends, and correlations, leading to more informed strategic decisions.

The roadmap for advancing the EDW must also include plans for integrating and leveraging these future AI analytic capabilities. This entails preparing the infrastructure to accommodate machine learning models, natural language processing, and other advanced analytics tools. By doing so, organizations can not only understand historical performance, make comparisons, and project future trends but also unlock the transformative power of AI in enhancing data-driven decision-making across the enterprise.

Orchestrated EDW™ (Sync/Summarize) | EDW²™ (Describe/Square/Project)

The Orchestrated EDW Framework utilizes a streamlined enterprise data model, providing a versatile foundation for enterprise data science initiatives. It operates on SQL-based schemas and tables, seamlessly integrating R and Python.

Seven Levels for gauging Enterprise Data Maturity and Optimization

Machine and External Data

With Enterprise Data crossing many applications and databases, understanding current business data and machine data is key to bridge the business-side to varied formats of technical data. From simple text, time and numerics to digital data.

Enterprise. Orchestrated.

Process

Enterprise Operating System (EOS)

Analytics

Data Mgmt.

Data Science

AI Policy