Over the past few decades, Data Architecture has focused almost exclusively on managing data for analytics, with operational data viewed as “data sources.” Operational data is much more than source data for analytics. It is the data used to run the business, whereas analytical data is used to observe the business. Business operations don’t work without operational data, yet we’ve largely ignored it as a critical part of Data Management architecture.

Over the decades that we’ve disregarded operational data, many changes have occurred in the world of operational systems and data. What was once primarily transactional systems has evolved to encompass transactions, workflow automation, process automation, commercial IoT, and industrial IoT. Today’s transactional systems are dominated by purchased products, each with unique semantics, data models, and proprietary data architecture. The result is a large accumulation of technical debt that limits data exchange, data sharing, data interfaces, Data Quality, and even data meaning.

It is time to step up to operational data architecture – to reduce technical debt and to prepare for changes yet to come. Concepts, techniques, and technologies exist to meet operational data challenges. But a fragmented, technology-first approach is time-consuming, costly, and often chaotic. Well-designed operational data architecture organizes the techniques and technologies to prevent chaos and improve data management.

You will learn:

• The variety of operational systems and the roles of each
• The variety of data created and managed by operational systems
• The implications of global data variations and mobile data variations
• The roles of MDM and RDM in operational Data Management
• The challenges of data sprawl, data disparity, and Data Management technical debt
• Architectural concepts, constructs, and techniques to manage sprawl, disparity, and debt
• Design techniques for adaptable and sustainable architecture