K
Kathleen Martin
Guest
Traditionally, the purpose, vision, and mission of a data warehouse have been driven by what, in most organizations, constitutes a relatively small set of users: the data engineers, data scientists, and business analysts interested in complex analytics. However, as the power of a data platform capable of running not just in the data center but also in the cloud or at the edge becomes more accessible, it will invariably attract a broader base of business users who want to use it to run queries and perform analytics to inform different operational decisions.
To satisfy this ever-expanding user base and their different requirements, organizations need to reconsider the purpose, vision, and mission of a data warehouse. In this new world, what purpose does the data platform serve? What should it deliver? What is its mission (and how will it achieve the vision)? Many aspects of the data warehouse’s purpose and vision will still apply to the data platform, but they will expand to encompass more strategic, tactical, and operational opportunities. The mission, though, must include a focus on data democratization, which requires a far different approach than was required of legacy data warehouse architectures.
New Purpose
Until recently, the data warehouse served as a central repository of historical data to help users analyze different time periods and trends. Data was consolidated from many sources to avoid impacting the performance of operations systems, improve data quality, optimize query performance, and provide a business representation of data that made it easier for users to access information.
A data warehouse’s ability to provide historical analytics will continue to be valuable, but capturing and understanding critical events in real time -- to improve operational decision making and response times -- will continue to grow in importance. True, a complementary operational data store (ODS), with its snapshot of transactional data that is often more current than that in the data warehouse, has provided additional support for operational decisions. However, even an ODS does not provide the real-time access required when decisions must be made in minutes, or perhaps even seconds. Examples include personalized e-commerce, supply chain optimization (scheduling, inventory, equipment use, etc.), credit and loan approvals, investment portfolio decisions, and many more use cases. Data fabrics and data meshes are emerging data architectures that can make data more accessible, available, and discoverable for real-time data ingestion (through built-in data warehouse integration) than a singularly-focused semantic layer can.
Continue reading: https://tdwi.org/articles/2022/01/31/dwt-all-find-new-purpose-vision-mission-for-your-data-warehouse.aspx
To satisfy this ever-expanding user base and their different requirements, organizations need to reconsider the purpose, vision, and mission of a data warehouse. In this new world, what purpose does the data platform serve? What should it deliver? What is its mission (and how will it achieve the vision)? Many aspects of the data warehouse’s purpose and vision will still apply to the data platform, but they will expand to encompass more strategic, tactical, and operational opportunities. The mission, though, must include a focus on data democratization, which requires a far different approach than was required of legacy data warehouse architectures.
New Purpose
Until recently, the data warehouse served as a central repository of historical data to help users analyze different time periods and trends. Data was consolidated from many sources to avoid impacting the performance of operations systems, improve data quality, optimize query performance, and provide a business representation of data that made it easier for users to access information.
A data warehouse’s ability to provide historical analytics will continue to be valuable, but capturing and understanding critical events in real time -- to improve operational decision making and response times -- will continue to grow in importance. True, a complementary operational data store (ODS), with its snapshot of transactional data that is often more current than that in the data warehouse, has provided additional support for operational decisions. However, even an ODS does not provide the real-time access required when decisions must be made in minutes, or perhaps even seconds. Examples include personalized e-commerce, supply chain optimization (scheduling, inventory, equipment use, etc.), credit and loan approvals, investment portfolio decisions, and many more use cases. Data fabrics and data meshes are emerging data architectures that can make data more accessible, available, and discoverable for real-time data ingestion (through built-in data warehouse integration) than a singularly-focused semantic layer can.
Continue reading: https://tdwi.org/articles/2022/01/31/dwt-all-find-new-purpose-vision-mission-for-your-data-warehouse.aspx
