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  • Writer's pictureKevin O'Connor

Leveraging your Data Warehouse for you AI initiatives

Artificial Intelligence (AI) is all the buzz today and figuring out where to start can be challenging. One place to start is to look at your existing data warehouse.

Older, on-premises or legacy data warehouses built on client/server technology may not be sufficient for today’s competitive business landscape. Today’s market demands organizations to rely on massive amounts of data to best serve customers, optimize business operations, and increase their bottom lines. Most older data warehouses were not designed to handle this volume, velocity, and variety of data and analytics.

Your business should look at making an investment to modernize your existing data warehouse. One that is built on the cloud which can automate data ingestion and analysis, which closes the loop on connecting data, insights, and analysis.

A modern data warehouse can then play a pivotal role in supporting your AI initiatives by providing a robust and scalable foundation for data storage, processing, and analytics. As organizations move along each phase of the data maturity curve, they continue to amass vast amounts of data from various sources and harnessing this data's potential is crucial for gaining insights and powering AI-driven decision-making.

One key aspect of a modern data warehouse is its ability to integrate disparate data sources seamlessly. These sources may include structured data from relational databases, unstructured data from social media, log files, and IoT devices, as well as semi-structured data from APIs and web services. By consolidating and harmonizing these data streams in a central repository, a data warehouse simplifies data access and enables AI models to access a comprehensive dataset for training and analysis.

Furthermore, data warehouses employ powerful processing capabilities like distributed computing and parallel processing to handle large datasets efficiently. This accelerates data retrieval and query execution, enabling AI models to access the required information quickly and process it at scale. Real-time or near-real-time data ingestion capabilities are also crucial, as they enable AI systems to leverage the latest data for improved accuracy and responsiveness.

In addition to supporting AI model development, data warehouses also aid in AI model deployment and monitoring, they can store model artifacts, facilitate version control, and provide infrastructure for serving AI models in production environments. Moreover, data warehouses allow for continuous monitoring of AI model performance, ensuring that models remain accurate and reliable over time.

Ultimately, a modern data warehouse's role in supporting AI initiatives is indispensable. It empowers organizations to unleash the full potential of their data, providing a solid foundation for data-driven AI applications and helping drive innovation, enhance decision-making, and achieve competitive advantages in today's rapidly evolving digital landscape.

Kevin O’Connor is the founder and CEO of Westberke Consulting who specialize in helping organizations leverage data to Improve business insights. Kevin is a senior technology professional with over 25 years of experience implementing data-driven technology solutions for organizations across diverse industries. His areas of expertise include business intelligence, data warehousing, data strategy, systems integration, business analytics, reporting and data governance.


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