Data Automation Day

AI in Data Management (Mike Ferguson)

The session looks at how artificial intelligence and other kinds of analytics are being integrated into data management to improve productivity and automation in order to shorten development times, and. It also looks at metadata standards and analytics and how vendors can combine their tools with other data management software to shorten time to value. Topics covered:

• What do we mean by AI?
• Classic machine learning, generative AI co-pilots and AI-Agents
• Agentic workflows - the power of workflow and AI
• Ways in which AI can assist in Data Management
• AI in the database 
• AI in data modelling
• AI in data catalogs
• Using AI in data engineering to speed up development and improve performance
• AI in data governance – Data quality, MDM, privacy, security, retention and sharing
• Knowledge graphs — the new way to store metadata
• Making data AI-Ready – vector databases, RAG and GraphRAG and Knowledge Graphs
• What can graph analytics on a metadata knowledge graph tell you?
• Using Co-pilots and AI in BI tools and Data Science

Workshops: Data Automation Levels

In an era where speed and adaptability are crucial, data automation is a game-changer for businesses aiming to stay competitive. In this workshop, we will explore the various levels of Data Automation, demystifying how they function and what each level means for businesses and technologists alike.

Accelerating Data Engineering Using a Data Catalog (Mike Ferguson)

Most companies today are drowning in data. Data is stored in multiple types of data store on-premises, in multiple clouds, in SaaS applications and streaming in from devices at the edge. This makes it harder to find and integrate data. Also more new data sources continue to emerge and with demand for clean. Integrated data now coming from every business department, companies have to do something to shorten the time it takes to engineer data. The session looks at this problem and how a data catalog can help. Topics covered:

• What is a data catalog?
• The data catalogue marketplace
• Using a business glossary within a data catalog to define data products
• Using a data catalog to automatically discover data in multiple data sources
• Using a data catalog to map raw data to common terms in a business glossary
• Using a data catalog to automatically detect sensitive data in data sources
• Using a data catalog to automatically profile data quality in your data sources and recommend fixes
• The power of metadata - Integrating data engineering tools and generative AI with a data catalog to rapidly build data pipelines to produce data products
• Publishing data products in a data marketplace within the data catalog

Using Data Warehouse Automation to Migrate Your Data Warehouse to the Cloud

As more and more companies utilize SaaS transaction processing applications and ingest data into cloud storage, the demand to migrate on-premises data warehouses to the cloud increases. However, migrating these systems can be complex as many data warehouses can often be up to 20 years old which means a lot has been added to them over the years. This means data warehouses can be large with a lot of data sources, dependent data marts, and hundreds or even thousands of BI users. So, migration can be a major challenge. This session looks at this problem and how data warehouse automation can help simplify, de-risk, and expedite data warehouse migration.

Workshop: Rethinking Data Warehouse Automation in the Modern Data Stack

 The "modern data stack" promises agility, scalability, and modularity—but how well does it deliver on those promises for today’s data professionals? What truly makes the stack modern, and where does data warehouse automation fit in? Should you dive headfirst into every new “modern data” tool as it hits the market? Or are tools adding more complexity than value? Maybe the tried-and-true approaches of “classical” techniques and data stacks still offer the reliability and structure that teams truly need. Join an open discussion and experience-sharing session on how data warehouse automation can complement or complicate the modern data stack.

Template Management in Data Warehouse Automation

Templates in data warehouse automation are used to automatically generate data models and customize them according to individual requirements. Due to the high level of standardization and automation capacity required, templates are particularly useful for Data Vault models. With growing demands from the business, customization and development of templates can become more complex and complicated. This session provides you with some best practices, tips and tricks to handle your templates successfully and simplifies their usage.