Data may be the language of modern businesses, but few organizations have fluency in this new language. The knowledge gap between data scientists and business users is often so significant that trying to share straightforward analyses may result in misunderstanding or worse, disbelief. What may seem like a simple question to an end user may require significant clarification and a series of precise questions by an analyst or data scientist before she has enough context to perform an analysis or run an experiment.
Having a common language that can short-circuit the frustrating back and forth between data professionals and business professionals is critical for businesses that wish to become more data driven. One approach to address this is to modernize the semantic layer for the data-driven enterprise. This effort requires enterprises to standardize the key terms and metrics that drive their business. This process can be very messy as it requires both business users and data professionals to come together to agree on common business measures while leaving room for the data professionals to map the term to the appropriate query.
In this session Matthew Roche walks you through building a business glossary that codifies your semantic layer and enables greater conversational fluency between business users and data scientists. He will share guidelines and examples for engaging the right stakeholders, how to scope the project so it doesn’t get bogged down by overcomplexity, and the key metrics for success - as well as real-world examples on how this was done successfully at Microsoft to support a multi-billion-dollar business unit representing multiple product teams that were each measuring and reporting their revenue and customer information using different criteria.
Matthew Roche is a Senior Program Manager in Microsoft’s Cloud and Enterprise Group, where he focuses on enterprise information management, crowdsourced metadata, and data source discovery.
Matthew is currently a member of the Azure Data Catalog team; previously, he worked on Power BI, SQL Server Integration Services, Master Data Services, and Data Quality Services. Before joining Microsoft Matthew worked as consultant and trainer focusing on ETL, data warehousing, and business intelligence.
When not enabling the world to get more value from its data, Matthew enjoys reading, baking, heavy metal, and competitive longsword combat.