DAMA Talk: Enabling Self-Service BI and Analytics

Selecting the right data integration tool for your needs
May 31, 2016
Be prepared for data preparation
June 29, 2016
Show all

DAMA Talk: Enabling Self-Service BI and Analytics

DAMARick Sherman is speaking at DAMA in Bloomfield, CT. It’s free and open to the public.

Title: Enabling Self-Service BI and Analytics

When: Friday June 17, 1:30

Where: CIGNA University Learning Facility, 1350 Hall Boulevard/Rte 218, Bloomfield, CT 06002 (link to Google map)

Preregistration: Just email your first and last name and Fri 6/17 Hartford to damabostonprereg@yahoo.com by 9pm, the day before the meeting. If it turns out you can’t make the meeting, there’s no need to cancel your preregistration. When in doubt, preregister!


The Holy Grail of business intelligence has long been self-service analytics where business people can access and analyze information without getting stuck in a lengthy IT backlog. Too often self-service is interpreted to simply mean buying the “right” analytical tool and getting IT “out of the way”. Although this appeals to the two myths perpetuated by tools vendors – tools are the answer and IT is the primary bottleneck – it is naïve and leads to an analytical “trough of disillusionment”.

Not all data, integration, governance and analytical needs are the same and neither are the business analytical skills. This session first examines how to identify and then position those needs into a self-service analytics needs matrix.

This session will discuss the different architectures, processes and best practices that have been successful in fulfilling different analytics needs and users in an enterprise. Also, the common mistakes made and pitfalls encountered by initial self-service efforts will be highlighted.

Who should attend:

– BI directors, managers and project leaders

– Data management and data governance practitioners

– Business analysts and data analysts

– Data architects

– BI architects and developers

– IT managers

Attendees will learn:

– How to classify analytical users by skills and needs

– How to position data integration, data preparation and data blending technologies

– How and when to leverage traditional BI versus data discovery tools

– Need for data and analytics governance

– Data architecture and database technologies needed to enable self-service analytics

Leave a Reply

Your email address will not be published. Required fields are marked *