Events

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Date/Time

02/07/2019
1:00pm-4:30pm

Location

Great-West Financial,
Training Rooms D-E, Level B2,
8525 E Orchard Road,
Greenwood Village, CO 80111

DAMA Winter Meeting: Small Team Governance & Machine Learning and Data Modeling

First Speaker:
Lowell Fryman
, Practice Principal & Services Capability Manager, Collibra

Title: Best Practices for Starting Data Governance with a Smaller Team
Abstract: Many organizations are just starting a Data Governance program with one or two core team members. Based upon all the seminars and articles that are out there the tasks and resource commitments identified and required for success can be overwhelming. Yes, you all have a long and exciting journey in front of you to achieve Data Governance success and the value that brings to your organization. However, many of the effective and successful Data Governance programs start small. Some even stay small. We will discuss examples of longer term successful programs that started with, and still have, one or two core team members. Data governance is NOT a program that you can just throw bodies at and be successful, but it is a program that requires many individual contributors.

This seminar will discuss

  • Why you should start small
  • What activities and deliverables you really need to be effective and successful
  • Methods to federate the governance processes across the organization
  • An approach and path for maturity while keeping the core team small
  • Best practices for adoption and rollout of the Data Governance program

Bio: Lowell Fryman, CDMP-CBIP, has been a practitioner in the data management industry for three decades and is recognized as a leader in data governance, analytics and data quality. Over the years Lowell has been a member of DAMA NCR, Chicago, and RMC chapters.

Lowell is a co-author of 2 data governance related books; “Business Metadata; Capturing Enterprise Knowledge” (isbn 978-0-12-3737267) and “The Data and Analytics Playbook; Proven Methods for Governed Data & Analytic Quality” (isbn 978-0-12-8023075).

Lowell is a past adjunct professor at Daniels College of Business, Denver University, held the position of President of RMC chapter for 5 years and VP of Programs for 3 years. He current is the DAMA-International VP of Online Services, Education Advisor for DAMA-Rocky Mountain Chapter (RMC), and DAMA-I Central member. He holds certifications in Data Management and Business Intelligence, as well as Data Modeling.

Lowell writes the Business Glossary and Metadata column on TDAN.com, contributes to Collibra Blogs and eBooks, and is the moderator for the Collibra Community, Governance Sponsors and Stewards Groups. During the day he is Practice Principal in the Customer Success team at Collibra where he is responsible for delivering thought leadership and advisory services. He conducts open discussions and presentations on Data Governance topics each month at https:/community.collibra.com.

 

Second Speaker:
John Myers, Senior Industry Analyst

Title: Machine Learning and Data Modeling: What we can learn from Moneyball
Abstract: Automation and Machine Learning are going to impact more than just manufacturing and self-driving vehicles. AI/ML is going to impact areas of knowledge workers in areas such as law, analysis…. and data management. AI/ML has the potential to make the lives of data management professionals better, but it won’t come without a cost. Data management teams in the areas of data modeling, data quality, etc need to be ready for the coming changes associated with AI/ML. Join John Myers as he talks about the future of data management and AI/ML and how organizations can overcome the technical and cultural hurdles.

Bio: John Myers is industry analyst with nearly 20 years of experience in areas related to business analytics and business intelligence in professional services, sales consulting, product management, industry analysis and research. He has helped organizations to solve their analytics problems whether they related to operational platforms such as customer care or billing; applied analytical applications, such as revenue assurance or fraud management. Established thought leadership in emerging data management paradigms such as Big Data (combination of multi-structured and relational data sets) applications and NoSQL access data stores.

 

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