November 2018: Data Management Maturity

Presented by Melanie Mecca

Director, Data Management Product and Services, CMMI Institute

Data Management Maturity – Why We Need It and How It Can Propel You to DM Leadership

Our industry is continually building capabilities based on its considerable accomplishments over the past decades. Some of the (roughly) sequential milestone markers that most organization share include: data design, data administration, data architecture / warehousing, data quality and governance, MDM, and predictive analytics using both structured and unstructured data.

So why haven’t organizations attained DM perfection? As we know, the data layer in the vast majority of organizations grew project by project, typically to meet specific needs of a line of business. Best practices were not usually shared, useful work products languished in project repositories, etc. – and above all, there was no universal mandate to manage data as a critical corporate asset.

The Data Management Maturity (DMM) Model’s primary goals are to accelerate organization-wide DM programs by: providing a sound reference model to quickly evaluate capabilities, strengths and gaps; accelerating business engagement; launching a collaborative vision / strategy; and identifying key initiatives to extend existing capabilities while building new ones – leading to efficiency, cost savings, creativity, and improved data quality.

In this seminar, we’ll address:

  • Data Management Capabilities and Maturity Evaluation
  • The DMM in action – interactive exercise with the Business Glossary – rate your organization!
  • Case study examples – how organizations have accelerated their progress
  • How to leverage Data Management Maturity to empower your career.
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June 2018: Data Management for the Internet of Things

Presented by Michael Scofield, M.B.A.

Assistant Professor, Loma Linda University

The “internet of things” is dependent upon the communication between various devices—such communication containing data. When data moves, it has architecture, and it is that architecture of “data in motion” (albeit small records within a transaction) which must be astutely designed.

The quality of any business or industrial process outcomes depend upon three major foundations:

  1. Quality and reliability of hardware (and physical network) supporting it.
  2. Quality of design of the process and decision rules. This includes anticipating all contingencies which would influence a decision made independent of human judgment and involvement.
  3. Quality of the data at capture, and quality of definition and clarity of data conveyed between devices.

Continue reading “June 2018: Data Management for the Internet of Things”