Dec 14 ADV Webinar: Strategies for Machine Learning Success

It is clear that Data Management best practices exist – and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both.

Data ethics covers a wide range of human-centric concerns. At heart, it refers to the need to ensure that enterprise data management and analytics practices don’t adversely impact the lives, safety, health, property, privacy, sensitivities, and reputations of customers, employees, communities, and other stakeholders. Ethical concerns affect many data and analytics practices. These concerns are present when considering how data collection, usage, or sharing could cause harm or expose individuals to the risk of being identified, when data is being used to target, profile, or categorize individuals, and when data gathered legitimately for one purpose is being reused in an entirely different context.