The Data and Analytics Playbook
Proven Methods for Governed Data and Analytic Quality
Lowell Fryman, Gregory Lampshire, Dan Meers
- 292 pagine
- English
- ePUB (disponibile sull'app)
- Disponibile su iOS e Android
The Data and Analytics Playbook
Proven Methods for Governed Data and Analytic Quality
Lowell Fryman, Gregory Lampshire, Dan Meers
Informazioni sul libro
The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration.
The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it.
In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization.
- Provides a mature and proven playbook approach (methodology) to enabling data governance that supports agile implementation
- Features specific examples of current industry challenges in enterprise risk management, including anti-money laundering and fraud prevention
- Describes business benefit measures and funding approaches using exposure based cost models that augment risk models for cost avoidance analysis and accelerated delivery approaches using data integration sprints for application, integration, and information delivery success