By John Ladley

This e-book is for any supervisor or staff chief that has the fairway mild to enforce an information governance application. the matter of handling facts keeps to develop with matters surrounding rate of garage, exponential development, in addition to administrative, administration and protection matters - the answer to with the ability to scale all of those matters up is facts governance which gives larger providers to clients and saves cash. What you'll find during this booklet is an outline of why facts governance is required, the right way to layout, start up, and execute a application and the way to maintain this system sustainable. With the supplied framework and case stories you'll be enabled and informed in launching your personal winning and funds saving facts governance program.

Provides an entire evaluate of the knowledge governance lifecycle, that may assist you figure expertise and employees needs
Specifically aimed toward managers who have to enforce a knowledge governance software at their company
Includes case reviews to element 'do's' and 'don'ts' in real-world occasions

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Additional resources for Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program

Sample text

Part of deploying DG means managing changes. INFORMATION ASSET MANAGEMENT The last concept has been deliberately positioned at the end of this chapter. So far, we have mentioned that data governance is a key element of managing data assets. We have contrasted DG with information management and reviewed the specific solutions that may trigger IM and DG. Now we need to talk about the asset aspect and frame our concepts within information asset management (IAM). We have found it better to save the whole “information as an asset” discussion until after the relationship of data governance to data management is established.

As you already know, this book does not separate the two concepts, as governance is governance for both of them. v • Information quality is the degree to which information consistently meets the requirements and expectations of knowledge workers in performing their jobs. vi Obviously, while the two definitions are different, they are certainly pointing in the same direction. The best way to understand data quality is that the content in question has to be effective or fit for its purpose. This means if your organization feels that customer data is not of “good quality,” you need to understand what purpose, action, or context is involved and how the shortfall is measured.

Data governance supports data quality solutions via: 1. Ensuring that data quality standards and rules are defined and integrated into development and dayto-day operations. 2. Ensuring that on-going evaluation of data quality occurs. 3. Ensuring that organization issues related to changed processes and priorities are addressed. Business Intelligence Business intelligence (BI) has grown from a term coined by Gartner Groupvii in the 1990s. It has since morphed (evolved is too complimentary) into a label that describes a self-perceived cool way of looking at data.

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Data Governance: How to Design, Deploy and Sustain an by John Ladley
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