"A big part of data governance should be about helping people (business and technical) get their jobs done by providing them with resources to answer their questions, such as publishing the names of data stewards and authoritative sources and other metadata, and giving people a way to raise, and if necessary escalate, data issues that are hindering their ability to do their jobs. Data governance helps answer some basic data management questions." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"A data lake is a storage repository that holds a very large amount of data, often from diverse sources, in native format until needed. In some respects, a data lake can be compared to a staging area of a data warehouse, but there are key differences. Just like a staging area, a data lake is a conglomeration point for raw data from diverse sources. However, a staging area only stores new data needed for addition to the data warehouse and is a transient data store. In contrast, a data lake typically stores all possible data that might be needed for an undefined amount of analysis and reporting, allowing analysts to explore new data relationships. In addition, a data lake is usually built on commodity hardware and software such as Hadoop, whereas traditional staging areas typically reside in structured databases that require specialized servers." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"Data governance presents a clear shift in approach, signals a dedicated focus on data management, distinctly identifies accountability for data, and improves communication through a known escalation path for data questions and issues. In fact, data governance is central to data management in that it touches on essentially every other data management function. In so doing, organizational change will be brought to a group is newly - and seriously - engaging in any aspect of data management." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"Data is owned by the enterprise, not by systems or individuals. The enterprise should recognize and formalize the responsibilities of roles, such as data stewards, with specific accountabilities for managing data. A data governance framework and guidelines must be developed to allow data stewards to coordinate with their peers and to communicate and escalate issues when needed. Data should be governed cooperatively to ensure that the interests of data stewards and users are represented and also that value to the enterprise is maximized." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"In truth, all three of these perspectives - process, technology, and data - are needed to create a good data strategy. Each type of person approaches things differently and brings different perspectives to the table. Think of this as another aspect of diversity. Just as a multicultural team and a team with different educational backgrounds will produce a better result, so will a team that includes people with process, technology and data perspectives." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"Lack of trust is closely associated with uncertainty about the quality of the data, such as its sourcing, content definition, or content accuracy. The issue is not only that the data source has quality issues, but that the issues that it may or may not have are unknown." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"The desire to collect as much data as possible must be balanced with an approximation of which data sources are useful to address a business issue. It is worth mentioning that often the value of internal data is high. Most internal data has been cleansed and transformed to suit the mission. It should not be overlooked simply because of the excitement of so much other available data." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"Typically, a data steward is responsible for a data domain (or part of a domain) across its life cycle. He or she supports that data domain across an entire business process rather than for a specific application or a project. In this way, data governance provides the end user with a go-to resource for data questions and requests. When formally applied, data governance also holds managers and executives accountable for data issues that cannot be resolved at lower levels. Thus, it establishes an escalation path beginning with the end user. Most important, data governance determines the level - local, departmental or enterprise - at which specific data is managed. The higher the value of a particular data asset, the more rigorous its data governance." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
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