Is Your Supply Chain A Cost Centre or a Value Creator?
The Supply Chain function within your company has many responsibilities. From planning to negotiating to buying, from moving goods to processing goods, and from managing data to managing inventory. These responsibilities are at the core of making your company run.
Strong data analytics is a digital business imperative — and it all begins with smart data governance practices and an emphasis on quality and context.Executives talk about the value of data in generalities, but Michele Koch, director of enterprise data intelligence at Navient Solutions, can calculate the actual worth of her company’s data.
The Business View of Data and Data Quality: The Six Dimensions of Semantic Quality – Part One
Business has a fundamental problem with Data Quality. In some places it’s merely painful, in others it’s near catastrophic. Why is the problem so pervasive? Why does it never seem to get fixed? Perhaps we’ve been thinking about the problem wrong. Time for a fresh look. The central flaw in the long-running discussion over Data Quality is literally its focus on ‘data’. Stored data is merely the system or database residue of things that have already happened in the business, a memory of past events.
“Metadata is hotter than ever,” said Donna Burbank, Managing Director at Global Data Strategy. “And there’s data to back up that assertion.” Speaking at DATAVERSITY® Database Now Online 2017 Conference, Burbank was referring the survey findings of the research report Emerging Trends in Metadata Management. 80 percent of survey respondents said that Metadata is as important – or more important – than in the past. Although not surprising, Burbank said, “It’s nice to have documentation that this actually is a growing trend.” She also remarked that at least one participant asks about Metadata in every webinar or conference presentation that she does.
The term golden record is a core concept within Master Data Management (MDM). A golden record is a representation of a real world entity that may be compiled from multiple different representations of that entity in a single or in multiple different databases within the enterprise system landscape.
A Conceptual Enterprise Framework for Managing Scientific Data Stewardship
Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement.