Whitepaper : ER/Studio Data Architect
Make the Most of Your Metadata
In many organizations, there are recurring challenges in meeting business process requirements that are driven by gaps in effective communication and information sharing among the data practitioners and the business domain owners. The scale of these gaps can range from the macro level (such as multiple yet variant definitions of key business terms such as “customer” or “material”) to the micro level (such as variances in field lengths for commonly-used data elements).
While data professionals don’t necessarily need to shoulder the blame for these variances, in a modern information-aware enterprise they are entrusted to seek out ways to mitigate the impacts of legacy variation. A reasonable approach is to understand where the variations occur, what the root causes are, and what can be done to address those root causes. This can be facilitated through a strategy for metadata management – collecting and managing the “data about the data” that provides the context for the formats, standards, definitions, and meanings associated with reference data sets, data element concepts, data elements, records, and tables.
Metadata management is not a new concept, although it is gaining new adherents as the value of enterprise data governance is increasingly recognized. The success of metadata projects may have been hampered in the past due to misguided attention on the deployment of metadata tools and population of the repository.
This whitepaper addresses considerations for collaborating on metadata within and across an organization. First, the paper considers how the organic and distributed evolution of the enterprise application ecosystem has allowed inconsistencies to be introduced that create challenges as the data sets from those applications are repurposed for reporting and analytics. Next, the question of the value of metadata management is raised, suggesting that a strategy for metadata management and use must clearly direct the types of activities for metadata discovery and capture.
Author: David Loshin
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David Loshin, president of Knowledge Integrity, Inc., (www.knowledge-integrity.com), is a recognized thought leader and expert consultant in the areas of analytics, big data, data governance, data quality, master data management, and business intelligence. Along with consulting on numerous data management projects over the past 15 years, David is also a prolific author regarding business intelligence best practices, with numerous books and papers on data management, including the recently published “Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph,” the second edition of “Business Intelligence – The Savvy Manager’s Guide,” and other books and articles on data quality, master data management, big data, and data governance. David is a frequently invited speaker at conferences, web seminars, and sponsored web sites and channels including www.b-eye-network.com.
Register to read the full whitepaper.
Topics :
Data Modeling,Metadata,
Products :
ER/Studio Data Architect,ER/Studio Enterprise Team Edition,ER/Studio Team Server Core,
In many organizations, there are recurring challenges in meeting business process requirements that are driven by gaps in effective communication and information sharing among the data practitioners and the business domain owners. The scale of these gaps can range from the macro level (such as multiple yet variant definitions of key business terms such as “customer” or “material”) to the micro level (such as variances in field lengths for commonly-used data elements).
While data professionals don’t necessarily need to shoulder the blame for these variances, in a modern information-aware enterprise they are entrusted to seek out ways to mitigate the impacts of legacy variation. A reasonable approach is to understand where the variations occur, what the root causes are, and what can be done to address those root causes. This can be facilitated through a strategy for metadata management – collecting and managing the “data about the data” that provides the context for the formats, standards, definitions, and meanings associated with reference data sets, data element concepts, data elements, records, and tables.
Metadata management is not a new concept, although it is gaining new adherents as the value of enterprise data governance is increasingly recognized. The success of metadata projects may have been hampered in the past due to misguided attention on the deployment of metadata tools and population of the repository.
This whitepaper addresses considerations for collaborating on metadata within and across an organization. First, the paper considers how the organic and distributed evolution of the enterprise application ecosystem has allowed inconsistencies to be introduced that create challenges as the data sets from those applications are repurposed for reporting and analytics. Next, the question of the value of metadata management is raised, suggesting that a strategy for metadata management and use must clearly direct the types of activities for metadata discovery and capture.
Author: David Loshin
Share This:
David Loshin, president of Knowledge Integrity, Inc., (www.knowledge-integrity.com), is a recognized thought leader and expert consultant in the areas of analytics, big data, data governance, data quality, master data management, and business intelligence. Along with consulting on numerous data management projects over the past 15 years, David is also a prolific author regarding business intelligence best practices, with numerous books and papers on data management, including the recently published “Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph,” the second edition of “Business Intelligence – The Savvy Manager’s Guide,” and other books and articles on data quality, master data management, big data, and data governance. David is a frequently invited speaker at conferences, web seminars, and sponsored web sites and channels including www.b-eye-network.com.
Register to read the full whitepaper.
Topics : Data Modeling,Metadata,
Products : ER/Studio Data Architect,ER/Studio Enterprise Team Edition,ER/Studio Team Server Core,