Databases Reference
In-Depth Information
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Is measured in percentage of corrections required per execution per table. The lower the
percentage of corrections, the higher the quality of data.
Data enrichment
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This is not a new subject area in the world of data. We have always enriched data to improve its
accuracy and information quality.
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In the world of Big Data, data enrichment is accomplished by integrating taxonomies, ontologies,
and third-party libraries as a part of the data processing architecture.
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Enriched data will provide the user capabilities:
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To define and manage hierarchies.
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To create new business rules on-the-fly for tagging and classifying the data.
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To process text and semi-structured data more efficiently.
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Explore and process multilingual and multistructured data analysis.
Data transformation
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Is implemented as part of ETL/ELT processes.
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Is defined as business requirements by the user teams.
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Uses master data and metadata program outputs for referential data processing and data
standardization.
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Is developed by IT teams.
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Includes auditing and traceability framework components for recording data manipulation
language (DML) outputs and rejects from data quality and integrity checks.
Data archival and retention
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Is implemented as part of the archival and purging process.
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Is developed as a part of the database systems by many vendors.
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Is often misquoted as a database feature.
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Often fails when legacy data is imported back due to lack of correct metadata and underlying
structural changes. This can be avoided easily by exporting the metadata and the master data along
with the data set.
Master data management
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Is implemented as a standalone program.
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Is implemented in multiple cycles for customers and products.
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Is implemented for location, organization, and other smaller data sets as an add-on by the
implementing organization.
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Measured as a percentage of changes processed every execution from source systems.
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Operationalized as business rules for key management across operational, transactional,
warehouse, and analytical data
Metadata
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Is implemented as a data definition process by business users,
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Has business-oriented definitions for data for each business unit. One central definition is regarded
as the enterprise metadata view of the data.
