IRI Workbench provides a number of features for working across multiple tables in a database. It includes wizards to: profile databases; classify columns; subset, mask and migrate data; generate test data; etc.
Editors Note, Q2’19: In addition to the method described below, there is now also available to IRI FieldShield (data masking product) or IRI Voracity (data management platform) users a Data Class Database Masking wizard, which can be used if you have pre-classified your data.
The Schema Pattern Search in IRI Workbench (WB) can be used to retrieve data in an entire schema matching specific patterns. The search process compares the patterns to all the data in every column of the selected data types in every table in every schema selected.
Given the amount of data businesses garner daily from human interaction, it is easy to understand how their sources become rife with redundant or erroneous entries.
The traditional or enterprise data warehouse (EDW) has been at the center of data’s transformation to business intelligence (BI) for years. An EDW involves a centralized data repository (traditionally, a relational database) from which data marts and reports are built.
Customers drive business, and they want to be understood and valued. That starts with getting their (only) name right, and having an accurate view of their transaction history, preferences, and related information.
IRI is now also delivering fuzzy search functions, both in its free database and flat-file profiling tools, and as available field-function libraries in IRI CoSort, FieldShield, and Voracity to augment data quality, security, and MDM capabilities.
Data architects and data scientists, as well as DBAs and governance teams, may need to use or migrate data in legacy file formats and databases. Additionally, the ability to mash-up those sources with newer file and database repositories is important in data integration (ETL) and analytic projects, as well as in data profiling for data loss prevention and privacy law compliance.
Master Data Management (MDM) is a strategic enterprise information management (EIM) life cycle initiative designed to foster the consistency and accurate maintenance of master (or reference) data.
Introduction
In this article, I suggest ways to move your company’s data towards a higher state of quality. The highest quality occurs when the data meets the needs of your company.
Master Data Management (MDM) is a discipline designed to make data more dependable, sharable and accessible. Here are some of IRI’s philosophies around MDM:
Most developers believe that data are, or should be, application-independent.