Data Quality (DQ)
It always bears repeating that being able to serve up high-quality data is really important. This is partly because the consequences of poor data quality can be severe – misleading analytics, stymied processes, greater storage costs (due to duplicated data), and so on – but also because historically many organizations have not treated data quality as the priority it should be. Read More
Business Intelligence (BI)
The names of IRI software products and how they run have at times been a source of mystery, or even confusion, to the uninitiated. This article spells out the pieces and clarifies their interplay, providing a quick primer for prospective users, partners, and new industry analysts. Read More
Data Masking/Protection
IRI Workbench contains a section of field-level Data Generation Rules for use in IRI RowGen test data synthesis, FieldShield data masking, and other SortCL-compatible tasks or Voracity (ETL) work flows. Read More
Data Quality (DQ)
IRI Workbench now contains a section of data cleansing, enrichment, and validation rules called Data Quality Rules for use in IRI CoSort data transformation and IRI Voracity ETL, reporting and data wrangling jobs. Read More
Big Data
To analyze data successfully, it must first be prepared successfully. Poor quality data creates poor results. Worse yet is data that takes too long to collect and clean because it is too big or too foreign. Read More
Data Masking/Protection
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. Read More
Data Quality (DQ)
Introduction This is my third installment of blog articles about Data Quality. In the first article, I postulated that data has quality when it has an acceptable level of errors. Read More
Data Masking/Protection
Update: Q2’16: In addition to the database profiling wizard in the data discovery menu group in IRI Workbench described below, IRI has introduced robust data classification that enables the application of field rules for multi-source data transformation and protection through data class libraries. Read More
Data Quality (DQ)
In Working towards Data Quality, we defined data quality (DQ) as a state in which data can be used for operations. What makes the quality of data high is the paucity of errors. Read More
Data Quality (DQ)
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. Read More
