by Blazent | Jan 26, 2017 | Data Quality
While many organizations boast of having good data or improving the quality of their data, the real challenge is defining what those qualities represent. What some consider good quality others might view as poor. Judging the quality of data requires an examination of...
by Blazent | Nov 29, 2016 | Data Quality
A recent white paper published at sapinsider, does a nice job of surfacing many of the challenges Blazent’s customers face. This blog will expand on the 5 reasons listed in the white paper, which focus on data quality implementations in the context of IT asset and...
by Blazent | Nov 23, 2016 | Data Quality
When considering the business value of good data quality, the primary purpose is to make a business more efficient and profitable. The 451 Group research that tabulated the top 5 benefits below included other downstream benefits, such as “better supplier performance”...
by Blazent | Nov 16, 2016 | Data Quality
3rd of a 4-part series A preceding blog provided an overview of the operational dimensions that are normally associated with data quality. To recap, these are: Integrity Accuracy Completeness Duplication Currency Consistency This blog post will...
by Blazent | Oct 27, 2016 | Data Quality
Enterprise automation, predictive analytics, and AIOps run exclusively on data. When IT leaders attempt to execute high-speed infrastructure transformations on a polluted CMDB baseline, the initiatives fail instantly. From the Archives: The Enduring Sources of Bad...
