Posted by David Kuketz on March 3, 2020
The four (4) key data quality (DQ) dimensions are completeness, consistency, conformity, and consolidation (uniqueness). Data quality can be improved via cleansing (normalization, standardization), classification/coding, creation (enrichment, enhancement, construction), and consolidation (deduplication). Once you get data clean, you need to keep it clean. Perfect data equals perfect business (assuming people, process, and technology are not potential sources of failure).
Posted by David Kuketz on January 14, 2020
“I cannot figure out the value of my data!” Exactly! Art or science, theoretical. Few have connected the value of data to the value of a business or shown causality of quantitative improvement in Data Quality (DQ) to a measurable increase in business outcomes. If a race car team has a new motor with more output than the previous generation (or competitors), should they use the same fuel or improve the fuel quality to maximize the output of the new motor? The answer is, it depends on what the motor requires; that may determine whether they win races, or not.
Posted by David Kuketz on October 11, 2019
Today, companies are moving at the speed of thought, moving the needle on the KPIs for the outcomes that make them great companies. Great companies provide optimized customer experience management (CX) and operational excellence (OX), not to mention accurate, real-time reporting with full transparency of information to stakeholders.