Start Your Innovation Journey – Without Risk

Posted by Todd Hall on February 20, 2020

Innovation Journey

In so many asset-intensive organizations, the integrity of the maintenance data is imperative to the success of the financial, operational, compliance, and EH&S operations. In most organizations, data managers are aware of a possible level, accuracy, and completeness of the data set. To manage the issues, most create a continual and manual process to review and cleanse, repeating the process over and over.

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How AI and ML Can Help Elevate the Vision of Better Data

Posted by Martin Printz on February 12, 2020

Retail Post

”Once upon a time” when the most complicated engine was Materials Requirements Planning (MRP), and the most common planning tool was MS Excel. Many would say that things were much simpler back then, and they probably were. We were used to stock-outs and lots of stuff on sale because of the misalignment between supply and demand. Oh, yes – the good old days.

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Measure Data Quality Impacts to Your Business

Posted by David Kuketz on February 6, 2020

SVM BLOG #3_ Measure Data Quality Impacts to Your Business

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).

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