The concept of Information Quality (InfoQ) is defined as the potential of a dataset to achieve a specific (scientific or practical) goal using a given empirical analysis method.
InfoQ is different from data quality and analysis quality, but is dependent on these components and on the relationship between them.
There are various statistical methods for increasing InfoQ at the study-design and post-data-collection stages -- how are these related to InfoQ?
Kenett and Shmueli (2012) proposed eight dimensions to help assess InfoQ:
Chronology of Data and Goal
Formalizing the concept of InfoQ increases the value of statistical analysis and data mining, both methodologically and practically. It is a foundation element in Data Science.
For a Facebook page on InfoQ, see https://www.facebook.com/infoQbook
For the information quality FB group see https://www.facebook.com/profile.php?id=139436410066165&ref=br_rs
For a LinkedIn article about the InfoQ book, see https://www.linkedin.com/pulse/what-experts-say-information-quality-potential-data-analytics-kenett
For a keynote presentation on InfoQ (starts at 5:20) see https://community.jmp.com/t5/Discovery-Summit-Europe-2017/Plenary-Session-From-Quality-by-Design-to-Information-Quality-A/ta-p/37537