InfoQ
This article may meet Wikipedia's criteria for speedy deletion as an article which plainly indicates that the subject was invented/coined/discovered by the article's creator or someone they know personally, and does not credibly indicate why its subject is important or significant. The criterion does not apply to any article that makes any credible claim of significance or importance even if the claim is not supported by a reliable source or does not satisfy Wikipedia's notability guidelines. See CSD A11.
If this article does not meet the criteria for speedy deletion, or you intend to fix it, please remove this notice, but do not remove this notice from pages that you have created yourself. If you created this page and you disagree with the given reason for deletion, you can click the button below and leave a message explaining why you believe it should not be deleted. You can also visit the talk page to check if you have received a response to your message. Note that this article may be deleted at any time if it unquestionably meets the speedy deletion criteria, or if an explanation posted to the talk page is found to be insufficient.
Note to administrators: this article has content on its talk page which should be checked before deletion. Administrators: check links, talk, history (last), and logs before deletion. Consider checking Google.This page was last edited by Shinoda.manu (contribs | logs) at 05:21, 23 January 2018 (UTC) (6 years ago) |
 | It has been suggested that this article be merged with Information_quality. (Discuss) Proposed since January 2018. |
Information quality (InfoQ) is 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. Formally, the definition is InfoQ = U(X,f|g) where X is the data, f the analysis method, g the goal and U the utility function.
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 (2014) proposed eight dimensions to help assess InfoQ and various methods for increasing InfoQ:
1) Data resolution
3) Data integration
4) Temporal relevance
5) Chronology of data and goal
8) Communication.
Formalizing the concept of InfoQ increases the value of statistical analysis and data mining, both methodologically and practically
The topic of information quality (InfoQ), as presented in this entry, has been applied in a wide range of domains. There are now publications on such applications in the context of healthcare, customer surveys, data science programs, advanced manufacturing and Bayesian network applications, to name a few. The topic is of special importance in the context of data science programs mostly driven by computer science perspectives. It brings in a complementary perspective that emphasizes a wide application perspective.
A detailed introduction to InfoQ with examples from healthcare, education, official statistics, customer surveys and risk management is available in the book by Kenett and Shmueli, Information Quality: The Potential of Data and Analytics to Generate Knowledge, John Wiley and Sons, 2016.
References
- Information Quality: The Potential of Data and Analytics to Generate Knowledge, Kenett, R.S. and Shmueli, G., John Wiley and Sons, 2016.
- An Information Quality (InfoQ) Framework for Ex-Ante and Ex-Post Evaluation of Empirical Studies, Shmueli, G. and Kenett, R.S., Proceedings of the 3rd International Workshop on Intelligent Data Analysis and Management, Kaohsiung, Taiwan, Springer Proceedings in Complexity, Eds. L Uden, L SL Wang, T-P Hong, H-C Yang and I-H Ting, pp. 1–13, 2013
- Chapter 1: The Role of Statistical Methods in Modern Industry and Services, in Kenett, R.S. and Zacks, S., Modern Industrial Statistics: with applications in R, MINITAB and JMP, Second Edition, John Wiley and Sons, 2014
- Chapter 1: Risk management: a general view, in Kenett, R.S. and Raanan, Y., Operational Risk Management: A Practical Approach to Intelligent Data Analysis, John Wiley and Sons, 2011
- From Data to Information to Knowledge, Kenett, R.S., Six Sigma Forum Magazine, 2008
- Modern Analysis of Customer Surveys with Applications using R, Kenett, R.S. and Salini, S., John Wiley and Sons, 2011
- Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis, Kenett, R.S. and Salini, S., Applied Stochastic Models in Business and Industry, 2011
- Bayesian Network Applications to Customer Surveys and InfoQ, Cugnata, F., Kenett R.S. and Salini S., Procedia Economics and Finance, 2014
- Statistics: A Life Cycle View, Kenett, R.S., Quality Engineering, 2015 http://ssrn.com/abstract=2315556
- Clarifying the terminology that describes scientific reproducibility, Kenett, R.S. and Shmueli, G., Nature Methods, Vol. 12(8), p 699, 2015
- Official Statistics Data Integration for Enhanced Information Quality, Dalla Valle L. and Kenett R.S., Quality and Reliability Engineering International, 2015
- On Information Quality, Kenett, R.S. and Shmueli, G., Journal of the Royal Statistical Society, Series A, vol 177 issue 1, pp. 3–38, 2014, http://ssrn.com/abstract=2128547
- On Generating High InfoQ with Bayesian Networks, Kenett, R.S., Quality Technology and Quantitative Management, 2016
- Helping Reviewers Ask the Right Questions: The InfoQ Framework for Reviewing Applied Research, Kenett R.S. and Shmueli G., Journal of the International Association for Official Statistics, 2016