Collaboration’s Impact On Supply Chain Creativity and Innovation

Supply Chain Data Audit Guidelines V 1.0

A Book Worth Reading.

Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary―useful in the hands of business analysts, communications strategists, designers, and more.

One of our International Association for Data Quality, Governance and Analytics Advisory Board Members – Dr. Stylianos Kampakis has written an excellent book on this subject. With the second edition of “The Decision Maker’s Handbook to Data Science”, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Dr. Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Dr. Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists"

We highly recommend his book.

The U.K. Government Advances It’s Data Strategy

The British Government has moved forward with the following announcement (September 9, 2020) on further refining its data strategy.

“Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents”

Click below to read an interesting article regarding data literacy.

“Statistical Inference on Membership Profiles in Large Networks” Dr. Jianqing Fan - Princeton University

Data Quality Deficiencies in Healthcare Records

Joseph A. Yacura, Founder – International Association for Data Quality, Governance and Analytics

The recent corona virus has drawn considerable attention to critical errors and gaps in the supply chain data quality in the healthcare field. The severity of the limited real time data, poor data quality and lack of vertical integration of data all contributed to the challenges of ordering, shipping and receiving critical medical supplies.

Prior to this pandemic, a study of health care records was conducted by Yili Zhang and Guney Koru whose results were published in the “Journal of the American Medical Informatics Association Volume 27, Issue 3 March, 2020 Pages 386 – 395”. The article is titled “Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions.”

The purpose of their study was to investigate and quantify the quality of the data contained in the health care records to develop a systematic approach to understanding and assessing data quality as data is becoming increasingly important as the volume and utilization of health care data increases. The data set they studied were comprised of Medicaid records from an undisclosed state. The data set contained 2.23 million rows and 32 million cells. They segmented the defects found into five (5) major categories and seventeen (17) subcategories. The five (5) major categories were:

1. Missing data

2. Correctness

3. Syntax violations

4. Semantic violations

5. Duplicity

Results:

Their study found more than 3 million (3,000,000) defects in the data set. Defect density exceeded 10% in five tables. The majority of the defects were format mismatches, invalid codes, dependency-contract violations, implausible value types, etc.

These findings suggest a significant opportunity for healthcare organizations to immediately address their data quality. Such an effort will result in lower operating and administrative costs, a higher level of data quality and improved patient care.

PyCaret 1.0.0

An “Open Source” low code machine learning library in Python.

The Governor of California Seeks Data Scientists Help to Fight Covid 19

The Governor of the State of California is asking for the help of citizen scientists (Data Analysts & Data Scientists) to help in the fight to end the spread of Covid-19

The Governor has made available all data regarding the spread of the virus in hopes of gaining the support of volunteer data professionals.

“Some Key Comparisons Between Statistics and Mathematics and Why Teachers Should Care”

Dr. Allan Rossman, Dr. Beth Chance, Dr. Elsa Medina
Dept of Statistics and Mathematics Cal Poly

We are pleased to have received permission from Dr. Allan Rossman (Chairman of Statistics Dept. Cal Poly) to post this article.

Ask Good Questions (Blog)

Dr. Allan Rossman, Chairman Dept of Statistics, Cal Poly

We are pleased to have permission from Dr. Allan Rossman to post a link to his blog which is a great resource for statistical literacy.