Data quality

3.1 Introduction High-quality data facilitate a precise analysis and the resulting statistics. Hence, high-quality data assist the organization to increase its business value. This chapter demonstrates the concept of data quality, the effects of inaccurate data, and the factors that cause low-quality data. 3.2 Data Quality The executive and the top management in organizations seek comprehensive reports and dashboards to enable them to understand on going processes and facilitate the decision- making

Principles of Data Quality There are many principles for the data quality that ensure the data quality for the data entered to a database. The most significant principles for the data quality include: 1- The Vision 2- The Policy 3- The Strategy 4- The collector has primary responsibility 5- User responsibility 6- Consistency 7- Transparency 8- Outliers The Vision It is very important for the big organization to get a high vision for their data and its quality especially when the same data will be shared

type of data, and it has a massive amount of processing power, and can handle a boundless number of jobs or tasks. Data Management, Data ingestion, Warehouse, and ETL provides features for effective management and data warehousing for data managing as a valuable resource. The Stream computing features pulls streams of data and then streams it back out as a single flow and then processes that data. Analytics/ Machine Learning features advanced analytics and machine learning. Content Management which

3.5 Data Quality Business Value: According to Umar at al. (1999), (as cited in Haug., & Arjborn., 2011 ) there are many big organizations losing millions of dollars annually because of Data Quality related problems. They lose this amount in terms of revenue opportunities and failure to address customer issues in a timely manner. In addition; poor Data Quality is one of the biggest reasons for failure of critical information projects. So, here there are some major benefits of using proper Data qualities

Introduction Data gathering methods are often loosely controlled, resulting in out-of-range values (e.g., Income: -100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), missing values, etc. Analyzing data that has not been carefully screened for such problems can produce misleading results. Thus, the representation and quality of data is first and foremost before running an analysis. If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge

Data management, statistical analysis & quality assurance Data collection The data and the values of the sensitivity scores would be collected at the general dental practice by the trained dentists who will report to the second investigator responsible for the overall collection of the data. Direct patient examination would be carried out at base line, immediately, 3, 6 and 9 months post application using visual analogue scale for tactile stimuli response and Schiff cold air sensitivity scale for

impact of data quality problems on the companies described in this case study? What management, organization, and technology factors caused these problems? BT Group had data quality issues within the product inventory and customer billing databases, resulting in poor efficiency of the system. The case doesn’t really give the reason that these databases contained inaccurate data. However, one can assume that the errors were caused by lack of structure within the organization at data collection

the file sets in the FAA database, Aircraft Series, to demonstrate a proposed process for quality data preparation (Federal Aviation Administration - Data Downloads, n.d.). This documentation includes a process overview, a description of the data files, two attempts at cleaning the data, and validation and standardization of the resulting content. A short discussion of data integrity, data validation, data governance, and documentation follows including recommendations to overcome the challenges

Water quality data (fixed-interval sample) collected bi-monthly from 1999-2008 and monthly from 2009-2013 for all 18 monitoring sites within the Reedy Fork and Buffalo Creek basins over a 15-year period was obtained from The City of Greensboro Stormwater Division, North Carolina. The sampled data were grouped in ranges of years from 1999-2002, 2003-2008, 2009-2010 and 2011-2013 so as to obtain a detailed analysis on the data. The sampling sites in the study area were numbered for simplicity of result

The Role of the Leader in Evaluating Data to Improve Quality and Safety Mary Slaton Walden University Leadership Competencies in Nursing and Health Care NURS 4021-9 Dr. Merilyn Long May 17, 2013 The Role of the Leader in Evaluating Data to Improve Quality and Safety Quality and Safety has been recognized as important issues in creating the delivery of effective and responsive health care. To improve Quality and Safety the leader must analyze data and interpret the information to develop