Data Management Skills

Data Management Skills - Data Management Skills Five Data...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Data Management Skills Five Data Management Skills that are important for successfully managing and using information. Data Usage . The ability to use data effectively to improve your programs. Includes familiarity with the data available to you, knowledge of the goals of your program and needs of your clients/audience, and creative approaches to using data. Software Skills . Knowing how to use database software to find records, sort, print, and other functions. Knowing how to use built-in forms and reports in a database. Exploring the software and learning new commands. Writing queries and reports in Access, using formulas in Excel or developing new layouts in FileMaker Pro. Data Integrity . Understanding definitions, program guidelines, and sources of data. Developing clear channels of communication. File Management . Knowing how to install files, import and export data, maintain backup files, make copies of files, create new folders. Knowing how to email files and how to download files from email or from websites. Understanding Windows concepts, including how to explore folders and file. Knowing how to create or remove shortcuts from the Desktop and/or the Start menu. Planning and Design Skills . Understanding database design concepts, including relational database design (table structure; one-to-many relationships). Understanding the benefits and limits of various types of databases, including PC and online databases. Ability to participate in short-term and long- term planning about database projects and to decide how to efficiently store and analyze various types of data. Data mining is the process of extracting patterns from data . Data mining is becoming an increasingly important tool to transform these data into information. Data mining can be used to uncover patterns in data but is often carried out only on samples of data. The mining process will be ineffective if the samples are not a good representation of the larger body of data. Data mining cannot show up patterns that may be present in the larger body of data if those patterns are not present in the sample being "mined". Inability to find patterns may become a cause for some disputes between customers and service providers. Therefore data mining is not fool proof but may be useful if sufficiently representative data samples are collected. The discovery of a particular pattern in a particular set of data does not necessarily mean that a pattern is found elsewhere in the larger data from which that sample was drawn. An important part of the process is the verification and validation of patterns on other samples of data Data Linkage: systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data - Data quality is the reliability and effectiveness of data , particularly in a data warehouse . Data quality assurance (DQA) is the process of verifying the reliability and effectiveness of data. Maintaining data quality requires going through the data periodically and
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 12/08/2010 for the course USE 3425 taught by Professor Raman during the Spring '10 term at Punjab Engineering College.

Page1 / 10

Data Management Skills - Data Management Skills Five Data...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online