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What are the two leading solutions for cleansing data and building decision support systems?
How can you use a data mart to cleanse your data?
What variables influence the acceptable margin of error of your information?
How can you determine the best places to fix your data?
What do you need to be aware of when you use data that's not perfectly accurate?
What are the key techniques for verifying data?
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Architecture and Infrastructure
Business Applications
Business Management
Customer Relationship Management
Data Management
IT Management
All decision support systems depend on good data. Any company that wants to perform analyses, run reports, and create queries depends on good, clean data. Unfortunately, no company’s data is 100% perfectly clean and accurate. That doesn’t mean that it’s not still valuable and can’t be used. In this program, Don Gerundo, a Principal of the Business Intelligence practice and Chief Methodologist of Exigent Partners, describes ways to improve the usefulness of even marginal data. Gerundo begins by describing two solutions for cleansing data, and explains how a data mart can cleanse your data. He provides background information and examples that explain how to work with attributes and metrics. Gerundo discusses where to fix your data, and explains what to do if you’re using data that’s not perfectly accurate. He concludes by discussing techniques for data verification, and offers advice on prioritizing data elements.
By watching this program, you will learn:
~ How information of various degrees of accuracy can be useful;
~ Different places in a decision support system to cleanse data;
~ Several methods to validate the accuracy and quality of data; and
~ How to tag invalid data.
PROGRAM TOPICS:
INTRODUCTION
PROGRAM ROI
AGENDA
TWO SOLUTIONS FOR CLEANSING DATA
Data Cleansing Solution Assumptions
Data Cleansing Solutions: The Decision Support System
HOW A DATA MART CAN CLEANSE YOUR DATA
How a Data Mart Can Cleanse Your Data: Sales Hierarchy Assignment
Building a Decision Support System: The Subject Area
Building a Decision Support System: Users
Building a Decision Support System: Purpose
Building a Decision Support System: Determining Data Requirements
Building a Decision Support System: Determining Data Origin
WORKING WITH ATTRIBUTES AND METRICS
Attributes and Metrics
Value Domains
The Unknown Category
WHERE TO FIX YOUR DATA
USING IMPERFECT DATA: Case Studies
USING IMPERFECT DATA: Knowing Your Error Tolerance
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