This document discusses data quality and its importance for business decision making. It defines data quality as ensuring information is fit for its intended purpose and helps data consumers make the right decisions. Poor data quality can significantly impact business performance, with 75% of companies reporting financial losses due to low quality data. The document outlines different data quality needs and metrics for various use cases and decision makers. It also presents examples of companies that have benefited financially from implementing thorough data quality management programs.

1. Stephen McCarthy
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IS6120 presentation

2.  Business intelligence
 Decision making and DSS
 Dashboards
 Data marts and warehouses
 Master Data Management
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3.  Firstly, it’s not the same as data integrity
 Data quality concerns business value,
integrity deals with data structure
 Information must be fit for purpose to helps
data consumers make the right decision
 PWC - 75% of companies suffered significant
bottom-line impact from poor data quality
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4.  Decisions madeWith good
data quality:
 Decisions made with poor
data quality:
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5. Case 1: Business Analyst vs. Operations Manager
 Business analysts might require quality data on a
product’s sales figures over a 12 month period for
predicting seasonal trends
 An operations manager however would be more
concerned with gauging next week’s stock
requirements
Case 2:Patient Record System vs. Social Media API
 Hospitals need high granularity data in order to
ensure right diagnosis and treatment is carried out
for patients
 In contrast, social media API don’t deal with
mission critical data so a lower level of data quality
is required
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6. 6
Accuracy Relevancy
Representation Accessibility
Data
Quality

7.  Define what data consumer means by data quality
and aim for conformance to expectations
 Develop a set of dynamic data quality metrics that
measure main dimensions i.e. goals and objectives
 Consider objective measures of data sets and
subjective measures of stakeholders
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8. Information Resources, Inc.
 Clients began to demand more complex
data delivery with reduced cycle times
 IRI created aTDQM program that included
technology, work process, and people (IS)
 Resulted in 80% of errors being
eliminated, reduced rework levels and
increased speed and delivery consistency
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9.  ClavisTechnology
- Cloud based SaaS
- Cleanses, augments and
integrates data
 Datanomic
- Enterprise solution
- Monitors and maintains quality
 InfosolveTechnology
- Open source solution
- Manages quality of data lifecycle
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10.  Data quality is a business issue not simply an IT
concern and must be managed as such
 Aims to provide data consumers with the best
information for making decisions
 Exponential growth in data capture will mean data
quality will be very important in future
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11. Any questions or comments?
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