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.
2. Business intelligence
Decision making and DSS
Dashboards
Data marts and warehouses
Master Data Management
2
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
3
4. Decisions madeWith good
data quality:
Decisions made with poor
data quality:
4
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
5
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
7
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
8
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
9
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
10