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	Data Quality

WHITE PAPERS:

HARVARD BUSINESS REVIEW: To Make the Best Decisions, Demand the Best Data
(HBR OnPoint Collection) Publication Date: Jan 1, 2006

Data Quality: Creating a Foundation for Customer Contact Center Management

RECOMMENDED READING:

Data Quality: The Field Guide

Data Quality: The Accuracy Dimension

Data Quality is a term used to describe the accuracy of data or sets of data and in particular how this data enters and flows through the organization. Most managers are unaware of the full implications of poor data quality.

An informal 2006 survey found the following:

30 % of Customer Contact Centers failed to meet Service Level Commitments as a result of bad or incomplete data.
25 % had Revenue Leakage as a result of faulty data.
40% had to completely scrap support systems or portions thereof because of untrustworthy reporting results
80% had significant manual processes in place to augment their existing reporting platform.
85% reported significant failure of their reporting system during the last 6 months.

Data quality is more than simply finding and repairing inaccurate data. Data quality is the reliable delivery of relevant and timely information to the right people. In a word – the underlying data collection, consolidation, management, and reporting facilities must be trustworthy.

Data Quality is achieved through the implementation of a data management system such as CENTREfuge. The following diagram shows how multiple information sources flow through the CENTREfuge data management system

click image to enlarge

In continual stages of refinement, the CENTREfuge Data Management platform reliably captures the data, cleanses it, normalizes and organizes it into appropriate data repositories – then presents it to end users using a combination of historical reporting and real-time displays.

CENTREfuge provides HIGH QUALITY DATA by:

Reliably capturing and maintaining critical data before being processed (Raw Capture). Data is protected even in the face of complete network failure.
Data is cleansed by identifying statistical outliers for resolution or discard. Data anomalies are investigated and corrected.
Data is normalized to ensure that only a single version of any given statistic is represented in the system.
Data is organized into pertinent data repositories so information isn’t lost in the noise of irrelevant data.
The result is a statistical platform that decision makers have confidence in – meaning the information is reliably received, faithfully interpreted and therefore is relevant to the task of managing the operation.

Questions about Data Quality?
Contact us at sales@newmetrics.com and we’ll show you how CENTREfuge can help you achieve a higher level of Data Quality in your business processes.