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Quality consideration for Importance of Data:Important data needs to be fixed first. This importance is gauged by the level of regulatory, shareholder, customer and employee impact. This is also the order of priority followed by most organizations. Typical rules applied are: - Data related to financial impact is important.
- Customer billing, statement and collection data is important.
- All the transaction sensitive data is more important compared to non-transaction OR analysis linked data.
- Historical data is less important compared to latest data.
- Sales automation data OR field CRM data is less important compared to the core production data.
Urgency of implementing data qualityThe Urgency is driven by the following: - Importance of Data
- Speed of build of data faults. For example- a wrong interest rate parameter could impact all new credit cards processing and statements every subsequent day. OR a faulty front-end data capture system for a global telecom company could result in thousands of erroneous records coming in every day.
- Critical Cut-off- You would like to first correct the data related to GL interface before the year-end processing.
- Visibility: Problem visibility may take precedence over its criticality. You have to manage perceptions before the reality.
Level of Data Quality Benchmark - Data, which is transaction and production oriented is given a higher quality benchmark.
- Business Case drives data Quality. The quality benchmark adjusts itself to meet the business case. For example- the mailing list of existing clients will end up having a quality bench-mark between 80 to 90%. This is because beyond a certain limit, one has to do field work to correct the addresses.
- Whatever is less important will have lower quality benchmark.
Organization level Data Quality Readiness The do-ability and effort linked to a data quality program varies sharply based upon the level of readiness with the organization. The readiness can differ across systems and functions. This readiness includes: - Management awareness and appreciation of need for data quality.
- Level of weight and importance given to internal audit and control.
- Level of resource availability with business and IT for data quality.
- Level of control & influence, an organization have with its supplier, customers and business partners.
- Past successes of any other quality initiative.
- Existence of a quality policy.
TIP- Higher is the level of readiness within an organization, more aggressive one can be in pursuing the data quality goals. Lesser is the level of readiness, more phased and cautious approach you will apply.
PLEASE REFER Execution-MiHPractice Tool Data Quality Approach, Scoping, Planning and Tracking Work-Tool |
