Do You Utilize Data Quality Tools Optimally?

Business consultants say that, “What shouldn’t be measured shouldn’t be managed”, and invariably what shouldn’t be managed makes it tough to take care of and enhance. Knowledge high quality needs to be thought-about as a measurable metric with quantifiable scores and effectively outlined information units. Devoid of assessable instruments it’s not potential to make sure high quality and the required enterprise influence that’s required.

The Knowledge High quality (DQ) Scorecard is an efficient method of figuring out your information DNA. The scorecard helps in retaining verify on high quality dimensions comparable to accuracy, completeness, consistency and compliance. With the intention to develop an insightful DQ Scorecard the next dimensions have to be thought-about:

Knowledge High quality Dimensions

• Out there and Full – To make sure that out there data are full and lacking information is added.

• Correct and Latest – To determine whether or not it’s up to date and proper.

• Constant – To establish if they’re associated to different components within the information set and coherent.

• Compliance with Requirements – to confirm whether or not they adjust to business requirements.

Knowledge High quality Metrics “Ought to Have’s”

• Definition -metrics ought to have effectively outlined goal of enterprise customers and high quality guidelines.

• Related -metrics ought to outline the way it improves efficiency and have a enterprise context.

• Measurable – scores needs to be quantifiable and measurable inside a specific vary.

• Controllable – metrics ought to outline a controllable facet of enterprise processes.

• Traceable – there needs to be a ‘time collection’ to hint and monitor the outcomes to be able to measure enhancements and supply insights.

• Knowledge forex – forex is outlined because the extent to which information must be up to date in the actual world context. Knowledge forex is figuring out the frequency at which information must be refreshed, or the ‘freshness quotient’.

Emphasize on Insightful DQ Scorecards

Get rid of Irrelevant ‘So What’ Metrics

Elaborate on Knowledge Metrics ‘Ought to Have’s’

To determine areas of enchancment and preserve high quality of information, effectively outlined Data Quality for Azure Data Lake dimensions and metrics needs to be developed. An necessary facet to bear in mind is that quantifying and measuring information high quality components with out certified relevance (what is named ‘so what’ metrics) is insignificant in growing an insightful DQ scorecard. Subsequently, via an organized and outlined method, applicable assessments and related enhancements could be created, to construct a powerful basis for companies, which is High quality Knowledge!


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