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The Key Data Quality Metrics You Need For DQA (Data Quality Analysis).

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5 KEY DATA QUALITY METRICS http://intellspot.com COMPLETENESS ACCURACY UNIQUENESS TIMELINESS RELEVANCE 1. 2. 3. 4. 5. Completeness means you are sure that there are no missing records and that no records have missing data parts. All available data entry fields are completed and this is a must. Completeness indicates if there is enough information to come up with conclusions. Decide on your unique value proposition. Ask what will make you stand out. Choose a name that delivers your brand message, audience, and story. Create a logo that reflects your brand and form a design guideline for consistency. Establish your voice online and deliver consistent communication. WHAT IS DATA QUALITY? Data quality is a kind of measurement of the adequacy and usefulness of a given data sets from different perspectives. In the business world, data need to be high quality in order to be used as a basis for making business decisions. Do your data accurately represent the “real world” values? Accuracy applies to whether the data values stored for a person or object are the correct values. To be accurate, data values must be the right value and must be represented in an unambiguous shape. Ensure that there no data duplicates reported. Uniqueness is a dimension that means you are sure no thing will be recorded more than once. Of course, you need to define “the thing” first. Uniqueness minimizes the risk of examining outdated information. Timeliness is the degree to which data present the reality from a given point in time. This metric shows whether the information is available when it is needed. There are two core aspects of timeliness: - Frequency - Currency Is all the data relevant to your organization needs and expectation? This question is what the relevance metric is all about. Here are 2 important points of the Relevance dimension: - Do the data meet the fundamental needs for which they were collected? - Can the data be used for additional purposes (e.g., a market sizing process)?

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Data quality metrics infographic

  • 1. 5 KEY DATA QUALITY METRICS http://intellspot.com COMPLETENESS ACCURACY UNIQUENESS TIMELINESS RELEVANCE 1. 2. 3. 4. 5. Completeness means you are sure that there are no missing records and that no records have missing data parts. All available data entry fields are completed and this is a must. Completeness indicates if there is enough information to come up with conclusions. Decide on your unique value proposition. Ask what will make you stand out. Choose a name that delivers your brand message, audience, and story. Create a logo that reflects your brand and form a design guideline for consistency. Establish your voice online and deliver consistent communication. WHAT IS DATA QUALITY? Data quality is a kind of measurement of the adequacy and usefulness of a given data sets from different perspectives. In the business world, data need to be high quality in order to be used as a basis for making business decisions. Do your data accurately represent the “real world” values? Accuracy applies to whether the data values stored for a person or object are the correct values. To be accurate, data values must be the right value and must be represented in an unambiguous shape. Ensure that there no data duplicates reported. Uniqueness is a dimension that means you are sure no thing will be recorded more than once. Of course, you need to define “the thing” first. Uniqueness minimizes the risk of examining outdated information. Timeliness is the degree to which data present the reality from a given point in time. This metric shows whether the information is available when it is needed. There are two core aspects of timeliness: - Frequency - Currency Is all the data relevant to your organization needs and expectation? This question is what the relevance metric is all about. Here are 2 important points of the Relevance dimension: - Do the data meet the fundamental needs for which they were collected? - Can the data be used for additional purposes (e.g., a market sizing process)?