Discuss the characteristics of good tool wrt validity reliability and usability

By now, you’ve heard how valuable data can be, how it can drive your company forward, how you can use it to make better decisions. There’s a caveat there, of course. Information is only valuable if it is of high quality.  How can you assess your data quality? Data quality meets six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Read on to learn the definitions of these data quality dimensions. 

• Accuracy
• Completeness
• Consistency
• Timeliness
• Validity
• Uniqueness

Six data quality dimensions to assess

Accuracy

The term “accuracy” refers to the degree to which information accurately reflects an event or object described. For example, if a customer’s age is 32, but the system says she’s 34, that information is inaccurate. 

What steps can you take to improve your accuracy? Ask yourself whether the information represents the reality of the situation. Is there incorrect data (that needs to be fixed)?

Completeness

Data is considered “complete” when it fulfills expectations of comprehensiveness. Let’s say that you ask the customer to supply his or her name. You might make a customer’s middle name optional, but as long as you have the first and last name, the data is complete.

There are things you can do to improve this data quality dimension. You’ll want to assess whether all of the requisite information is available, and whether there are any missing elements. 

Consistency

At many companies, the same information may be stored in more than one place. If that information matches, it’s considered “consistent.” For example, if your human resources information systems say an employee doesn’t work there anymore, yet your payroll says he’s still receiving a check, that’s inconsistent.

To resolve issues with inconsistency, review your data sets to see if they’re the same in every instance. Are there any instances in which the information conflicts with itself?

Read our eBook

4 Ways to Measure Data Quality

See what data quality assessment looks like in practice. Review four key metrics organizations can use to measure data quality

Timeliness

Is your information available right when it’s needed? That data quality dimension is called “timeliness.” Let’s say that you need financial information every quarter; if the data is ready when it’s supposed to be, it’s timely.

The data quality dimension of timeliness is a user expectation. If your information isn’t ready exactly when you need it, it doesn’t fulfill that dimension.

Validity

Validity is a data quality dimension that refers to information that doesn’t conform to a specific format or doesn’t follow business rules. A popular example is birthdays – many systems ask you to enter your birthday in a specific format, and if you don’t, it’s invalid. 

To meet this data quality dimension, you must check if all of your information follows a specific format or business rules. 

Uniqueness

“Unique” information means that there’s only one instance of it appearing in a database. As we know, data duplication is a frequent occurrence. “Daniel A. Robertson” and “Dan A. Robertson” may well be the same person. 

Meeting this data quality dimension involves reviewing your information to ensure that none of it is duplicated. 

How does your data measure up?

Are you fulfilling all possible data quality dimensions? Download a free scorecard to assess your own data quality initiatives. Data quality solutions can help improve your score and ensure your data is accurate, consistent and complete for confident business decisions. 

To learn more, read our eBook: 4 Ways to Measure Data Quality

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Usability, Reliability, & Validity
and their types

Usability, Reliability, & Validity
and their types

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4. 4. Reliability refers to how dependably or consistently a test measures a characteristic. If a person takes the test again, will he or she get a similar test score, or a much different score? A test that yields similar scores for a person who repeats the test is said to measure a characteristic reliably. Reliability tells you how consistently a method measures something. When you apply the same method to the same sample under the same conditions, you should get the same results. If not, the method of measurement may be unreliable. Reliability Hina Jalal (Ph.D.) @AksEAina
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6. 6. Validity tells you how accurately a method measures something. If a method measures what it claims to measure, and the results closely correspond to real-world values, then it can be considered valid. There are four main types of validity: •Construct validity: Does the test measure the concept that it’s intended to measure? •Content validity: Is the test fully representative of what it aims to measure? •Face validity: Does the content of the test appear to be suitable to its aims? •Criterion validity: Do the results correspond to a different test of the same thing? Validity Hina Jalal (Ph.D.) @AksEAina
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