6 Dimensions Of Data Quality

6 Dimensions Of Data Quality

Have you ever questioned what it takes to be a truly data-driven company? To make important decisions, you must have faith in the accuracy and reliability of your data.

Many firms discover that the data they collect is not adequately reliable. 74% think they need to improve their data management to thrive, according to Experian’s 2021 Global data management research survey. That means that more than half of corporate leaders are unable to make confident decisions based on the data they collect.

Let’s look at why data quality is crucial to a company and how it can benefit your end result.

What is the significance of data quality?

Data quality is crucial because it allows you to make informed decisions that benefit your customers. A positive customer experience leads to happy customers, brand loyalty, and improved revenue. With low-quality data, you’re just guessing what people want. Worse, you might be doing things your clients hate. Collecting credible data and updating existing records helps you get a better picture of your clientele. It also provides verified email, postal, and phone numbers. This data helps you sell more successfully and efficiently.

Keeping data quality might help you stay ahead of the competition. Reliable data keeps your firm agile. You’ll be able to spot new opportunities and conquer challenges before your competitors.

To gain the greatest outcomes, you must regularly manage data quality. Data quality is crucial as data is used more extensively for more complex use cases. 

Personalization, accurate marketing attribution, predictive analytics, machine learning, and AI applications all rely on high-quality data. Working with low-quality data takes a long time and requires a lot of resources. Poor data quality, according to Gartner, can cost an extra $15 million per year on average. It isn’t only about money loss, though. 

Poor data quality has a number of consequences for your company including:

  • Bad data leads to incomplete or erroneous insights and erodes faith in the data team’s work inside the team as well as the enterprise.
  • Companies’ data analytics efforts don’t pay off.
  • To confidently use business data in operational and analytical applications, you must understand data quality. Only credible data can allow accurate analysis and thus reliable business decisions.
  • The rule of ten states that processing faulty data costs 10 times more than processing the right data.
  • Unreliable analyses: Managing the bottom line is difficult when reporting and analysis are distrusted.
  • Poor governance and noncompliance risks: Compliance is no longer optional; it is essential for corporate survival.
  • Brand depreciation: Businesses whose judgments and processes are regularly incorrect lose a lot of brand value.
  • Poor data impacts a company’s growth and innovation strategy. The immediate concern is how to increase data quality.

What criteria are used to assess data quality?

Data quality is easy to detect but hard to measure. Numerous data attributes can be evaluated to gain context and assessment for data quality. To be effective, customer data must be unique, accurate, and consistent across all engagement channels. Data quality dimensions capture context-specific features.

What is the definition of a data quality dimension?

Data quality dimensions are data measurement qualities that you may examine, interpret, and improve on an individual basis. Data quality in your given context is represented by the aggregated ratings of many variables, which show the data’s feasibility for usage.

On average only 3% of DQ scores are graded acceptable (with a score of >97%), indicating that high-quality data is the exception.

Data quality dimension scores are usually expressed in %ages, which serve as a benchmark for the intended purpose. A 52 % comprehensive customer data collection, for example, indicates a lesser level of confidence that the planned campaign will reach the proper target segment. To increase data trust, you can specify the acceptable amounts of scores.

What are data quality dimensions?

The following 6 major dimensions are commonly used to gauge data quality on several dimensions with equal or variable weights.

Accuracy

The degree to which information accurately reflects an event or thing represented is referred to as “accuracy.” Data accuracy refers to how closely data matches a real-world scenario and can be verified and ensures real-world entities can participate as anticipated. A correct employee phone number ensures that the person is always reachable. Incorrect birth dates, on the other hand, can result in loss of benefits. Verification of data accuracy requires legitimate references like birth certificates or the actual entity. Testing can sometimes ensure data accuracy. You can check customer bank details against a bank certificate or perform a transaction.  Accurate data can support factual reporting and reliable business outcomes. Highly regulated businesses like healthcare and finance require accuracy.

Completeness

When data meets the requirements for comprehensiveness, it is deemed “complete.”  For customers, it displays the bare minimum required for effective interaction. Data can be considered complete even if a customer’s address lacks an optional landmark component. Completeness can help customers compare and pick products and services. A product description is incomplete without a delivery estimate. Customers can use historical performance data to analyze financial products’ suitability. Completeness assesses if the data is sufficient to make valid judgments.

Consistency

The same information may be maintained in multiple locations at many businesses. It’s termed “consistent” if the information matches. For instance, if your human resources information systems indicate that an employee no longer works there, but your payroll system indicates that he is still receiving a paycheck, that is inconsistency. Consistency of data enables analytics to appropriately gather and utilize data. Testing for consistency across numerous data sets is tough. These formatting mismatches can be swiftly remedied if one enterprise system utilizes a customer phone number with international code separate from another. If the underlying data is conflicting, resolving may necessitate a second source. Data consistency is generally linked to data correctness, therefore any data set that has both is likely to be high-quality.

Review your data sets to determine if they’re the same in every instance to resolve inconsistency issues. Is there any evidence that the information contradicts itself?

Timeliness

Is your data readily available when you need it? “Timeliness” is one of the data quality dimensions. Let’s say you need financial data every quarter; if the data is available when you need it, it’s timely.

The timeliness dimension of data quality is a user expectation. It doesn’t satisfy that dimension if your information isn’t available when you need it.

Validity

Validity is a data quality attribute that refers to information that does not meet business standards or conforms to a specified format. Example: ZIP codes are legitimate if they contain the appropriate characters. Months are legitimate in a calendar if they match the global names. Using business rules to validate data is a methodical strategy.

To achieve this data quality criterion, make sure that all of your data adhere to a certain format or set of business standards.

Uniqueness

The term “unique” refers to information that appears just once in a database. Data duplication is a common occurrence, as we all know. It’s possible that “George A. Robertson” and “George A. Robertson” are the same people. This data quality dimension necessitates a thorough examination of your data to guarantee that none of it is duplicated.

Uniqueness is crucial to avoid duplication and overlap. Data uniqueness is assessed across all records in a data set. With low duplication and overlap, high uniqueness builds trust in data and analysis.

Finding overlaps can help keep records unique, while data cleansing and deduplication can remove duplicates. Unique client profiles help offensive and defensive consumer engagement initiatives. This increases data governance and compliance.

Conclusion

The fundamental goal of identifying essential data quality dimensions is to provide universal metrics for measuring data quality in various operational or analytical contexts. 

  • Define data quality rules and expectations
  • Determine minimum thresholds for acceptability
  • Assess acceptability thresholds.

In other words, the claims that correlate to these thresholds can be utilized to monitor how well-measured quality levels fulfill agreed-upon business objectives. Consequently, metrics that match these conformance measures help identify core problems that hinder quality levels from achieving expectations.

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