Primarily, information quality ensures that the information is appropriate to the needs and expectations of the users. Human involvement is involved here, and no level of tools can provide guarantees for quality. Focusing solely on numerical variables, such as accuracy, without considering the context in which the data will be used, overlooks a critical dimension of the situation.
Through discussions with data professionals and managers, we’ve identified 6 key data quality dimensions that encompass both the technical factors and the human-oriented considerations necessary for creating truly high-quality data.
What Are Data Quality Dimensions?
Data quality dimensions are the quantifiable features of data that can be quantified, assessed, and improved separately. Together as a package, the results in every dimension give a general impression of data quality in a particular environment and indicate to what extent the data is qualified for its specific purpose.
These are normally measured as percentages against which usability is measurable. For example, if patient data is only 87% accurate, then the other 13% risks being erroneous in processes like billing, where accuracy is vital.
Data quality dimensions are the quantifiable features of data that can be quantified
What Are the 6 Dimensions of Data Quality?
The following are the 6 dimensions of data quality that you should be aware of to improve data quality effectively:
Accuracy
Under data quality dimensions, accuracy is one of the most basic dimensions of data quality, as it guarantees data reflects actual objects or occurrences in the real world that the data was attempting to represent. Reliable data can be trusted to generate sound facts and inform well-educated decisions.
For example, in a dataset of sales where there is one transaction per row, accuracy is such that every value in the “amount” column reflects the exact amount that was charged to the customer.
It must always be remembered, however, that absolute accuracy is never necessarily possible or even wanted. The standard of acceptable accuracy must be scaled to the particular purposes of analysis or decision-making, weighing precision against practicality.
To ensure your data truly reflects reality and supports informed decisions, explore our guide on the 16 Key Factors To Improve Data Accuracy.
Under data quality dimensions, accuracy is one of the most basic dimensions of data quality
Completeness
As regards data quality dimensions, completeness is the most significant because it guarantees all data elements required are available to avoid gaps in analysis and allow thorough, comprehensive insights. Completeness is not about collecting massive amounts of information; it’s possessing all the necessary data to answer a question or solve a puzzle correctly.
For example, in sales, completeness would refer to the fact that all transaction records have such fundamental data as product information, customer information, salesperson identification, and date/time of sale.
There is a misunderstanding that completeness involves collecting all available information. It actually has to be defined on the basis of the analysis or decision task requirements, with an emphasis on relevance, not complete collection.
Completeness is the most significant because it guarantees all data elements
Consistency
Amongst the data quality dimensions, consistency is an important data quality dimension, providing homogeneity between datasets and measurements to avoid contradictions or inconsistencies that would erode reliability as well as interpretability. This usually means keeping standardized currencies, naming conventions, data formats, and rules of encoding in different databases or sources of data.
For instance, the common inconsistency would be to call a field “user_id” in one data set and then revert to “userid” sometime later in another. Such naming or formatting inconsistencies lead to confusion, particularly when coming together to report or analyze sales information from multiple systems with growing data volume and team size.
One myth is that source data is always uniform. In reality, structural heterogeneity, timestamp types, and formats are the rule, and thus most of the effort at consistency arises at the data transformation stage.
Consistency is an important data quality dimension, providing homogeneity between datasets and measurements
Validity
Within data quality dimensions, validity is one of the 6 data quality dimensions that ensures values in data comply with given domain, standards, or requirements. ZIP codes are valid, for example, when they have adequate characters for the concerned area, and calendar months are valid when they coincide with recognized global month names. Using well-defined business rules offers a formal method for testing and ensuring data validity.
Incorrect information can negatively impact the completeness of a dataset as a whole. Organizations may establish standards for excluding or fixing incorrect data so that only data meeting validity requirements remains available for analysis and decision-making purposes.
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Validity is one of the 6 data quality dimensions that ensures values in data comply with the given domain
Uniqueness
In the data quality dimensions, uniqueness is critical in avoiding duplicate records and ensuring every piece of data for a unique, single entity or occurrence. With this single source of truth, organizations avoid ambiguity, remove redundant effort, and trust their data-even when the same value is copied in various places.
For instance, the imposition of uniqueness constraints on identifiers or primary keys in a sales database eliminates redundant sales records and yields reliable reporting.
Uniqueness has traditionally been thought to be confined to the imposition of primary keys. In practice, though, it is also employed in designing a data warehouse with distinctive tables and models, but without repeating efforts in the database system, without needing to gaze at, “Why in the world do we have DIM_ORGS_FINAL and DIM_ORGS_FINAL_FINAL?”
Uniqueness is critical in avoiding duplicate records and ensuring every piece of data
Integrity
Under the data quality dimensions, integrity falls under one of the fundamental dimensions of data quality that will ensure the relationship between attributes is accurate and consistent while data is being transported and processed across a number of systems. Having data integrity is all about the ability to trace, connect, and join all enterprise data reliably, irrespective of where and how it is processed and stored.
Integrity is especially important in maintaining consistency among data elements. For instance, a customer profile can contain a customer’s name and several addresses. If any of the addresses is violated in integrity at some point during the data life cycle, the related profile can be invalid or incomplete, eventually affecting the accuracy and reliability of the dataset as a whole.
Integrity falls under one of the fundamental dimensions of data quality that will ensure the relationship
Why These Data Quality Dimensions Matter?
In data governance, data quality dimensions are important to guarantee the reliability, compliance, and appropriateness of data for its purpose. Quality data saves money, avoids compliance issues, facilitates correct decision-making, and protects the reputation of an organization. Poor data causes expensive remediation, bad information, regulatory compliance issues, and loss of trust.
- Completeness: Guarantees that all the necessary data attributes are available, facilitating accurate analysis and compliance.
- Accuracy: Ensures information reflects actual world objects or occurrences, enabling sound decisions.
- Consistency: Ensures like meaning, form, and structure across systems to avoid integration mistakes.
- Validity: Ensures information adheres to specified forms, regulations, and constraints, minimizing processing faults.
- Uniqueness: Avoids duplicate records, and there is one truth source for all objects.
- Integrity: Safeguards inter-element relations between information across systems to provide traceability and proper links.
Data quality dimensions are important to guarantee the reliability
Mastery of data quality dimensions is crucial for all businesses that want to establish a strong data governance structure. By verifying that data is accurate, complete, consistent, valid, unique, and maintains integrity, businesses can make better decisions, minimize operating threats, and establish trust throughout all levels of the organization. High-quality data is not merely a technical necessity; it’s a strategic driver of sustainable growth.
At DIGI-TEXX, we realize the maximum importance of these elements of data quality to unleash the highest potential of your data. We at DIGI-TEXX deliver this ability to help organizations construct robust processes that make information purpose-fit, reliable, and poised to drive business success.
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