Knowledge Hub

Good data in the data world possesses several essential qualities that contribute to its reliability and effectiveness. Firstly, it is accurate, ensuring that the information it represents aligns with the real-world context it intends to portray, free from errors or inconsistencies. Completeness is another hallmark of good data, implying that it includes all necessary details without any missing or incomplete records. Consistency is maintained throughout, following standardized formats and structures, facilitating uniformity and coherence. Relevance ensures that the data is directly applicable to its intended purpose, meeting the specific goals of data analysis or application. Timeliness is a key aspect, as good data is up to date, reflecting the most recent and relevant information. Reliability is associated with trustworthiness, ensuring the data is unbiased and collected through reliable methods. Validity is maintained by adhering to predefined rules and standards, and accessibility ensures that authorized users can easily retrieve and utilize the data.

‘Data Quality is when we compare data against Business Rules.’

Organizations aspire to be data-driven, but the reality is that without quality, data becomes a liability rather than an asset. Decisions made on inaccurate, incomplete, or untimely data are not just ineffective, they can be damaging. Poor data quality has been repeatedly linked to regulatory fines, reputational loss, failed AI models, and massive operational costs. Simply put: bad data costs more than no data at all.

The Six Dimensions of Data Quality

• Accuracy – Data should reflect the real-world event or entity it represents. Accuracy depends on traceability through data lineage; without lineage, accuracy is always questionable.
• Completeness – Data must contain everything required to serve the business purpose. Completeness is not about “all data,” but about ensuring critical attributes exist to fulfill KPIs or regulatory needs.
• Consistency – Data values must not conflict across systems. If a customer’s birthdate appears in three formats across platforms, trust collapses.
• Validity – Data must adhere to business rules, constraints, and formats. For example, a date in DDMMYYYY or an amount rounded to four decimals.
• Uniqueness – Records must be free of duplication. Duplicate customer IDs or accounts lead to wasted marketing spend, skewed analytics, and customer frustration.
• Timeliness – Data must be available when promised. Missing overnight batches or delayed real-time feeds translate into broken business processes and lost opportunities.

Data Validation is about verifying or validating the correctness and quality of data against the source. In other words, reconciliation of imported and processed data with its source data.

Data Validation can be done before pulling data and as well after data is processed but must be done before passing data to end users.

Sometime, data changes its shape after transformation from source to target, in this situation Data Validation is conducted against business rules implemented during transformation.

Types of Data Validation

• Constraints Validation checks the accuracy of all types of constraints like primary key, foreign keys, unique, not null, values check and query performance constraint.
• Structured Validation checks the structures of tables and columns.
• Data Range Validation checks the allowable data range to be allowed for processing e.g., if only latest data is allowed to be process the only latest data should get processed.
• Code Validation checks the quality of the syntax. There are ways to write quality and optimized code.
• Data Type Validation checks the type of data to be process e.g., if a data type of a column is Date then only data format with date values should be processed.