The past decade has seen an explosion of data and it is fundamentally transforming how businesses make decisions. Data is being used by business leaders to learn more about their organizations, and, in turn, they translate that knowledge into improved performance and business advantages. While data science, business intelligence, and statistics can be utilized by business leaders to make sound business decisions, a well implemented data quality solution is vital to first ensure that quality data is being used in these systems and processes.
It is very common to find data errors in most business environments. Bad data can be caused by a variety of factors, including:
- User entry errors
- Extraction errors
- Validation errors
- Transmission errors
- Storage errors
- Aggregation errors
All of these factors can negatively influence or completely obstruct a company’s ability to access high quality data. Untimely or inaccurate data can result in loss of revenue, stakeholder and customer dissatisfaction, as well as many other inefficiencies and drawbacks.
Automated and interactive systems often fail to capture and synchronize bad data, wasting time and energy to perform fixes in the process. A well implemented data quality solution will allow data to be more reliable and accessible and will resolve problems caused by incorrect data in business intelligence or data warehouse workloads. Implementing this type of solution will improve all of the main dimensions of data quality, including:
- Completeness — indicates if all the data required to meet current and future business information needs is available in the data resource
- Precision or degree of disaggregation — indicates the depth of knowledge available in the data. For example: An aggregated view has lower precision than the source table.
- Accuracy — indicates if the data reflects reality in an unambiguous and consistent way
- Uniqueness — indicates if the same piece of information is being stored multiple times
- Validity — indicates if the presented value is acceptable given a set of constraints or is in the collection of possible accurate values.
- Consistency — indicates if the represented data does not fluctuate across all of its instances
- Timeliness — multiple timestamps may be associated to the data and used to enforce data quality
- Reasonableness — indicates if the presented value is within a reasonable range based on historic data (aka ‘business as usual’) or satisfies aggregated business rules
High quality data is vital for businesses efficiency. When data fits the intended use, it enables companies to perform at optimal efficiency and provides better products and services to consumers. A well implemented data quality solution allows data to be more reliable and accessible and thus can greatly impact a company’s bottom line.