Data quality can be ensured at peak or near-peak levels by engaging effective data management tools to facilitate and provide a sound framework to implement data quality measurement, monitoring and subsequent improvements.
Fundamentally, data quality requires a hybrid discipline that combines data cleansing and defect prevention into your enterprise-wide best practice. Specific topics include domain choice, codes and location reference data sets, repair, normalization, identity resolution, and sampling versus whole-set processing. The plan includes where to collect data, how to collect it, when to collect it and who will do the collecting.
Include data providers and data processors in decisions to establish what is feasible. Accordingly, the quality requirements of data typically depend on the intended usage of the data. The primary meaning of data quality is data suitable for a particular purpose (fitness for use, conformance to requirements, a relative term depending on the customers needs).
The first area where data lineage has its impact is the existence of the business itself. But in the context of machine learning and AI, data quality is held to even higher standards. The data warehouse is the core of the BI system which is built for data analysis and reporting. When data is being collected on a regular basis to monitor a system or process, the frequency and size of the sample should be reviewed periodically to ensure that it is still appropriate.
In essence, it takes raw data and subjects it to a range of tools that use algorithms and business rules, coupled with expert judgment, to analyze, validate, and correct the data as appropriate. Traditional performance measurement, focusing on external accounting data, is obsolete. Develop integrated governance and management processes that promote standards-based services that are technical enablers for new and innovative methods for using data to accelerate the pace and quality of analytic insight.
A data warehouse is a central repository of information that can be analyzed to make better informed decisions. Data analysis may be required to better understand data content, context, and quality. As a result, users can work with current data that is of better quality and thus more usable. The quality management solution selected must be one that closely aligns to the unique business objectives of your enterprise.
Sustainable data governance requires a solid foundation of quality data to measure and monitor critical data elements. The right mix of planning, monitoring, and controlling can make the difference in completing a project on time, on budget, and with high quality results. Accessing and extracting data and information become easier if standard tools are used in organizations.
The issue with these tasks is that information comes in so quick organizations think that its hard to play out the majority of the data preparation activities to guarantee ideal data quality. Data quality is a key component of data management, ensuring decisions are based on fit-for-purpose data. Extract, normalize and standardize your data with confidence across multiple inputs and formats.
Want to check how your Data Quality Processes are performing? You don’t know what you don’t know. Find out with our Data Quality Self Assessment Toolkit: