Data Quality Methodology
Datactics combines the experience of data analysts and next generation technology providing excellence in every data quality project.
Our methodology, which has been developed from our experiences in real life data quality situations, provides a consistent framework within which our techniques and technology exist. We harness and combine technology with proven data quality techniques, all of which can be executed by business and technical personnel alike.
The Datactics methodology contains 6 distinct elements:
- Analysis
- Unfortunately you don’t know what you don't know. So, the initial step of any comprehensive data quality management strategy has to be the discovery of exactly what your data contains and, as importantly, what it doesn’t.
- Re-Engineering
- Once you have determined your data content, the re-engineering phase allows you to correct errors; transform data to required standards; enhance data with additional information and if required, extract key value items from it. In short, ensure that your data is “fit for purpose”.
- Matching
- Having re-engineered data to an appropriate format and standard, data matching can be carried out at its most effective level. Matching allows the identification of equivalent entities or groups of entities.
- Integration
- Data quality cannot be viewed as an isolated process; therefore the ability to integrate a data quality management methodology into existing business processes is critical.
- Reporting
- After the previous phases have been completed, a large amount of knowledge and information about data has been collected. Being able to review, audit and share this information is vital if a genuine data quality improvement culture is to be created and iteratively enhanced throughout an organization.
- Management
- The orchestration and management of all the above processes within a single, easy to use environment provides great benefit. It facilitates increased productivity by streamlining workflow and also provides a fully transparent layer from which data quality professionals can both monitor and if necessary, execute data quality processes.
