Tools should be selected and tool architectures should be set in the.planning phase of the enterprise Data Quality program. Tools provide a.partial rule set starter kit but organizations need to create and input their.own context specific rules and actions into any tool.
- Modeling and ETL Tools: The tools used to model data and create ETL processes have a direct impact on the quality of data. If used with the data in mind, these tools can enable higher quality data. If they are used without knowledge of the data, they can have detrimental effects.
- Data Querying Tools: Data profiling is only the first step in data analysis. It helps identify potential issues. Data Quality team members also need to query data more deeply to answer questions raised by profiling results and find patterns that provide insight into root causes of data issues.
- Data Profiling Tools: Data profiling tools produce high-level statistics that enable analysts to identify patterns in data and perform initial assessment of quality characteristics. Some tools can be used to perform ongoing monitoring of data. Profiling tools are particularly important for data discovery efforts because they enable assessment of large data sets. Profiling tools augmented with data visualization capabilities will aid in the process of discovery.
- Data Quality Rule Templates: Rule templates allow analyst to capture expectations for data. Templates also help bridge the communications gap between business and technical teams. Consistent formulation of rules makes it easier to translate business needs into code, whether that code is embedded in a rules engine, the data analyzer component of a data-profiling tool, or a data integration tool. A template can have several sections, one for each type of business rule to implement.
- Metadata Repositories: Defining data quality requires Metadata and definitions of high quality data are a valuable kind of Metadata. DQ teams should work closely with teams that manage Metadata to ensure that data quality requirements, rules, measurement results, and documentation of issues are made available to data consumers.
