As more and more enterprises realize the unique strategic importance of data quality, a new class of data quality analysis tools has emerged. Like all processes, to achieve the highest level of data quality excellence, the order of operations matters. Properly putting things first means that the first thing that must be done, even before planning, is to analyze. The truism that you can’t fix what you’d don’t measure is especially true as it relates to data quality
Data quality analysis tools are available for a wide range of measurements and fall into the following categories:
This type of data analysis tool is used to assess metrics like the duplicate rate (such as RingLead’s dedupe tool), data completion, compliance to the enterprises data standards, as well as other metadata related to an enterprise’s contact and company data. This data quality analysis is helpful to provide insight about data quality and will assist in the recognition of data quality issues.
These data analysis tools are helpful in analyzing and controlling the ongoing conformance of data to the agreed business rules defining the data quality for the enterprise.
Data Quality Checklist
It is useful to create a data quality checklist to be used for data improvement.
Data analysis tools help enrich the value of internal data by appending related attributes.
These tools help in data identification, the merging and linking of related entries across the different sets of data.
Data Parsing and Standardization
Data analysis tools help in formatting the values into layouts which are consistent, based on local and industry standards. They also help in decomposition of text fields into components.
These tools help in cleansing the data by modifications of data values to meet restrictions, business rules and the other requirements.
Growing Importance Of Data Quality Analysis Tools
Data quality is of vital importance for any organization striving for peak revenue performance. With the best quality data, the decision making capabilities of every individual in the organization is enhanced, allowing for business objectives to be reached in a more efficient manner. Employing best practices for data quality analysis is essential since you can’t fix what you don’t measure.
Many tools are provided by vendors and consumed by users for internal deployment. Now, however, hosted tools are also growing in popularity and continuing to emerge. The tools are regularly implemented by many organizations in support of other data quality initiatives and data solutions, which is bringing about a continuous improvement in organizational data quality.
Contact data quality is a foundational level enabler across an enterprise’s other critical applications like ERP, BI, since they are all inter-connected.
With the increasing focus on data quality initiatives in the enterprise the key role that the right data quality analysis tools has can not be overstated.