Does your organization trust your data?
Too often, the answer to this question is ‘no.’ Worse is the condition where people inside the organization won’t speak up, but don’t trust the data and don’t use the data.
We work with our clients to correct this fundamental issue of untrusted data. Our tools, methods, and expert practitioners provide a prescribed, repeatable path to turn untrusted data into trusted, pristine, and business actionable information. We enable our Clients to move from the current state of data quality to the high performing organization that management and stakeholders expect.
We have partnered with many diverse organizations to deliver high value Data Quality initiatives. We have a comprehensive understanding of the investment of the time and the risk of taking on the responsibilities of this transition– and the corresponding risk of doing nothing about the data quality problem at all. We have developed a comprehensive Information Governance process that starts with strategy and continues through to executing a data quality program.
Our practice specializes in planning, executing, and systematizing the ability of an enterprise to share high-quality data across business practices, data repositories, and lines of business:
- Managing Data Quality. Recommending tools for Enterprise Data Quality management solution that provides a view into the quality of your data throughout its current and historical lifecycle, ensuring the data is capable of meeting its intended purpose.
- Data Cleansing. We automate the process of investigating, standardizing, matching, and life cycling data. The investigation task parses, classifies, and analyzes patterns in source data.
- Standardizing the Data. This process ensures that each data type has a correct and consistent content and format. Unfortunately, even after standardizing, data values in the records often are not identical. In one record, a person’s name might be “J. Smith,” in another, it might be “John Smith”. One challenge when matching records is to determine how likely it is that “J. Smith” is the same as “John Smith”.
- Data Lifecycle and Formatting. This process ensures that the best available data survives and is correctly prepared for the target destination.
- Data Enrichment. If necessary, the existing information is enriched with data from external sources.