Data quality resembles human health. Accurately testing how any one element of our diet and exercise may affect our health is difficult. In the same way, accurately testing how any one element of our data may affect our business is fiendishly difficult too. Moreover, business owners and stakeholders are in the market for a data strategy and have their own business challenges, problems, and opportunities. They ultimately want to make their business successful. By thinking big and starting small, eSimplicity works with our customers on Data Governance, Data Profiling, Data Matching, and Data Quality Reporting to improve the data quality and business agility.
A business glossary is a valuable outcome from data governance used in data quality management. The business glossary is a primer to establish the metadata used to achieve common data definitions within an organization and eventually in the business ecosystem where the organization operates. eSimplicity works with data owners, data stewards, and data custodians or similar to balance what makes sense in our customer environment.
For each data quality issue raised, eSimplicity starts with a root cause analysis. Data quality problems only go away if the solution addresses the root cause. When finding solutions, we strive to implement processes and technology that prevents the issues from occurring as close to the data onboarding point as possible rather than relying on downstream data cleansing.
Data profiling is a method often supported by dedicated technology and used to understand the data assets involved in data quality management. These data assets most often have been populated over the years by different people operating under varying business rules and gathered for bespoke business objectives.
In data profiling, the frequency and distribution of data values is counted on relevant structural levels. Data profiling can also be used to discover the keys that relate data entities across different databases and to the degree not already done within single databases.
