{"id":514536,"date":"2022-04-06T14:06:10","date_gmt":"2022-04-06T12:06:10","guid":{"rendered":"https:\/\/www.devoteam.com\/expert-view\/bad-data-quality-is-a-business-killer\/"},"modified":"2022-04-06T14:06:10","modified_gmt":"2022-04-06T12:06:10","slug":"bad-data-quality-is-a-business-killer","status":"publish","type":"expert-view","link":"https:\/\/devoteam.info\/be\/expert-view\/bad-data-quality-is-a-business-killer\/","title":{"rendered":"Bad Data Quality is a Business Killer!"},"content":{"rendered":"\n

How Garbage-In > Garbage-Out practice contributes to bad decision making?<\/strong><\/p>\n\n\n\n

We all heard about garbage in, garbage out. This is especially true if we consider data that we use for any elegant, well tested model or software. If we only focus on the quality of code and libraries when developing a solution and ignore the quality of the data used, then it should be no surprise that the output of the models \/ software will never be what we expect.<\/p>\n\n\n\n

What is Data Quality?<\/strong><\/p>\n\n\n\n

Today dependency on data for many activities and processes is increasing rapidly. Considering also the hyper automation trends, the quality of data itself is growing in importance. According to the Global Data Management Community (DAMA), Data Quality consists in \u201cplanning, implementation and control of the activities that apply quality management techniques to data, in order to assure it is fit for consumption and meets the needs of data consumers.\u201d Data quality management is a core component of the overall data management process.<\/p>\n\n\n\n

Why Data Quality? <\/strong><\/p>\n\n\n\n

Organizations are running many Business Intelligence solutions for analytical and decision-making purposes. Reports generated are reliable only when the data quality is good. Data Quality is imperative to the success of any Business Intelligence solution. In today\u2019s era, where the data is being ingested from multiple sources, the quality of the data might differ between sources. The basics to a successful business intelligence capability implementation is ensuring that only the highest quality data makes it to the repository destination used for reporting and dashboarding. <\/p>\n\n\n\n

Why a Data Quality Framework?<\/strong><\/p>\n\n\n\n

A data quality validation solution must be in place, that will constantly measure the quality of data and act upon, applying automated correction preferably or at minimum report the quality issues found. Data management teams should use these inputs not only to fix the specific quality issue found, but also to design & implement processes to automate further the repeated occurrence of the similar data quality issue. Welcome to a Data Quality Framework<\/strong>.<\/p>\n\n\n\n

Key must-have characteristics of a Data Quality Framework<\/strong>:<\/p>\n\n\n\n