Fertile data & it's quality
All technologies that drive a business forward including the use of AI & machine learning that automates decision making works clearly only with good data. The data quality gets considered a top priority due to this reason. All data-quality improvements get realized by the application of various principles routinely used in data-development. Proper data management & its analysis for deriving a significant value from it, is the most sought out thing while deriving its quality!
Invest time for quality data
Investing time & effort for data-cultivation helps with generating quality data. One example or a case study in this regard is: studying an online supermarket & why customers behave in certain ways at times? Edge cases keep coming up throughout the analysis where customers didn’t follow an expected path. Understanding these cases becomes vital in order to fully know about the data-quality! For the proper investigation, both user clicks as well as logs for back-end processes got thoroughly examined. Once these minor data issues got fixed or taken care of, a pretty good picture emerged that answered the quest successfully thus improving data quality while showing its worth during events such as these researches! Hence, investing time in properly analyzing all kinds of data helps with generating quality data that everyone is looking for!
Data Cultivation
Data cultivation is most effective when it’s close to the source. Data quality is an integral element of everything one does. All kinds of data need to be cultivated, reviewed, analyzed and fixed about a source for proper usage of the same. For instance, aggregated data from a business enterprise gets used as an input into transactional systems as a part of fixing any quality issues.
Data quality testing differs from a software code evaluation
A bunch of tests gets used for determining any code issues as part of its evaluation & data quality testing does not go about in the same way! A published schema of events and an idea of its streamlined flow helps keep a check on quality data. This process gets done through a Schema Evolution Testing and Publishing that aids with the data deployment pipeline; ensures that all changes are forward-and-backward compatible.
Production and Fixing Data Quality should go hand-in-hand
An entire production process does not go into a stand-still in-order to fix various data quality issues. An approach similar to Software Development Practices for QA in production is employed, which includes monitoring & keen observations about improving the data quality. Thresholds get defined for normal expectations and actions taken when it gets crossed.
Conclusion
Attaining fertile data and ensuring its quality through time-investing, and other processes help businesses to grow and evolve well.
Comments