To succeed, businesses primarily driven by data must understand data issues as quickly as they can before wasting valuable resources on a ready-made solution. Your company will require outsourced IT development for big data from providers like Cloud Xtension to be able to control it.
Keep in mind that operating with customer data or product using different sources can be disastrous if you don’t consider data quality. It might seem easier to just rely purely on data governance solution to resolve problems encountered by both data governance and data quality. However, this tactic will have an effect on the completeness and timeliness of Big Data concerning business deliverables.
Know the differences and similarities between these two to understand the issues better. This is the only way to guarantee that you don’t waste resources and time in coming up with solutions.
Data Quality
To better understand this, consider data quality when you’re grabbing tomatoes to consume. Data quality designates a system that will keep something beneficial. For instance, those tomatoes you grabbed from the fridge could lead you to consume sweet-smelling, shiny and firm tomatoes compared to moldy, putrid and soft-spotted ones.
Data Quality is the dependence on consistency, completeness and accuracy of data to be beneficial throughout the enterprise. Issues in this matter can revolve around enrichment, monitoring, profiling, matching, generalized “cleansing”, and parsing and standardization.
Data Governance
On the other hand, data governance defines practices and procedures in place, which will control data assets management procedures and practices. This involves authority and control, such as preparation, observation and implementation.
Think about data governance as when you were a child. Your Mom asked your older brother to buy vegetables to cook for dinner. She will more likely recommend a trustworthy farm stand or store where he can purchase it. If your older brother chooses to take the easy way out and just pick the ingredients from your neighbor’s garden without asking her, then she might disapprove. In this case, you’d have to face problems in terms of data compliance, selection, consistency, availability, and consistent analytics, reporting and metrics.
Businesses must learn to use their resources wisely by knowing the differences and overlapping of data quality compared to data governance before trying to resolve issues.
