Analytics over erroreonus bad data quality generates maintenance and repair costs. Beyond these economic aspects, poor data quality can also affect customer satisfaction, reputation or even strategic decisions which may result in monetary loss to the organization. It is therefore very important to be able to measure the quality of data residing in their data lake.Usually Data Scientist spends 60% of their efforts in cleaning the data so that they can create effective data science models over it.
Data quality is entire a process, it’s not something you do just once. Jumbune helps at every stage, making it easy to profile and identify problems, manage the entire data quality life cycle to maintain a high level of data quality. Jumbune Data Analysis framework provides users much needed visibility into the quality and profiles of the data present on the Hadoop distributed file system. Users can assess the quality of the data within the dataset over a period of time for consistancy and logic, also profile them to quickly categorise them based on the present data. The user need not write any code and entire functionality is carried out without any data movement.With Jumbune Data Quality, you can manage the entire data quality life cycle: cleansing, profiling, standardizing, matching and monitoring.It is a fast, easy way to improve your data lifespan.
Jumbune unblemished module Analyze Data conform to Data Quality Validation Process.Analyze Data module have very comprehensive feature like Data Validation, Data Profiling, Data Quality TimeLine, Data Cleansing.