While downtime in the system occurs, most of the time organizations put their engineers to spot where the issue is. This takes lots of time and human resources.
Large organizations have several workloads where data has to be managed. Having different data platforms for different purposes creates chaos for the Data Stewards.
When there is too much data to manage, data quality is always at stake. Having no centralized documentation and data platform makes it difficult to automate.
Augmented Data Management gives data stewards control over the management of different data sets from across the organization.
Having access to master data helps the growth of the team to analyze the Data Patterns for Proof of concept.
Automate issue spotting if the system falls into some downtime.
Data Quality metrics can be improved with the passive approach of Data Transformation.
Data is centralized for access sharing with the teams across organizations.
Reduced human efforts and errors on similar issues if identified at other stages later in the product life cycle.
Engage: Start engaging the custom Tags and classification for different needs. This will help to locate the data quicker by the teams and needs.
Proof of Concept: Having the classification of Data enables better proof of concept for Analytics and Forecasting purposes.
Evaluate: When POC is finalized, then evaluation of the POC data can be done in the System.
Improve: It becomes easy to identify the improvement points on the POC data. More improvements bring Data Quality and Quick Decisions making.