With over 2 decades of vast experience in IT consulting working with different IT tools we’ve honed our expertise in understanding and importance of features in IT tools that empower the users.
Our experienced Salesforce team has identified one such standard feature in Salesforce Data Cloud i.e., Data Explorer which has few limitations, and developed a Custom Lightning Component “Data Pathfinder” which will overcome these limitations and empower the users experience while using it.
Problem
Solution Overview
Solution Benefits
Data Pathfinder - A Custom Lightning Web Component using Salesforce Platform tools.
Data Pathfinder is a custom LWC component which when utilised helps the users to overcome few gaps of the Standard Data Explorer feature of Salesforce Data Cloud.
Problem
Salesforce Data Cloud has Data Explorer which acts as a great entry for users to explore the data ingested from different sources. Although a great feature to have the standard data explorer has few limitations:
Record Limit: Only 100 records are accessible. If you need to analyse the entire dataset, consider using other tools or exporting the data.
Performance: Complex queries or large datasets may impact performance. Users should optimize their queries and avoid unnecessary operations.
No Pagination: There is no built-in pagination for navigating beyond the initial 100 records. If you need to explore more data, consider using other Salesforce features or APIs.
Solution Overview
To overcome these limitations, the custom Data Pathfinder has been developed.
To fetch and display the first 500 records by default and with capability to fetch and display entire data with user action if needed.
Perform excel like basic aggregations.
Pick and choose 8 columns of the selected object.
Display the top 10 most repeated value.
Solution Benefits
Analyse entire ingested data set(s) effectively and get insights, identify patterns and make informed decisions.
Pick and choose specific column heads to display the data based on the business context or use case and clearly analyse the data.
Identify the top 10 recurring or repeated values based on the business context for e.g., what are the top 10 reasons because of which cases are being created by the customers.
Aggregate numerical values in a data set, for e.g., aggregate the amount values of opportunities or successful orders of a particular account.
