User Engagement Dashboard

High Level Design

Overview

As a BI driven organization that uses the Business Intelligence to monitor and investigate various aspects of your company – sales, marketing, operations, and others you might find it useful to measure the BI implementation success and usage within your organization. Understanding the system usage can help in managing licenses, Improving current data visualization and knowing which data is the most interesting for your users.

 

Goals

• Increase users’ engagement to the BI solution delivered to them.

Objectives

• Identify popular dashboards to learn on what triggers its users to use it and why it’s more useful\appealing than others.
• Identify dashboard which are not in use to be able to analyze what are the reason for the poor engagement for further implementations.
• Identify dashboards owners who gain better engagement so they could share their experience with others.
• Identify more\less engaged users so we can find focal points for getting feedbacks and to re-arrange licenses when needed.

 KPIs Architecture

Objectives	KPIs	Measures	Data Source
Identify popular dashboards to learn on what triggers its users to use it and why it’s more useful\appealing than others	Active dashboards	# of active dashboards in the last 30 days: Count(active dashboards within the last 30 days) # of active dashboards: Count(active dashboards)	TRACE; DASHBOARDS
		Top 5 active dashboards in the last 30 days: Count(actions per dashboard within the last 30 days)  filtered by top 5 dashboards per their actions.
	Action Trends	# of active dashboards per day: Count(active dashboards) per day
Identify more\less engaged users so we can find focal points for getting feedbacks and to re-arrange licenses when needed	Active Users	# of active users in the last 30 days: Count(active users within the last 30 days) # of active users: Count(active users)	TRACE; USER_ACTIONS
		Top 5  Active Users per Role Last 30 Days: Count(actions per user per role within the last 30 days) filtered by top 5 users per their actions.
	Action Trends	# of active users per day: Count(active users) per day
	Top 5 Non Active Users	Top 5 Non Active Users: Top 5 users with longest duration (in days) since their last action
Identify dashboard which are not in use to be able to analyze what are the reason for the poor engagement for further implementations.	Dashboards not in use	Dashboards not in use: Dashboard with less than 10 actions during the selected period	TRACE; DASHBOARDS
Identify dashboards owners who gain better engagement so they could share their experience with others.	Dashboard per Owner	Avg: count(dashboards)/count(dashboard owners) Max: Max(group by owner(count(dashboards))	TRACE; DASHBOARD_OWNERS
		# of Dashboards per Owner: count(dashboards) per owner

 Data Requirements

#	Source	Table Name	Table Details (Type, #Rows, Key field/s)
1	MongoDB	DASHBOARDS	SCD table, contains all dashboards data
2		ELASTICUBE	SCD table, contains all EC data
3		TRACE	Fact Table, contains all transaction and actions within all dashboards by all users
4		USER_ACTIONS	SCD table, contains all users data
5		DASHBOARD_OWNERS	SCD table, contains all dashboards data
6		TRACE_WIDGET_DASH_USERS	Key table to map between all tables’ keys

Entities Relationship Diagram