Automated dashboard sharing with executive team using cloud-native scheduling

I wanted to share how we solved a persistent problem with getting timely sales dashboards to our executive team. Previously, our sales ops team was manually exporting dashboard data every Monday morning and emailing PDF reports to executives, which was time-consuming and error-prone.

We implemented an automated dashboard sharing solution using Zendesk Sell’s cloud-native scheduling features combined with role-based access control. The system now automatically generates and distributes customized dashboards to different executive stakeholders based on their roles and data access permissions, and we’ve leveraged the cloud analytics sharing capabilities to provide real-time access when needed.

The implementation took about two weeks and has saved our team approximately 6 hours per week while significantly improving executive visibility into sales performance. I’ll detail the approach we used and lessons learned.

Happy to provide full implementation details since this has worked so well for us. Here’s the comprehensive approach we used:

Automated Dashboard Scheduling: We leveraged Zendesk Sell’s native Dashboard Scheduler feature available in the cloud deployment. Configuration steps: Navigate to Analytics > Dashboard Settings > Scheduling, create schedule profiles for different distribution cadences (daily at 7 AM for VP Sales, weekly Monday 8 AM for C-suite, monthly first business day for Board reports). Each schedule specifies recipient list, delivery format (PDF or interactive link), and optional personalized message.

The key was creating dashboard templates with parameterized date ranges and filters. Instead of hardcoding “last 30 days”, we used relative date expressions like “current_month” and “previous_quarter” so dashboards automatically adjust. This eliminated the need to manually update date parameters each reporting period.

For delivery reliability, we configured the scheduler to retry failed deliveries up to 3 times with 30-minute intervals. We also set up email notifications to our ops team if a scheduled delivery fails all retry attempts, ensuring we catch issues before executives notice.

Role-Based Access Control: This was the most complex part of the implementation. We created a matrix mapping executive roles to data visibility: CEO sees all territories, Regional VPs see only their regions, Sales Directors see only their teams. In Zendesk Sell, we implemented this through custom user roles and data access policies.

We built dynamic dashboards using user context variables rather than creating separate dashboards per role. The dashboard filters automatically adjust based on the logged-in user’s role and assigned territories. For example, the Revenue Pipeline dashboard uses a filter like “territory IN user.assigned_territories” which dynamically restricts data to what that user should access.

For scheduled email delivery, we used the “Personalized Delivery” option which generates the dashboard using each recipient’s access context. This means the same scheduled job sends different data to different executives based on their roles, eliminating the need for multiple schedule configurations.

Cloud Analytics Sharing: Beyond scheduled delivery, we enabled self-service access through Zendesk Sell’s cloud analytics portal. Each executive received a custom portal URL with single sign-on integration using our corporate identity provider. The portal provides real-time dashboard access (with 15-minute refresh intervals) for ad-hoc analysis needs.

We created a tiered dashboard structure: Executive Summary (high-level KPIs, scheduled daily), Operational Details (drill-down metrics, on-demand access), and Custom Analysis (self-service exploration tools). This balances automated delivery of key metrics with flexibility for deeper investigation.

For mobile access, executives use the Zendesk Sell mobile app which syncs cloud dashboards to their devices. This was particularly valuable for our CEO who frequently travels and needs access during flights with cached data.

Implementation Timeline: Week 1: Requirements gathering (2 days meeting with executives to understand needs), dashboard design (3 days creating templates and testing data accuracy).

Week 2: RBAC configuration (2 days setting up roles and permissions), scheduler setup (1 day configuring delivery schedules), testing and refinement (2 days with pilot user group).

The main technical challenges: 1) Ensuring dashboard performance with complex queries - we optimized by pre-aggregating data in materialized views, 2) Handling timezone differences for global executives - configured scheduler to use recipient’s local timezone, 3) Managing dashboard version control as requirements evolved - implemented a staging environment for testing changes before production deployment.

Lessons Learned: Start with a small pilot group (we used 3 executives) to validate the approach before full rollout. Gather feedback on dashboard content, delivery timing, and access mechanisms. We made significant adjustments based on pilot feedback that would have been disruptive if deployed to entire executive team initially.

Document the RBAC configuration thoroughly. Six months later when we needed to add a new executive role, having clear documentation of the logic and filters saved hours of reverse-engineering.

Set clear expectations about data latency. We created a dashboard header that displays “Data as of [timestamp]” so executives understand they’re seeing near real-time but not instantaneous data. This prevented confusion during critical business moments.

The 6-hour weekly time savings came from eliminating manual export (2 hours), formatting and customization (2.5 hours), distribution and follow-up (1.5 hours). Additionally, we’ve seen qualitative improvements in decision speed as executives now have data available when they need it rather than waiting for Monday morning reports. Several strategic decisions were accelerated by 2-3 days because executives had real-time visibility into emerging trends.

This is exactly what we need! We’re still doing manual exports and it’s becoming unsustainable as our executive team grows. Can you share more details about the scheduling configuration? Did you use Zendesk’s built-in scheduler or integrate with external tools? Also curious about how you handled different dashboard versions for different executive roles.

I’d love to understand your implementation timeline better. Two weeks seems fast for getting this working reliably. Can you break down the phases: requirements gathering, configuration, testing, rollout? Also, what were the main technical challenges you encountered and how did you overcome them? We’re planning a similar project and want to learn from others’ experiences.

The role-based access control piece is critical. We tried automated sharing initially but ran into issues where executives were seeing data they shouldn’t have access to based on their organizational scope. How did you configure the RBAC to ensure each executive only sees their relevant territories or business units? Did you create separate dashboards or use dynamic filtering?

The cloud analytics sharing capabilities in zs-2021 are solid but have some limitations around real-time data freshness. Dashboards refresh every 15 minutes in cloud deployments, which is usually sufficient but occasionally executives want truly live data during critical business moments. Did you encounter this issue and if so, how did you address the expectation management around data latency?

One challenge we’ve faced with automated dashboard distribution is executives wanting different metrics or time periods than what’s configured. How do you handle ad-hoc requests without falling back into manual processes? Do you provide self-service access alongside the scheduled reports, or maintain the scheduled delivery as the primary mechanism?

Six hours per week savings is substantial. That’s over 300 hours annually that can be redirected to higher-value analysis work. Beyond the time savings, have you noticed improvements in decision-making speed or quality now that executives have more timely access to data? I’m building a business case for similar automation and quantifiable benefits would be helpful.