Event Information
WHEN
ON DEMAND
Guest Webinar with Jeffrey Haguewood, Owner & Managing Director of Sidekick Solutions
In this webinar, we explore the benefits of cloud data warehousing strategies for Business Intelligence (BI) reporting and AI data analytics. We demonstrate how these tools can leverage RunSignup data to produce insights from race and event data, to create compelling visualizations and automated reporting. Additionally, we demonstrate centralized dashboard reporting of RunSignup and Salesforce CRM data, shown together in an integrated data model.
Sidekick Solutions, a data cloud provider, has been a trusted RunSignup partner since 2022. That’s when we set out to provide easier access to our APIs with a no-code solution and decided to use Zapier for integrations. Sidekick now maintains our Zapier Apps that are available free of charge to customers. Additionally, some customers engage with Sidekick directly to build Zapier Integrations for their specific needs.
Sidekick’s expertise in data integrations and streamlining databases led to this new product that is covered here, an automated data pipeline. The webinar shows how their data warehouse provides both modern Business Intelligence reporting and AI insights for better reporting and analysis of data across platforms without requiring complex integrations.
This webinar is ideal for organizations that use a CRM or DMS in addition RunSignup and want an easier solution for accessing data from multiple platforms.
Summary of Webinar
The problem they’re solving
“I want insights from my data, but reporting is painful”
Common pains Jeff called out:
Too much manual reporting: exports → Excel → cleaning → reporting.
Need more compelling visuals + faster KPI/trend reporting.
Growing desire to use AI for analysis, but with control over data access.
“CRM as the center” is creating friction
Many orgs put everything into a CRM (Salesforce/Microsoft), but also run a “constellation” of tools (RunSignup + email tools + processors + etc.).
Centralizing everything into the CRM creates heavy integration burden.
Reporting often gets harder as complexity grows.
Conclusion: consider an alternative architecture for data + reporting.
Sidekick’s solution: a “data cloud” (data warehouse) + a playbook
Jeff frames it as technology + people/process.
Technology: a data cloud / data warehouse (their backbone)
Centralized place to aggregate data for BI reporting and AI analytics.
Fully automated, “assembly line”: raw data in → standardized/usable outputs out.
Demo architecture: pipelines from RunSignup + Salesforce → warehouse → BI + AI tools.
People/process playbook (how value actually happens)
Standardize a data dictionary (what each field means across sources)
Normalize into data marts (clean shapes built for end-users)
Automate regular reporting (stop “running reports”; start analyzing them)
Democratize access (push insights down the org chart, not just analysts)
RunSignup pipeline specifics (what they ingest)
Default: daily refresh (can be more frequent for key objects).
Objects highlighted:
Races, events, registrations
Add-ons + questions
Donations + fundraisers
Goal: data in the warehouse should “look like” what you experience in RunSignup (not raw API dumps).
Connects to BI tools like PowerBI, Tableau, AWS QuickSight (they demoed QuickSight; showed PowerBI briefly to emphasize tool-agnostic approach).
Demo Part 1: BI (Business Intelligence) dashboards
Jeff’s BI emphasis: automated refresh + interactive filtering + KPIs + drill-down.
Core BI capabilities shown
Dashboards for races, events, registrations, donations, fundraisers, add-ons.
Dynamic filtering: “one report becomes many” (filter by race/event/date/etc.).
KPI tiles: month-over-month, quarter/year comparisons.
Trend charts + heat maps + demographic breakdowns (gender/age examples).
Money-side views:
Paid vs deposited (processing-fee delta)
- Donations: counts/amounts, averages, tier breakdowns
Add-ons reporting (called out as especially useful)
- Treat add-ons as their own analyzable object (not just buried inside registrations).
- Use add-on dashboards for:
- uptake analysis (shirts/meal plans/etc.)
- order quantity patterns
- time-based add-on trends
Jeff credited RunSignup/Eric for encouraging an add-ons dashboard because it’s a common operational pain point.
Portfolio-level race views (not just one race at a time)
Dashboards that compare a whole set of races (example: breakdown by event distance across races).
Purpose: understand patterns across your “race portfolio,” not only inside single events.
Demo Part 2: advanced BI use cases
1) Race-to-race comparison
Use case: compare the “same” race year-over-year (or similar races across locations) and see whether you’re tracking ahead/behind.
Compare race A vs race B
Then event within race A vs event within race B
Track net change in:
registrations
donation averages
even add-on behavior (conceptually)
2) Multi-source reporting without forcing everything into CRM
Key point: you can union Salesforce + RunSignup in the warehouse/BI layer instead of pushing all RunSignup data into Salesforce.
Example shown:
Salesforce donations (outside RunSignup) + RunSignup fundraising totals → “total impact”
Identify top contributors even if they aren’t frequent registrants
(He noted demo data was de-identified / “fake” for privacy.)
AI portion: “natural language insights” with controlled data access
Jeff’s positioning:
BI requires you to know what to measure.
AI helps you ask broader questions and discover insights (forecasting/prediction/composites).
Important constraint: controlled/private AI
AI agent operates on explicit datasets you provide (closed model setup).
Emphasis on control: “AI only sees what you’ve allowed.”
AI Demo 1: “Insights tree” / scenario analysis
He showed a pre-run scenario flow (so nobody watched loading spinners):
Ask: “Top 10 individual fundraisers across all races” → AI generates a table.
Ask: “Which states had greatest increase in registrations from ’24 to ’25?”
AI produced a chart that wasn’t ideal
He iterated: “make it side-by-side” → better visualization
Key idea: AI is iterative; you may need to “bring it back” when it goes off-track.
He liked that the tool shows underlying table data for validation/traceability.
AI Demo 2: “Chat agent” (RunSignup Analytics Advisor)
Configured to:
Review dashboards
Suggest insights you might not think of
Provide strategic advice
Example prompt: “How can I increase donations based on historical data?”
AI returned bulleted strategy recommendations + supporting validation.
Follow-up: asked about “registration-to-donation conversion” and how to implement a tiered donation structure.
Then: asked for a forecast of potential outcomes (conservative vs optimistic).
There was an AI error; he “pressed it” to get it over the hump.
Output: chart summarizing potential impact.
AI → report automation (board-ready outputs)
He demonstrated generating a one-page board report:
Ask AI for patterns → AI summarizes
Then request: turn it into a printable report
AI generates an HTML layout designed to print as an 8.5×11 PDF
Pitch: once you refine format/branding, this becomes repeatable monthly/weekly
Security + deployment claims (Sidekick pitch section)
Private cloud per customer (no multi-tenancy; data not mixed)
Compliance certifications + annual pentest
Can be used as:
your primary warehouse, or
a “node” feeding your existing warehouse/lake (Azure/Snowflake examples)
Implementation timeline claims:
RunSignup + Salesforce pipelines + cloud: deployable “in 48 hours”
Customization/capacity building continues over time, but plumbing is fast
Q&A highlights
Q: Are AI agents custom-built by Sidekick or can we connect ours?
Agents are built within an AWS QuickSight module they deliver; customizable per customer.
They can also provide database access/connectors if you have external agents that can query relational data.
Q: How long does setup take? Is this a year-long project?
Claimed: core pipelines + cloud environment live in ~48 hours; not a multi-year build.
Long-term work is customization and building internal reporting/analysis muscle.