How Bear IQ Data Tuning Works

When data arrives from your source systems, it’s a starting point — not a finished product. Before anything appears in your Bear IQ dashboards, Bear Analytics runs it through a structured tuning process to make it clean, consistent, and ready for real decisions.

Here’s what that process involves.

What Is Data Tuning?

Data tuning is how Bear Analytics transforms raw source data into analysis-ready data. It’s the work that turns inconsistent exports from multiple systems into a dashboard you can trust.

It’s not a one-time setup — it’s an ongoing process calibrated for each client, each event, and each data source. As your event evolves, Bear Analytics adjusts the tuning rules to keep your data sharp.

The Five Pillars of Data Tuning

1. Normalization

Source systems don’t follow a universal standard. Field names, formats, and value structures vary from platform to platform. Normalization standardizes everything so it can be compared and aggregated consistently.

What this looks like:

• Date formats are unified across systems (e.g., MM/DD/YYYY, YYYY-MM-DD, Unix timestamp all become one standard)

• Name and company fields are cleaned and reconciled where possible (e.g., “IBM,” “I.B.M.,” and “International Business Machines”)

• Registration type labels are mapped so “Full Conf,” “Full Conference,” and “Full-Conference Pass” all roll up to the same segment

• Currency and revenue formats are standardized across all connected systems

2. Grouping and Rollups

Raw data exists at a granular transactional level. That’s useful for source systems, but not ideal for analysis. Bear IQ creates meaningful groupings so you can see patterns.

What this looks like:

• Individual exhibit sales line items are grouped into categories: booth sales, sponsorships, add-ons

• Registration types roll into broader attendance categories: full conference, single-day, expo only, exhibitor, speaker

• Revenue is segmented by product type, sales rep, and time period

• Organizational data is grouped so parent companies and subsidiaries roll up correctly

3. Contextualization

Numbers without context are just numbers. Bear Analytics adds layers of meaning so your dashboards tell a story.

What this looks like:

• Year-over-year comparisons are built by aligning data across event years, even when categories or pricing structures changed

• Pacing data is calculated so you can see how this year’s registrations compare to the same point in prior years

• Derived metrics are created — like revenue per attendee, conversion rates, or registration-to-attendance ratios — that don’t exist in any single source system

4. Exclusion and Filter Rules

Not everything in your source data belongs in your analytics. Data tuning defines what to exclude so your numbers stay meaningful.

What gets excluded and why:

• Cancelled registrations — a cancelled record shouldn’t count in your active pipeline

• Test and QA records — internal transactions created during setup are identified and removed

• Incomplete or abandoned registrations — partial form submissions are filtered out to avoid inflating counts

• Non-event transactions — if a shared platform contains data from other events or products, Bear IQ scopes to your event only

• Duplicate records — where the source system allowed duplicates, Bear IQ applies dedup rules for accurate unique counts

5. Validation and Quality Checks

After tuning rules are applied, Bear Analytics runs validation checks before data goes live in your dashboards.

What this includes:

• Sanity checks on key metrics — are total counts within expected range? Did revenue move unexpectedly?

• Null and missing data detection — are required fields populated?

• Referential integrity — do attendee records link correctly to the right event, registration type, and organization?

• Trend comparison — does pacing data make sense relative to prior years? Are there anomalies to investigate?

When validation flags an issue, Bear Analytics investigates before the data goes live. This might mean working with an integration partner, consulting your team about a process change, or adjusting a tuning rule.

How Tuning Is Maintained Over Time

Several things can trigger a tuning update:

• Your event adds a new registration category or pricing tier

• A source system changes its API, data model, or field structure

• You switch to a new registration platform, AMS, or exhibit sales vendor

• Your team identifies a new reporting need

• Bear Analytics detects an anomaly during routine validation

When any of these happen, Bear Analytics updates the configuration and validates the result. This is part of the ongoing service — you don’t need to manage it yourself.