Building Data Streams That Actually Power Campaigns

Unify and Normalize at the Right Layer

Success depends on consistent identity resolution—but that’s impossible when streams carry broken naming conventions or mismatched keys.

We often step back and build a clean data model in BigQuery or dbt before Lytics sees any data:

• Translate inconsistent campaign naming
• Normalize brand_name, asset_type, utm_source
• Establish a single user_id hierarchy
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Soli & Co's Take: Don’t ask Lytics to do what your warehouse or models should’ve done first.

Validate the Stream Behavior—Then Trust It

Stream quality isn’t assumed. It’s tested.

We:

• Monitor event flow against expectations
• Verify presence of key fields (email, brand, timestamp)
• Sample profiles to ensure stream updates are populating correctly
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In one case, email activity was logged—but profiles didn’t update due to misaligned field names. Schema fixes restored team trust.
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Soli & Co's Take: QA isn’t a phase. It’s part of stream management.

Build for Audience Logic From Day One
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Streams aren’t just for storage—they’re the building blocks of segmentation.
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We design streams to answer campaign questions like:

• Who engaged with a certain product line in the last 60 days?
• Who downloaded gated content but skipped events?
• Who engaged with two or more brands?
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Often that means adding custom tags, derived fields, or brand-channel mappings.
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Soli & Co's Take: The best streams are built with segmentation in mind—not just ingestion.

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