Databricks Commercial and Professional Services Global

MiQ

Enterprise Knowledge Search · Marketing Personalization · Sales Automation

MiQ needed a better way to support agent-assisted media planning and campaign optimization because massive audience and campaign datasets were spread across separate tools, forcing planners to manually stitch together insight before making recommendations.

Number of campaigns for more than 2,300 advertisers powered by Sigma (since launch)
Turning 700T customer signals into media intelligence
at Data + AI Summit 2026 to learn more.

Reported impact

Business outcome

MiQ improved agent-assisted media planning and campaign optimization with measurable results including Turning 700T customer signals into media intelligence, Number of campaigns for more than 2,300 advertisers powered by Sigma (since launch), and at Data + AI Summit 2026 to learn more., while giving teams a faster path from governed data to production decisions.

  • Productivity Improvement
  • Revenue Growth
  • Speed to Market

Solution

MiQ uses Databricks Data + AI Platform to support agent-assisted media planning and campaign optimization. The implementation replaced a legacy state where massive audience and campaign datasets were spread across separate tools, forcing planners to manually stitch together insight before making recommendations. Campaign performance data and audience data move through production data pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. Databricks SQL, Genie, and Agent Bricks operationalize models, agents, and analytics that deliver agent-assisted campaign plans and audience insights.

Data flow

Campaign performance data and audience data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver agent-assisted campaign plans and audience insights.

Solution architecture

7 components · 4 layers
  1. Compute
    • Databricks Data + AI Platform Databricks Data + AI Platform unifies engineering, analytics, governance, and AI workloads for agent-assisted media planning and campaign optimization.
  2. Storage
    • Delta Lake Delta Lake stores curated campaign performance data and audience data so every team works from one trusted data foundation.
  3. Serving
    • Databricks SQL Databricks SQL serves curated dashboards and ad hoc analysis to agent-assisted campaign plans and audience insights.
    • Genie Genie gives business users natural-language access to governed data for faster decisions around agent-assisted media planning and campaign optimization.
    • Agent Bricks Agent Bricks packages domain-specific agents that automate parts of agent-assisted media planning and campaign optimization.
    • AI Search AI Search keeps enterprise content searchable for assistants embedded in agent-assisted media planning and campaign optimization.
  4. Governance
    • Unity Catalog Unity Catalog applies centralized access controls, lineage, and auditability across sensitive datasets.

Architecture clues

  • Centralized governance, lineage, and access controls are enforced through Unity Catalog.
  • Delta Lake provides the shared storage layer for curated lakehouse datasets.
  • Number of campaigns for more than 2,300 advertisers powered by Sigma (since launch)
  • Operational AI workflows are deployed directly beside governed data and analytics assets.
  • Turning 700T customer signals into media intelligence

Evidence from the source

Number of campaigns for more than 2,300 advertisers powered by Sigma (since launch)
There were too many data silos. Teams worked across separate internal platforms for traders, sellers and analytics, each with its own pipelines and data stores,
Without Databricks, this agent wouldn’t be possible. With Genie, our teams can easily interact with complex campaign and audience data to make decisions that improve our clients’ ad performance.
people who enjoy baking with chocolate in New York
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Databricks

    MiQ customer story

    First-party
    https://www.databricks.com/customers/miq
    Source type
    Vendor-published customer story
    Published
    Jul 8, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 8, 2026 · Databricks · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.