A playbook for VPs of Data Product Management at media companies
If you are a VP of Data Product Management at a media company, this is your playbook. It covers what your team ships, where its week goes today, the use cases agents take over first, the autonomy you can safely grant in each domain, and what to show leadership after the first quarter.
Key takeaways
- •Who this is for. Data teams at media groups and streamers: the direct-to-consumer data platform, the audience and identity platform behind ad sales, viewership analytics for programming and finance, and studio distribution reporting.
- •The problem today. Weekly debates about whose viewership number is right, quarterly vendor reconciliation and logging changes mistaken for audience behavior.
- •What changes. Playback is reconciled against vendor measurement every morning, so divergences are explained the next day, not next quarter. Logging is checked on every app release, so a change in how starts are logged never looks like an audience leaving.
- •What stays with your people. Reported viewership and sellable audiences: programming and ad sales depend on them, so a named lead approves.
- •The result. One viewership number that programming, ad sales and finance all use.
The PRD for agentic TV and streaming viewership data teams
This is the requirements document we would write with you for your team. Everything below expands on it.
| Who | Media group data platforms, DTC data product teams, audience and viewership analytics |
| The job | One definition of a view across every org. |
| Environment | App SDKs and set-top box data; vendor measurement feeds; Snowflake or Databricks; dbt; identity and audience platforms. |
| Constraints | Reported viewership and sellable audiences always go to a named approver. Subscriber data carries consent rules per brand and region. |
| Success is measured by | Playback vs vendor reconciled: from about 3 months to 8-15 min; Programming question answered: from about 2 days to seconds; Sellable segment published: from about a week to 10-15 min; catalog upkeep closed autonomously 88% of the time. |
| Owned by | A VP of Data Product Management at a media company, with domain owners as named approvers. |
| Pilot scope | Daily playback-vs-vendor reconciliation. |
What going fully agentic and autonomous means for TV and streaming viewership data teams
- •Put your data pipeline on autopilot. Playback is reconciled against vendor measurement every morning, so divergences are explained the next day, not next quarter.
- •Run the data back office without the busywork. The work that fills the week today moves to agents: weekly debates about whose viewership number is right, quarterly vendor reconciliation and logging changes mistaken for audience behavior.
- •Scale data without scaling headcount. Logging is checked on every app release and consent rules are applied to every segment by brand and region, so the data you manage grows without the team having to.
- •Get the plumbing off your plate. One viewership number that programming, ad sales and finance all use.
Who this is for
Data teams at media groups and streamers: the direct-to-consumer data platform, the audience and identity platform behind ad sales, viewership analytics for programming and finance, and studio distribution reporting. In large media companies these sit in four or more separate orgs. Titles include VP of Data Product Management, Data Product Manager, Data Platform PM and Audience Data Lead.
The product you ship
- •A first-party identity spine across every brand and app.
- •Viewership data joined across linear ratings, streaming playback and set-top box data.
- •Audience segments ad sales sells against.
- •Reporting programming, finance and partners rely on.
Where the week goes
- •Every app and brand logs differently. Unifying playback across a portfolio is a permanent project.
- •Measurement vendors change methods. Ratings, panel and streaming data have to be reconciled again.
- •Identity breaks. Device graphs and login rules change and audience sizes jump.
- •Consent and privacy vary by brand and region. Every segment has to respect them.
- •Four orgs, four definitions. DTC, ad sales, programming and finance count a view differently.
Add it up and upkeep is most of the team's week. Here is how a typical week splits today, and once agents own the back office:
Time spent on new data products moves from 16% of the week to 50%. Most of the rest becomes review: people steer and approve agent work instead of doing it by hand.
Then and now
| Work | Today | With Data Workers |
|---|---|---|
| App logging changes | Found in the weekly viewership review | Caught per app release |
| Definition of a view | Different per org | One governed definition |
| Vendor reconciliation | Quarterly | Daily |
| Audience-size jumps | Investigated for days | Traced to identity changes on the first jump |
| Consent per brand and region | Checked by hand | Enforced on every segment |
| Duplicate tables after mergers | Kept forever | Retired once lineage proves it is safe |
These are the impact metrics we move for TV and streaming viewership data teams:
- •Playback vs vendor reconciled: from about 3 months to 8-15 min
- •Programming question answered: from about 2 days to seconds
- •Sellable segment published: from about a week to 10-15 min
Across the routine work in this domain:
The biggest change is Playback vs vendor reconciled: from about 3 months to 8-15 min. Work that waited in queues finishes in minutes or hours, because agents detect, fix and verify in one pass.
Use cases, in depth
The autoplay logging change
Today. Minutes per view fall for one app and programming spends a week debating whether it is real.
With Data Workers. Streaming detects the change, Quality Monitoring isolates the app, Incident Debugging confirms it is logging, Pipeline Building separates autoplay starts and the report waits for one approval.
What changes. Programming decisions are made on real behavior.
Unifying viewership across five apps
Today. Each app's data team reports its own numbers.
With Data Workers. Pipeline Building builds one viewership model across apps, Context Wizard governs the definition of a view, and Quality Monitoring ties each app to its own source.
What changes. One number across the portfolio.
Audiences ad sales can sell the same day
Today. Each new segment needs consent checks and a data request.
With Data Workers. Access & Governance checks consent per brand and region, and Spellbook lists sellable segments for ad sales.
What changes. Ad sales builds packages without waiting on data.
Agents working together: The autoplay logging change
A streaming app release changes how autoplay starts are logged. Streaming detects the change, Schema Evolution maps it, Quality Monitoring sees average minutes per view drop for that app, Incident Debugging confirms it is logging rather than behavior, Pipeline Building separates autoplay starts, and Conductor holds the weekly viewership report for the programming analytics lead.
Minute by minute
Illustrative example. Playback events stream into Databricks; the weekly viewership report publishes Monday.
| Time | Who | What happens |
|---|---|---|
| Fri 10:00 | App release | Autoplay starts logged differently |
| 10:20 | Schema Evolution agent | Playback schema change detected |
| Sat | Quality Monitoring agent | Minutes per view drop for one app |
| Sat | Autonomous Data-Conductor | Confirmed logging, not behavior |
| Sun | Pipeline Building agent | Autoplay separated with tests |
| Mon 07:00 | Spellbook Data Catalog | Programming analytics lead approves |
The agents on the job
| Agent | What it does for viewership teams | Tools it calls |
|---|---|---|
| Real-Time Streaming | Keeps playback events, schemas and consumers in step across apps | register_kafka_schema, register_stream_schema, detect_schema_change |
| Schema Evolution | Maps app releases and vendor format changes into the viewership model | detect_schema_change, validate_schema_compatibility, generate_migration, rollback_migration |
| Pipeline Building | Builds unified viewership and audience models across brands | generate_pipeline, validate_pipeline, sandbox_test, deploy_pipeline |
| Quality Monitoring | Audience-size, completion-rate and vendor tie-out checks | set_sla, run_quality_check, get_anomalies, create_quality_tests_for_pipeline |
| Data Access & Governance | Applies consent and brand-level policies to every segment and request | provision_access, check_policy, request_governance_review, generate_audit_report |
| Data Security | Classifies subscriber PII and keeps it out of context | scan_pii, review_pii_writes, pci_card_data_masking |
Context Wizard gives DTC, ad sales, programming and finance one governed definition of a view, a viewer and a reached household. Conductor reconciles streaming playback against vendor measurement daily. Spellbook gives every audience and dataset a page ad sales can search.
What goes on autopilot
Once a domain has earned it, this work runs on its own, and each item removes a problem your team deals with today:
- •Playback is reconciled against vendor measurement every morning, so divergences are explained the next day, not next quarter.
- •Logging is checked on every app release, so a change in how starts are logged never looks like an audience leaving.
- •Consent rules are applied to every segment by brand and region, so ad sales can sell segments without a manual review.
Where your people stay in charge
Agents never get blanket access. Each domain has its own autonomy level, set by you, and it only rises when the receipts show the agents are reliable. Everything starts at L1, observing only; reversible upkeep such as reruns, staging fixes, documentation and cleanup is usually first to run on its own.
- •Reported viewership and sellable audiences always goes to a named approver: programming and ad sales depend on them, so a named lead approves.
- •Programming and partner communications: these stay with people.
Once a deployment has matured, here is how that ladder plays out across task classes:
Catalog upkeep closes autonomously 88% of the time. For Reported viewership, autonomous closure is 0% by design: every change is proposed with a full diff and approved by a named person.
What happens when an agent is wrong
- •At L2 propose, nothing changes until a named person approves. The proposal shows the full diff, the blast radius and how to roll it back. Unanswered requests expire and escalate.
- •At L3 act, reversibly, every change keeps a rollback point, and Conductor verifies the downstream number after the change. If verification fails, the change is reversed and the domain drops a level.
- •No agent approves or promotes its own work. Autonomy only grows on evidence, per repair class, and repeated errors shrink scope automatically.
- •One org-wide stop halts all autonomous dispatch, and every action lands in a hash-chained audit trail you can export with generate_audit_report.
How it fits your stack
Data Workers connects to what you already run: App SDKs and set-top box data; vendor measurement feeds; Snowflake or Databricks; dbt; identity and audience platforms. Each agent is an MCP server, so the same agents work inside Claude Code, Cursor or Codex and in Slack or Teams through Spellbook. Nothing migrates.
- •Runs in your infrastructure with your credentials and your model key. The hosted Autonomous Data-Conductor sees workflow metadata only, never rows.
- •Uses the access you already govern. Agents act under scoped service identities and your existing roles; every grant goes through provision_access and is logged.
- •Proposes changes as diffs in your dbt project, orchestrator and warehouse, with blast radius, tests and a rollback point, and applies them only at the autonomy level you set for that domain.
- •Open-source core. The Apache-2.0 community edition ships 11 agents and 160+ MCP tools, so your engineers can read exactly what the agents call.
Governance and regulation
Subscriber data carries consent rules per brand and region. Data Workers enforces them on every segment and logs access.
Data Workers doesn't make you compliant; your controls, people and assessors do. It supplies controls and evidence wherever agents touch data. Not legal advice.
The alternatives TV and streaming viewership data teams weigh
Build it yourselves. A strong team can wire a coding agent to MCP servers for the warehouse and dbt. The work is everything around it: routing approvals to the right owner, blast radius, rollback, receipts, and current context about every other system.
Observability, catalogs and point tools. Observability alerts, catalogs document, and quality tools test. Data Workers takes their signals, diagnoses, proposes the fix, verifies it and leaves the receipt.
Platform-native agents. Snowflake made its managed MCP server and Cortex Agents generally available on Nov 4, 2025, and Databricks made Genie One generally available on Jun 16, 2026. They are excellent inside their platform; this work crosses the source systems, orchestrator, warehouse and BI, and Data Workers works across all of them.
In this space specifically. Measurement vendors and audience platforms count and sell; Data Workers keeps one governed viewership layer under them.
The case for your CTO
What this replaces. Weekly debates about whose viewership number is right, quarterly vendor reconciliation and logging changes mistaken for audience behavior.
What your team gets back. One viewership number that programming, ad sales and finance all use.
Integration effort. Connecting is configuration, not a migration. The agents attach over MCP to the warehouse, orchestrator, dbt project and source systems you already run, under the roles you already govern, inside your own infrastructure. Your engineers can read every tool the agents call in the open-source core.
Risk and control. Autonomy is set per domain. Everything starts at L1, observing only; reversible work moves up only when the receipts justify it; reported viewership and sellable audiences stay at L2 with a named approver; and one org-wide stop halts all autonomous work.
Why now. Ad-supported streaming makes viewership revenue data.
The first win. Daily playback-vs-vendor reconciliation. The pilot below lays out the scope, what you see each week and the cost.
The climb: from assisted to autonomous to agentic
Our thesis is that the data platform climbs the way analytics did, in stages, and that the hard part of data work was never knowing what to do but doing it. For TV and streaming viewership data teams, the climb looks like this:
- •Assisted. Agents work alongside your team. They read the whole stack, show what needs doing and propose every fix with a receipt; reported viewership and sellable audiences always go to a named approver.
- •Autonomous, under your governance. Routine work runs on its own: playback is reconciled against vendor measurement every morning, logging is checked on every app release, and consent rules are applied to every segment by brand and region. Your team governs the agents instead of doing the upkeep.
- •The agentic enterprise. One viewership number that programming, ad sales and finance all use. People set direction, and the platform carries it out.
You choose the altitude, every step is visible and reversible, and the pilot below is the first rung.
The pilot: how TV and streaming viewership data teams start
The pilot is how your team proves the value on its own data before committing.
- •What we connect. App SDKs and set-top box data; vendor measurement feeds; Snowflake or Databricks; dbt; identity and audience platforms, over MCP, inside your infrastructure. Nothing migrates.
- •Scope. Daily playback-vs-vendor reconciliation.
- •First week. Agents run at L1 across the scoped domain. They map every pipeline, definition and lineage path and show the fixes they would have made, with receipts, so your team sees the value before anything changes.
- •By week two. Playback is reconciled against vendor measurement every morning and logging is checked on every app release start running on reversible work. Reported viewership and sellable audiences stay at L2 with a named approver.
- •At day 30. We review the impact metrics against your baseline:
- •Playback vs vendor reconciled: from about 3 months to 8-15 min
- •Programming question answered: from about 2 days to seconds
- •Sellable segment published: from about a week to 10-15 min
- •the share of catalog upkeep closed autonomously, targeting 88% as the deployment matures
- •What you decide. Whether to expand to the next domain. The pilot fee is credited in full against the first year.
- •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.
The pilot is the start of a longer curve:
By month 12, more than 46% of agent actions run fully autonomously and manual work falls below 10%, while reported viewership stay with a named approver.
FAQ
Does it replace measurement vendors? No; it reconciles against them.
Can it unify apps? As a mission.
Who approves reported viewership? A named lead at L2.
Where do the agents run? In your infrastructure, with your credentials and model key. The hosted Conductor sees workflow metadata only.
Do we have to migrate anything? No. Your warehouse, dbt project, orchestrator, catalog and BI stay where they are.
Sources
- •NBCUniversal, VP Data Product Management: https://builtin.com/job/vice-president-data-product-management-global-streaming-platform/3306080
- •Marketing Dive, WBD Olli: https://www.marketingdive.com/news/warner-bros-discovery-first-party-data-platform-olli-ctv-marketing/714484/
- •Data Workers pricing: https://dataworkers.io/pricing/
- •Data Workers open-source repository (tool registrations): https://github.com/DataWorkersProject/dataworkers-claw-community
- •Snowflake, managed MCP server and Cortex Agents GA release note (Nov 4, 2025): https://docs.snowflake.com/en/release-notes/2025/other/2025-11-04-cortex-agents-mcp
- •Databricks, Genie One launch (Jun 16, 2026): https://www.databricks.com/company/newsroom/press-releases/databricks-launches-genie-one-all-new-agentic-coworker-every-team