A playbook for the Director of Value Creation, Data and Analytics
If you are a Director of Value Creation, Data and Analytics, 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. The in-house team at a private equity firm that collects, standardizes and analyzes financial and operational data across portfolio companies, supports value creation plans and integrates acquisitions.
- •The problem today. Monthly spreadsheet chases across portfolio companies, manual chart-of-accounts mapping and caveated portfolio packs.
- •What changes. Each company's data is pulled from source and mapped, so the pack is ready on day four. Portfolio KPIs tie to each company's ledger, so investors get accurate numbers.
- •What stays with your people. KPIs shared with investment committees and LPs: decisions and reporting depend on them, so the operating partner approves.
- •The result. A value creation team that spends its time on the plan, not the pack.
The PRD for agentic portfolio data and value creation teams
This is the requirements document we would write with you for your team. Everything below expands on it.
| Who | Portfolio operations, value creation and data and analytics teams at private equity firms and their portfolio companies |
| The job | Comparable KPIs across every portfolio company, every month. |
| Environment | Portfolio companies' ERPs (NetSuite, Sage, SAP, QuickBooks), CRMs and BI; a firm-owned Snowflake or Databricks; dbt; BI. |
| Constraints | KPIs shared with investment committees and LPs always go to a named approver. Portfolio data is shared under each company's agreements and feeds investor reporting. |
| Success is measured by | KPI question: from about 2 days to seconds; Chart of accounts mapped: from about 2 weeks to 4 h; Monthly pack assembled: from about 2 weeks to 8 h; catalog upkeep closed autonomously 88% of the time. |
| Owned by | A Director of Value Creation, Data and Analytics, with domain owners as named approvers. |
| Pilot scope | Three portfolio companies on one KPI model. |
What going fully agentic and autonomous means for portfolio data and value creation teams
- •Put your data pipeline on autopilot. Each company's data is pulled from source and mapped, so the pack is ready on day four.
- •Run the data back office without the busywork. The work that fills the week today moves to agents: monthly spreadsheet chases across portfolio companies, manual chart-of-accounts mapping and caveated portfolio packs.
- •Scale data without scaling headcount. Portfolio KPIs tie to each company's ledger and add-on integrations run as missions, so the data you manage grows without the team having to.
- •Get the plumbing off your plate. A value creation team that spends its time on the plan, not the pack.
Who this is for
The in-house team at a private equity firm that collects, standardizes and analyzes financial and operational data across portfolio companies, supports value creation plans and integrates acquisitions. It works with CFOs and leadership at each company and reports to the deal and operating partners.
The shift
Firms now expect data-driven value creation across every holding, but every portfolio company runs its own ERP, chart of accounts and reporting habits. Value creation teams spend the month collecting and reconciling. Agents can connect to each company where its data lives, map it to one KPI model and keep it current, so the team spends its time on the plan.
The product you ship
- •A standard KPI model across the portfolio
- •Monthly portfolio reporting
- •Value creation plan tracking
- •Integration data for add-on acquisitions
Where the week goes
- •Companies submit data late and in different formats.
- •Charts of accounts don't map.
- •KPIs mean different things per company.
- •Add-on acquisitions need integration.
- •LP questions need answers fast.
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 14% 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 |
|---|---|---|
| Monthly submissions | Spreadsheets by email | Pulled and mapped automatically |
| Chart of accounts mapping | Manual | Mapped once, maintained by agents |
| KPI definitions | Per company | Governed across the portfolio |
| Add-on integration | Consultants | A mission |
| LP questions | Days | Minutes, with lineage |
These are the impact metrics we move for portfolio data and value creation teams:
- •KPI question: from about 2 days to seconds
- •Chart of accounts mapped: from about 2 weeks to 4 h
- •Monthly pack assembled: from about 2 weeks to 8 h
Across the routine work in this domain:
The biggest change is Chart of accounts mapped: from about 2 weeks to 4 h.
Use cases, in depth
A new platform company
Today. Onboarding a new holding into portfolio reporting takes a quarter.
With Data Workers. Connectors reach its ERP, Schema Evolution maps its chart of accounts, Context Wizard applies the portfolio KPI model.
What changes. The company reports in the standard pack within weeks.
An add-on acquisition
Today. An add-on's data has to merge into the platform company on a 100-day clock.
With Data Workers. Data Migration moves workloads in waves with parity checks.
What changes. Integration hits the plan.
Value creation tracked on live data
Today. Initiatives are tracked in slides.
With Data Workers. Pipeline Building links initiative metrics to operating data.
What changes. Progress is current every month.
Agents working together: A new platform company joins the portfolio
Minute by minute
Illustrative example. Portfolio data lands in a firm-owned Snowflake; the monthly pack publishes on day 10.
| Time | Who | What happens |
|---|---|---|
| Day 1 | Portfolio companies | Books close |
| Day 2 | Connectors agent | Pulls ERP data from 12 companies |
| Day 2 | Schema Evolution agent | Maps two chart-of-accounts changes |
| Day 3 | Quality Monitoring agent | Ties KPIs to each ledger; flags one gap |
| Day 4 | Spellbook Data Catalog | Operating partner approves the pack |
The agents on the job
| Agent | What it does for you | Tools it calls |
|---|---|---|
| Connectors | Reach each company's ERP, CRM and BI | get_connector_status, trigger_airflow_dag, get_dbt_run_history |
| Schema Evolution | Map charts of accounts and formats | detect_schema_change, validate_schema_compatibility, generate_migration, rollback_migration |
| Data Context & Catalog | Govern portfolio KPIs and lineage | trace_cross_platform_lineage, blast_radius_analysis, define_business_rule, generate_documentation |
| Quality Monitoring | Check submissions against the GL | set_sla, run_quality_check, get_anomalies, create_quality_tests_for_pipeline |
| Data Migration | Integrate add-on acquisitions | generate_migration, validate_migration, verify_data |
| Data Access & Governance | Scope access by company and fund | provision_access, check_policy, request_governance_review, generate_audit_report |
Data Context Wizard keeps one governed graph of definitions, lineage and owners that every agent and person answers from. Autonomous Data-Conductor runs each job from detection to a verified result. Spellbook Data Catalog is where your team approves, audits and rolls back, and asks questions in plain English.
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:
- •Each company's data is pulled from source and mapped, so the pack is ready on day four.
- •Portfolio KPIs tie to each company's ledger, so investors get accurate numbers.
- •Add-on integrations run as missions, so integration hits the 100-day plan.
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.
- •KPIs shared with investment committees and LPs always goes to a named approver: decisions and reporting depend on them, so the operating partner approves.
- •Investment decisions: 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 KPIs shared with ICs and LPs, autonomous closure is 0% by design.
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: Portfolio companies' ERPs (NetSuite, Sage, SAP, QuickBooks), CRMs and BI; a firm-owned Snowflake or Databricks; dbt; BI. 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
Portfolio data is shared under each company's agreements and feeds investor reporting. Data Workers scopes access by company and fund and logs every change.
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 portfolio data and value creation 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. Portfolio monitoring tools collect submissions; Data Workers connects to source systems, maps them and keeps the data current.
The case for your CTO
What this replaces. Monthly spreadsheet chases across portfolio companies, manual chart-of-accounts mapping and caveated portfolio packs.
What your team gets back. A value creation team that spends its time on the plan, not the pack.
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; kPIs shared with investment committees and LPs stay at L2 with a named approver; and one org-wide stop halts all autonomous work.
Why now. Value creation now depends on data across holdings.
The first win. Three portfolio companies on one KPI model. 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 portfolio data and value creation 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; kPIs shared with investment committees and LPs always go to a named approver.
- •Autonomous, under your governance. Routine work runs on its own: each company's data is pulled from source and mapped, portfolio KPIs tie to each company's ledger, and add-on integrations run as missions. Your team governs the agents instead of doing the upkeep.
- •The agentic enterprise. Private equity firms will run every holding on one governed data model, onboarded at close and integrated on plan, so value creation teams spend their time on the operating levers.
You choose the altitude, every step is visible and reversible, and the pilot below is the first rung.
The pilot: how portfolio data and value creation teams start
The pilot is how your team proves the value on its own data before committing.
- •What we connect. Portfolio companies' ERPs (NetSuite, Sage, SAP, QuickBooks), CRMs and BI; a firm-owned Snowflake or Databricks; dbt; BI, over MCP, inside your infrastructure. Nothing migrates.
- •Scope. Three portfolio companies on one KPI model.
- •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. Each company's data is pulled from source and mapped and portfolio KPIs tie to each company's ledger start running on reversible work. KPIs shared with investment committees and LPs stay at L2 with a named approver.
- •At day 30. We review the impact metrics against your baseline:
- KPI question: from about 2 days to seconds
- Chart of accounts mapped: from about 2 weeks to 4 h
- Monthly pack assembled: from about 2 weeks to 8 h
- 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 42% of agent actions run fully autonomously while KPIs shared with ICs and LPs stay with a named approver.
FAQ
Do companies need a new system? No; agents connect to what each company runs.
Who sees which company's data? Access is scoped by company and fund.
Can it integrate add-ons? Yes, as a migration mission.
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
- •Data Workers pricing: https://dataworkers.io/pricing/
- •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