CalSTRS Investments Branch - Roadmap Resource and Study
Open-access learning paths for investment staff modernizing their toolkit. All resources below are free/open and accessible without required signup (checked March 18, 2026).
Operating model workstreams for scaling and modernization, structured for execution across data, systems, controls, and capability uplift.
Redesign trade-to-book workflows, exception handling, approvals, and reconciliation to reduce manual cycle time.
Use cases: automated allocations, exception queues, same-day reconciliation.
KPI targets: STP rate > 92%, break resolution < 24h, settlement fail rate < 0.10%.
Define ownership model, quality controls, lineage standards, and stewardship accountability by domain.
Use cases: data owner matrix, golden-source policy, lineage for board metrics.
KPI targets: critical-data quality > 98%, lineage coverage 100%, issue SLA < 5 business days.
Run end-to-end vendor governance: sourcing, fit scoring, SLAs, renewal strategy, and concentration risk controls.
Use cases: vendor scorecards, renewal playbooks, concentration stress test.
KPI targets: tier-1 SLA attainment > 99%, renewal lead-time > 120 days, concentration index trend down YoY.
Industrialize recurring board and IC reporting with standardized data products, QA gates, and narrative templates.
Use cases: monthly board pack pipeline, standardized attribution narrative, auto-refresh scorecards.
KPI targets: cycle-time -40%, first-pass QA > 97%, ad-hoc request turnaround < 2 days.
Integrate ODD, compliance checks, surveillance, and audit evidence into one operating cadence.
Use cases: pre-trade checks, post-trade surveillance, quarterly control attestation.
KPI targets: unresolved control exceptions < 5 per quarter, critical incident response < 4 hours, audit readiness 100%.
Deploy structured rollout playbooks, role-based enablement, and adoption KPIs for every major system release.
Use cases: role-based launch plans, super-user network, post-go-live adoption clinics.
KPI targets: active user adoption > 85% in 90 days, training completion 100%, support tickets -30% by Q2.
Standardize GP data ingestion, document extraction, cash-flow validation, and private-asset entity mastering.
Use cases: GP statement OCR pipeline, ILPA mapping, entity and deal hierarchy mastering.
KPI targets: GP data timeliness < 5 days, extraction accuracy > 95%, coverage completeness > 90%.
Implement release trains, testing standards, and model/version controls across PMS, analytics, and data layers.
Use cases: quarterly release calendar, regression suite, model approval gates.
KPI targets: release predictability > 90%, change failure rate < 10%, rollback frequency -50% YoY.
Institutionalize continuous upskilling in AI/LLM, data engineering, and investment analytics for front-to-back teams.
Use cases: role pathways, applied labs, desk-level learning sprints.
KPI targets: certified skill coverage > 80%, internal mobility +20%, external dependency -15%.
Track benefits, risks, and milestones with executive scorecards tied to cost, control, and decision-speed outcomes.
Use cases: value dashboard, milestone governance, benefits realization reviews.
KPI targets: on-time milestones > 90%, value capture vs plan > 85%, decision latency -30%.
Common stacks across institutional investors - what's working, what needs to scale
Platforms commonly deployed at large pension funds and institutional allocators
| Asset Class | Core Platforms | Status | What's Working / What Needs to Scale |
|---|---|---|---|
| Public Equity | Aladdin, FactSet, Bloomberg AIM, Charles River | Mature | Working: Well-integrated with custodians & market data. Scale: Real-time risk analytics, AI-driven alpha signals, ESG data at position level. |
| Fixed Income | Aladdin, Bloomberg PORT, ICE BondEdge, Tradeweb | Mature | Working: Pricing, analytics, OMS. Scale: Liquidity analytics, credit surveillance automation, structured product modeling. |
| Private Equity | Burgiss, eFront (BlackRock), Cobalt, Chronograph | Evolving | Working: Cashflow tracking, IRR/TVPI. Scale: GP data ingestion automation, look-through transparency, secondaries modeling. |
| Real Estate | Yardi, RealPage, ARGUS, Altus | Evolving | Working: Property-level financials & valuations. Scale: Portfolio-level analytics, climate risk overlay, cross-manager aggregation. |
| Inflation Sensitive | Custom models, Bloomberg, internal spreadsheets | Gaps | Working: Basic position tracking. Scale: Consolidated infra/commodities/TIPS view, scenario analysis, benchmark alignment. |
| Risk Mgmt Strategies | Aladdin, Numerix, custom overlays | Evolving | Working: Derivatives pricing. Scale: Cross-asset hedging dashboards, real-time Greeks, liquidity stress testing. |
Typical layers in a modern pension fund technology stack
| Layer | Common Tools | Status | What's Working / What Needs to Scale |
|---|---|---|---|
| Data Ingestion | Snowflake, Databricks, Azure Data Factory, FiveTran | Scaling | Working: Batch ETL from custodians/admins. Scale: Real-time streaming, API-first ingestion, unstructured data (PDFs, GP letters). |
| Data Warehouse / Lake | Snowflake, Azure Synapse, Databricks Lakehouse | Scaling | Working: Reporting marts for performance. Scale: Single source of truth across asset classes, position-level lineage. |
| Transform / Modeling | dbt, Python/Pandas, Spark, stored procedures | Scaling | Working: Ad-hoc SQL transforms. Scale: Version-controlled pipelines, automated testing, CI/CD for data models. |
| Analytics / BI | Tableau, Power BI, Looker, custom dashboards | Mature | Working: Board reporting, periodic views. Scale: Self-service analytics, embedded AI, natural language queries. |
| Investment Book of Record | Aladdin, SimCorp, Eagle PACE, Geneva | Mature | Working: Position keeping, NAV. Scale: Real-time IBOR, multi-asset views, private market integration. |
| Cloud / Infra | Azure (Gov), AWS GovCloud, hybrid on-prem | Scaling | Working: Core hosting. Scale: Full cloud migration, FedRAMP alignment, zero-trust security. |
| AI / ML Platform | Managed ML services, lakehouse compute, open-source frameworks, internal notebooks | Early | Working: Exploratory analysis. Scale: Production ML pipelines, model governance, LLM-assisted research & reporting. |
Portfolio Management, Order Management, Risk, and supporting systems
| System | Top Platforms | Who Uses It | What's Working / What Needs to Scale |
|---|---|---|---|
| PMS Portfolio Mgmt |
Aladdin, Charles River, SimCorp, FactSet | PMs, CIOs, Risk | Working: Exposure/position views, compliance. Scale: Cross-asset unified view, what-if analysis, direct GP data feeds for privates. |
| OMS Order Mgmt |
Bloomberg EMSX, Charles River, Aladdin, FlexTrade | Traders, Ops | Working: Public market execution. Scale: TCA analytics, multi-broker algo strategy, FX hedging automation. |
| Risk | Aladdin, MSCI RiskMetrics, BarraOne, Axioma | Risk Team, CIO | Working: Factor models, VaR. Scale: Liquidity risk, climate scenarios, total-fund stress testing incl. privates. |
| Performance | StatPro, FactSet, MSCI, Eagle Performance | Perf Analysts, Board | Working: GIPS-compliant returns, benchmarks. Scale: Attribution granularity, real-time perf, private market IRR automation. |
| Middle Office | Eagle PACE, Geneva, Advent/SS&C, internal tools | Ops, Accounting | Working: Recon, settlement. Scale: Exception-based workflows, STP rates, AI-assisted breaks resolution. |
| Reporting | Tableau, Power BI, SSRS, custom portals | All stakeholders | Working: Standard periodic reports. Scale: On-demand self-service, interactive drill-downs, mobile board decks. |
| CRM / LP Mgmt | Salesforce, DealCloud, internal trackers | Relationship Mgmt | Working: Contact tracking. Scale: Pipeline analytics, commitment tracking, automated LP reporting. |
Critical data inputs powering investment analytics and operations
| Data Domain | Sources / Vendors | Coverage | What's Working / What Needs to Scale |
|---|---|---|---|
| Market Data | Bloomberg, Refinitiv, ICE, MSCI | Good | Working: EOD pricing, indices. Scale: Intraday feeds, alt data (satellite, sentiment), consolidated platform. |
| Positions / Holdings | Custodian feeds (BNY, State Street), admins | Good | Working: Daily public positions. Scale: Intraday, private look-through, cross-custodian golden record. |
| Private Markets | GP reports, Burgiss, Preqin, PitchBook, Cobalt | Mixed | Working: Quarterly financials. Scale: Automated GP ingestion (ILPA templates), real-time NAVs, deal-level transparency. |
| Performance & Benchmarks | MSCI, Cambridge, NCREIF, Bloomberg indices | Good | Working: Standard benchmarks. Scale: Custom benchmark construction, peer comparison, private benchmark lag adjustment. |
| ESG & Climate | MSCI ESG, ISS, Sustainalytics, CDP, PCAF | Mixed | Working: Portfolio carbon footprint. Scale: Forward-looking climate scenarios, net-zero tracking, biodiversity metrics. |
| Reference Data | Bloomberg, Refinitiv, internal MDM | Mixed | Working: Security master basics. Scale: Entity resolution, LEI mapping, cross-asset golden master, hierarchy mgmt. |
| Risk Factors | Barra, Axioma, Northfield, internal models | Good | Working: Equity factor models. Scale: Multi-asset factors, illiquidity premia, macro regime indicators. |
| Regulatory / Compliance | Internal systems, Thomson Reuters, state-specific | Mixed | Working: Basic checks. Scale: Automated monitoring, cross-mandate violation flagging, full audit trail. |
Publicly disclosed platforms at $50B+ pension funds and multi-asset managers
Technology platforms disclosed or widely reported at major U.S. and global pension systems
| Fund | AUM | Key Systems (Public/Reported) | Known For |
|---|---|---|---|
| CalPERS California |
~$503B | AladdinBloombergFactSetEagle PACESnowflakeTableauSAPAzureStateRAMP | Notable: Multi-year technology modernization program. Migrated to Aladdin for total-fund PMS/risk. Active Snowflake data warehouse buildout. Public Board agenda documents detail tech procurement. |
| CalSTRS California |
~$397B | BloombergFactSetEagle PACEBurgissTableauPower BIPreqinPitchBook | Notable: Active procurement for investment technology roadmap consultant. Seeking Senior Head of Investment Data & Analytics. Investments tech modernization is a stated priority. |
| NY State Common New York |
~$268B | AladdinBloombergMSCI BarraOneEagleTableau | Notable: Long-standing Aladdin deployment. Robust internal risk analytics. Climate risk analysis a focus area with MSCI tools. |
| Florida SBA Florida |
~$260B | SimCorp DimensionBloombergFactSetAxiomaMSCI | Notable: SimCorp Dimension as core IBOR/PMS - one of few U.S. pensions on this platform. Strong internal quant team. Significant internal management (>70% of assets). |
| Texas TRS Texas |
~$210B | AladdinBloombergBurgisseFrontSnowflakePower BI | Notable: Undergone technology transformation. eFront for private markets, Snowflake for data centralization. Growing internal management capabilities. |
| WSIB Washington State |
~$190B | BloombergFactSetEagleBurgissInternal tools | Notable: Lean technology team leveraging outsourced investment operations. Strong private markets program with Burgiss as main platform. |
| SWIB Wisconsin |
~$155B | Charles RiverBloombergFactSetAxiomaSnowflakePython/ML | Notable: Advanced quant/data science team. Charles River IMS for OMS/PMS. Known for internal alpha generation and systematic strategies. Active ML research program. |
| CPP Investments Canada |
~$590B CAD | AladdinSnowflakeDatabricksAzurePythonTableauInternal AI Lab | Notable: Industry-leading tech investment. Dedicated AI/ML lab. Snowflake + Databricks data platform. Extensive internal management across all asset classes. ~$1B+ annual tech spend reported. |
| OTPP Ontario Teachers' |
~$255B CAD | AladdinSimCorpBloombergSnowflakeAWSPythonDatabricks | Notable: Pioneer in direct investing infrastructure. Large internal tech team (~500+ in technology). Multi-year cloud migration. Known for innovation in private markets data. |
| GPIF Japan |
~$1.6T | BloombergMSCIFactSetS&PCustom internal | Notable: World's largest pension fund. Primarily externally managed. Major ESG data consumer. Published technology RFIs for portfolio analytics modernization. |
| NBIM / Norges Bank Norway (Gov Pension) |
~$1.7T | SimCorp DimensionBloombergInternal systemsPythonCloud-native | Notable: Best-in-class transparency - full holdings published. Massive internal tech team. SimCorp Dimension as core platform. Known for sophisticated in-house risk & data systems. |
Technology platforms at major institutional asset managers - internal stacks and client-facing tools
| Firm | AUM | Key Platforms / Stack | Known For |
|---|---|---|---|
| BlackRock | ~$11.5T | AladdinAladdin StudioeFrontSnowflakeAzureDatabricksTableau | Notable: Aladdin is the firm - also licensed to 200+ institutions. eFront acquired for private markets. Aladdin Studio (API platform) expanding ecosystem. Largest investment technology company in the world. |
| Vanguard | ~$9.3T | Internal proprietaryAWSPythonJavaSnowflakeDatabricksTableau | Notable: Heavily proprietary stack. Major AWS cloud migration. Large engineering org (~10,000+ in IT/tech). Custom portfolio construction and index tracking systems. |
| State Street / SSGA | ~$4.1T | Charles River (owned)Alpha PlatformSnowflakeAWSGX (data) | Notable: Owns Charles River Dev (IMS). Building State Street Alpha as front-to-back platform. GX for data management. Major custodian + asset manager combo. |
| PIMCO | ~$1.9T | AladdinInternal analyticsBloombergPythonCloud (hybrid) | Notable: Sophisticated internal analytics for fixed income. Aladdin for core PMS/risk. Advanced credit and macro modeling. Known for proprietary trading and research tools. |
| J.P. Morgan AM | ~$3.4T | AladdinSpectrum (internal)BloombergAWSPythonDatabricks | Notable: Spectrum internal OMS. Large quant research team (JP Morgan AI Research). Publishing ML research papers. Athena-based risk systems from banking side. |
| Goldman Sachs AM | ~$2.8T | Simon (internal)MarqueeSecDBBloombergAWSPython | Notable: SecDB - one of the most legendary internal platforms in finance. Marquee client-facing analytics. Simon for portfolio management. Massive internal engineering culture. |
| Wellington Mgmt | ~$1.3T | AladdinBloombergFactSetInternal research toolsPythonNLP pipelines | Notable: Early Aladdin adopter. Known for fundamental + quant research integration. Building NLP pipelines for earnings analysis. Collaborative culture across tech and investment teams. |
| T. Rowe Price | ~$1.6T | AladdinBloombergFactSetAWSSnowflakeInternal analytics | Notable: Cloud-first strategy with AWS. Snowflake data platform. Investing heavily in data science team. Migrated to Aladdin for front-office operations. |
| Nuveen / TIAA | ~$1.2T | SimCorpBloombergYardi (RE)eFrontAzurePower BI | Notable: SimCorp Dimension as core IBOR. Large real estate & private markets (Yardi, eFront). Azure cloud strategy. Farmland & infra - unique asset class data needs. |
| Bridgewater | ~$124B | Fully proprietaryAWSPythonInternal AI/MLCustom data platform | Notable: Entirely proprietary tech stack. Massive data engineering operation. Known for systematic macro - all models built internally. AWS-based cloud infrastructure. ~1,500 employees, large % in tech. |
| Man Group / AHL | ~$175B | ProprietaryPythonArctic (open-source)AWSKubernetesInternal ML | Notable: Open-sourced Arctic (high-performance datastore for financial time series). Python-first culture. Major contributions to open-source finance tooling. Systematic + discretionary hybrid. |
| AQR Capital | ~$105B | ProprietaryPythonC++Cloud-nativeInternal risk engine | Notable: Factor-based investing pioneer. Fully proprietary research + execution stack. Strong academic research culture. Published influential papers on systematic strategies. |
Updated visual dashboard with richer styling, stronger hierarchy, and responsive interaction.
Interactive visuals summarize maturity, platform breadth, pension scale, and architecture shape.
Benchmark, visuals, graphs both lagging and forward-looking from People, Process, Technology, and Industry intelligence. Executive signal foundation: statistically significant samples from multiple signals, including AI news, market signals, operating datasets, and peer intelligence.
Frequency counts across pension funds and asset managers reveal consolidation risk and control points.
Dominance vs innovation scoring, with bubble size by institutional penetration.
Maturity vs platform coverage by asset class.
People, Process, Technology, Industry benchmarks across pension and peer institutions.
Pensions vs asset managers across data platform, PMS/OMS, private markets infra, cloud adoption, analytics depth, and AI/ML investment (normalized from the full CSV corpus).
Tool popularity from role requirements and stack mentions across peers.
Highest-frequency skills across investment technology and data roles.
Skills where private/corporate peers demand more than pensions today.
Layered end-to-end blueprint from foundation through AI decision support, with continuous People and Process tracks.
Data journey path from source onboarding through trusted foundation, decision analytics, and operating controls.
Evolution of dominant technology patterns from legacy tooling to cloud-native analytics.
Finance and asset-management AI hype curve with numbered milestones to keep events readable and non-overlapping.
Institution-vendor dependency network highlighting dominant backbone providers.
Maturity heatmap by asset class and capability layer.
Cohort split across pensions vs asset managers, with readiness scores overlaid.
Adoption and analytics capability positioning of major platform vendors.
Buy vs build operating model split, comparing pensions and asset managers.
Decision posture by capability area, normalized so each row sums to 100%.
How investment data moves from market data ingestion to executive decisioning.
Bubble map of complexity vs impact across governance, quality, stewardship, definitions, journey, and analytics initiatives.
Demand vs gap view to prioritize capability-building tracks for investment technology teams.
Procurement-focused visuals to evaluate existing tools, shortlist new platforms, and shape a practical technology roadmap for the Investments branch.
X-axis: overall fit for CalSTRS. Y-axis: lock-in risk from concentration and low openness. Bubble size: innovation support.
Estimated integration load across data, order/risk, private markets, and reporting workflows.
Category-level view across size of effort, delivery risk, dependency load, and business impact.
Capability intensity by delivery phase: POC, implementation, and scale.
Compare Fast Track, Balanced, and Risk-First rollout flavors by capability area.
Bubble map of complexity vs impact across governance, quality, stewardship, definitions, journey, and analytics initiatives.
Demand vs gap view to prioritize capability-building tracks for investment technology teams.