Portfolio Management

Data Governance, Catalog, and Quality Tools: How Are They Different? Organizations rely on three essential tools to ensure their data is usable, compliant, and trustworthy: Data Governance Platforms, Data Catalog Platforms, and Data Quality Platforms. Each plays a unique role, but together they form a robust data ecosystem. Here’s how they compare: Data Governance Platforms • Focus on ensuring compliance and managing regulatory requirements. • Key features include: • Secure data access and mitigate risks. • Manage audit trails and enforce quality standards. • Approve access workflows to control data use. Data Catalog Platforms • Empower users to discover relevant datasets and collaborate. • Key features include: • Discover datasets with ease. • Visualize basic data and collaborate with annotations. • Track data usage and manage datasets through proxies (data virtualization). Data Quality Platforms • Ensure the quality of data assets, making them reliable for business use. • Key features include: • Define and validate data quality rules. • Standardize data cleaning and monitor alerts. • Build dashboards and calculate quality KPIs. Why Does This Matter? In 2025, businesses cannot afford to make decisions based on incomplete, inaccurate, or inaccessible data. These platforms work together to ensure that: • Data is secure and compliant. • Teams can easily find and use relevant datasets. • The quality of data meets enterprise standards for decision-making. Building a solid data foundation requires integrating these tools into your workflows. Organizations that succeed in combining governance, cataloging, and quality platforms will be ahead in their data-driven transformations. Join our Newsletter with 137000+ followers — https://lnkd.in/dbZPj6Tu How is your organization leveraging these tools? Let’s discuss in the comments! #data #ai #datagovernance #theravitshow

So you're thinking of building an #electrolyzer to make green #hydrogen. But how much #wind, #solar and #battery capacity do you need to power the electrolyzer in order to minimize the cost of hydrogen it produces? BloombergNEF has just the tool you need to find out - the Hydrogen Electrolyzer Optimization Model (H2EOM). A vastly enhanced version 2.0 was published yesterday by my brilliant colleagues Xiaoting Wang and Ulimmeh-Hannibal Ezekiel. For an example project in #California, the optimal setup for a 1MW electrolyzer is to power it by 1.14MW of wind and 0.83MW of solar, skipping the batteries. That gives you a levelized cost of hydrogen (LCOH) or $4.63 per kilogram and a utilization rate of 65% on your electrolyzer (excluding any #IRA #45V #taxcredits). If you wanted to increase the utilization rate to 90%, you'd need to be happy with a #LCOH of $7.28 per kilogram as you pay for batteries, as well as more solar and wind capacity. Users can do this modeling for any location on the planet by using BNEF's Solar- and Wind Capacity Factor Tool to get 8,760h of capacity factor data anywhere. Users can tweak any cost and financing assumption to suit their project, making this a super versatile tool for #H2 modeling. Oh, and did I say you can model up to 50 projects at once? BNEF clients can download the model here: https://lnkd.in/e9vTYc7G

📊 𝗧𝗼𝗽 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗘𝘃𝗲𝗿𝘆 𝗤𝘂𝗮𝗻𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄  When I first stepped into quant finance, I realised: 👉 Picking assets is important. 👉 But constructing a portfolio that balances risk & return? It is equally important. Portfolio optimisation is where math meets markets, turning uncertainty into structured allocations. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝘆𝗼𝘂 𝗺𝘂𝘀𝘁 𝗸𝗻𝗼𝘄 (𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗵𝗲𝘆 𝗮𝗿𝗿𝗶𝘃𝗲 𝗮𝘁 𝗳𝗶𝗻𝗮𝗹 𝘄𝗲𝗶𝗴𝗵𝘁𝘀) 👇 𝟭. 𝗠𝗲𝗮𝗻-𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗠𝗮𝗿𝗸𝗼𝘄𝗶𝘁𝘇)  • Classical Modern Portfolio Theory.  • Balances expected return vs variance.  • Weights chosen to maximise return for a given level of risk. 𝟮. 𝗕𝗹𝗮𝗰𝗸-𝗟𝗶𝘁𝘁𝗲𝗿𝗺𝗮𝗻 𝗠𝗼𝗱𝗲𝗹  • Blends equilibrium market portfolio with investor views.  • Avoids extreme/unrealistic weights from MVO.  • Final weights = equilibrium + adjusted views. 𝟯. 𝗠𝗶𝗻𝗶𝗺𝘂𝗺 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗠𝗩𝗣)  • Ignores return forecasts, minimizes volatility only.  • Popular in risk-sensitive mandates.  • Weights tilt toward low-volatility assets. 𝟰. 𝗥𝗶𝘀𝗸 𝗣𝗮𝗿𝗶𝘁𝘆 & 𝗘𝗾𝘂𝗮𝗹 𝗥𝗶𝘀𝗸 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 (𝗘𝗥𝗖)  • Allocates based on risk contribution, not dollar amounts.  • Risk Parity → equalizes volatility contributions.  • ERC → ensures balanced marginal risk. 𝟱. 𝗙𝗮𝗰𝘁𝗼𝗿-𝗕𝗮𝘀𝗲𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻  • Allocates across style factors: value, momentum, quality, low-vol.  • Weights optimized for factor exposure rather than securities.  • Core of smart beta ETFs. 𝟲. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 / 𝗔𝗜 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝘀  • Genetic algorithms, reinforcement learning, Bayesian optimization.  • Learn optimal weights dynamically.  • Increasingly common in systematic hedge funds. 𝟳. 𝗖𝗩𝗮𝗥 (𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗩𝗮𝗹𝘂𝗲-𝗮𝘁-𝗥𝗶𝘀𝗸) 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻  • Minimises extreme tail losses.  • Looks beyond VaR → focuses on worst-case scenarios.  • Final weights skew conservative under fat tails. 𝟴. 𝗥𝗼𝗯𝘂𝘀𝘁 & 𝗥𝗲𝘀𝗮𝗺𝗽𝗹𝗲𝗱 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆  • Handles input uncertainty in returns/covariances.  • Michaud’s resampling → Monte Carlo to stabilise weights.  • Prevents fragile allocations. 💡 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁: There is no “one best” model. Each optimisation method reflects your 𝗽𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆 𝗼𝗳 𝗿𝗶𝘀𝗸 & 𝗿𝗲𝘁𝘂𝗿𝗻. As a quant, you’re not just investing, you’re engineering a risk engine. 🔁 Save this for your quant prep. 💬 Comment: Which optimisation technique do you rely on (or struggle with)? 📌 Follow Puneet Khandelwal for more insights on Quant, ML, and Finance. #QuantFinance #PortfolioOptimization #Investing #RiskManagement #MachineLearning #FinanceCareers #Quant

PORTFOLIO OPTIMIZATION WITH UNCERTAINTY: BAYESIAN MEAN-VARIANCE 📊 In portfolio construction, the classical mean-variance optimization often produces extreme, unstable allocations due to parameter estimation errors. Bayesian Mean-Variance elegantly addresses this challenge by incorporating uncertainty directly into the optimization process. 🎯 This approach updates prior beliefs with observed data to create more robust portfolios through Bayesian inference: μ_post = (Σ_prior^(-1) + T·Σ_sample^(-1))^(-1) · (Σ_prior^(-1)·μ_prior + T·Σ_sample^(-1)·μ_sample) When properly implemented, Bayesian portfolio optimization involves three core elements: 📌 Prior Specification: Setting initial beliefs about expected returns, typically using market equilibrium or equal-weight assumptions as a conservative starting point 📈 Likelihood Function: Incorporating historical return data to update beliefs, with sample size T determining the weight given to observed versus prior information 🔄 Posterior Distribution: Combining prior and likelihood to obtain updated parameter estimates that reflect both beliefs and data Key steps to implement Bayesian Mean-Variance: 1. Define prior distributions for expected returns (often μ ~ N(μ₀, τ²Σ)) 2. Calculate posterior parameters using precision-weighted averaging 3. Optimize portfolio using posterior estimates instead of raw sample statistics 4. Apply standard mean-variance optimization with updated parameters 5. Monitor shrinkage intensity as new data arrives Applications in modern portfolio management: • Institutional Portfolios: Managing large diversified portfolios with parameter uncertainty • Robo-Advisory: Providing stable allocations for retail investors • Multi-Asset Strategies: Combining assets with limited historical data • Dynamic Rebalancing: Adapting portfolios as market regimes change • Risk Management: Reducing concentration risk from estimation errors By shrinking extreme positions toward more balanced allocations, Bayesian Mean-Variance delivers portfolios that are both theoretically sound and practically robust—particularly valuable when historical data is limited or market conditions are uncertain! 💡 #PortfolioOptimization #BayesianFinance #QuantitativeFinance #RiskManagement #InvestmentStrategy

David McNellis, Christian Olinger, and I published a note today on the Total Portfolio Approach. For those who are not as close to this framework, TPA encourages/forces CIOs and investment teams to look across asset classes, not just in their strategic asset allocation vertical, to allocate capital (versus are you the best manager in a select vertical, which is akin to the more traditional Strategic Asset Allocation). This mindset shift represents a potential important change for some of the largest allocators in our industry towards more relative value comparisons across asset classes, geographies, and capital structures. In our experience, the practical advantages often show up most clearly in Private Markets because Private Markets introduce complexity around liquidity, pacing, and governance that traditional asset-class buckets were not always designed to handle. Therefore, we at KKR believe a more holistic total portfolio lens can help investors better manage liquidity, provide flexibility, particularly during drawdowns by avoiding mechanical reactions to denominator effects, improve vintage diversification, and better compete for capital across strategies. That said, we do not think TPA is a silver bullet. It is not a substitute for skill in forecasting, manager selection, or underwriting. And for many organizations, a ‘minimum viable portfolio' version, strengthening portfolio-level analytics and refreshing relative value views more frequently, may be the most practical starting point. In a world where return ranges may be narrower and dispersion wider, how we allocate capital across the whole portfolio could matter as much as where we allocate it. Read more at https://go.kkr.com/4badWlI #TotalPortfolioApproach #CIO #AssetAllocation

Discover → Control → Trust → Scale Governance is not a tool. It’s a layered system: Catalog – discover, tag, and connect data + AI assets. Quality – enforce correctness, freshness, and reliability. Policy – codify who can do what, where, and how. AI Control – govern models, prompts, and usage. Break one layer → trust breaks. Good governance doesn’t slow data down — it makes it usable, trusted, and AI-ready. With so many tools out there, the real question is simple: what helps your team trust data faster? Here's the breakdown to adapt and integrate with Data Governance: ⚙️ 1. ENTERPRISE GOVERNANCE TOOLS Collibra – Enterprise‑grade governance platform for glossary, lineage, and policy‑driven stewardship. Atlan – AI‑powered data catalog that enables self‑service discovery and governance‑as‑code. Informatica Axon – Unified governance hub for policies, lineage, and MDM‑integrated data. Alation – AI‑driven catalog and search engine built for analyst‑centric discovery. OvalEdge – Governance and compliance platform focused on sensitive‑data detection and templates. Secoda – Lightweight AI catalog for modern data teams with simple issue tracking. ☁️ 2. CLOUD‑NATIVE GOVERNANCE Databricks Unity Catalog – Single governance layer for data and ML across the Databricks lakehouse. Google Cloud Dataplex – Unified data governance and profiling layer for GCP data lakes. Microsoft Purview – Cross‑Azure catalog, classification, and sensitivity‑label governance engine. Snowflake Horizon – Native governance and access control layer built into Snowflake. Google Cloud Data Catalog – Metadata discovery and integration layer for BigQuery and Vertex AI. 🔄 3. PIPELINE + QUALITY LAYER dbt Labs – Transformation‑forward framework that enforces data contracts and testing in pipelines. Great Expectations – Validation framework that codifies data quality expectations and tests. Soda – Observability tool for monitoring data freshness, distribution, and anomalies. ⚡How to decide, where to begin with? Single platform  → Start with Unity Catalog / Dataplex / Purview / Snowflake Horizon. Multi‑cloud  → Add Atlan / Collibra as cross‑platform governance. Data quality issues  → Enforce contracts with dbt + Great Expectations. The smartest governance stacks don’t rely on one tool, Instead they combine catalog, quality, lineage, and policy where each matters most. #data #engineering #AI #governance

With public equity and fixed income markets in turmoil in recent weeks the traditional 60:40 portfolio model has again been challenged. There's little doubt uncertainty will pervade these markets for the foreseeable future. Therefore it is timely to release further research on the beneficial portfolio characteristics of private market assets. In this paper "Optimising private market asset allocations" we examine the integration of this asset class within traditional asset allocation strategies to  assess performance impacts across investor risk profiles. We believe that including private market assets can significantly enhance portfolio returns for investors who adopt a risk-based utility-maximising strategy in portfolio construction. Additionally, we find that unlisted infrastructure has the most potential of the private market assets considered to improve portfolio Sharpe ratios, especially for ‘Defensive’ and ‘Balanced’ investors. Our research applies a utility maximisation framework which facilitates risk appetite aware optimisation to tailor portfolios to match specific investor risk preferences and lifecycle stages. A novel two-stage returns unsmoothing approach is used to more accurately estimate true private market return volatility. We show that even after returns unsmoothing, private markets can significantly enhance portfolio outcomes. This study finds that defensive investors benefit from allocations  to infrastructure and private credit, achieving lower volatility and higher returns. Balanced investors see similar advantages with  a stable allocation to infrastructure, while growth investors lean towards private equity for higher risk-reward profiles. This analysis adds further weight to our assertion that private market assets have a material role to play in optimising investor portfolios. With IFM Investors Economics & research Frans van den Bogaerde, CFA and Christopher Skondreas #investment #assetallocation #risk #privatemarkets #portfolioconstruction

What's the next big tech headache you've successfully solved in your family office? If you're in a family office, you know the drill. Every year, we talk about software to fix our "reporting problem." Yes, using spreadsheets to stitch together multi-asset portfolios is awful. The manual data entry costs us huge amounts of time—20% to 40% of our team’s working hours—and getting to 100% data accuracy feels like a constant, exhausting battle. Next-gen tech isn't about fancy features; it's about solving the three biggest, most personal challenges we face: 1. The Burden of Family Dynamics The Problem: Managing money across generations is often a maze of complex relationships, different levels of financial understanding, and conflicting goals. Trying to align three different family branches on an investment strategy is harder than any reconciliation task. The Fix: Integrated digital platforms offer a lifeline. They provide unified, transparent data and customized dashboards so every family member can see what they need, without having to call an advisor every five minutes. Governance tools formalize oversight, creating a clean, unassailable audit trail of every decision and approval, which is the best defense against long-term family conflict. 2. The Fear of Being a Single Point of Failure The Problem: We hold generational wealth, yet our operational setup often relies on fragmented systems and key staff members. This patchwork is a huge cybersecurity liability. That fear of a breach or a sudden resignation leaving us exposed is very real. The Fix: We have to treat technology as core infrastructure, not an accessory. Moving to a consolidated, cloud-native platform provides institutional-grade security. It hard-wires our processes so they can withstand turnover, and uses AI within reconciliation and workflow automation to create that crucial second layer of defense. 3. The Private Markets Time Sink The Problem: Our focus is increasingly on private assets (PE, venture, real estate), but the data comes in as a flood of unstructured documents (K-1s, capital calls). Our smartest analysts spend their days as data entry clerks, wasting time that should be spent on strategy. The Fix: AI is now operational infrastructure. The smartest vendors are embedding AI to automatically ingest, categorize, and validate that messy document data. This is what truly frees up lean teams to focus on due diligence and value creation, rather than getting lost in the "grunt work." The goal is simple: technology must make the family office more resilient, less reliant on any single hero, and more transparent. That's the key to protecting both the wealth and the family's legacy. Share your thoughts below! #FamilyOffice #WealthTech #FinTech #PrivateMarkets #Technology

I have spent years building data pipelines, and governance was always the hardest part. SOC 2 audits. PII handling. Lineage documentation. We always treated them as afterthoughts, something to “add later.” That’s why Express by Nexla feels different. It’s built with compliance at the core, not as a feature, but as a foundation. Here’s what stood out to me in their governance layer 👇 SOC 2 compliance baked in:  Every pipeline runs with enterprise-grade controls and encrypted operations. PII masking by default: Sensitive data gets identified and protected automatically. Data lineage visibility: Every transformation and flow is tracked, versioned, and auditable. Policy automation:  Access, validation, and monitoring rules run silently in the background. It’s the kind of compliance that doesn’t slow teams down.  It empowers adoption. When governance becomes invisible, innovation accelerates. If you’ve ever battled the friction between speed and control, this is worth a look: https://lnkd.in/dDEhWF3e

Govern to Grow: Scaling AI the Right Way    Speed or safety? In the financial sector’s AI journey, that’s a false choice. I’ve seen this trade-off surface time and again with clients over the past few years. The truth is simple: you need both.   Here is one business Use Case & a Success Story. Imagine a loan lending team eager to harness AI agents to speed up loan approvals. Their goal? Eliminate delays caused by the manual review of bank statements. But there’s another side to the story. The risk and compliance teams are understandably cautious. With tightening Model Risk Management (MRM) guidelines and growing regulatory scrutiny around AI, commercial banks are facing a critical challenge: How can we accelerate innovation without compromising control?   Here’s how we have partnered with Dataiku to help our clients answer this very question!   The lending team used modular AI agents built with Dataiku’s Agent tools to design a fast, consistent verification process: 1. Ingestion Agents securely downloaded statements 2. Preprocessing Agents extracted key variables 3. Normalization Agents standardized data for analysis 4. Verification Agent made eligibility decisions and triggered downstream actions   The results? - Loan decisions in under 24 hours - <30 min for statement verification - 95%+ data accuracy - 5x more applications processed daily   The real breakthrough came when the compliance team leveraged our solution powered by Dataiku’s Govern Node to achieve full-spectrum governance validation. The framework aligned seamlessly with five key risk domains: strategic, operational, compliance, reputational, and financial, ensuring robust oversight without slowing innovation.   What stood out was the structure: 1. Executive Summary of model purpose, stakeholders, deployment status 2. Technical Screen showing usage restrictions, dependencies, and data lineage 3. Governance Dashboard tracking validation dates, issue logs, monitoring frequency, and action plans   What used to feel like a tug-of-war between innovation and oversight became a shared system that supported both. Not just finance, across sectors, we’re seeing this shift: governance is no longer a roadblock to innovation, it’s an enabler. Would love to hear your experiences. Florian Douetteau Elizabeth (Taye) Mohler (she/her) Will Nowak Brian Power Jonny Orton