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

Most portfolios fail in the first 10 seconds. Here’s why: I'll tell you exactly when I know a portfolio won't make it past my screen. The moment I land on "Hi, I'm a passionate designer who loves solving problems..." Listen. I've already read your CV. I know your name, your experience, and where you're based. I don't need a repeat performance. What do I need? To see if you can actually design. Here's what happens when I review portfolios: I have 10 seconds to decide if your work is worth 5 minutes of my additional review and hours of the interview process. And you're wasting those seconds telling me you "love design." Of course, you love design. You're a designer. That's expected. Show me this instead: → Your work / style / taste (Immediately) → The problems you've solved → The impact you've created → Your actual design thinking When I land on your portfolio, I'm looking for: First impressions that matter. Is it accessible? Any animations that show craft? Does it load fast? Can I navigate intuitively? Your portfolio IS the first design problem I see you solve. And if you can't design for me, your user, why would I trust you with my users? What actually gets you hired: ✓ Business context as a stage setting ✓ Your specific role (not "I did everything") ✓ Team composition and timeline ✓ The REAL problem you solved Not 20 personas. Not 50 wireframes. Not your entire design process is outlined. Give me: - 2-3 key research insights - 1 example of iteration that mattered - The final solution (3 screens max) - Actual impact or expected metrics Here's the brutal truth: I don't care about your design philosophy. I care if you can move my metrics. Design isn't just about beauty or experience. It's about business impact. Show me you understand that balance: - Skip the autobiography. Start with your best work. - Make me think "I need to talk to this person". Not "I need to read more about them." Your portfolio should work like your best designs: Clear. Intuitive. Impactful. Remember: I've hired dozens of designers. The ones who got offers? They showed me their thinking through their work. Not through their "About Me". Designers, what's the first thing visitors see on your portfolio? Time for some honest self-assessment (and a potential change).

I’ve reviewed > 400 portfolios this year. Observation #1: The ones that got interviews weren’t the prettiest. They were the clearest. → Clear intent (what roles they’re targeting) → Clear structure (who they helped + what changed) → Clear thinking (how they made decisions) Observation #2: Hiring managers responded best to portfolios that made it easy to scan, not admire. → 3-5 second headlines that told the story → Metrics up top, visuals in the middle, lessons at the end → Less storytelling. More signal. Observation #3: The portfolios that ‘failed’? → Opened with “Hi, I’m Alex and I love solving problems” → Contained 30+ screenshots with no explanation → Didn’t articulate business impact or their role → Had no opinion, no POV, no process If I were applying today? → I’d restructure my case studies to lead with outcomes → I’d add a design philosophy section to show how I think → I’d cut 40% of the fluff and focus on what actually matters → I’d communicate my USP and elevator pitch up front Your portfolio isn’t a gallery. It’s a business case for why you’re worth hiring. ----- Just thought I'd share this after reviewing some notes over the weekend. Hope it helps! ----- #ux #tech #design #ai #business #careers

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

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

Five years ago I would not have believed this. The biggest names in CPG are quietly taking food out of the center of the plate. Unilever is carving out an $8B ice cream portfolio to focus on beauty and wellness. Nestlé is leaning harder into health science. The categories with pricing power are not pantry staples. They are skincare, supplements, functional hydration, and performance nutrition. Why the shift is rational, not trendy: Food margins are getting squeezed. Trade down is real, private label is sharper, and price elasticity in core staples is hitting its ceiling. Health and wellness carry willingness to pay. Consumers accept a premium for outcomes, routines, and performance. They do not reward cost plus in pasta sauce. Loyalty is drifting in food. Promotions move share week to week. Self care and efficacy-led categories hold repeat. You can already see where momentum lives. L'Oréal skincare growth outpaced many classic food portfolios last year. The Coca-Cola Company is pushing deeper into functional and non-carbonated. PepsiCo’s most defensible engine is Gatorade’s ecosystem of hydration, not soda. These are not side bets. They are where pricing power and repeat accrue. What I am advising leadership teams to do now: • Reweight the portfolio. Map pricing power, repeat, and trade down risk by category. If the math says wellness and self care carry the margin story, allocate accordingly. • Build credibility before you buy it. If you are a food-first house moving into health, you need scientific muscle, regulatory fluency, and communities that care. Partnerships, acqui-hires, and advisory benches matter. • Treat personalization as a revenue lever. Recommendations, routines, and subscription logic are table stakes in self care. Own the data and make it useful. • Keep the core honest. Food will not disappear, but it must earn its space with cleaner RGM, fewer zombie SKUs, and real reasons to stick around outside of price. I am not declaring the death of food. I am pointing at where the next decade of pricing power is likely to sit. The winners will rebalance now, not after a third year of elasticities telling the same story. If you are leading a CPG portfolio, are you future proofing around outcomes and routines, or are you managing a slow decline in categories that no longer set the pace? #FMCG #CPG #ConsumerTrends #GrowthStrategy #Beauty #Wellness #RevenueShift #BrandEvolution

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

Modern quantitative analysis methodologies used in portfolio management mainly fall into the following categories: • Predict-then-optimize: These methods first forecast asset prices or returns and then solve an optimization problem (e.g., mean-variance model) to determine the portfolio. While easy to implement, their performance heavily depends on accurate predictions, which are challenging due to market volatility. • RL (Reinforcement Learning) based methods: Instead of focusing on accurate price prediction, the RL approaches directly learn portfolio allocations by maximizing a reward function; e.g., cumulative return using PPO (Proximal Policy Optimization). However, they often inefficiently optimize from surrogate losses, as portfolio optimization differs from typical RL applications where rewards are more straightforwardly differentiable. • DL (Deep Learning) based approaches: These methods address RL limitations by directly optimizing financial objectives (eg, Sharpe ratio). Despite this advantage, they still face some limitations. First, the dynamic market and low signal-to-noise ratio in historical data hinder model generalization. Solutions like simple architectures or external data (e.g., financial news) either fail to capture essential features or rely on information that may be unavailable. Second, DL methods produce fixed portfolios that overlook varying investor risk preferences and lack fine-grained risk control. To address these shortcomings, the authors of [1] propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of 2 main phases: • return-related maximization • risk control In the return-related maximization phase, 2 auxiliary objectives; return rate prediction and return rate ranking, are introduced and combined with portfolio optimization to mitigate overfitting and improve the model's generalization to future markets. Subsequently, in the risk control phase, 2 methods; portfolio interpolation and portfolio improvement, are introduced to achieve fine-grained risk control and rapid adaptation to a user-specified risk level. For the portfolio interpolation method, the authors show that the adjusted portfolio’s return rate is at least as high as that of the minimum-variance optimization, provided the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method achieves higher return rates than portfolio interpolation while maintaining the same risk level. Extensive experiments on 3 real-world datasets: NAS100, DOW30 and Crypto10. The results, evaluated using metrics such as Annualized Percentage Rate (APR), Annualized Volatility (AVOL), Annualized Sharpe Ratio (ASR), MDD, demonstrate the superiority of MILLION compared to the baselines: MVM, DT, LR, RF, SVM, LSTM-PTO, LSTMHAM-PTO, FinRL-A2C, FinRL-PPO, LSTMHAM-S, LSTMHAM-C and LSTMHAM-M. Link to the preprint [1] is provided in the comments.