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

A roadmap is not a strategy! Yet, most strategy docs are roadmaps + frameworks. This isn't because teams are dumb. It's because they lack predictable steps to follow. This is where I refer them to Ed Biden's 7-step process: — 1. Objective → What problem are we solving? Your objective sets the foundation. If you can’t define this clearly, nothing else matters. A real strategy starts with: → What challenge are we responding to? → Why does this problem matter? → What happens if we don’t solve it? — 2. Users → Who are we serving? Not all users are created equal. A strong strategy answers: · What do they need most? · Who exactly are we solving for? · What problems are they already solving on their own? A strategy without sharp user focus leads to feature bloat. — 3. Superpowers → What makes us different? If you’re competing on the same playing field as everyone else, you’ve already lost. Your strategy must define: · What can we do 10x better than anyone else? · Where can we persistently win? · What should we not do? This is where strategy meets competitive advantage. — 4. Vision → Where are we going? A roadmap tells you what’s next. A vision tells you why it matters. Most PMs confuse vision with strategy. But a vision is long-term. It’s a north star. Your strategy answers: How do we get there? — 5. Pillars → What are our focus areas? If everything is a priority, nothing really is. In my 15 years of experience, great strategy always come with a trade-offs: → What are our big bets? → What do we need to execute to move towards our vision? → What are we intentionally not doing? — 6. Impact → How do we measure success? Most teams obsess over vanity metrics. A great strategy tracks what actually drives business success. What outcomes matter? → How will we track progress? → What signals tell us we’re on the right path? — 7. Roadmap → How do we execute? A roadmap should never be a list of everything you could do. It should be a focus list of what truly matters. Problems and outcomes are the currency here. Not dates and timelines. — For personal examples of how I do this, check out my post: https://lnkd.in/e5F2J6pB — Hate to break it to you, but you might be operating without a strategy. You might have a nicely formatted strategy doc in front of you, but it’s just a… A roadmap? a feature list? a wishlist? If it doesn’t connect vision to execution, prioritize trade-offs, and define competitive edge… It’s not strategy. It’s just noise.

Many founders treat pricing as a revenue optimization problem. Figure out the product first, scale usage, then monetize. That's backwards. Pricing isn't about extracting money. It's about discovering whether you built something people actually value. At Gamma, we used pricing as a proxy for value and kept it pretty much the same for over 2 years. Free usage will lie to you (especially for B2B and prosumer products). Usage spikes feel like PMF. They're not. Usage without payment tests your onboarding, not your value. If you come out with too generous of a free plan, you'll never know what true willingness to pay looks like. Here's how to use pricing as a proxy for value: 1. Pick your value metric Choose the thing customers actually hire you for. Documents generated. API calls. Minutes transcribed. At Gamma, we gated by AI credits as the primary value metric, with business levers like custom branding. 2. Draw a hard boundary between free and paid Let people experience the "aha," then stop them at a generous but bounded gate. We gave users plenty of AI credits up front. Once they hit the limit: upgrade for access to more AI. 3. Research your range, then let behavior decide We used Van Westendorp to find our starting range. Ask users four price points: too cheap to trust, good value, getting expensive, too expensive to consider. Plot where these intersect to bracket your range. Then test a few prices within it. Research shows what people say they'll pay - conversion shows what they actually do. We watched free-to-paid conversion and early churn signals, picked the winner, and moved on. 4. Instrument retention and talk to customers Track whether paid users keep crossing your value threshold each week. Stay close to customers through power-user communities or direct outreach. Ask questions like: "What job were you hiring us for?" and "What would justify a higher price?" 5. Treat pricing changes like product pivots Once you've validated pricing, the only reason to change it is if you've fundamentally changed what you're selling. We haven't changed ours in two years because the value metric (AI usage) hasn't changed. Constantly repricing means you're still searching for product-market fit. Why this matters: Pricing early clarifies who values you, which channels convert, and which segments to double down on. You're better off launching pricing way earlier so you can see who's actually willing to pay for it.

𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗲𝗶𝗴𝗵𝘁 𝘀𝗶𝗺𝗽𝗹𝗲 𝘀𝘁𝗲𝗽𝘀 𝗳𝗼𝗿 𝗖𝗙𝗢𝘀 𝘁𝗼 𝗺𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹𝘀... You need to know your numbers. No one else will. But how can you best monitor and analyze the financials? First an overview of the eight steps to improve... 1. Establish KPIs 2. Financial reporting 3. Variance analysis 4. Financial ratios 5. Forecasting 6. Financial planning 7. Technology and Analytics 8. Financial reviews ---------- 1️⃣ Establish KPIs Identify and track key financial metrics that are relevant to the organization. These may include revenue growth, profitability margins, cash flow, ROI, and working capital ratios. Establish benchmarks and targets to assess performance. 2️⃣ Financial reporting Implement a robust financial reporting system that provides timely and accurate financial information. Regularly create financial statements, including income, balance sheets, and cash flow statements. 3️⃣ Variance analysis Perform variance analysis to compare financial results against budgets, forecasts, and prior periods. Identify and analyze the reasons for significant variances. Use variance analysis to identify trends, opportunities, and potential risks. 4️⃣ Financial ratios Utilize financial ratios and KPIs to assess financial health and performance. These may include liquidity ratios, profitability ratios, efficiency ratios, and leverage ratios. Monitor changes in these ratios over time and benchmark them. 5️⃣ Forecasting Develop financial forecasting models and conduct scenario analysis to project future financial performance. Assess the impact of different scenarios on financials, like market fluctuations, pricing changes, and legal shifts. 6️⃣ Financial planning Collaborate with the executive team on the development of long-term financial plans, budgeting processes, and resource allocation. Provide financial insights and analysis for strategic initiatives, investment decisions, and growth strategies. 7️⃣ Technology and Analytics Use financial technologies and analytics tools to enhance financial monitoring and analysis. Implement data visualization tools to present financial information. Explore advanced analytics techniques, such as predictive modeling and data mining. 8️⃣ Financial reviews Schedule regular financial reviews with the executive team and relevant stakeholders. Present financial performance reports, discuss key findings and address any questions or concerns. Provide financial insights and highlight risks and opportunities. ---------- I have used these steps many times with success to create tangible results and business leaders are eager for you to step in and get it done. Are you currently following these eight steps? Anything you'd add or change? #finance #cfo #accountingandaccountants #analytics 🎧 Listen to our #FinanceMaster Podcast here: https://bit.ly/3NLSt73 🧑🎓 Learn how we can help your finance team here: https://bit.ly/3prsWXH

Profitability is #Amazon's top priority in 2026. Vendor Managers will happily delist 25% of your revenue if it helps stabilise their bottom line. This is new territory that brands now need to adjust to, which has left most sales teams that I've been talking to stunned. Amazon no longer wants to: ❌ List your entire assortment ❌ Gain market share ❌ Meet your growth targets Unless their profitability targets are met. Interestingly, profitability no longer just refers to Net PPM. It increasingly refers to internal Contribution Margin figures your VM has to report on. I've seen countless brands being CRAP'ed on Net PPM-accretive ASINs, because Amazon's CM figures were becoming unsustainable. That means you can no longer use Net PPM as the North Star in your trade relationship. Yes, cleaning up your distribution and launching Amazon exclusives still helps stabilise Amazon's bottom line. But vendors also need to address the hidden cost centres leading to profit erosion: That means: ✅ Fixing process defects leading to chargebacks ✅ Unlocking joint cost savings via SCM initiatives ✅ Improving packaging for SIPP/FFP ✅ Launching bundles to reduce variable costs ✅ Etc. If you're a CCO, SVP, or Commercial Director and you don't have a roadmap for the above for the ecommerce channel, your margins will continue to erode with Amazon in 2026. (And no, Amazon won't come to the rescue.) ♻️ Repost to share, and 💭 Comment your thoughts below. #amazonvendor #amazonstrategy

Should You Buy the Asset or its Producer? Are you better off allocating to Gold or Gold Miners? Bitcoin or BTC Miners? SPX or Brokerage Firm? Generally, it is a win-win, however on the margin, and performance is measured in relative value, one is better off in the asset than the producer. Yes, I would rather own gold or BTC than their miners, despite strong performance by the miners, as the 3 charts below demonstrate. When you buy the physical your exposure is directly tied to its price, including ETF of physical. The miners add an additional layer of risk and are often not long significant quantities of the physical in deliverable form. While the miners’ stock is impacted by the price of the physical, it is also impacted by management of the company and its decisions, labor costs, CapEx, production costs, regulatory risks, its capital structure, and debt costs to finance its operation, and operating costs and profit margins. Net-net, the physical commodity usually has less risk because of the additional consideration of the company’s operations and its equity risk. Look no further than Lehman Brothers and Bear Steans, two proud companies that failed (2009) v. the performance of S&P 500. Likewise, many Bitcoin miners collapsed during the dark white crypto winter just 2+ years ago and many more would’ve if not for the pivot to AI compute, or the gold miners that must contend with strikes and geopolitical risks. When you own the commodity, there isn’t operational risk. When capital allocators consider their portfolio mix and real assets including commodities it’s an interesting conversation for your investment team to ponder. Long term wealth creation can be captured by buying the equity of the producer, however, in select cases the physical asset has performed better over the long run for assets such as gold, BTC and SPX. Oil, industrial metals, food commodities which have a seemingly endless supply and are produced in large quantities globally - the result is the opposite since the companies most often outperform the physical commodity given supply dynamics for the commodity. As a creditor, we are active in financing strong low-cost producers, yet we pay close attention in underwriting a commodity producer to the commodity bear case scenario to ensure the company can withstand trough prices. Commodities are inherently volatile, and while there are times to be constructive, as a creditor we always focus on protecting the downside. Huge CapEx to build a mine or plant can take years with cost over runs and uncertain sales projections, as well as transportation costs to deliver the commodity to its distributor. My preference is always to see it operational before providing financing. Project finance is too often mis-priced relative to risk. The price for Gold & BTC is up significantly in the past 2+ years. Meanwhile the miners are relatively flat, as is the Commodity Index (CRB), including metals, food, lumber, and the oil/gas/energy complex.

ACCOUNTING SOFTWARE vs ERP SYSTEMS 🧮 💻 Financial systems make or break a business - but how do you know which one is right for YOU? Choosing the right financial management solution can save your business THOUSANDS of dollars while setting you up for future growth. ➡️ THE CORE DIFFERENCE Most folks think these are the same thing, but they're not. Accounting software handles your transactions and financial reporting. That's it. ERP is the entire orchestra - connecting everything from accounting to inventory to HR to sales. ➡️ FEATURE SHOWDOWN Your typical accounting package includes: - General ledger stuff - Accounts receivable/payable - Bank recon tools - Invoicing capabilities - Financial reports - Tax features - Some basic inventory tracking Meanwhile, ERP systems give you: - Everything above PLUS - Serious inventory management - Full warehouse systems - Supply chain tools - Complete HR management - Production scheduling - Data analytics/dashboards ➡️ WHICH SIZE FITS YOU? Accounting software makes sense if: - You're small/medium sized - Have one main location - Deal with simple finances - Run a service business - Have under 50 team members ERP becomes necessary when: - You're mid-sized or larger - Operate multiple locations - Manufacture or distribute products - Deal with complex financials - Have 50+ people on payroll ➡️ TIMING IS EVERYTHING With accounting software, you're looking at: - 1-2 weeks to pick one - A few days to set it up - 1-2 weeks for basic config - Another week for data transfer - Few days training the team - About a week to go live TOTAL: 1-2 months from start to finish ERP timelines are a whole different story: - 1-3 months just gathering requirements - 1-2 months picking the right system - Several weeks for initial setup - 2-4 months configuring everything - 2-6 months on customization - 2-5 months moving data and testing TOTAL: 3-6 months before you're fully operational ➡️ WHAT'S THE DAMAGE? Accounting software won't crush your budget: - $10-200 monthly per user - $0-2,000 for implementation help - $0-2,000 for training - 0-20% annual maintenance - Minimal IT investment TOTAL: As little as $50 / month to get started ERP systems demand serious investment: - $15,000 to $1M+ licensing - $150-500+ monthly per user - $50,000-250,000+ implementation & customization - Major IT infrastructure needs TOTAL: $10,000-250,000 minimum ➡️ POPULAR SOLUTIONS Accounting software players: - QuickBooks - Xero - FreshBooks - Wave Major ERP contenders: - NetSuite - Microsoft Dynamics - Acumatica - Odoo - SAP ➡️ MAKING THE RIGHT CALL The right solution comes down to several key factors: - Look at your 3-year plan. Multiple locations coming? ERP might save pain later. - Count your departments. More teams needing the same data = stronger case for ERP. - Be honest about your budget. Start with accounting software if money's tight. === What system runs YOUR finances right now? Drop me a comment below 👇

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

Gold miners not more than just a quick trade? 🏇 Bloomberg with an update on the gold mining sector that appeals to my contrarian instincts. Could this time 🕰️ be different? (Famous last words! ☺️) Here’s a summary of the article: Investors are pulling out of gold miner ETFs, suggesting fading enthusiasm for the sector—even as gold prices remain high. Gold-mining stocks have outperformed both gold itself and the broader market this year. The VanEck Gold Miners ETF, the largest in the sector, is up 57% year-to-date, compared to gold’s 24% rise and the S&P 500’s more modest gains. Despite that, investors have been steadily withdrawing funds from the ETF in every month of the year except May. The Sprott Gold Miners ETF also experienced outflows in May, even as gold reached record highs. “People are selling their shares of gold-mining ETFs into the rally,” said John Ciampaglia, CFA, FCSI, CEO at Sprott Asset Management. “We’re not seeing new money coming into the sector.” Several factors are contributing to the outflows. Past overspending by mining companies has made investors cautious, even though some firms have improved financial discipline. Greg Taylor, CIO at PenderFund Capital Management, said the sector is still seen more as a short-term trading opportunity than a long-term investment. The recent surge in mining stocks may also be prompting traders to seek better returns elsewhere—such as in tech and cryptocurrency. The Nasdaq 100 Index is up 10% since late April, slightly outpacing the VanEck ETF’s 8.4% rise in the same period. In a May 29 note, Bank of America Securities recommended investing in oil 🛢️over gold 🥇, citing relative valuations. Still, advocates for gold-mining stocks argue the sector remains undervalued. Rising gold prices have boosted miners’ profits, yet valuations are low. For instance, Newmont Corporation, the largest miner by market cap, trades at 13x forward earnings—below its five-year average of 20. Scotiabank's Ovais Habib noted that gold miners are being valued as if gold were $1,454 per ounce, far below the spot price of $3,380. Lower energy costs, including cheaper oil and diesel, could also enhance miners’ profitability by improving cash flow, according to Brooke Thackray of Global X ETFs. Additionally, ongoing gold purchases by central banks may continue to support gold prices and mining shares. Goldman Sachs estimates central banks are buying 80 metric tons of gold monthly, with central banks and sovereign wealth funds collectively acquiring 1,000 tons annually—about a quarter of yearly global production. “They’ve been in the market and they’re really price-indiscriminate,” Thackray said. (+++Opinions are my own. Not investment advice. Do your own research.+++) Enjoying my posts? Tap the 🔔 next to my photo and set it to 'All' and you'll be notified when I post. 💸