Tech Market Analysis Tools

Revolutionizing Data Integration: ETL, ELT, and Reverse ETL in the AI Era In today's data-driven world, efficient data integration is crucial for businesses to gain insights and make informed decisions. Let's dive into the evolution of data integration techniques and how AI is reshaping the landscape. ETL: The Traditional Powerhouse Extract, Transform, Load (ETL) has been the go-to process for decades. It involves: 1. Extracting data from various sources 2. Transforming it to fit operational needs 3. Loading it into the target system (usually a data warehouse) Enter ELT: Flipping the Script Extract, Load, Transform (ELT) emerged with the rise of cloud computing and big data. The key difference: - Data is loaded into the target system before transformation - Leverages the power of modern data warehouses for transformation - Offers more flexibility and scalability Reverse ETL: Closing the Loop A newer player in the field, Reverse ETL: - Moves processed data from warehouses back into operational systems - Enables data activation, turning insights into action - Bridges the gap between analytics and operations AI: The Game Changer Artificial Intelligence is revolutionizing data integration: - Automating data mapping and transformation rules - Identifying data quality issues and anomalies - Optimizing data pipelines for performance - Providing predictive maintenance for data workflows Tools of the Trade Open Source: - Apache NiFi - Talend Open Studio - Airbyte Proprietary: - Informatica PowerCenter - IBM DataStage - Fivetran As data volumes grow and complexity increases, mastering these techniques and leveraging AI will be key to staying competitive. What's your take on the future of data integration?

Most people lump PostGIS, DuckDB, and Apache Sedona into the same mental bucket. They’re not. And if you don’t understand the difference, you’re either over engineering your workflows or breaking things at scale. Here’s the breakdown: 🐘 OTAP (Operational Transactional Analytic Processing) → PostGIS What it’s good at: Small- to mid-scale operational workloads. Think: a city planning department running spatial queries or updates against parcels, buffers, or intersections. It shines when you need transactional consistency, live updates, and integration with operational apps. What it’s not good at: PostGIS is not your friend when you dump 500M records of building footprints or retrieve a country-size batch of road data. You’ll end up with timeouts and an angry DBA. Running live queries to support a permitting system in a mid-sized city → ✅ Querying global-scale datasets on PostGIS → ❌ 📈OLAP (Online Analytical Processing) → DuckDB, Snowflake, BigQuery, Redshift What it’s good at: Analytical slicing and dicing of large but mostly tabular datasets. Think: millions (or billions) of rows of geotagged events, aggregated by region, month, or customer. They’re optimized for scans, filters, group-bys. Pair with formats like GeoParquet and you’ve got fast analytics. What it’s not good at: OLAP engines aren't the best at heavy processing. Even clipping NYC sidewalks by buffers around trees fell flat. They don’t handle complex geometries, raster data, or more complex spatial problems. You might hack around it with extensions or UDFs, but that’s like bringing a butter knife to cut down a tree. Example: Calculating average delivery times by ZIP code across billions of trips → ✅ Running viewshed analysis or cost distance models in an OLAP → ❌ 🏭 Processing Engines → Databricks, Wherobots, Sedona, Dask What they’re good at: Heavy lifting. These are built for ETL pipelines, spatial joins across hundreds of millions of features, distributed raster analysis, machine learning, and crunching petabytes of data. This is the layer where you do the big lift before pushing summaries downstream. What they’re not good at: You don’t spin up Spark to answer “how many parcels intersect this buffer?” Also, they’re not your transactional system: no one is building a permit approval workflow directly on Spark. Example: Intersecting 780M building footprints with global flood rasters → ✅ Checking whether a bike rack falls inside a single polygon → ❌ 👉 Don't do everything in one tool. The modern spatial stack is about using the right tool at the right layer so you don’t waste time (or money) fighting against the wrong one. 🌎 I'm Matt and I talk about modern geospatial. 📬 Want more like this? Join 8k+ others learning from my newsletter → forrest.nyc

Machine learning beats traditional forecasting methods in multi series forecasting. In one of the latest M forecasting competitions, the aim was to advance what we know about time series forecasting methods and strategies. Competitors had to forecast 40k+ time series representing sales for the largest retail company in the world by revenue: Walmart. These are the main findings: ▶️ Performance of ML Methods: Machine learning (ML) models demonstrate superior accuracy compared to simple statistical methods. Hybrid approaches that combine ML techniques with statistical functionalities often yield effective results. Advanced ML methods, such as LightGBM and deep learning techniques, have shown significant forecasting potential. ▶️ Value of Combining Forecasts: Combining forecasts from various methods enhances accuracy. Even simple, equal-weighted combinations of models can outperform more complex approaches, reaffirming the effectiveness of ensemble strategies. ▶️ Cross-Learning Benefits: Utilizing cross-learning from correlated, hierarchical data improves forecasting accuracy. In short, one model to forecast thousands of time series. This approach allows for more efficient training and reduces computational costs, making it a valuable strategy. ▶️ Differences in Performance: Winning methods often outperform traditional benchmarks significantly. However, many teams may not surpass the performance of simpler methods, indicating that straightforward approaches can still be effective. Impact of External Adjustments: Incorporating external adjustments (ie, data based insight) can enhance forecast accuracy. ▶️ Importance of Cross-Validation Strategies: Effective cross-validation (CV) strategies are crucial for accurately assessing forecasting methods. Many teams fail to select the best forecasts due to inadequate CV methods. Utilizing extensive validation techniques can ensure robustness. ▶️ Role of Exogenous Variables: Including exogenous/explanatory variables significantly improves forecasting accuracy. Additional data such as promotions and price changes can lead to substantial improvements over models that rely solely on historical data. Overall, these findings emphasize the effectiveness of ML methods, the value of combining forecasts, and the importance of incorporating external factors and robust validation strategies in forecasting. If you haven’t already, try using machine learning models to forecast your future challenge 🙂 Read the article 👉 https://buff.ly/3O95gQp

Grateful to be featured in the "Shoptalk Hot Takes" interview by Blenheim Chalcot and ClickZ.com alongside George Looker to unpack omnichannel commerce. 5 key takeaways and tactics from my conversation: 1. Design for Customer Continuity, Not Just Channel Expansion 💡 71% of customers expect brands to personalize interactions across every touchpoint. Tactical: Map out customer journey across channels, then design experiences that recognize and reward continuity—cart persistence, loyalty rewards, browsing history sync, etc. 2. Build the Infrastructure: Unify Data Streams Across All Touchpoints 🧠 Data fragmentation = missed opportunity Tactical: Integrate POS, e-commerce, mobile, social, and marketplace data into a centralized data lake or unified commerce platform. 3. Establish a Single Source of Truth for Customer Profiles 🔍 Brands with unified profiles see up to 2x better campaign performance. Tactical: Implement Customer Data Platforms (CDPs) to consolidate behavioral, transactional, and engagement data into unified customer profiles. 4. Partner Strategically for Scale, Not Just Stack ⚙️ A bloated tech stack doesn’t equal agility As I noted, Retailers are getting sharper about which partners can scale with them. Ecosystem efficiency matters more than ever. Tactical Step: Audit your tech stack and partnerships consistently. Prioritize partners that offer extensibility, future-proofing, and proven omnichannel success. 5. Measure What Matters: Unified KPIs Across Commerce 📈 You can’t optimize what you don’t measure holistically Tactical: Align your analytics stack to report holistically across channels—tie marketing to merchandising, CX to LTV, and inventory to revenue. 🧠 Bottom line: think holistically, move strategically, and build ecosystems that scale experience with agility, not just transactions. Complete list in comment 👇 #ecommerce #omnichannel #unifiedcommerce

✨ What’s New Wednesday: Refreshed in-product industry benchmarks with the latest data from 2023 for metrics like time to fill, offer accept rate, and DEI representation in your funnel 📊 As a data nerd, this one’s pretty cool… In 2018, Gem launched the first full-funnel analytics product of it’s kind (Talent Compass), where we combined all the data from your CRM (Gem) with all the data from your ATS. The response was incredible. Recruiting operations & leadership had never seen this level of data and insights. Neither had the C-suite or the board. Finally, the TA industry had access to the same level of data that Sales & Marketing teams had had for years. The very next question for so many customers became…  → is this a good pass-thru rate?  → is the diversity representation in my funnel any good?  → how does my engineering offer accept rate stack up? Essentially, customers wanted to know how their data compared to the industry, and more specifically, to other companies… 𝗟𝗜𝗞𝗘 𝗧𝗛𝗘𝗠? Fast-forward to 5 years later. Gem now has the most comprehensive benchmarks in the industry for key metrics like offer acceptance rates, time-to-hire, and DEI representation, and we’ve built them directly into Talent Compass dashboards.   1️⃣ These benchmarks, drawn from Gem's unique dataset of 1,000s of customers and 28 million candidate data points, enable our customers to measure their performance against some of the world's top recruiting teams. 2️⃣ Given the wide range of Gem customers across industries, segments, and regions, we've designed Talent Compass to be customizable. 3️⃣ Customers can tailor benchmarks to their specific industry, location, company size, and more, ensuring relevant comparisons. Now that 2023 is over, we’ve refreshed the dataset for these benchmarks with the latest data from the past year so you can use the latest trends and patterns from this past year to help inform strategic goals and priorities for 2024.

Entering a market isn’t guesswork. It’s math. And the equation is simpler than you think. When a new player shows up, incumbents move fast: → Drop prices until rivals run out of cash → Lock up distributors and suppliers → Flood the market with brand spend → Sign long contracts with penalties → Lobby regulators to raise barriers That’s 5 of 10 ways big companies protect their turf. For new entrants, fighting head-to-head rarely works. The smarter play is partnership. Instead of burning years and millions, you can borrow scale, credibility, and access. Here are 5 proven ways to do it: Co-distribution ⤷ Partner with a non-competitor who already sells to your target customers ⤷ You get reach without building your own network. Joint innovation ⤷ Collaborate with an incumbent to launch a new product ⤷ You share costs and inherit their credibility White-label supply ⤷ Sell your product under an incumbent’s brand ⤷ You scale quietly, while learning how the market really works Adjacent alliances ⤷ Enter through a related industry ⤷ Bypass the strongest defences Anchor partnership ⤷ Land one marquee partner ⤷ Their endorsement signals trust and opens doors The question is: how do you know if you have a real chance? Use the Entry Equation. Success Score = (Distribution × Incentive × Differentiation) ÷ (Switching + Regulatory + Capital) Score each factor 1–5 (5=Excellent): • Distribution Access • Incumbent Incentive • Differentiation • Switching Costs • Regulatory Barriers • Capital Intensity Interpretation: 0–5 = Low viability 6–10 = Conditional entry 11–15 = Strong entry Need an example? An EV battery startup partners with a Tier-1 auto supplier. Here's the assessment: • Distribution = 4 • Incentive = 5 • Differentiation = 5 • Switching = 3 • Regulatory = 4 • Capital = 3 Score = (4×5×5) ÷ (3+4+3) = 10 Interpretation → Conditional entry The path forward: reduce regulatory drag or switching pain This is how experienced CEOs think about market entry. Not just, “Can we compete?” But, “Who can we partner with to get through the defences?” Remember: Go-to-market partnerships aren’t a growth lever for new entrants. They’re the only way in. --------------------------- Was this helpful? Get cheatsheets like this each Wednesday. Subscribe to my free newsletter: https://philhsc.com ♻️ Repost this to help a founder or CEO assessing a new market ➕ Follow me, Phil Hayes-St Clair for more like this

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

Startup valuations have traditionally relied on qualitative assessments—evaluating founders’ experience, market potential, and investor sentiment. But in today’s fast-paced ecosystem eChai Ventures, investors Udit Goenka and incubators Jatin Chaudhary are turning to predictive analytics and AI-driven insights to make smarter, data-backed decisions. 𝑯𝒐𝒘 𝒊𝒔 𝑨𝑰 𝒓𝒆𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏𝒊𝒛𝒊𝒏𝒈 𝒔𝒕𝒂𝒓𝒕𝒖𝒑 𝒗𝒂𝒍𝒖𝒂𝒕𝒊𝒐𝒏𝒔? 📊 Data-Driven Market Analysis: AI models analyze market trends, competitive landscapes, and customer demand to estimate potential market size more accurately. 📈 Financial Health Assessment: Machine learning models evaluate burn rates, revenue projections, and cash flow patterns to predict long-term financial stability. 🤖 Founder & Team Evaluation: AI can assess founders' track records, leadership skills, and even social sentiment to predict their likelihood of building a successful company. 📡 Predicting Growth Trajectories: Advanced analytics track key metrics like user acquisition costs, retention rates, and unit economics to forecast a startup’s scalability. 🚀 Risk Mitigation: AI-powered due diligence helps investors identify red flags early, reducing the risk of overvaluation or investing in unsustainable ventures. With AI-enhanced decision-making, the guesswork is fading, and data is taking the lead. While human intuition remains essential, startups that embrace data transparency will gain a competitive edge in attracting investors. Mohit Sureka 💬 𝑫𝒐 𝒚𝒐𝒖 𝒕𝒉𝒊𝒏𝒌 𝑨𝑰 𝒘𝒊𝒍𝒍 𝒄𝒐𝒎𝒑𝒍𝒆𝒕𝒆𝒍𝒚 𝒓𝒆𝒑𝒍𝒂𝒄𝒆 𝒕𝒓𝒂𝒅𝒊𝒕𝒊𝒐𝒏𝒂𝒍 𝒔𝒕𝒂𝒓𝒕𝒖𝒑 𝒗𝒂𝒍𝒖𝒂𝒕𝒊𝒐𝒏𝒔, 𝒐𝒓 𝒘𝒊𝒍𝒍 𝒉𝒖𝒎𝒂𝒏 𝒋𝒖𝒅𝒈𝒎𝒆𝒏𝒕 𝒂𝒍𝒘𝒂𝒚𝒔 𝒑𝒍𝒂𝒚 𝒂 𝒓𝒐𝒍𝒆? #startupecosystem #startups #Startupvaluation #DataDrivenDecisionMaking #Fundraising

How I use Claude.ai to save time and hassle chasing submissions from businesses We’ve all had to wait on forecast information, or received incomplete submissions and then had to chase. The good news is that automating this task, which isn’t my favourite(!) is quick and easy using AI. To give an example, I’ve generated a script using a simple prompt in Claude.ai to read reports (in this case csv, word, pdf and powerpoint) and query unclear items or where an explanation is needed. This saves time and allows me to move onto other things. Here’s what the script does: Reads reports in CSV, Word (DOCX), PDF, and PowerPoint (PPTX) formats Automatically detects the file type and processes accordingly Analyzes the report content for unclear items or those needing explanation Generates a formatted HTML email with the findings Sends the email to relevant stakeholders Saves a copy of the email content as an HTML file for record-keeping This versatile automation can save hours of manual work across different document types and ensure that important issues are promptly addressed. And it’s not just useful for treasurers, it can be used by project managers, team leads, and anyone dealing with regular reporting processes using various file formats. Key features of the script: Uses pandas for data manipulation Implements file type detection and appropriate reading methods Utilizes libraries like python-docx, PyPDF2, and python-pptx for different file formats Generates clean, formatted HTML emails using Arial font Easily customizable for different report formats and email content I've included the full code in the comments below. Feel free to adapt it to your needs or reach out if you have any questions! This is just one example of the type of automation that we’re focussing on at Your Treasury - AI your way I’d encourage you to go to Claude.ai and try creating something similar yourself. #Python #Automation #ProjectManagement #DataAnalysis #EmailAutomation #DocumentProcessing ——————————————————- Example prompt Create a Python program where a user uploads a file and python then detects the file type (doc, excel, pdf, PowerPoint) and Python then reads a report and send out emails to relevant stakeholders if anything is unclear or needs more explanation. Produce example code to show this, including the report and a sample email which should be saved as an html file and formatted in Arial text. Prompt me if you have any questions

Data-backed decisions will always outperform guesswork. Test small, learn fast, and scale smart. As global trade dynamics shift, brands must adapt quickly and strategically. Here are four key strategies to help you evaluate new markets in today's landscape: 1. Test New Markets with Purpose Market testing isn't just a buzzword, it’s a structured approach to learning. Start with a small advertising budget to run targeted campaigns and gather actionable insights. Which products resonate? What messaging converts? Remember, a hero product in Australia or the US might fall flat in France, the UK or Korea. Track performance by product and by region. A top-seller in one market could be unprofitable elsewhere due to preferences, competition or costs. 2. Speak Directly to Your Customers Already have customers in the EU? Don’t just analyse their data, speak to them. Why did they choose your brand? Where do they usually shop? Would they buy again? These conversations uncover real, on-the-ground insights that data alone can’t provide. Use this qualitative input to inform your go-to-market strategy and better understand your competitive positioning. 3. Diversify Your Supply Chain Tariffs aren’t just a sales problem, they’re a cost structure issue. Consider whether your manufacturing partners can support a split shipment strategy or help mitigate the impact through alternate production hubs. Explore supplier networks in countries less impacted by tariffs. Nearshoring or reshoring might be more viable than you think, especially when factoring in lead times, shipping costs, and political risk. 4. Consider Local Partnerships and Market Entry Support Entering a new market doesn't mean going it alone. Look into local distributors, marketplace platforms, or fulfilment partners who already understand the regulatory environment and consumer behaviour. Strategic partnerships can speed up validation and reduce the cost of entry. When market dynamics shift this is when the true entrepreneurial opportunity to re-write the game comes to the forefront.