Nathan A Cunningham’s Post

So, we recently had this client who was in a bit of a pickle with their data lake setup. Their data was all over the place, making it a real pain to get any useful insights out of it. They were feeling pretty overwhelmed and stuck. But fear not! Our team swooped in to save the day. We sat down with the client, got the lowdown on their needs and goals, and got to work on a solution. Fast forward a bit, and voila! With our data lake in place, everything's running like a dream. The client's got all their data neatly organized in one spot, making it super easy to pull out those juicy insights and make some serious business moves. Our fancy algorithms and machine learning magic have even got them predicting customer needs and spotting trends before they happen. Seeing the relief and excitement on the client's face when they saw what our solution could do? Priceless. We're all about delivering results that make a real difference, and this was a prime example. So, if you're dealing with data chaos and need a hero to swoop in and save the day, look no further. We've got your back.

Obvious but still worth reading to get bit of common sense. Data is only one of inputs when running and building a product. https://lnkd.in/dxXmJ-G2

Data-driven, data-informed or data-ignorant? Which type of organisation are you? Check out this read, those guys know what they're talking about.

Decision Making : At each internal node, the tree makes decisions by evaluating one of the input features. Click for more https://bsapp.ai/flOTBxOPF #MachineLearning

Unlock the Power of Predictive Analysis with Linear Regression 👇 The key to mastering linear regression? Datasets. Here’s why they’re so important: 1. Foundation for Analysis: Datasets provide the raw data you need to build and test regression models. Without data, there’s no way to uncover relationships between variables. 2. Establishing Relationships: Linear regression helps you discover how independent variables influence the dependent ones, whether that’s predicting sales based on advertising spend or housing prices based on location. 3. Making Predictions: Historical data allows you to forecast future outcomes. From sales projections to stock trends, linear regression models can give you valuable insights. 4. Quantifying Impact: With the right dataset, you can measure how a change in one variable impacts another. For example, understanding how a 1% increase in marketing spend affects overall revenue. 5. Evaluating Model Performance: Using datasets, you can test and validate your model’s accuracy. This ensures your predictions hold up when applied to new data. 6. Data-Driven Decision Making: Datasets transform guesswork into evidence-based insights, helping you make informed decisions and optimize strategies. 7. Handling Complexity: Real-world data is often complex, and good datasets help capture that complexity. Linear regression allows you to break down these relationships into something actionable. Sign up now - https://dataful.in/ ------------------------------------------------------- P.S.: Dataful offers access to 16000+ clean, accurate, and easy-to-use datasets on 50+ Indian sectors. Join 8500+ trusted users to create impactful models or research work. #data #bigdata #datascience #linearegression #machinelearning #datadriven #datainsights #quantitativeanalysis #trustworthy #accurate #reliable

🛠️ DATA PRE-PROCESSING SECRETS UNVEILED!🚀✨ Embark on the transforming voyage of data, where raw becomes polished! 🔄 🔧 Pre-Processing for Excellence in Crafting 1. Feature Selection: Picking the stars that will shape insights! 2. Categorical Feature Encoding: Cracking the Code to Unlock Categorical Stories! 3. Scaling Wonders: Unleashing Standardised Power for Fair Comparison! 4. Balancing Act: Correcting class imbalances for unbiased model insights! Join the Data Revolution, where every preprocessing move is a deliberate stride towards powerful insights! 💡🌐 #datapreprocessing #datatransformation #linkedinanalytics 💼

I am always curious about how DataFlow works. starting the journey.

We've been busy refining our product-market fit with initial customers, including a promising 50-60 person company that could become a key lighthouse client. Product updates: Crawling multiple customer-specified websites Extracting unique insights using LLMs Organizing facts by categories (company overview, pricing, features, use cases) Developing two key outputs: Activity feed for real-time updates Weekly/biweekly newsletter summarizing changes across tracked companies We're focusing on vertical markets, particularly in the observability space, to optimize our resource-intensive process of website crawling and LLM processing. Looking ahead, we're exploring a graph RAG (Retrieval-Augmented Generation) approach. This involves entity and fact extraction to create a traversable graph format, potentially improving accuracy and efficiency in our information processing pipeline.

Frustrated by hidden customer insights in data silos? Thor Olof P., CEO and CO-founder of Stravito, shares his mission to empower EVERYONE in your company with actionable knowledge. Discover how Stravito creates an "internal Google" for businesses, unlocking true competitive advantage through data-driven decisions. Read the excerpts from the interview;

Observability isn’t just about catching issues—it’s about seeing the full picture with confidence. With our insight engine, you get a bird’s eye view across your warehouse, dbt models, and BI tools, surfacing issues that matter most. These aren’t just alerts; they’re context-rich insights that take you to resolutions letting your team build trust in the data from the ground up. When you know what’s happening and how to fix it, you confidently drive results