How to Extract Value From Data

Having a lot of data isn’t the same thing as having high-value data. If you’re having a hard time explaining that to executive leaders, try a different approach. Teach them how to put a dollar value on the business’s data. Every curated dataset creates new opportunities for the business, and that’s the connection between data and profit. The simplest data valuation method is called ‘With & Without’. The business thinks that every dataset creates the same value, so I run an early experiment to disprove that assumption. I turn off access to datasets that stakeholders believe are high value and wait for the complaints to roll in. In most cases, no one notices. Three months later, I propose putting the dataset into cold storage. Business leaders push back, saying their teams would grind to a halt without access to those datasets. I tell them about the experiment. Now I can start a rational conversation about connecting data to use cases and putting a dollar value on each dataset. Data doesn’t create value for two reasons: 1️⃣ It’s incomplete. The data required to support the use case isn’t being gathered holistically. Sometimes that’s an accessibility issue. Other times, the use case, workflow, and outcomes aren’t understood well enough to know what data is necessary. 2️⃣ It lacks context. Data points aren’t enough to support use cases. Context about the process, product, person, intent, and outcome is required. Until data is gathered contextually, its value creation is limited. Connecting datasets with opportunities creates the justification for changing how the business gathers and leverages data. Putting a dollar value on contextual datasets quantifies the ROI of information architecture and engineering initiatives. That’s the shortest path to getting budget and buy-in. Quantify value in terms that business leaders care about and show them a clear connection with outcomes they believe are essential.

"Data is our most valuable asset," they say. Cool. So where’s the P&L? If data is truly an asset, treat it like one. That means hard numbers: quantified costs, tangible value, and a clear strategy to increase the net value over time. Yet, when I look at most data strategies, the financial perspective is conspicuously absent. ▪ Data valuation? Handwaving at best. ▪ Revenue, cost, and ROI? Deferred to use cases. Maybe. ▪ Depreciation schedules? Crickets. Somehow, that "most valuable asset" often gets a free pass from the financial perspective. Imagine trying that with any other business asset. A data strategy worth its salt shouldn't flinch from the money talk. 1️⃣ How much cash is our data generating and saving? 2️⃣ What’s the cost and value of our data? 3️⃣ What's our plan to create and maintain high-value data? 4️⃣ How does the potential value of our data translate to measurable business outcomes? 5️⃣ How do we increase the net value of our data assets? You don't need the numbers down to the last digit. But the overall mechanics need to work out - otherwise, chances are you're probably nurturing a liability, not an asset. The future belongs to organizations that understand the connection between data and their top and bottom line. They'll be turning terabytes into treasure. Everyone else? Data hoarders, rich in bytes but poor in insights, wondering why their "valuable asset" is a money pit. Time to put in the work. Give your data a P&L – or admit that “data as an asset” is probably just another buzzword.

Unlocking Hidden Insights: How #Snowflake's AI_EXTRACT is Revolutionizing Data from Scans and Engineering Drawings For industries like Energy, Manufacturing, Infrastructure, and Finance, vital data is often sitting in scanned PDFs, images, and engineering drawings. Traditional methods are slow, manual, and rely on complex custom code or basic OCR that misses context. Enter AI_EXTRACT.. Snowflake's AI_EXTRACT (powered by Cortex AI) is an innovative LLM function that instantly extracts this data. It provides sophisticated content understanding—not just raw text. The Fun part? No complex model training or custom machine learning. You simply use a SQL query to tell the AI, in plain English, what information to extract from your staged document. Use Case: Engineering Drawings Imagine an energy company needing to inventory grid assets. Their data (e.g., base voltage, transformer counts) is trapped in complex schematics (datablock-1.pdf). Using AI_EXTRACT, you ask specific questions: "What is the Base Voltage of Bus 77?" and "How Many Transformers are in the diagram?" and the function returns the values directly as structured, queryable columns. Top Benefits: - Speed & Efficiency: Automate data extraction that once took hours of manual effort. - Accuracy: Reduce human error and gain structured data, not just raw text. - No Code/Low Code: Integrate powerful AI directly into your existing SQL workflows. - Scalability: Effortlessly process thousands of documents stored in the Snowflake Data Cloud. - Accessibility: Unlock data previously stuck behind specialized, expensive tooling. Key Industry Applications: - Utilities & Energy: Digitizing grid assets, infrastructure maps, and maintenance records. - Manufacturing: Extracting specifications from product designs and assembly instructions. - Construction & Engineering: Pulling crucial details from blueprints, schematics, and project documentation. - Finance & Legal: Automating data capture from legacy contracts, applications, and legal documents. #AI #GenAI #unstructureddata

Around 2 years ago, I attended a HubSpot admin HUG that changed my entire approach to reporting. Kit Lyman, a product manager for the reporting product, shared her framework around data and reporting. Ever since, I've been carrying this screenshot on my desktop whenever I want to discuss reporting with a client or a colleague. Some of you might look at this and think it's simple. But if you've ever tried to work with a leader that keeps asking for random reports until they give up and ask you for a CSV export of the data, you will find a lot of value in this. Whenever you're approaching a reporting exercise, always start your thought process with value, even though everything begins with data. The value of a report is the action that it informs. Your framework should look like this 👇 "I want to know X, so I can Y" For example, "I want to know how often my top reps engage with contacts, so I can establish activity goals." "I want to know my average deal size for new business, so I can implement a discounting strategy." "I want to know which email send times yield the most clicks, so I can adjust my sending schedule." Once your report statement is clear, its time to identify the elements you need to build that report 👉 Data Sources: Define primary and secondary sources, crucial for accurate joins. 👉 Dimensions: Categories or groups by which data is segmented (e.g., activity owner). 👉 Measures: Quantifiable metrics (counts, sums, averages, etc.). 👉 Detail: Additional specifics about activities or data points. The rest becomes an exercise of familiarity with your reporting tool of choice. If you want to check out that HUG, I'll leave a link 👇

As a CDO, how do you extract real business value from GenAI? Today, let me share a framework I use with my clients - the AI Value Pyramid™. This framework gives you a clear path to implementing AI, in synchronization with your data management capabilities. Here's how it works: Level 1: Productivity This is your entry point - where GenAI shines right now. Think email drafting, code documentation, and meeting summaries. There is minimal dependency here on your organization’s data maturity since it’s all about picking the best 3rd party tools/APIs already trained on external data. There is a first-mover advantage here, but no real competitive edge. Level 2: Information Retrieval Let’s take the simple example of law firms. Traditionally, highly paid associates spend countless hours reviewing boxes of documents, searching for critical information to support lawsuits. GenAI can perform these tasks much faster and more accurately, transforming the efficiency of legal practices. Similarly, call centers are slashing handle times by having GenAI fetch information across systems instead of agents doing it manually. But here's the catch - you need higher data maturity to make this work, especially when dealing with multiple systems. Level 3: Product & Experience Differentiation This is the game-changer. At this level, you're training models on your own data to create completely new value propositions. Think of using ML on your CRM data to identify high-probability prospects and having GenAI deliver those insights to your sales team in real time. The catch? This requires serious data maturity, governance, and skillset. To summarize, the AI Value Pyramid™ shows you the trajectory and all the moving pieces to go from AI-friendly to business value with AI. - Start with productivity. - Move up to information retrieval. - Win with product and experience differentiation. Your AI can only go as far as your data maturity.

One enterprise we spoke to faced what seemed like an impossible challenge: how to unlock analytical value from regulated industry data WITHOUT compromising individual privacy. The scale of the problem was massive. This organization was one of the world's largest collectors of unstructured data. It processes millions of forms daily, containing everything from personal health information to patterns of financial behavior. They serve industries including financial services and life sciences, two of the most heavily regulated on earth. The data sitting in their systems represented extraordinary business intelligence potential. It included modeling of financial risks, market trend analysis, research into patient outcomes. If they could glean insights from the data, it could transform entire sectors. But within the unstructured text, the data contained a minefield of personal information: names, medical conditions, financial details and countless other sensitive personal identifiers. Traditional approaches to solve this problem couldn’t work. Manual review couldn't scale to millions of forms. Blanket restrictions left valuable insights locked away. Broadstroke anonymization destroyed the utility in the data. Legal risk paralyzed innovation initiatives. What was needed was surgical precision. What was needed was to identify (and de-identify) sensitive information, while preserving the core analytical value that gave that data so much potential. This problem, and opportunity, was exactly the one we built Fides for. It can automatically detect personal information within unstructured data at massive scale, remove or synthesize identifying elements, and maintain data utility for sophisticated enterprise analysis and use. The result is that they can now safely leverage their data (literally decades of collected insights) to power things like research initiatives, business intelligence, innovation. For regulated industries sitting on similar data goldmines, this approach allows them finally to answer the question: How do we unlock value from our data, safely and at scale? How much analytical value is currently locked away in your organization's unstructured data?

Stop guessing what your customers want. Start stealing insights directly from your competitors' customers. Here’s my 5-step method to extract gold from public reviews (with prompts included): Step 1: Auto-collect reviews across platforms Prompt: Deep Research mode “Collect up to 100 English-language reviews for [Competitor Product/Service] from platforms like Amazon, Reddit, Google, and their official website. Include both praise and complaints. Organize into a platform-based table: Positive | Negative.” Why this matters: → Captures raw, unfiltered customer voice → Reveals praise + pain in one view → Forces GPT to mine multiple sources, not just one Step 2: Extract emotional pain points Prompt: “Analyze these reviews and identify 5 recurring customer pain points. Include real quotes and rate the emotional intensity (1-10).” Why this matters: → Emotional language = marketing gold → Filters out one-off rants → Prioritizes based on what customers feel most Step 3: Find the gaps no one’s solving Prompt: “Create a matrix showing unmet needs across all competitors. Highlight the most glaring market gaps.” Why this matters: → Exposes blind spots in the market → Compares multiple players → Spots real whitespace, not just noise Step 4: Validate before you build Prompt: “Generate 5 survey questions to test these unmet needs with my audience.” Why this matters: → Cheap way to de-risk ideas → Keeps research laser-focused → Helps you build what people actually want Step 5: Rank by ROI potential Prompt: “For each opportunity, estimate: revenue impact, dev complexity, time to market, and competitive advantage duration. Then rank them.” Why this matters: → Turns insights into action → Balances speed + strategy → Helps you make smart, fast moves Want help customizing this for your product or market? Drop what you're working on in the comments.

From Raw Data to Insights: A Simple Step-by-Step Guide If you’ve ever opened a dataset and thought, “Where do I even start?”, you’re not alone. Whether you’re a student, beginner, or data enthusiast, here’s a clear roadmap from raw data to meaningful reporting: 1. Understand Your Data: Start by exploring the dataset. What kind of information is inside—numbers (sales figures, test scores), text (names, comments), dates (birthdays, order dates)? Example: In a sales file, identify columns like Customer Name, Product, Quantity, Price, and Date of Purchase. 2. Clean the Data: Data is rarely perfect. Remove duplicates, correct typos, and decide what to do with missing values. Consistency is key. Example: If “USA” and “U.S.A.” appear in the country column, standardize them. If some prices are missing, you may replace them with averages or flag them for review. 3. Transform for Usefulness: Prepare the data so it can answer questions. Create new variables, restructure tables, or convert formats. Example: Calculate “Total Sales” by multiplying Quantity × Price, or group ages into brackets (18–25, 26–35, etc.) for clearer insights. 4. Analyze Thoughtfully: Now ask: What story is this data telling? Use descriptive stats like averages, totals, and percentages. Compare across groups to find patterns. Example: Which product sold the most last quarter? Which region had the highest growth? 5. Report with Clarity: Turn numbers into visuals. Use summary tables for detail and charts for trends. Keep them simple and easy to digest. Example: A bar chart to show top 5 products by sales, or a line chart to show revenue growth over months. 6. Tell the Story: Data becomes powerful when it connects back to decisions. Always answer “So what?” and link your findings to action. Example: Instead of saying, “Product A sales increased 20%,” say, “Product A’s growth suggests we should boost inventory and marketing in Q4.” 👉 The journey from raw data to insight is less about fancy tools and more about following a disciplined process. If you master these steps, tools like Excel, Power BI, SQL, R, or Python simply become enablers—not barriers. 💡 Over to you: When you first receive a dataset, what’s the very first thing you do? Watch one of my previous presentations on "Introduction to Data Analysis" on this YouTube channel >>> https://lnkd.in/d6fEhKkr for additional context. #DataAnalysis #DataVisualization #Mentorship #LearningData #StorytellingWithData Adeola Raji Tayo Asaolu, MBA Alawode, Gbadegesin MPH, B.Tech Ayokunle Faniku Datametrics Associates Limited Abdulmalik Abubakar, MPH, PMD Pro, PMP

🚨 𝐇𝐨𝐰 𝐭𝐨 𝐔𝐧𝐥𝐨𝐜𝐤 𝐕𝐚𝐥𝐮𝐞 𝐟𝐫𝐨𝐦 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐃𝐚𝐭𝐚 — 𝐛𝐲 DEUNA 👇 As commerce evolves into an intelligent, agent-driven ecosystem, payments data is emerging as a critical strategic asset — but one that remains vastly underutilized. This post outlines the key limitations holding teams back — and how leading merchants are transforming data into a competitive advantage. — 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐃𝐚𝐭𝐚 Today’s commerce stack generates rich and interconnected data from multiple systems: → Payments Data: transaction metadata, approval rates, methods, issuer response → Orders Data: cart value, SKUs, timestamps, discounts → Identity Data: device fingerprinting, customer behavior, fraud signals → Other Sources: CRM, logistics, loyalty, geography, and more — 𝟑 𝐒𝐲𝐬𝐭𝐞𝐦𝐢𝐜 𝐁𝐥𝐨𝐜𝐤𝐞𝐫𝐬 𝐓𝐨 𝐕𝐚𝐥𝐮𝐞 𝐑𝐞𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 1️⃣ Fragmented & Inconsistent Data → 80% of data teams’ time is spent cleaning instead of analyzing → Siloed PSPs, fraud tools, and BI platforms prevent unified decision-making 2️⃣ Context-less AI doesn't deliver  → 90% of AI projects fail to operationalize → Without business context, generic AI outputs remain disconnected from strategic goals 3️⃣ Constant Firefighting, Minimal Strategy → Operational teams are stuck responding to incidents instead of driving outcomes — 𝐇𝐨𝐰 𝐋𝐞𝐚𝐝𝐢𝐧𝐠 𝐓𝐞𝐚𝐦𝐬 𝐔𝐧𝐥𝐨𝐜𝐤 𝐕𝐚𝐥𝐮𝐞 The process to transform raw payments data into actionable intelligence: 1️⃣ Data Ingestion → Consolidating data streams from PSPs, checkout systems, CRMs, fraud tools, and commerce platforms into a central layer 2️⃣ Variable Definition & Structuring → Translating raw fields into business metrics (e.g., GMV, approval rate, ARPU) 3️⃣ Real-Time Monitoring & Alerts → Establishing live observability for key indicators (e.g., spikes in declines, fraud signals, latency issues) 4️⃣ Cleaning, Deduplication & Standardization → Removing duplicates, normalizing formats (e.g., date, currency, identifiers) 5️⃣ Industry-Specific Contextualization → Applying domain knowledge (e.g., travel refunds, retail loyalty...) to reclassify and enrich data 6️⃣ Addition of Exogenous Variables → Integrating external factors such as FX volatility, holidays, weather, or marketing campaigns to reveal hidden correlations and refine models. 7️⃣ AI Implementation → Using ML and intelligent agents to detect anomalies, identify high-impact opportunities, and automate routing, retries... in real time. — 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧: 𝟑 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐓𝐡𝐞 𝐍𝐞𝐰 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 1️⃣ Unified Data Foundation 2️⃣ Context-Aware Opportunity Detection 3️⃣ Real-Time Execution on Intelligence → Automate retry logic, fraud triggers, routing adjustments, and more — Source: DEUNA ► Subscribe to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬: https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... | Marcel van Oost

𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐖𝐢𝐬𝐝𝐨𝐦 𝐅𝐮𝐧𝐧𝐞𝐥 - 𝐅𝐫𝐨𝐦 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐨 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 I recently came across a beautiful sketch illustrating T.S. Eliot’s poetic words about the transition from data to wisdom, and it resonated deeply with the work we do in Data. 𝐄𝐥𝐢𝐨𝐭'𝐬 𝐰𝐨𝐫𝐝𝐬: "Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information? Where is the information we have lost in data?" These lines speak to the subtle but crucial progression of turning raw data into actionable wisdom—something many of us in tech live by. In our world of data science, this evolution isn't just philosophical; it's practical, forming the backbone of how we build systems, make decisions, and create value. Let me take this opportunity to reflect on a similar progression in our field—a funnel that transforms Data into Wisdom with distinct stages and roles that each have a part to play: 𝟏. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 This is the foundation. Data engineers ensure raw, unstructured data is collected, processed, and stored efficiently, preparing it for the journey downwards. They create the pipelines that bring data to life—an essential first step. 𝟐. 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Here, data becomes information. Analysts dive deep, using tools like SQL and visualization platforms to extract meaningful insights from the data. The focus is on understanding what happened and answering key business questions. 𝟑. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 Data science transforms that information into knowledge. Through statistical analysis, experimentation, and modeling, data scientists not only describe the past but also predict the future, adding layers of understanding to drive better decisions. 𝟒. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 Machine learning takes that knowledge and applies it repeatedly to solve problems at scale, bringing a system closer to something like wisdom. With every iteration, the models learn, adapt, and improve, leading to more accurate predictions and deeper insights over time. Just like a seasoned chef who starts with raw ingredients and over time develops an intuitive understanding of how flavors blend, we, too, move through these stages in our data journey. With enough practice, we can build that “instinct” for how data behaves, spotting patterns and trends that lead us to insights. As we consider this funnel, it's important to recognize that sometimes one person might tackle every stage—from engineering the data pipelines to applying machine learning models. Other times, we might focus on mastering just one role within the funnel and collaborate with others to complete the picture. In the end, the goal isn't just to understand data—it’s to create value, make an impact, and hopefully, in some cases, reach the wisdom that comes from mastering our craft over time. ♻️ Repost this if you found it helpful! 👋 Follow for daily data and career insights!