80% OF DATA IS UNSTRUCTURED Here’s what we mean: Data exists across different systems, locations, and sometimes, formats and versions. Emails between customers and sales reps. Feedback in spreadsheets. Call notes on a shared doc. (Chaotic. 😵💫) We all know AI can help, but how do you actually get started? 1. Get the data right. Data lives beyond the obvious places, and often without business context. But without access to this data, any analysis will be incomplete. 2. Translate unstructured data into structured data. Even the simplest business questions start with correlating discrete, structured values. 3. Know that LLMs won't perform magic. They’re not built for classification tasks, which are the mechanism for converting unstructured data to discrete values. Gong CPO and co-founder Eilon Reshef shares his tips on how to unlock business value from your data today in the latest publication of The Edge. Check it out here: https://lnkd.in/gfpXF_sf
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🤖 Sublimity AI bridges the gap between business experts who need data-driven insights and technical teams responsible for managing enterprise data. 🔍 Imagine a CFO asking, 'What was our mid-market retention last quarter?' This question often ends up with a data analyst who may lack the original context. What exactly is 'mid-market'? Does 'retention' refer to revenue or customer numbers? ❌ The outcome? Delayed or irrelevant insights. And trust in the data team is undermined by a lack of reproducibility with hidden and evolving assumptions built into the analysis. ✅ We are building a platform that helps business users ask the right questions—questions directly answerable with existing structured data. Meanwhile, technical teams experience 10x productivity gains delivering consistent, meaningful answers. 📆 Schedule a demo here: https://lnkd.in/gwjpjMSE
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Fact: "45% of data leaders believe improving how data is used in business decision-making and operational processes is pivotal for an effective data strategy" My learnings: * It is hard to implement AI if you don't have a data strategy * It is hard to implement AI if you don't have data infrastructure * It is hard to implement AI without knowledge of data engineering 👀 reactions inbound? 💣
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Data Drowns Without Direction Do you feel overwhelmed by the data deluge? I’ve navigated these waters, transforming data chaos into clear, actionable insights. Success Dashboard: Your new command center. Tailored for consultants who value precision and efficiency, this tool isn't just about displaying numbers—it's about understanding them and making them work for you. Here's how to harness the true power of your data: 💡 Elevate Your Service Offerings: Integrate compelling, smart data visualizations and real-time analytics into your consultancy. Show your clients not just numbers, but insights. 💡 Boost Client Conversion & Retention: With our API integrations and conditional alerts, you'll keep your clients ahead of risks and opportunities. 💡 Enhance Time & Client Management: Automated action plans and synced calendars free you up from the minutiae, so you can focus on strategy. 💡 Expand Your Impact: More efficient operations mean you can help more clients, more profoundly. 💡 Lead with AI Innovation: Position yourself at the forefront of consultancy tech with our AI Advisor, Huxley. Are You Ready to Master the Data Deluge? Don't let your data potential slip away! Transform data from overwhelming to overachieving starting now: http://qrco.de/bevqpD ✍️ Interested in seeing how it works? Comment below and I'll respond to all comments! ♻️ Know someone overwhelmed by data but underwhelmed by results? Share this—they’ll thank you later.
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2nd Runner Up CFA RC '24 National | Finance | Data | Design | Fin-Tech | ESG | Emerging Technologies | Strategy & Transformation | Private Equity | Venture Building | Consulting
Excited to share my experience with this fantastic course on Excel for Business Analysts. This course gave a thorough introduction, which was ideal for enthusiastic analysts like me. Here's a breakdown of the key takeaways: Foundations: Clarifying the differences between business analysis and project analysis. Data Powerhouse: Analyzing data aggregation strategies from several databases. Data Integrity is Important: Learn how to clean Excel data and avoid data bias to ensure the soundness of our study. Predictive Analytics: Understanding how businesses foresee in a range of scenarios, including key investment decisions. Data-Driven Decisions: Learned how to understand business analytics outputs, identify patterns, and conduct variance analysis to compare actual and predicted outcomes. The Future of Business Analytics: Investigated the exciting promise of Artificial Intelligence, including generative AI tools such as Copilot and ChatGPT, while also recognizing possible biases and limitations. Excel is a formidable force when it comes to substantial business analysis; these insights demonstrate its capacity to assist strong business decisions. https://lnkd.in/dFvjCHwC #microsoftexcel #businessanalysis
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🔍 𝐇𝐨𝐰 𝐭𝐨 𝐌𝐚𝐱𝐢𝐦𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚'𝐬 𝐕𝐚𝐥𝐮𝐞 𝐛𝐲 𝐒𝐞𝐭𝐭𝐢𝐧𝐠 𝐂𝐥𝐞𝐚𝐫 𝐆𝐨𝐚𝐥𝐬 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐭𝐡𝐢𝐬 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐢𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞𝐟𝐮𝐥 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧: By defining specific goals, you ensure that every piece of data collected serves a clear purpose. This minimizes irrelevant data and maximizes relevance, leading to more informed decisions. 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐅𝐨𝐜𝐮𝐬: Goals provide a roadmap, helping your team stay focused on what truly matters. This focus translates into more efficient data analysis and better insights. 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲: When data is collected with a clear purpose, its accuracy and quality improve. Goal-driven data collection helps filter out noise and ensures consistency. 𝐀𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Data aligned with strategic goals is more likely to yield actionable insights. These insights drive impactful decisions that propel your organization forward. 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: By aligning data collection with business goals, you optimize resource allocation. This prevents wastage of time and effort on irrelevant data and directs resources towards valuable information. 𝐀𝐜𝐭𝐢𝐨𝐧 𝐒𝐭𝐞𝐩𝐬 𝐭𝐨 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐆𝐨𝐚𝐥-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧: 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐊𝐞𝐲 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: Start by defining the core objectives that data will support. 𝐒𝐞𝐭 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐃𝐚𝐭𝐚 𝐆𝐨𝐚𝐥𝐬: For each business objective, set clear and measurable data goals. 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐃𝐚𝐭𝐚: Assess your existing data against these goals. Identify gaps and areas for improvement. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐳𝐞 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Develop a plan to collect data that aligns with your goals, ensuring relevance and quality. 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲 𝐑𝐞𝐯𝐢𝐞𝐰 𝐚𝐧𝐝 𝐀𝐝𝐣𝐮𝐬𝐭: Continuously review data against goals and adjust strategies as needed to stay on track. 𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚’𝐬 𝐯𝐚𝐥𝐮𝐞 𝐚𝐧𝐝 𝐮𝐭𝐢𝐥𝐢𝐭𝐲? Schedule a call with us today for a comprehensive AI & Data Audit. Together, we'll identify opportunities, align your data strategy with your business goals, and set you on the path to data-driven success. https://lnkd.in/gDM_ttMc Empower your business with data intelligence and stay ahead of the competition!
Free AI + Data Audit of your business with Pooja Lokesh
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#ICYMI Susan Walsh - The Classification Guru, and 🦋 - Laura Elizabeth Quelch joined likeminded professionals in the data governance group - Data Governance Know How, hosted by the lovely Nicola Askham, to take part in a fun and interactive session discussing the challenges and solutions to dirty data! Susan presented her talk Between the Spreadsheets: Classifying and Fixing Dirty Data. Here were the key takeaways: 🔵 Dirty data is everywhere and everyone has dirty data, so we need to expand this conversation outside of the tech and data space. 🔵 Data problems are people problems. 🔵 The data must have a purpose. 🔵 There are so many factors to consider when working worldwide - time zones, date formats, postcodes etc 🔵 Always look at CONTEXT. 🔵 Misclassification can lead to incorrect data, which can have a dramatic impact on your business. 🔵 Check before Tech - clean your data before implementing AI. The software learns from what you give it! 🔵 Don't worry about AI - jobs will adapt to work alongside AI. Follow Data Governance Know How to join the next session.
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Founder of Satisfyly. Innovation. Analytics. SEO. Strategy. Conversion Rate Optimization. Data Science.
Data preparation. It is the most important thing that organizations can do to prepare for the future. If you want to be able to take advantage of Media Mix Modeling, AI tools to query your data, and just make your life easier, having a data warehouse with all your data is crucial. Get in touch if you want to learn how you can have a data warehouse for less than you think.
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At Wovenware a Maxar Company, we understand that Insights as a Service is reshaping the landscape of business intelligence. In his recent Forbes article, our COO Carlos Melendez, sheds light on the evolving paradigm of data utilization, demonstrating that it's not merely the data itself, but the actionable insights it yields, that hold the key to success in the data-driven era. Let's dive Insights as a Service – the game-changer. Shifting from selling data to selling insights is the competitive edge, as Randy Bean notes. It allows outsourcing data analytics to experts who not only decipher data but translate it into actionable insights. Insights as a Service involves: 🔍 Collaborative Design: Understand challenges, needs, and goals. 🔍 Data Sourcing: Identify data types, even using synthetic data. 🔍 Data Classifying: Cleanse and categorize data for algorithm training. 🔍 Predictive Modeling: Refine machine learning for accuracy. 🔍 Data Analysis: Offer actionable steps based on AI trends. Start Here: 🔑 Key Questions: - Have unresolved challenges due to data gaps? - Is your data strategically utilized? - Struggling with data volume? - Lack impartial analytics resources? - Clear goals for insights as a service? Insights as a Service bridges the data revolution gap, transforming data into invaluable gold. With it, you unearth decision-driving insights. Read Carlo's Forbes piece for more information: https://lnkd.in/eCFdUpE4 #InsightsAsAService #DataRevolution #SmartDecisions
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📊 Diving Deeper into Data Quality: The Pillar of Informed Decision-Making Building upon our recent post about data quality, let's dive deeper into its key attributes: Validity: Data must adhere to specific rules and formats, like consistent capitalization in customer names. Accuracy: Data should reflect real-world facts precisely to avoid misinformed decisions. Completeness: All necessary information must be present to provide a full picture. Consistency: Data from various sources should be uniform and reliable for comparisons. Uniformity: Standardized formats, units, and measurements are vital for combining data. Relevance: Data should be current and applicable to the decision-making context. Poor data quality can lead to financial losses, inefficiency, and damage to reputation. High-quality data is especially crucial for technologies like AI and machine learning to produce accurate results. Addressing data quality issues requires effective data management practices, including data quality software tools, data governance, and continuous monitoring. Whether it's financial analysis or business intelligence, high-quality data is what sets us apart. Ready to discover how we can turn data into your strategic advantage? Reach out! 🚀📈 #DataQuality #DecisionMaking #BetterDecisions
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There is a huge difference between data and strategic assets. One is time-consuming and borderline useless. The other is controlled, focused and has real business value. So, what makes data a strategic asset? 1. Data strategy The data is focused on organizational objectives. As a whole, it has purpose and is insightful. 2. Data governance The data is high quality, easily usable by anyone and is secure. In other words, the data can be trusted. If you get these things right, your data goes from 'dusty storage'. To being the business pillar you need to thrive. #data #analytics #ai
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