Arun Philip

Data governance significantly impacts organizational decision-making by ensuring data quality, security, and compliance. Key components include data stewardship, quality management, data ownership, classification, retention, privacy compliance, lineage, cataloging, and risk management. A robust data governance framework enhances data-driven decisions, ensures compliance, and reduces risks, ultimately leading to better business outcomes.

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October & November bring 5 new updates on www.CJ-Mayes.com 🏏 Yash S. shares more on understanding web structures to collate cricket data ⚪ We take a look at some geometric transformations behind recreating the WaterWorld visual in Tableau 📊 I chat business dashboard asesthetics and storytelling with Gbóláhàn Adébáyò 🥇 We take a look at what it takes to make the final of the worlds biggest data competition #IronViz 📈 We close on a quick tableau tip to zoom in on scatter plots in Tableau

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From AI Assistants to AI Enterprise 🚀 In the recent times, the data and analytics landscape has been flooded with AI-powered tools - "assistants", "copilots", and "agents". We have probably seen these terms used interchangeably. But are they really the same thing? Understanding the distinctions between these AI-powered tools is crucial before considering their purposeful usage & adoption. 💎 AI Assistants: Think of these as your digital sidekicks with low autonomy. They require significant human input and guidance, often performing basic tasks and act as productivity boosters and not decision makers or action takers. Some use cases - Basic customer service, data entry, task management (scheduling appointments, setting reminders, and managing to-do lists). 💎 AI Copilots: Positioned between assistants and agents, copilots offer a balance of human assistance and autonomy. They not only enhance productivity but also provide valuable support in intelligent decision-making through recommendations by asking the right questions & analyzing data patterns, while not taking actions on behalf of humans.  Some use cases - Content creation (documents, music, design), recommendation systems (sales / marketing / HR copilots), code generation & debugging, research. 💎 AI Agents: These are the autonomous powerhouses. They can act independently to achieve specific goals, such as automating complex workflows or making data-driven decisions and can learn from their experiences. They decompose multi-step problems into intermediate stages, mimicking a "thinking process" to solve the problem without human supervision. Some use cases - Autonomous Systems (self-driving car, drones / robots), personal assistants, self-sustaining processes (supply chain, dynamic pricing, trading), personalized healthcare. By understanding these distinctions, you can choose the most appropriate AI tool for your specific needs. ⭐ Autonomy: Agents are the most autonomous, capable of acting independently. Copilots offer a balance of autonomy and human guidance, while assistants require more human input. ⭐ Complexity: Agents are often used for more complex tasks, while assistants and copilots are better suited for simpler tasks. ⭐ Scope: Agents may operate across multiple domains, while assistants and copilots are often focused on specific tasks or industries. #GenerativeAI #GenAI #AIAgents #AgenticAI #Copilot (Image Source: Bain Capital Ventures)

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In today’s digital age, effective leadership goes beyond having a vision; it demands a comprehensive data strategy. Leaders must recognise that data is the foundation upon which informed decisions are built. But the real power of data lies in its utilisation. When insights derived from data are applied effectively, they can guide strategic decisions and foster a culture of data-driven innovation. It’s for this reason that empowering teams with the right tools and training is crucial. When everyone understands how to use data effectively, the organisation shifts from guesswork to precision, enhancing decision-making and positioning itself for long-term success. #Data #Strategy #Innovation

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When working as a #DataAnalyst, don’t let overconfidence lead you to dive into the data without clear objectives. Always start by asking the right questions and understanding what the analysis needs to achieve. 📊 Clear goals are essential for meaningful insights—without them, you risk getting lost in irrelevant details. 💡 #DataScience #Analytics #Learning #Data #BusinessIntelligence #Growth

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Efficient data pipelines are crucial for unlocking insights and driving innovation. They ensure secure and efficient data ingestion, clean and standardize data, validate data quality, and curate data for analysis. This process transforms raw data into valuable insights, enabling better decision-making and fostering innovation.

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Data Analyst scenario based case study at ride-hailing company (let's say X) with solution: Problem Statement: How to increase the revenue per active-rider in the next six months for X? Few Proposed Features: 1. On-Demand Refreshments During Rides Pain Point: Busy users often feel fatigued and hungry during rides, especially after long days. Solution: Introduce a feature allowing riders to order snacks and drinks via the cab app. This not only enhances the user experience but also generates additional revenue through a convenience fee. 2. "X" Money Wallet Integration with External Merchants Pain Point: Users prefer a seamless payment experience but currently must navigate multiple apps. Solution: Enable "X" Money to be used for payments across various platforms directly through the "X" app. This drives higher engagement with the wallet, increasing transaction volume and revenue from merchant commissions. 3. Enhanced Visibility for "X" Select and "X" Pass Pain Point: Users often overlook subscription options that can provide long-term savings. Solution: Promote "X" Select during the ride booking process to highlight savings and benefits, mirroring successful strategies used by Q-Commerce companies like Swiggy. Strategic Impact: By implementing these features in one major city initially, we project significant growth in revenue per active rider. Each feature is designed not only to meet user needs but also to foster loyalty, ensuring a steady revenue stream through subscriptions and enhanced service offerings. Marketing & Success Metrics: 1. We will actively promote new features across social media channels. 2. Metrics such as subscription sales, transaction counts, and user engagement with the refreshment section will be monitored closely to gauge performance. Add your thoughts in comments for any other feature that can make sense! For career guidance in Data Analytics, job search strategies, resume review, interview preparation, etc., you can book a session with me here: https://topmate.io/itshb

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Well Well Well !!! Look who is taking the stage on our next DataPub event. One and only Mahmoud Yassin from Booking.com will be talking about power of internal data marketplace in dealing with Data Mess, a topic which is very close to my heart. Please do register & RSVP for the event at the link below. Do reach out to me and Daniëlle Bourgondiën for any questions or clarifications. https://lnkd.in/eq-fPuM4 #datapub #datamess #datamesh #cloud #snowflake #microsoft #dataproducts #lakehouse #mesh #data #dataanalytics

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The 'age of the machine' is upon us. With AI/ML capabilities expanding rapidly, it is inevitable that the conversation around building and managing machines in business will become critical in the coming years. While humans will still be necessary, it is clear that our work will undergo drastic changes. It's time to start adjusting. #AI #business #futureofwork

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Analytics India Magazine writes "AI4Bharat Introduces BhasaAnuvaad, Speech Translation Dataset of 13 Indian Languages with 44,400 Hours of Data - AI4Bharat also developed Indic-Spontaneous-Synth, a synthetic evaluation set to highlight how current models, though effective on datasets like FLEURS, tend to underperform in realistic, spontaneous language translation scenarios, underscoring the need for more robust datasets. " https://lnkd.in/eT6wXx8k. #ai4bharat #indianlanguages #speechtranslaten #trainingdata #artificialintelligence #analyticsindiamagazine

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This is the second of three notes "on transitions" Let me excerpt the farewell note I sent to the #dataScience teams (previously part of #AI, before that #EDABI) at #Target that I was leading at the time of my recent departure, they were working on #forecasting and #generativeAI. I sent this on my penultimate day in role. "Dear Team, As many of you know, tomorrow, [], will be my last day in the office. This will indeed be a bittersweet transition for me as I have quite enjoyed my tenure at Target and the amazing warmth and friendliness of the Target culture. Reflecting on the past five years, it has been an honor and a privilege to have led this incredible team. From our humble beginnings with a handful of scientists, engineers and product owners building an uncertain product, the Demand forecasting engine (DFE) has transformed into a cornerstone of Target's enterprise operations model. This remarkable journey would not have been possible without each one of you. Thank you for your dedication, hard work, and unwavering commitment to making this vision a reality. More recently, I have been involved in building and scaling our generative AI efforts. I am confident that this initiative is on the right path and will achieve great success with your continued efforts. As I bid farewell, I want to leave you with some advice: take calculated risks, step out of your comfort zone, and set high standards for yourselves. Push beyond the boundaries set by others. Find your internal metrics that guide you to accomplish all that you are capable of. The future is bright, and I believe in each of your abilities to drive innovation and excellence. Good luck to all of you, individually and collectively. It has been a pleasure working together, and I look forward to seeing the amazing things you will achieve. I do hope our paths cross again in the future, and that this is au revoir rather than adieu. PS: Please do stay in touch and feel free to reach out at any time, even if it is just to say hello! " To be concluded.

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A data analyst and a social media nut case both tell stories, but what’s the difference? Data analyst digs up the data and tells a story based on relevant and concrete facts. A social media nut case tells a story first and then tries to find edge cases data points to support it. Don’t you agree ? #data #analysis #friday #thoughts

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Microsoft Copilot 🤖 , the next Big Thing after Chat GPT? Coming back to the corporate and already realizing how Generate AI is boosting productivity. It wasn't long ago when OpenAI released ChatGPT for public use in November 2022. It garnered significant attention and saw rapid adoption across various platforms and applications due to its advanced natural language processing capabilities and versatility. Its success can be attributed to its ability to generate coherent and contextually relevant responses to a wide range of prompts, making it a valuable tool. What is so called Copilot and why does it look like the next big thing? It is an OpenAI-powered AI assistant that has been integrated into Office 365. To say, something that can crawl across your Outlook, Teams, Excel and even Power B claiming to boost productivity as it can go across all your office suits and help you in your day-to-day tasks. To keep it short I would only mention 2 applications : 1️⃣ Summarizing all your Emails in your mailbox: Imagine being a person who receives a huge pile and wants a quick summary with all the important schedules and deadlines lying ahead. All you have to do is go to Copilot and give a prompt to get a summary or even write mails for you that SOUND LIKE YOU!!! 2️⃣ Sales Data Science Assistant for all you Sales guys using Excel: Are you a commerce major? Never written code your entire life. So unfamiliar with the topic that Python is no more than a snake species to you all. How does it sound you would be given an assistant who does not only know Python, but makes FORECASTS right there in your Excel just by giving a prompt!! And many more!!! It would be interesting to see how it would help change business as they slowly add the feature and start using it. Even more interesting to how it would affect Data Analytics, Data Science stack and different roles and skills. Feel free to share your thoughts or correct mine 😊 #MicrosoftCopilot #AI #DataAnalytics #DataScience #productivity

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Artificial Intelligence is here to stay, and here are a few trends in AI/ML that are shaping our future: - Bias For Value Delivery: From C-suite to operational teams, there is a consistent need for understanding incremental value delivery from AI/ML - be it gaining efficiencies (time and cost savings) or driving effectiveness (revenue, profits, competitive advantage). - AI Agents: AI agents are like virtual personal assistants that can automatically perform tasks, make decisions, and interact with their environment to achieve goals, making our lives easier and more efficient. - Zero Shot Learning: Zero-shot learning enables AI models to recognize and categorize objects or concepts without having seen any examples during training, by leveraging related knowledge and contextual understanding (just like humans). - Explainable AI (XAI): As AI systems make more decisions in our lives, understanding “how” they make these decisions is crucial. XAI focuses on making AI’s thought processes transparent and understandable. - Edge AI: Instead of sending data to the cloud for processing, Edge AI processes data locally. This means faster responses and reduced internet bandwidth usage, which is particularly beneficial for smart devices and real-time applications. - Quantum AI: Combining the principles of quantum computing with AI could solve incredibly complex problems much faster than current technologies allow. This is still in its early stages but holds enormous potential. - Responsible Compute: AI can be expensive, both in terms of compute costs as well as carbon footprint. Users may need to weigh costs vs. benefits of using high compute algorithms like large language models, and where possible substitute with relatively lighter programs. Another emerging trend in this space is the use of bio computing (organoids) that require significant less energy to function. I am sure I missed a few trends, interested in hearing what others think… #AI #FutureTech #Innovation #MachineLearning #ArtificialIntelligence

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What Data Modeling is crucial?

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Valuing Wearable data not only takes into account the data born out of the wearable to the gateway but also the time-series data that is generated and actioned. Do not underestimate the power of the value of wearable data.

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Visualizing data brings it to life by telling the story behind the numbers. 📈 Tools like Power BI, Tableau, and others make it easier to present data in a clear and impactful way. 💡 Visualizing data is key to unlocking the full potential of data-driven strategies, empowering decision-making and driving success. 📚📉 #DataVisualization 📊 #PowerBI #Tableau #DataScience #DataAnalytics 📊 #DataStorytelling #DataTransformation #DataStrategy #BusinessIntelligence

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The next word prediction loss function is the usual cross-entropy which will give loss value if the model predicted say cookie instead of biscuit as the next word. What if we have a way of telling the trainer to use a list of synonyms so that the penalty for predicting cooking in place of biscuit is not that high? Or this is something which is handled internally by the trainer?

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