Nitin Srivastaavaa

Decoding the Buzzword - Analytics Over the last two decades , have seen analytics grow from a backend team to something the group CEOs mention in their earning calls. While the purpose/ benefits/ role of Analytics has gained more understanding and acceptance, it continues to be used very liberally so much so that it seems to me that almost everyone does analytics now a days. In order to decode the buzzword I put together a video to understand major disciplines in analytics. Let me know what you think about it ? https://lnkd.in/e8_yUzig

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Unlock the Power of Data Products with the Model-First Approach! Join our CTO Animesh Kumar at #CDOIQ2024 to learn how to build impactful Data Products that drive business value! Discover: > How to shape data requirements for productized outcomes > Creating clear pathways from data to measurable impact > Achieving faster business alignment and value delivery Session 15-F (Virtual) Cracking the Data Product Code: The Model-First Approach to Building Impactful Data Products 4:45 PM - 5:30 PM Don't miss this opportunity to transform your data strategy! #CDOIQ2024 #DataProducts #ModelFirstApproach #DataStrategy

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Meet our RCG AI experts at  Databricks Data + AI Summit 24    Discover why the world's leading Retailers and CPGs trust Tredence to power over $2T in sales. Learn from Tredence and Databricks RCG leaders about Tredence's deep expertise in data modernization. Explore how Tredence's GenAI & Databricks Data Intelligence Platform enables customers to democratize insights and drive business decisions.   Register here: https://lnkd.in/gM3sr_5q Morgan Seybert | Sagar Balan #Tredence #DataAISummit #Databricks #BeyondPossible

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Unsupervised Learning by Maven Analytics was one of those courses I didn’t want to end. It was absolutely brilliant—full of insightful content, delivered with enthusiasm and expertise. I became fascinated by the endless possibilities it offers beyond standard data analytics. From understanding customers and employees to improving products, the potential applications are vast. The final project was especially engaging, reinforcing Alice Zhao's view that unsupervised learning is both a science and an art. Many assignments made this clear. While the scientific results may be the same for everyone, our interpretations are shaped by domain knowledge, experience, and personal context. I am looking forward to working on more projects to keep my skills sharp. In the meantime, I have added the final course project to my growing portfolio. #DataScience #UnsupervisedLearning #MachineLearning #LearningbyDoing #ContinuousLearning #SkillsDevelopment #Clustering #PCA #Python #DataAnalytics

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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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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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SAS, announced its participation in the newly launched Coalition for Responsible Evolution of AI (CoRE-AI), India’s leading multi-stakeholder coalition, that brings together over 35+ members including India’s AI-first startups, think tanks, research organizations, universities, and industry players. The coalition, hosted by The Dialogue as its secretariat, is set to play a pivotal role in shaping the responsible development, governance, and deployment of AI in India. CoRE-AI aims to provide a collaborative platform for stakeholders to address critical aspects of AI governance, skilling, awareness, and technical competencies. By fostering cross-sector partnerships, the coalition seeks to drive AI innovation while ensuring ethical standards and mitigating potential risks, making everyone feel part of the journey. Read more: https://lnkd.in/gtx-iBEu #IndiaTechnologyNews #SAS #CoREAI #ResponsibleAI #AIInnovation #EthicalAI #AIDevelopment #AIIndia #TechForGood #AIRegulation #AIandEthics #AIFuture #TechInnovation #AIForBusiness

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Alas 📣, a hands on and insightful read. Data is « everywhere » in the organisation , not a responsibility only for those who work in data teams 👩‍💻but for everyone in organisation irrespective of your job title! If you want that your data works harder, better and get an insight on monetizing data frameworks , this is worth a read 📖📚#dataiseverybodysbusiness #datastrategy #monetization #knowyourdata

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Calling out 2 key points : 1. Data is fundamentally additive 2. It's the marginal lift that matters The value of any given data element is not just in it's application/use case but is also a function of the orthogonal lift over the status quo data used in a given workflow. Great essay Abraham Thomas https://lnkd.in/gWxRBr8h

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Generative AI and Technical Debt The rapid emergence of generative AI tools is transforming software development, but it also raises concerns about increasing technical debt. As organizations rush to integrate these advanced technologies, the influx of new APIs, libraries, and frameworks, and accelerated code output could all be producers of technical debt. The urgency to adopt AI capabilities often results in hastily implemented solutions that lack proper oversight. Incorporating AI requires a long-term commitment, involving continuous management of models and strategies. Some companies focus on fixing past tech debt but doesn't put effective controls for new debt from creeping in. Technical debt is not a one-time fix; it’s an ongoing commitment. However, generative AI also offers opportunities to mitigate technical debt. By focusing on smaller proof-of-concept projects and iterating based on learnings, organizations can better manage their resources. Orgs should be deliberate in tool selection and ensuring teams are well-trained to navigate this evolving landscape. They should invest in best practices and tools that will proactively keep their solution architectures clean and keep them ready for the future tech-tide. While generative AI can accelerate development, it is crucial to implement guardrails to prevent indiscriminate usage that could exacerbate technical debt.

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Snowflake is making significant strides in India, with a vision that transcends traditional go-to-market strategies. At the recent Data Cloud Summit 2024, Vijayant Rai, managing director of Snowflake India, highlighted the company's multifaceted approach to establishing a robust presence in the country. “We’re looking at India in a multi-dimensional way,” Rai said, emphasizing their diverse operations. Snowflake's presence in Pune boasts a team of 500 professionals handling operations and support. Additionally, India serves as a hub for global customers through Snowflake’s Global Capability Centers (GCCs). Snowflake is not just focused on the local market but also on how India can support global customers. With the country’s rapid economic growth and digital transformation, Snowflake aims to drive innovation by partnering with enterprises and SMBs. Explore Snowflake’s Vision for India Link in the comments below 👇

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Technologies play a pivotal role in enhancing FP&A : 1. Data Integration and Management - Data Warehousing allows for the integration of data from various sources, ensuring a single source of truth. - ETL Tools : Extract, Transform, Load (ETL) tools help in cleaning, consolidating, and preparing data for analysis 2. Advanced Analytics - Predictive Analytics : Leveraging machine learning and AI, predictive analytics can forecast future financial trends, customer behaviors, and market movements based on historical data. - Prescriptive Analytics : not only predicting outcomes but also recommending actions. 3. Business Intelligence (BI) Tools - Dashboards and Visualization : BI Tools help FP&A professionals to quickly interpret and present data. 4. Automation and Efficiency - Robotic Process Automation (RPA) : RPA can automate tasks such as data entry, report generation, and consolidation of financial statements - AI and Machine Learning : AI can automate complex analytical tasks, identify patterns, and provide insights that are not easily detectable by humans. 5. Scenario Planning and Modeling - What-If Analysis - Dynamic Financial Modeling 6. Cloud Computing - Scalability and Accessibility - Cost Effective 7. Artificial Intelligence and Machine Learning Applications - Natural Language Processing (NLP) : NLP can be used to analyze unstructured data such as market news, earnings calls, and social media, providing additional insights for financial analysis. - Anomaly Detection : AI can identify anomalies in financial data that may indicate errors, fraud, or unusual patterns that require further investigation. 8. Regulatory Compliance and Risk Management - Automated Compliance : Technology can ensure that all financial reporting complies with relevant regulations, reducing the risk of non-compliance. - Risk Analytics : Advanced analytics tools can assess financial risk by evaluating various risk factors and their potential impact on the business. 10. Continuous Improvement and Learning - Feedback Loops : Advanced analytics platforms provide feedback on the accuracy of forecasts and models, enabling continuous improvement. - Learning Algorithms :Machine learning algorithms improve over time with more data, enhancing the accuracy and reliability of predictions. By leveraging these technologies, FP&A professionals can move beyond traditional spreadsheet-based analysis to more sophisticated, data-driven decision-making processes.

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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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A good refresher on "designing modern data architectures" - DataCamp. #data #dataarcchitectures #datamodernization

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Discover how Treasure Data's Live Connect feature enables seamless zero-copy integration with Databricks, reducing costs and enhancing customer insights. Read the Latest full News - https://lnkd.in/dPMvxjeK #Martech #MarTechedge #TreasureData #Databricks #DataIntegration #CustomerDataPlatform #LiveConnect #DeltaSharing #MarketingTechnology

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