Innovation and Data Analytics

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  • View profile for Brij kishore Pandey
    Brij kishore Pandey Brij kishore Pandey is an Influencer

    AI Architect | Strategist | Generative AI | Agentic AI

    684,416 followers

    Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

  • View profile for Armand Ruiz
    Armand Ruiz Armand Ruiz is an Influencer

    VP of AI Platform @IBM

    200,834 followers

    How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

  • 🚀 Unlocking Public Value with Non-Traditional Data: New Use Cases, Emerging Trends 🤔From mobile phone records to social media posts, satellite imagery to grocery shopping data—Non-Traditional Data (NTD) is rapidly expanding how we understand and respond to today’s public challenges. 👉 In our latest curation, we spotlight how these often privately held, passively generated datasets are driving impact across domains like: 💳 Financial Inclusion 🏥 Public Health & Well-being 🏙️ Urban Mobility & Planning 📉 Economic & Labor Dynamics 🌐 Digital Behavior & Communication 🧭 Socioeconomic Inequality 📲 Data Systems & Governance 🔍 What’s new? We’re seeing more interdisciplinary research, hybrid use with traditional data, and stronger attention to ethics and impact. 👇A few standout examples: ➡️ In South Africa, grocery shopping data helped assess creditworthiness for 8M individuals without formal credit history. ➡️ In NYC, researchers used Google Street View + AI to challenge assumptions about urban health interventions. ➡️ In Chile, mobile phone data revealed stark inequalities in wildfire evacuation patterns. ➡️ A team in the US used Reddit and NLP to track how insomnia treatments are perceived over time. ➡️ Global wastewater surveillance via aircraft is proving a scalable early-warning system for pandemics. 📚 Check out the full set of curated cases and reflections here (with ✍️ Adam Zable) 👉 https://lnkd.in/eUDkqyQi #DataForGood #NonTraditionalData #PublicInterestTech #DataGovernance #DigitalInnovation #SocialLicense #DataStewardship #AIForPublicValue

  • View profile for Prukalpa ⚡
    Prukalpa ⚡ Prukalpa ⚡ is an Influencer

    Founder & Co-CEO at Atlan | Forbes30, Fortune40, TED Speaker

    45,945 followers

    A quiet shift is happening in the world of data. As AI becomes more embedded in real products, data is stepping into the spotlight. For years, data teams have lived under G&A or “cost center” budgets. But that’s starting to shift. But now I’m hearing things like: “We’re funding our data platform like product R&D.” “Data isn’t just analytics anymore - it’s our AI foundation.” We’re now seeing: → Data infrastructure classified as CapEx, not just OpEx → Data initiatives moving into R&D and product orgs This isn’t just a re-org. It’s a revaluation. Data is becoming an innovation asset. My prediction? In a few years, the best companies will treat data like software: A core R&D investment - not an internal service. If you’re a data leader today, your org chart - and your budget - might look very different in 12 months. What shifts are you seeing in your organization?

  • View profile for Brent Dykes
    Brent Dykes Brent Dykes is an Influencer

    Author of Effective Data Storytelling | Founder + Chief Data Storyteller at AnalyticsHero, LLC | Forbes Contributor

    71,104 followers

    In #datastorytelling, a lot of the emphasis is placed on communicating an insight to an audience in a clear and effective manner. If you’ve done a good job explaining your insight, the next thing your audience will wonder is the following: 🙋🏻 What should we do about it? 🙋🏻♀️ How do we move forward? 🙋🏾♂️ How does this change things? Today, many analysts and data scientists don’t always attempt to answer these questions and fail to provide recommendations. 👉 They feel it isn’t their job to tell decision-makers what to do. 👉 They feel they lack the business knowledge to make meaningful recommendations. 👉 They don’t want to bias the audience on any specific course of action. 👉 They don't have time to explore and develop solid recommendations. In my view, if you don’t offer recommendations to your audience, your data story is incomplete. Your data story will be less effective because, without a recommended action, it is less likely to inform or influence a decision. No action, no value. Frequently, for each insight, there will be more than one potential course of action and the different options need to be weighed against each other. To me, that sounds like important analysis work and still a data professional’s responsibility. If you don’t feel it’s your job to tell decision-makers what to do, I’ve found many executives welcome suggestions from analysts who are knowledgeable about the data. Just because you’re making a recommendation, that doesn’t mean managers must accept it. It also doesn’t mean they can’t modify what you recommend as they see fit. If you’re worried that you lack the domain expertise to make solid recommendations, that’s an opportunity to partner with the business side before you present your findings. The more you learn about the business, the better your analysis will become too. If you’re nervous about biasing the audience on what they should do, you can mitigate that concern by exploring different options and then recommending the best one based on some objective criteria. If you only have time for the analysis and not for forming recommendations, you need to reevaluate how you’re spending your time. If you continue throwing insights over the wall without accompanying recommendations, you’re not going to see many of your analyses translate into action and value. Don’t prioritize being efficient over being effective. Data stories should drive action and influence positive change. Providing recommendations is a crucial component of effective data storytelling. If you don’t steer your audience to a potential solution and next steps, you can’t expect your data stories to have much impact. Yes, in some cases, it may be difficult to come up with recommendations on your own. That’s why I’ve always viewed #analytics as a team sport with the #data and business teams working together. How have your data stories benefited from having solid recommendations? #businessanalytics #businessintelligence #dataanalytics

  • View profile for Vin Vashishta
    Vin Vashishta Vin Vashishta is an Influencer

    AI Strategist | Monetizing Data & AI For The Global 2K Since 2012 | 3X Founder | Best-Selling Author

    203,358 followers

    Few have ever done what I’m lucky enough to be doing for the third time: building a data and AI strategy for a startup at zero. To build Parachute Group as a data and AI-first company, I must overcome the data cold start problem: Where do you begin at a business with no data? Here’s how to start. The answer is built from the frameworks I teach in my courses, which I have used for over a decade for clients. Phase 1: Engineer Access To Data Generating Processes. The first 6 months focus on engineering the data flywheel’s foundations. We’re starting with the highest value internal operations, apprentice, and client workflows. Data that’s connected to a user or customer workflow is contextualized and can be transformed into information. Phase 2: Introduce Data & Analytics Into The Workflow. Going directly to AI is always a mistake. Once we have workflow transparency, it’s time to measure the impact of providing experts with information at critical task and decision points. Phase 2 is iterative and experimental. If we introduce relevant information into a workflow (experiment), we expect to see improvements in key outcomes (results). We validate or refute our understanding of the workflow one experiment at a time. That’s how knowledge graphs are assembled. For an AI-first business, its knowledge graph creates information asymmetry. We know something competitors don’t, and we will use AI to monetize and scale that competitive advantage. Startups are a tug-of-war between value delivery and efficiency. The data and AI strategy helps the business rapidly scale value delivery without scaling costs. Start by engineering access to key internal and external customer workflows and iterate forward using the simplest approach possible.

  • View profile for Jahanvee Narang

    Linkedin Top Voice | Analytics @ Walmart | Podcast Host | Featured at NYC billboard

    31,434 followers

    As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

  • View profile for Alfredo Serrano Figueroa
    Alfredo Serrano Figueroa Alfredo Serrano Figueroa is an Influencer

    Senior Data Scientist | Statistics & Data Science Candidate at MIT IDSS | Helping International Students Build Careers in the U.S.

    8,462 followers

    A few years ago, breaking into data science meant learning Python, machine learning, and building a solid portfolio. That’s still important—but the job market is shifting, and many people are focusing on the wrong things. Companies are no longer just looking for "SQL experts" or "deep learning specialists." They want problem solvers who understand data, business, and execution. Companies are prioritizing practical, real-world data skills over advanced modeling. The ability to clean, analyze, and communicate insights is often more valuable than knowing how to fine-tune a neural network. AI is exciting, but many businesses still struggle with basic data infrastructure, and that's why companies need professionals who can: - Work with real, messy data instead of perfect Kaggle datasets. - Build dashboards and reports that drive actual decisions. - Explain findings to leadership in clear, non-technical language. Hybrid Roles Are on the Rise - The lines between data analyst, data scientist, and analytics engineer are blurring. Many companies expect data scientists to: + Know SQL and database management. + Understand cloud platforms and deployment. + Work closely with product teams, not just focus on models. What Should You Focus On to Stay Competitive? 1. Master SQL and Data Manipulation – Almost every data job requires it. 2. Strengthen Your Business Acumen – Companies care about insights, not just models. 3. Improve Your Communication Skills – If leadership doesn’t understand your findings, they won’t act on them. 4. Work on Real-World Projects – Hiring managers want to see impact, not just academic exercises. The best data professionals aren’t just great at coding—they understand how to use data to solve real business problems. If you’re learning data science today, ask yourself: Are you focusing on what hiring managers actually need, or just chasing what looks impressive on paper?

  • View profile for Jaret André
    Jaret André Jaret André is an Influencer

    Data Career Coach | I help data professionals build an interview-getting system so they can get $100K+ offers consistently | Placed 60+ clients in the last 3 years in the US & Canada market

    24,967 followers

    The Data Analyst roadmap that helped 50+ of my clients Tier 1: Excel & SQL – Your bread and butter for handling data. Tier 2: Data Cleaning & EDA – Messy data = useless insights. Tier 3: Data Visualization & BI Tools (Tableau, Power BI) – Communicating insights clearly. Tier 4: Statistical Analysis & ML Basics – The deeper layer of understanding. Then, you need projects. Not just any projects, but ones that actually make an impact: - Find, clean, and analyze real-world data – No pre-cleaned Kaggle datasets. - Build dashboards that tell a story – Not just charts, but insights that drive decisions. - Solve real business problems – Show companies you understand their needs. - Create a compelling case study. Write about your process, results, and impact.- Record a video breakdown – Prove you can explain complex data in simple terms. - Target specific industries – Finance, healthcare, e-commerce, whatever excites you The market is tougher than ever. You could be the most skilled data analyst out there, But if you can’t communicate your value, you’ll be overlooked. Focus on: -> Networking & outreach – Talk to hiring managers and industry professionals. Cold applications aren’t enough. -> Building a personal brand – Share insights, create content, and let recruiters come to you. -> Positioning yourself as a problem-solver – Companies don’t just need analysts. They need people who drive business impact. Note: This is an example roadmap that you should customize for your own goals and needs. (Like I customize everything to my clients.) Analyze your situation and realize what skill or habit is missing from your process that is on this roadmap, then double down on that. Follow me, Jaret André and let’s land you your next data job!

  • View profile for Tom Arduino
    Tom Arduino Tom Arduino is an Influencer

    Chief Marketing Officer | Trusted Advisor | Growth Marketing Leader | Go-To-Market Strategy | Lead Gen | B2B | B2C | B2B2C | Revenue Generator | Digital Marketing Strategy | xSynchrony | xHSBC | xCapital One

    9,647 followers

    Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.

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