Marketers shouldn’t need a data scientist to forecast their metrics. That’s why I patented Metric Forecasting — AI that automatically projects future performance and updates in real time, right inside your dashboards. Patented AI forecasting (built on Prophet) Upper/Lower confidence bands auto-generated No-code, marketer-friendly Forecasting without spreadsheets. Forecasting without data scientists. Just forecasting built in. https://lnkd.in/giSZZmCk #MarketingAI #AIForecasting #NoCode #MarketingAutomation #PatentedAI #Japio #FutureOfMarketing

Marketers shouldn’t need a data scientist to forecast their metrics. That’s why I patented Metric Forecasting — AI that automatically projects future performance and updates in real time, right inside your dashboards. Patented AI forecasting (built on Prophet) Upper/Lower confidence bands auto-generated No-code, marketer-friendly Forecasting without spreadsheets. Forecasting without data scientists. Just forecasting built in. https://lnkd.in/gJn8Fu8b #MarketingAI #AIForecasting #NoCode #MarketingAutomation #PatentedAI #Japio #FutureOfMarketing

A Data Lakehouse is a modern way to organize and manage data that brings together the best of both worlds: the flexibility of data lakes and the reliability of data warehouses. Think of it this way - data lakes let you store all kinds of raw, unstructured data, which is great for flexibility but can get messy. Data warehouses, on the other hand, keep data structured and ready for analysis, but they are less adaptable.   A lakehouse combines these strengths. It lets businesses store data in its raw form while still running fast, dependable analytics. For companies working with big data, AI, or machine learning, this means cleaner pipelines, less duplication, and easier access to the insights that really matter. #WisdomWednesday #datalakehouse #datamanagement #bigdata #dataanalytics #datawarehouse

AI isn’t coming for your job. It’s coming for your data questions. Many business users struggle to extract actionable insights quickly, often limited by traditional dashboards and static reports. The challenge: leveraging AI to ask better questions and get better answers. Phil Seamark from the Power BI Product team will show how Copilot and Chat with Your Data features can help you drive deeper insights and stay ahead of the curve. Speaker: Phil Seamark, Power BI Product Team Join the conversation: https://lnkd.in/gVNJjd7W #DataSaturday #PowerBI #AI #Copilot #DataAnalytics #Melbourne

🧠 Why Data Quality is the Next Competitive Advantage In today’s data-driven world, companies are racing to adopt AI, machine learning, and advanced analytics — but here’s the truth: Even the smartest models can’t fix bad data. 💡 Data quality isn’t a checkbox — it’s a strategic advantage. When your pipelines deliver clean, reliable, and timely data, you gain: ➤ Faster and more confident decision-making ➤ Trusted AI insights (no hallucinations, no bias) ➤ Stronger compliance and governance posture ➤ Lower rework and operational costs Modern tools like dbt tests, Great Expectations, Monte Carlo, and Soda are making data observability a reality — helping teams catch issues before they reach the dashboard. In the end, it’s simple: The organizations that prioritize data quality today will lead the data economy tomorrow. #DataEngineering #DataQuality #DataGovernance #ETL #DataOps #BigData #AI #Analytics #DataObservability #CloudComputing

When building large Data Vaults with Copilot, I work in bounded domains. Here's why: Large vaults have hundreds of interdependent objects. Copilot's context window can't track them all at once. Errors accumulate. The mechanism: context decay. Copilot prioritises recent instructions. Early standards get deprioritised. Hash keys drift. Naming becomes inconsistent. My approach: Work one domain at a time, Reference key standards in every prompt, Validate after each domain completes This keeps consistency across 100+ objects while maintaining AI speed. Quick note: This assumes you're already familiar with Data Vault 2.0. AI accelerates experienced architects, it doesn't replace foundational knowledge. What's your approach to managing context limits with AI? #DataArchitecture #DataVault #MicrosoftCopilot #AI #EnterpriseAI

When building large Data Vaults with Copilot, I work in bounded domains. Here's why: Large vaults have hundreds of interdependent objects. Copilot's context window can't track them all at once. Errors accumulate. The mechanism: context decay. Copilot prioritises recent instructions. Early standards get deprioritised. Hash keys drift. Naming becomes inconsistent. My approach: Work one domain at a time, Reference key standards in every prompt, Validate after each domain completes This keeps consistency across 100+ objects while maintaining AI speed. Quick note: This assumes you're already familiar with Data Vault 2.0. AI accelerates experienced architects, it doesn't replace foundational knowledge. What's your approach to managing context limits with AI? #DataArchitecture #DataVault #MicrosoftCopilot #AI #EnterpriseAI

The latest #Qlik Agentic AI Study shows that while budgets are ready, most organizations still struggle to bring AI to life. As Qlik’s Chief Strategy Officer James Fisher says, it’s not a question of ambition — it’s about building the trusted data foundation that makes AI work across the business. Read more now. 

The latest #Qlik Agentic AI Study shows that while budgets are ready, most organizations still struggle to bring AI to life. As Qlik’s Chief Strategy Officer James Fisher says, it’s not a question of ambition — it’s about building the trusted data foundation that makes AI work across the business. Read more now. 

Data Analysts: Your Dashboards Are Evolving For years, the dashboard was king. But in top companies, the role of a data analyst is rapidly moving beyond building static reports to becoming an orchestrator of AI-driven insights. This is a critical shift. Here’s why your focus needs to change: AI Automates the Mundane: Generative AI tools are becoming adept at basic querying, data cleaning, and initial report drafting. This automates the repeatable tasks that once consumed much of an analyst's time. Your value no longer comes from just retrieving data. From Reporting to Explaining & Predicting: Stakeholders now expect more than just "what happened." They need to know "why it happened," "what will happen next," and "what should we do about it." This requires leveraging advanced analytics and AI models that can unearth deeper patterns and forecast future outcomes. Insights Embedded, Not Just Displayed: The goal is no longer to drive users to a dashboard. It's about delivering actionable insights directly into operational workflows, triggering proactive alerts, or even automating decisions. Think of insights pushing actions, not just being passively consumed. Your new edge lies in mastering prompt engineering, validating AI outputs, understanding model explainability, and translating complex AI-generated insights into clear, strategic business recommendations. This is about elevating your role from data presenter to strategic foresight provider. Are you ready to build the next generation of intelligent insights? #DataAnalytics #AI #BusinessIntelligence #DataScience #CareerDevelopment