Mark Wan

"Find me dbt models called ‘dim_account’" The query sounds simple but what it reveals in Euno’s metadata management platform might surprise you. With a stack like Snowflake x dbt x Tableau you’ll instantly discover: → Usage insights → Cost optimization opportunities → Schema and column details → Upstream and downstream relationships → Metadata and tags If you had that kind of visibility, what would you be curious to explore first?

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How AI Has Changed My Buying Decisions (and Everyone Else’s) Recently, I’ve been reflecting on how much AI has reshaped the way I make buying decisions, and it’s fascinating to see how these changes are playing out for others as well. Take a recent trip my wife and I planned. We had our destination in mind, but beyond that, the entire itinerary came together with the help of AI. We asked GPT to sketch out a 7-day plan for us in Country A. After getting that draft itinerary, we dove into hotel options. I was on Booking.com, my wife was on Agoda, and we compared notes. But the process didn’t stop there. We saw good reviews, then bad reviews, and went back and forth, making and canceling free reservations multiple times. Eventually, we asked GPT again to help validate some of the negative reviews and give us more confidence in our final choice. We also checked travel groups on Facebook to see what others were saying. What used to be a straightforward discussion between the two of us and a quick look at a couple of sites has turned into a multi-touch journey involving AI tools like GPT and Gemini. We rely on these tools not just to get information, but to help us filter, validate, and feel confident about our choices. And I know we’re not alone. Many people are doing the same, turning to AI for restaurant suggestions on the go, or using it to narrow down options in ways that weren’t possible before. It’s a shift that makes buying behavior more complex and less linear than ever. I’m sharing this to highlight how quickly the landscape is evolving. Our buying patterns are changing, and businesses will need to adapt to this new, less measurable reality.

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👀 Power BI + Python: The Analytics Duo You Didn’t Know You Needed As Australian organisations continue to double down on data-driven decision making, many are turning to the seamless integration of Power BI and Python for a competitive edge. If you haven’t explored this pairing yet, 2024 is the year to get started. 🇦🇺📈 Why does this combo matter? Power BI brings intuitive dashboards and rich visualisations to the table, but when paired with Python, data professionals unlock powerful modelling, custom analytics, and advanced transformation possibilities — all within the same reporting environment. Here’s why the buzz is growing: 🐍 **Enhanced Data Prep**: Python scripts can handle data cleansing, complex ETL, and feature engineering right inside Power BI. No more jumping between disparate tools. 🎨 **Custom Visuals**: Move beyond standard charts. Python visualisations in Power BI mean predictive models, geospatial plots (think seaborn, plotly, matplotlib), and interactive storytelling become everyday assets. 🛠️ **Advanced Analytics**: Whether you’re creating churn prediction models or complex segmentation, having sklearn or statsmodels in your Power BI arsenal is a massive enabler. Hot Tip: Libraries like pandas and matplotlib are native to most data pros, but don’t overlook packages like prophet for time series forecasts or nltk for text analytics — all embeddable in your dashboards. The best part? Power BI’s cloud service and Power Platform ecosystem mean these analytics can be scaled, shared, and automated with non-technical audiences, driving true data democratisation. So — are you using Power BI + Python in your workflow? If not, what’s holding you back? 🚀 Would love to hear your experiences and favourite use cases! #DataAnalytics #PowerBI #Python #AUNZData

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