DrSimonJ Pty Ltd

Join Hypergrowth Data CEO Simon Jackson at our upcoming YOW! Tech Leaders Summit in Sydney to gain insights and strategies to increase the velocity of your engineering teams to ship product changes and deliver customer value at exceptional paces! 📅 Date: Thursday 12 Sept 📍 Location: Sydney 🎟 Snag your 1-day Summit tickets here: https://lnkd.in/gZaZKZaD #YOWTLS24 #techleadership

Gain insights and strategies to increase the velocity of your engineering teams to ship product changes and deliver customer value at exceptional paces with Hypergrowth Data CEO Simon Jackson at our upcoming YOW! Tech Leaders Summit in Sydney! 📅 Date: Thursday 12 Sept 📍 Location: Sydney 🎟 Snag your 1-day Summit tickets here: https://lnkd.in/gZaZKZaD Don't miss this chance to boost your tech leadership game! #YOWTLS24 #techconference #techleadership

What does "monetize data" mean to you?? I think of 4 things but feel others either fixate on one or probably know more. The four things I think of... 1️⃣ Boosting Productivity Using data to enhance internal operations and help people produce more output for their time. Eg Analytics or experimentation to focus attention or make better decisions. 2️⃣ Powering Products Using data behind the scenes to power product experiences that acquire, retain, or increase the value of customers. Eg AI/Machine Learning to personalise a product experience or journey. 3️⃣ Being the Product Gathering and directly selling proprietary data that is valuable to other businesses to do (1) or (2). Eg advertising business selling raw data or aggregated insights about their audience. 4️⃣ Procuring Investment A bit different to the others but important nonetheless - using data to tell stories about your customers and business that inspire confidence and secure investment in your endeavours. So tell me... I'd love to hear from business leaders and data experts alike. What comes to mind when you think of "monetizing data?"

If you're paying someone to build you a Machine Learning / AI model, please please please... Make sure you at least understand this one thing: *Validation* I've spoken with a number of business leaders who've engaged a contractor/consultancy/etc to build them ML / AI models. These can be great! Sometimes game-changers. BUT... It's easy if you're not an expert in ML / AI to be taken for a ride. If you're really out of your depth, "model validation" is what I'd suggest you learn a little about. It's the process of testing the model actually does what you want. Ask questions like: - How will we measure performance? - What baseline will we compare against? - Are we able to measure in-production performance? - What are the caveats or risks we should be aware of? You don't need to be an ML / AI expert to leverage its value. But it pays to have a firm grasp on what counts as success. Have you faced this? What did you find challenging?

A priority that every established business should have: Making analyses and learnings more accessible. Here's why 👇 - They're unique insights about your customers and business. - They have a major influence over decisions. - They're not cheap to gather. So everyone (especially new hires) should be able to immerse themselves in past learnings to: - Get why past decisions were made - Break faulty assumptions they might have - Shortcut the path to being experts in YOUR business - Proceed with their work being more informed (enlightened?) So here's the tactical take-away: Create a discoverable repository of past analyses, experiments, and learnings to help staff take weeks to deliver the sort of impact they'd otherwise take years to land. Have you seen this done well? Tell us about it below!

"Why isn't my data analyst adding value?!" 😡 Here are 11 common, maybe non-obvious reasons: - The decision maker isn't clear on their priorities - The decision maker can't share priorities - The analyst can't understand priorities - The analyst can't form data questions - The analyst can't efficiently query - The data is too convoluted - The data quality sucks - The data has nothing interesting in it - The analyst can't communicate their findings - The decision maker can't understand the findings - The decision maker doesn't know what to do next The "Analyst" isn't the only factor in useful analytics! What would you add?

We interrupt your regularly scheduled data conference posts to present our round-up of our data articles this week. If you're missing out on all the Snowflake fun, our guest contributor Jagdish Purohit from 5X has got you covered with a day-one overview. All Hands on Data is our team's newsletter where we share our favorite articles and podcasts from the data world. Make sure to subscribe so you don't miss a beat on what's happening in the world of data! This week's featured articles: Building a data pipeline using Snowflake primitives by Dan Golding at Twing Data Day 1 Snowflake Summit Overview by Jagdish Purohit by the team at 5X Data Libraries – the Secret Sauce to Regulatory Environments by Ryan Lougheed at Onspring for insideBIGDATA Data by Design, a project directed by Lauren Klein This week's featured podcasts: How to build terrible AI systems by Hugo Bowne-Anderson I've Got 99 Analytical Methodologies and Need to Pick Just One featuring Simon Jackson from Hypergrowth Data for Analytics Power Hour #data #datanews #datapodcasts #AI #podcasts #dataviz #datavisualizations #snowflake #snowflakesummit

The more analytical methodologies the analyst learns, the more they realize they *don't* yet know. On the latest episode of the show, Simon Jackson from Hypergrowth Data joined us for a discussion about the vast—and seemingly impossible to even cleanly organize—pool of analytical methodologies and the challenges involved in identifying the (or "a") right one for each given business need. https://lnkd.in/gugnB7-g #analytics #analyticsmethodologies #podcast