Passiona Cottee, Data Ethicist / Machine Teacher / UTS Sessional Lecturer / Solicito
Lead Product Manager, Prospa
Anyone who has had a seat on a 'rocket ship' can appreciate the tensions experienced when making decisions with data. Sometimes, to maintain momentum and remain agile, sacrifices need to be made. As Lead Product Manager for Organisational Scale at Prospa, Andrew Walker has survived this very journey and is alive to tell the tale (he has the scars to prove it). He covered the tough choices made, particularly in developing Prospa's Credit Decision Engine, in order to move at an industry-leading pace but also maintain a market-leading NPS.
Principal Researcher, Gradient Institute
Consequential decisions are increasingly being made by AI, like selecting who to recruit, who receives a home-loan or credit card, and how much someone pays for goods or services. AI systems have the potential to to make these decisions more accurately and at a far greater scale than humans, but, if improperly designed they run the risk of doing unintentional harm -- especially to already disadvantaged members of society. Only by building AI systems that accurately estimate the real impact of possible outcomes on a variety of ethically relevant measures can we ensure this powerful technology improves the lives of everyone. This talk focused on the anatomy of these ethically-aware decision-making systems, and some design principles to help the data scientists, engineers and decision-makers collaborating to build them.
Data scientist was described as the sexiest career of the 21st century. Data engineering is one of the fastest growing technical roles. And data analysts? Well, you don’t exactly see people stampeding to Lambda School to become one. I think this should change.
In this session Claire covered why she believed that good data analysts are the most valuable members of a data team. We learnt how you can “scale yourself” by applying software engineering best practices to your analytics code, and how to turn this knowledge into an impactful analytics career.
Tableau Zen Master
David spoke about the power of feedback and the data viz community, with regards to him sharing his Game of Thrones death data.
It's an interesting journey that should inspire people to embrace feedback and how crowd sourcing data vizzes can be a super powerful and great learning experience for everyone involved.
Director of Customer & Growth Analytics, IAG
In this corollary to Austin Byrne's talk in June, Willem shared his team's journey to escape the typical value-providing drudgery of dashboards, reports, and PowerPoints - and move towards more advanced challenges with more diverse, longer term, business values. Is there a binary choice between elementary reporting and advanced statistical work? How do you harness the curiosity, learning, and skills from the latter to motivate and propel your team towards both? This talk put the spotlight on the passionate individuals and how they can actually make differences for customers and their organisation.
Co-founder & Practice Director, Intellify
Time-series, as a field of study, has largely focused on statistical methods that work well under strict assumptions. Specifically, when there is sufficient history, there is little meta-data and a well-formed auto-correlation structure. However, as an applied practitioner Kale knows that most real-world time series problems violate these assumptions. This leaves us with an opportunity to use more modern time series methods, based on machine learning, to overcome these deficiencies.
This session spoke about the unique properties of time-series, how statistical methods work and how and why machine learning (and deep learning) methods can be used to improve accuracy.
Head of Product Analytics, Atlassian
Let's face it: your job rarely involves the depth of technology and innovation read on Airbnb's Data blogs. Over the past ten years, Austin has come to see a trend across hundreds of analytics professionals: we are an unhappy bunch when we feel there is more advanced analytics work being done by our peers. This is true even in "cutting edge" companies, based on his experience working on and leading several Analytics teams at Facebook and Atlassian. He lends thoughts into why he think this plight exists, share what he feels is a representative view into the "normal" work done by the majority of analytics professionals in companies of all sizes, and contest what "impactful" really means to an analyst job-well-done.
Hosted by Simon Rumble
Insights Manager, Rezdy
So, you need an SQL data model but don't know how to get started? This talk was about Rezdy's journey using DBT over the last 7 months, and the things you may need to consider as you're getting started.
Director, Small Multiple
How can you leverage skills and tools you already have to create charts and data viz that are useful to others? We learnt about the three parts of a good data viz and some general tips and tricks for making charts that support day-to-day reporting and communications.
Director - Technology & Enablement, Venntifact
A brief history of the evolving role of data in the customer experience, and an overview of the key platform “categories” in play. Aimed at anyone overwhelmed by acronyms and trying to answer “what does what?
Managing Director, Panalysis
Creating exceptional customer experiences is a key component to success in today's digital world. Google Analytics is a powerful tool to help you understand the journey your customers take, but has so many options and so much data, it can be hard to get started. By taking a structured approach to your analytics you can learn a lot about the customer's journey and identify areas for improvement - by tackling your own customer journey map using what's possible in Google Analytics.
Principal Developer Advocate, Poplin Data
Raw event data like you get from GA360, Adobe Analytics or Snowplow gives you much more information than the aggregated data you see through reports but they can be unwieldy to use. Building an SQL data model allows you to build centralised business rules for things like conversions, traffic sources and rollup events while still being able to change them. When something screws up, you can paper over those changes so your business users only see a perfect timeline. This talk walked through building a data model on top of raw Snowplow data using reasonably simple SQL.
Head of Data, Excite Holidays
Tableau was a game changer for interactive data visualisation and analysis. It hasn't been quite as strong as an enterprise Business Intelligence tool. Nicholas has worked with Tableau for over 7 years and is currently responsible for every aspect of its deployment in his organisation. In this talk he shared from his experiences some of the strengths, weaknesses, and then demonstrated some more advanced technical tips/tricks/hacks that he's developed over the years. (e.g. manicured self-service, drill-through and big data).
Head of BI, Big Red Group
6 months ago BRG ran all their business insight by connecting 200+ tables directly to Tableau. Obviously, something had to change… Mark talked about how they went from nothing to something and explains some of the challenges they faced and continue to face on the journey to data nirvana!
Hosted by Sam Redfern
The world of data is full of buzz words. Inspirational posts on LinkedIn talking about how "data lakes" will transform the industry, CEO articles talking about how they should kill BI and have only “data science teams”, ad-tech, attribution and more. Separating fact from fiction is tricky if you’re an outsider. Here a panel of 3 industry experts discussed different terms and discovered if they are underrated / overrated.
Hosted by Simon Rumble
How can you tell when people are actively engaged with your content, not just clicking a vacuous headline? Once you know, how do you distribute this information around the business to help optimise content creation?
This month’s panel session featured great minds who’ve been working and thinking deeply on these matters in publishing and content marketing. We covered the challenges faced by digital publishers, platforms and solutions to the problems, and approaches to getting the data to the decision makers
The Great Debate
Google Analytics vs Adobe Analytics vs Snowplow Insights.
Location and structure of the data in the cloud doesn't matter. Or does it?
Vojtech Kurka, Solution Architect, Keboola
Offline is the new online
James Fogelberg, Founder, LANDMARKS ID
Data Layers and KPIs: Together at last!
Jason Lam, Senior Consultant, 2Datafish
Automating everything in Google Scripts/BigQuery/Data Studio
Scott Sunderland, Founder at Tribalism
Analysis of competing hypotheses (ACH)
Moe Kiss, Product Analyst & Digital Analytics, THE ICONIC
Holistic data capture
Mike Robins, Co-Founder, Poplin Data
Dashboard Design Principles
Dr. Statistics : Or How I Learned to Stop Worrying and Learned to Love The Equations
Kshira Saagar, Head of Data Sciences, Datalicious
And here's the embedded video.
Triggered emails on a budget
Tim Griffin, Head of Data & Innovation, Appliances Online
Doing Personalisation When You're Not Amazon
Willem Paling, Personalisation Engineering Director, IAG
Adam King, Personalisation Engineering Lead, IAG
When Data Fails & The US Election
Internet of things for $25
Simon Rumble, Co-Founder, Poplin Data