It’s hard to admit failure, and I consider myself to be an all-star analyst, but if you can learn and become a better analyst then it’s ok to fail, right? Here are some of the key topics that I’ll address from failing: What key skills do you need to be a more successful analyst? What is the importance of Objective Analysis? How do you overcome negative results, noise and naysayers? How can you continue to grow in this field? What do I see as the future of Digital Analytics?

While most of the consultancies are focused on delivery, not many of them are creating inspiration and foundation for delivery of continual business value. We would explore reasons why common narrative is not aligned with most client needs, how to fix it and how to sell and grow analytics services.

News and other types of content consumption patterns are changing challenging media to transform and adjust to new readers' behavior. New distribution platforms appear and evolve, formats of content delivery appear and die. What are the key metrics modern publishers have to analyze if they want to increase profits and retain their audience? How we can track visitors' behaviour and get insights on publishers content policy, articles formats and distribution channels?

In this talk Anastasiia and Mariia will show how they use data for their company purposes. Collecting data from everywhere: Office, Task Manager, Google Calendar, Gmail, SalesForce and internal systems.

I'm giving away my ideas for free. I'll describe I want to tackle in 2019. These are my personal moonshots, stuff that pushes my boundaries, expands my envelope and challenges me. In essence, this is the output from SPWK - start the year with the hardest list of things you want to tick off. None of these ideas has gone beyond ideation. They're doable with time, effort and new features. I want someone to pick them up and run with them...maybe even capture a Golden Punchcard in 2020? You're welcome to lift them and use them - I don't expect any credit, just go do something interesting!

Past attendees of Superweek have ridden along with Tim as he explored R, and then as he dove deeper into some of the fundamental concepts of statistics. In this session, he will provide the latest update on that journey: how he is putting his exploration into the various dimensions of data science to use with real data and real clients. The statistical methods will be real, the code will be R (and available on GitHub), and the data will only be lightly obfuscated. So, you will be able to head back to your room at the next break and try one or more of the examples out on your own data! (But, don't do that -- the food and conversation at the breaks is too good to miss!)

This is a continuation of the stuff Tim Wilson spoke about. I am a relatively new entrant to the whole data science side of things (about 12months in as of now) and tried learning a bit of R / deployed snowplow etc but still struggled with code and not having intuitive interface that I was used to.

The top speed recorded by an F1 car is currently 372.6 km/h (231.5 mph). Juan Pablo Montoya of the McLaren-Mercedes F1 Team achieved that after racing professionally for 13 years (and 'Karting' most of his early life). The F1 car itself is an amazing tool, but does anyone think that you could put just anyone behind the drivers seat and achieve the same results as Montoya? Tools are only as good as the people using them (at least until said tools learn how to murder us). In a recent study 55% of college students said they believed the full moon caused people to behave oddly, despite a complete lack of evidence. The often unspoken problem in Analytics is the people. Even if they don't believe in Bigfoot, they may lack the humility to admit they have blind spots, or the intellectual empathy to understand things that aren't all about them. We must elevate our practice of analytics through the promotion of Critical Thought, which leads to better Empirical Analysis, and insight and value for our companies and clients.

Demonstrate any Digital Analytics solutions or method of your own that is way beyond the defaults. Who decides who's gonna win? The audience. Send your nomination to [email protected]!

On his first visit he shared the Digital Analytics Maturity Model, next came the Radical Analytics Manifesto, and after skipping a year, he is back in force with interesting stories about digital transformations, customer centricity, use and abuse of data, and the secret life of digital analysts.

Since the dawn of analytics we have strived to improve both the quality and volume of our data, with no other ambition to ensure the largest possible dataset – not because we need it, but because we might need it. GDPR have temporarily put a wrench in our original approach, but it takes more that the law to keep a good analyst away from his data and with Machine Learning as an active part of the toolbox the value of data have grown exponentially. The sessions in a reflection on how we have done things so far and where we might end if you don’t stop doing business as usual and instead calibrate our efforts in a more strategic and ethical direction.

The Google Analytics Suite of products is now part of the Google Marketing Platform. We will cover how key pieces of the Platform can be used including the Salesforce connectors, Display & Video 360, Google Optimize integration, and Google Cloud integrations. We will review how data can be used actionably for advertising, e-mail, personalization, and surveys.

This thing called agile development starting popping up more and more around me. Last year, I decided to investigate it: talking to scrum masters, discussing implementation issues with fellow analysts, and reading about the lean methodology. After that, something clicked into place. In this talk, I’ll share my ideas on and experiences in applying lean to my analytics team.

Zorin's personal experiences gathered with small family businesses to large enterprises in terms of expectation management. If we look at Analytics through ages, how far it has gone, the massive tooling around it and the widespread ability to test, predict and sustainably create mistakes or better say learn one would think the expectations should be easy to manage. One would be somewhat wrong or?

These days there is a strong case for Machine Learning in Analytics and everywhere you turn you are presented with case and examples on how ML have made the analysis better, faster and more consistent. But for a man with a new hammer everything looks like a nail and sometimes we forget to draw a line when machine learning is the right tool and when an analyst actually is required to do the job right. Join this session to dive into some big thoughts on how to distinguish what is needed when.

Mobile Apps are not the same as web, so why have we been measuring them as such? With the old GA Services SDK turning down for some users, it's time to look into how to use Google Analytics for Firebase to measure and action on your mobile app's data. Krista will walk you through the benefits and power of the tool, explain the differences in data model and implementation best practices, and tips for how to migrate.

We'll look ahead into the rapid changes hitting Analytics as an industry and all of us as people needing to protect our digital profiles and review where we've come from, what needs to change, and where we headed.