MinneFRAMA

Dave and Matt will be doing a live taping of the Data Able podcast interviewing a couple to be named Twin Cities analytics professionals that will telling data stories. What was most successful analytics projects? What was the definition of success? What made it successful? Along with taking questions from the audience.

2017 saw the most doors closed for retail. 2018 is set to pass that, yet more brands like Nike and Under Armour, as well as retail startups, are opening more doors than ever. What's with this conundrum?

The reality is, retail is thriving, more than ever; due in large part to machine learning and analytics on the customer. This presentation will explore success stories of retail winners in this "retail apocalypse" age. The presentation will cover market share on brands in the footwear, sports, apparel and fashion industries. It will also include consumer habits, the importance of segmentation, shopping preferences, and ultimately, shopper destinations.

This presentation will reveal how analytics and big data informs merchandising decisions, merchandising, branding and more. A few examples to be included will be AllBirds, Warby Parker and will showcase how Nike's new store, Nike by Melrose uses crowdsourcing technology to fulfill merchandise in the store - This has never been done before in the history of retail.

Our panel will discuss ways in which the California Consumer Privacy Act and EU’s General Data Protection Regulation can present opportunities for data analytics and how innovation can co-exist with regulation, including (i) a brief introduction to such regulation, (ii) the impact of such regulation on the performance of analytics and the impact on the resulting insight, (iii) the journey involving tracking and analyzing the California Consumer Privacy Act and (iv) what to monitor and take into account going forward, including prospects for federal US privacy regulation.

Developing professional skillsets requires building a mental representation in order to use them easily and effectively. Spending some time reviewing your professional mental model will improve its use and help you find weak areas to improve. This will be a discussion at one of the tables during the lunch break.

HealthPartners had a data integration problem - their digital data was isolated across numerous sources and didn’t logically map together. Additionally, they didn’t have the infrastructure or automation to move, transform, and load the data into multiple systems. As a result, they turned to Amazon’s cloud to build an automated, scalable solution to ingest data from multiple sources in multiple formats, transform, and load the data into multiple data warehouses. Learn more about the business problems that were solved and the underlying technology that was built to solve this common analytics problem.