Data Tech

This presentation will discuss graph databases, graph algorithms and where graph based approaches can be used in Healthcare analytics use cases.

General Mills is one of the largest food companies in the US, with an ever-widening family of brands that consumers love. Our presentation will focus on how we track, collect and synthesize branded impression data from web, social, email and beyond for Pillsbury.com. We'll demonstrate how we are using Tableau to visualize this impression data in a simple and easy-to-understand format for our brand teams. Join us for a deep dive into the Doughboy's data!

We introduce new tools for modeling and forecasting multivariate time series data using vector autoregression, and demonstrate the use of the BigVAR and bigtime R packages.

Organizations often underestimate the technical complexities and the resources needed to build an effective data lake—-not to mention operating and maintaining the data lake once it’s developed. But you can circumvent these obstacles by learning the most common pitfalls faced in developing and deploying your data lake.

In this session, learn how to manage:

• Data ingestion issues with managing source side change data capture and subsequent merging and syncing of data
• Data preparation challenges including effective troubleshooting of data pipelines and how to prep data to scale and support high-speed BI queries
• Data lake operational hazards including scaling up for agile enterprise-grade performance
• Constantly changing data platforms and how to deal with hybrid and multi-cloud environments
• Data lake governance challenges such as managing access control and maintaining regulatory compliance

Geospatial analysis has become an increasingly important part of many companies tool set, to understand everything from supply chains to consumer demand. With petabytes of geospatial data analyzed using machine learning and deep learning models at Descartes Labs, this session will expose some of our "secrets" to getting the most value from geospatial data.

Most companies understand the need to develop an analytics and big data strategy to enhance customer or partner experience or to improve business processes and outcomes. Internal data is where most companies start as they build out a data platform. Integrating external data sources such as location, government or weather information can provide new opportunities to create more meaningful outcomes that are greater than simply combining the data would suggest.

James Peterson, a meteorologist from IBM, and Michael Downs, CTO at Evolving Solutions share some fascinating use cases for combining weather data with internal data to produce high value and sometimes surprising outcomes.

Buy flights on a Tuesday! Compare multiple websites! Buy months in advance!

These are all the common pieces of advice that you'll hear when you ask Google how to get the best deals on flights. Unfortunately, they're all misleading at best and completely wrong at worst. But what these things really point to is the psychology of human nature -- we want to feel like we got a great deal! Even if the price we're paying is higher than necessary.

But being data people, (hashtag) facts matter! What is a good deal? The answer, as always, is "it depends!". The truth is, most airlines excel at revenue management. Fare pricing is oftentimes an arbitrary exercise based on external requirements instead of a predictable, patterned behavior. That means, flight pricing is effectively chaotic in nature and difficult to predict on a time domain.

Using machine learning (ML) and artificial intelligence (AI) on Azure we've built and proven our ability to find that great deal. A couple of recent deals we've booked using this process (though manual, instead of automated) include Bahamas for $200, Argentina in Business for $1500, and Portugal for $550.

Come learn what we've accomplished and how we're doing it.

Data Science projects are difficult to plan, particularly for those coming from a software development tradition. In this talk, I'll discuss some approaches we use in data science consulting to bring projects from conception to productionalization. The main trick: engage data engineering and DevOps early in the process and produce prototypes that allow them to begin work.