MinneFRAMA

With advancement in technologies and the ability to process huge amounts of data, the application of individual level personalization algorithms to harness consumer engagement is becoming critical for marketing efforts. This talk will cover how technology and machine learning algorithms could build loyalty and drive sales in the retail and healthcare industries.

When a hurricane is bearing down on a major metropolitan area, accurately predicting the extreme spikes in demand for home improvement categories is extremely difficult, and too often based on inaccurate instinct. See how North Highland used TensorFlow to train a recursive neural network on hundreds of terabytes of weather data, to help a major home improvement retailer better protect their customers' homes and loved ones in times of need.

The integration of Voice of Customer Research and On-Site (Store & Branch) Journey Measurement provides a remarkably fruitful way to understand and improve the customer experience. In this presentation, we'll not only explain how this works, but we'll show several real world case-studies that illustrate the remarkable power of putting attitudinal and behavioral data from the real world together. We'll also cover the technologies necessary to do the work. The keys to successful integration. And the way to handle common issues around store operations, data collection and security, and consumer privacy. This integration may be fairly common practice in the digital world, but it's almost unheard of in the store.

In the spirit of Twin Cities Data Visualization Group we will start our session with data viz critiques - audience participation encouraged. Then, we will have a session two mini-session speakers. One around the ABC's of data viz and how to start. Another around using visualizing geographic data with GIS.

As organizations accumulate more data, insights teams struggle to analyze it all and make sure they discover the most important insights. Today, Fortune 500 companies analyze less than 20% of the data they have — the 80% of data that never gets analyzed contains important hidden insights that will never be discovered and used.

These hidden insights are the key to beating the well-organized, digital challengers in your business. You have more data than these challengers, but if you can't get more insight out of that data, it's not serving you.

Noah Horton, the former head of product and core engineering for Oracle Cloud, has invented a new approach to AI — the first based on unsupervised machine learning — that is able to analyze all of a businesses data without human guidance, and discover the insights that matter automatically.

Data Science, machine learning, and AI have long been promised as a solution to dealing with this data growth, but every other AI or machine learning company you've heard about still requires data scientists to start with a hypothesis and to run bespoke projects. Even simple projects still require months of cleaning data, building and training models, and repeated iteration — before you see any results. These approaches are too manual and too slow for you to be able to analyze all your data. In this keynote session, you'll learn why Unsupervised AI is the key to automatically discovering the important insights in your data — saving your team the manual effort of generating and testing hypotheses, and allowing everybody, from the C-level to line-level, to learn faster and make better data-driven decisions.

Noah will also answer questions on improving your insights process, show you ways to get very quick wins in your organization using AI (with real examples), and share what successes we've seen with other key industry executives (like the first Chief AI Officer in the Fortune 500, Pavan Arora, who I've quoted below).

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“Almost everyone underestimates the time it takes to train a machine. Supervised learning requires an enormous amount of data cleaning and transformation. Unsupervised is leaping beyond by reducing AI development from months to minutes. They are on the cutting-edge, helping lead the AI revolution.“
PAVAN ARORA
Chief AI Officer, ARAMARK (first CAIO in the F500)
Former Chief Data Officer, IBM Watson

More than $410 billion dollars was donated to charity in 2017, 70% of which came from individuals. How, then, can it be that no consumer research exists on the topic of personal financial management -- no infographics or app solutions -- that includes charitable giving as a component within consumers' full financial picture? What's with that? There's no shortage of information that conveys how passionate and committed Millennials are about making a difference -- they intentionally work for (and invest in) companies, buy brands and support causes that demonstrably contribute to a better world. Due to our patented technology, intense data integration and use of AI and Machine Learning, the data gathered within our app will be like no other currently available in market. Researchers, retailers, marketers, manufacturers, advertisers, financial institutions, nonprofits will all benefit from the highly predictive, realtime information that will be available. The goal is to empower individuals and organizations to produce, position and promote products that are relevant and meaningful to all.

Over the last two decades agile has gone from obscurity to ubiquity, evolving a lot along the way. Agile forerunners continuously push boundaries and realize major productivity gains as a result. But despite agile being everywhere these days, many data teams struggle to adopt it effectively. Even small agile misapplications can end up decreasing productivity instead of increasing it. This creates a growing productivity gap between data teams that use agile effectively and those that don’t, which can mean the difference between a data team being a competitive asset or a costly liability. In this talk, we’ll dig into modern agile concepts and how they can be applied to manage and run high-performing data teams starting from the basics and working up to recent trends in areas like DataOps, lean workflows, continuous delivery, and objective reliability management.

The data hype machine continues to be in full effect.  There are very public statistics on the percentage of analytics and data efforts that fail outright, or at a minimum do not meet their advertised goals. As data volumes rise, so does the opportunity for risk and efforts to control your data consumption will often backfire. We will plan to cover a simple analogy, and will discuss behavioral metrics you can use on your organization to gauge the effectiveness of your data and analytics strategy.  Positive side effects will be accelerated adoption of both data & solutions, and the ability to connect your tribe of data ninjas to rise the tide of your entire organization.

AI is hard! Is your company building a digital assistant, incorporating image or audio analytics into an application, or developing an intelligent recommendation engine? The road to AI-enabled solutions is paved with good intentions but littered with roadblocks such as incomplete cognitive/AI software platforms, difficult-to-use open source components, and a lack of staff with relevant expertise. In this session, Sean examines requirements that span over a dozen functional technology categories and draws on lessons from his deploying solutions in organizations adopting open source and commercial AI technology. Recommendations from this session will help your company develop a framework for assessing whether and when to build, buy, or partner for AI capabilities.

In late 2017, it was reported that more than 20 million Amazon Echoes and over seven million Google Homes were being used in our homes and offices, and this number continues to grow everyday. While they're good at some things, most aren't very conversational. In order to evolve this medium, we need to move beyond simple one-turn interactions into more human-like conversations. So how might we do this? This talk explores pairing knowledge graphs and graph technologies with voice user interfaces to create more natural conversational interactions between human and machine that engage, inform, and delight users.

The goal of every analytics department is to turn data into actionable insights.  For organizations that are in the early stages of the analytics maturity model, a project as simple as automated reporting can make a huge difference in speed to decision and ability to craft data-driven strategy.
 
In this session, we will describe the steps a small team of analysts followed to build an automated report suite using Alteryx and Tableau that provided tremendous business value to stakeholders and enabled the broader marketing analytics team to get out of the report-pulling business and re-invest in deeper, more complex analyses.