February 2018: Business Applications of Blockchain Technology

Presented by Mark Wheeler

RSVP via Eventbrite

Blockchain technology is being talked about as one of the next big innovations to take hold across multiple industries. Businesses who focus on transactions, contracts, and assets management are just a few of the initial sectors of the economy which will be disrupted. As the underlying technology begins to take hold, time-consuming and labor-intensive functions will be replaced by automated and “always on” smart processes. Centralized models of trust and authenticity will be replaced by decentralized services and crowd-based consensus algorithms.

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January 2018: Hybrid Cloud Data Architecture

RSVPs have been closed as we are at capacity for this month’s chapter meeting.

Presented by Parth Patel

Big Data Solutions Engineer, Zaloni

UPDATE: Montgomery Park lot may be full due to new tenants.

As hybrid clouds become more prevalent, IT organizations struggle to deal with moving and managing data. While computation and applications can be outsourced, once data is generated, IT is on the hook to control it as long as it exists. Data governance and data security are particularly difficult to address in hybrid environments. Parth Patel will discuss the importance of creating a standardized data fabric with a common data management platform that can tier access, providing hierarchical data lifecycle management for data lakes. They will discuss “in production” use cases of this tiered data lake approach for Hybrid “Ground to Cloud” environments across multiple industries. Topics during this session include: · Data governance · Data security · Metadata management · Hybrid “Cloud to Ground” architectures

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November 2017: Good Data, Bad Info

Presented by Michael Scofield, M.B.A.

Assistant Professor, Loma Linda University

Producing decision-able information from lots of raw data often requires complex processes which must be carefully designed and architected.

This presentation looks at the gap between raw data and decision-able information. Most of the chatter about data quality focuses upon raw, granular data describing discrete events and entities inside the enterprise and around it. But for data warehouses and decision-support, data must be converted into useful information meaningful to the decision-maker.

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September 2017: Cartographic License and Building Maps That Work

Presented by Sarah Battersby

Senior Research Scientist at Tableau Software

UPDATED: A PDF of the presentation is available for download

We live in a data rich world and many of the data that we encounter have a geographic link, such as a point coordinate, an address, a state, or country name.  Maps are a key part of understanding geographically linked data and utilizing it in decision making.  In designing maps, virtually anyone with a computer and an internet connection can be a cartographer – regardless of knowledge of principles of spatial data or visual and cartographic communication best practices.  The good news about this democratization of cartography is that everyone is empowered to explore their own data with maps.  The bad news is that anyone can make a map.  While many map authoring tools help guide the design, it is still easy to unintentionally (or occasionally intentionally) mislead readers.  The variant of the truth that we find in maps is largely driven by choices that the cartographer makes in the data collection, cleaning, analysis, and visualization process.  In this presentation I consider issues of how the map designer and reader perceive mapped data and where “noise” can creep into the communication to distort the intended message.

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July 2017: Data Science, Predictive Analytics

Presented by Dean Abbott

Chief Data Scientist, SmarterHQ

UPDATED: A PDF of the presentation is available for download

Most of what data scientists do is nothing new, and much of what’s new is really a throwback to what we used to do 20 years ago. So why data science so popular now? In this talk, Dean will describe what differentiates data science from related fields like Business Intelligence, Predictive Analytics, and Statistics, and will illustrate the use of data science from case studies in customer analytics and fraud detection.

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