DataWorks San Jose 2018

June 17-21, 2018

Join us from June 17-21 at DataWorks San Jose to learn about the latest developments in big data, while networking with industry peers and pioneers to learn how to apply open source technology to make data work and accelerate your digital transformation.

Whether you are ready to kick off a visual analytics project or have technical questions for our engineers, we'd love to talk to you.

Swing by Booth #G7 or request a meeting in advance.

DataWorks San Jose Highlights:

Can’t make it to DataWorks this time? Download Arcadia Instant and maybe you’ll be speaking on behalf of Arcadia Data at Strata next year!


Key objectives and principles for building predictive models on big data

Satya Ramachandran

VP of Engineering
Neustar

Thursday, June 21, 11:30 AM - 12:10 PM
Executive Ballroom 210D/H

Business analysts spend a lot of time today looking at what happened in the past. But what about trying to grasp what will happen in the future? For example, what if you are given 10% more budget for next quarter’s marketing spend? Do you know how you’ll use that extra money, and do you know what impact it will create? Or, suppose you want an increase in your budget, but need to show what you expect that increase to do, then what?

Many of today’s data applications are simply “decision support systems” designed to be useful in the aforementioned scenarios. They help business professionals use data to better understand their environment and to make better decisions. But with larger volumes of data and increased ambitions of competitive businesses, the end goals become tougher to achieve. As the VP of Engineering for MarketShare DecisionCloud at Neustar, which provides planning and analytics capabilities for marketers, Satya Ramachandran has taken on these challenges by leveraging big data technologies.

In this talk, Satya will talk about some of the high expectations he’s faced at MarketShare and also some of his successes. For example, despite the fact that data has grown significantly in recent years, business users still want faster results. This phenomenon led to efforts that supported orders of magnitude more data within his organization and also demanded orders of magnitude speed improvements –going from several minutes to sub-second responses. Satya will share some guiding principles that helped him successfully develop and deploy systems that his customers needed to be successful within their big data projects.


A Tale of Two BI Standards: Data Warehouses and Data Lakes

Steve Wooledge

VP of Marketing
Arcadia Data

Tuesday, June 19, 7:40 PM - 8:00 PM
DataWorks Expo Theater

Data lakes as part of the logical data warehouse (LDW) have entered the trough of disillusionment. Some failures are due to lack of value from businesses focusing on the big “data” challenges and not the big “analytics” opportunity. Data is just data until you analyze it.

While the data management aspect has been fairly well understood over the years, the success of business intelligence (BI) and analytics on data lakes lags behind. In fact, data lakes often fail because they are only accessible by highly-skilled data scientists and not by business users. But BI tools have been able to access data warehouses for years, so what gives?

In this talk, we’ll discuss:

  • Why existing BI tools are architected well for data warehouses, but not data lakes.
  • The pros and cons of each architecture.
  • Why every organization should have two BI standards: one for data warehouses and one for data lakes.

 

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