Strata Data Conference New York 2018

Sept 11 - 13, 2018

Let's talk about faster insights for your business users at Booth #1307

Join us from September 11 - 13 at Strata Data Conference New York, to tap into the minds of expert speakers from around the world and network with thousands of data scientists, engineers, analysts and business managers.

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 #1307 or request a meeting in advance.

  • Fill out this Eckerson Survey for a free t-shirt - Bring a print out of your landing page to claim your prize

 

Strata New York Highlights:

Tuesday, September 11th 

2:00 - 2:30 PM | Location: 1A 08

AI to Spot New Trading Opportunities

with Paul Lashmet

 

Wednesday, September 12th 

2:05 - 2:45 PM |  Location: 1E 06

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

with Randy Lea

 

Wednesday, September 12th

5:25 - 6:05 PM | Location: 1E 12/13

If You Thought Politics Was Dirty, You Should See the Analytics Behind It

with John Thuma

 


Visualize AI to Spot New Trading Opportunities

Paul Lashmet

Practice Lead
& Advisor for Financial Services
Arcadia Data

Tuesday, Sept 11 | 2:00 PM - 2:30 PM
Location: 1A 08

The use of artificial intelligence (AI) and deep learning to generate and execute trading strategies is becoming more common in 2018. As more and more business decisions are based on AI and advanced data analytics, it is critical to provide transparency to the inner workings of an algorithmic black box. This is because regulators and investors are demand it.

Read More

The financial services sector is a leading adopter of AI and has the most ambitious AI investment plans. Adoption will center on AI technologies like neural-based machine learning and natural language processing because those are the ones that are beginning to mature and prove their value. However, the rapid adoption of these technologies by hedge funds and asset managers comes with a major challenge: lack of transparency.

The decision-making processes of machine learning based technologies are opaque and don’t provide reasoning behind the results. This is concerning because many machine based decisions have wide-ranging compliance and business development implications.

This is not much different from working with people because their decision making processes aren’t fully captured. AI, however, creates data and that is a major opportunity. That data can be audited and visualized to expose lineage and correlation that offers fund managers deeper insight into what makes one strategy better than another. The demand by regulators and investors for transparency into investment decisions will drive new innovations in visual data analytics.

In this talk, Paul Lashmet, a veteran of the financial services industry, will demonstrate how the right visualization tools can be used to visualize the decisions behind market simulations and automated trade execution. He will also show how data visualization can enable fund managers to learn from the decisions made by machines and spot new trading opportunities.


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

Randy Lea

Chief Revenue Officer
Arcadia Data

Wednesdayy, Sept 12 | 2:05 PM - 2:45 PM
Location: 1E 06

The use of data lakes continue to grow, and the right business intelligence (BI) and analytics tools on data lakes are critical to data lake success. In this talk, we’ll discuss why traditional BI tools don’t work well on data lakes, so every organization should have two BI standards: one for data warehouses and one for data lakes. We’ll also discuss innovations that justify this position.

Read More

The use of data lakes continue to grow, and a recent survey by Eckerson Group shows that organizations are getting real value from their deployments. However, there’s still a lot of room for improvement when it comes to giving business users access to the wealth of potential insights in the data lake.

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, organizations often struggle with data lakes 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 traditional BI tools are architected well for data warehouses, but not data lakes.
  • Why every organization should have two BI standards: one for data warehouses and one for data lakes.
  • Innovative capabilities provided by BI for data lakes

If You Thought Politics Was Dirty, You Should See the Analytics Behind It

John Thuma

Director of BI Solutions
Arcadia Data

Wednesday, Sept 12 | 5:25 PM - 6:05 PM
Location: 1E 12/13

As a seasoned data analyst with deep political campaign experience, I love to follow the data around elections. With mid-term elections just around the corner, the ability to crunch voter data has never been more important. It is big money. The final price tag for the 2016 election is in: $6.5 billion for the presidential and congressional elections combined, according to campaign finance watchdog OpenSecrets.org.

Read More

There is also more data than ever as social media plays a critical role in voter and donator influence. The use cases are endless. The names may be different, but the goals are the same:

- Channel Optimization: Tracking voter online and offline behaviors is all about delivering the right message, to the right people, at the right time. This will impact handshaking, texting, to social media, and television ads.

- A Voice to the Voiceless: With social media there is a sense of belonging and inclusion. The volume of social media provides the perception that makes even the most extreme positions seem mainstream.

- Advanced Targeting: Ads for donations will push people who normally do not donate money to campaigns to open up their wallets. Campaigns will leverage deep-rooted psycho-analytics to emotionally trigger people to act.

People who run for office are nothing more than mere products that expire on election day. Much like a recommendation engine influences you to purchase a product, election campaigns are using similar tactics to persuade you to vote and to donate. And every member of the voting public is sort of like an oil well with tons of sensor data being emitted and analyzed at real time. Through the use of real-time streaming technologies like Kafka and modern BI platforms it is easy to leverage high speed data to change a candidates position even if they don’t agree with it, if only to get more votes. Helping them do this as an analytics professional can make you feel dirty.

Not only is this a data velocity opportunity it is also a data scale and complexity matter. Being able to capture, analyze, and deliver information requires modern data infrastructure as well as a different kind of business intelligence. Elections and campaigns cannot wait for a star schema or a semantic layer to be completed. They need tools that enable them to act fast both on a national and down to the street level. They need to be able to merge desperate data together rapidly in order to capitalize on your emotional vulnerabilities.

There are many similarities between those in the business world and political campaigns:

- Data Heartbeat: Being able to measure the heartbeat of your business is now possible through real time analytics.
Analyze the Voice of the Customer: Your customers are speaking to you all the time, are you listening?

- Be Nimble / Be Disruptive: Understanding what messages resonate and generate revenue is what matters. It is no longer fire and forget marketing. It is about changing direction on a dime.

- Your Data and Alternative Data: Today it takes more than just the data in your shop to be successful. You need to look at other data like weather, social media, and demographics to see unknown correlations and behaviors.

In this session I will discuss:

- How campaigns use analytics to achieve their mission.
- The technologies they use to address their big data challenges.
- The lessons you can learn from politics, without the slime.
- The moral and ethical challenges I faced (soap, lots of soap).


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