Hackernoon logo"Connect, Analyze and Learn from Data" - Dr. Yu Xu by@shahmeer

"Connect, Analyze and Learn from Data" - Dr. Yu Xu

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@shahmeerShahmeer Khan

Tech writer sharing insights in a fun and informative way.

Welcome to "Mondays with Entrepreneurs". This week we have an Entrepreneur and tech expert who thinks Monday is the most exciting day of the week. His previous experience includes working for Tech giants like Twitter, Teradata, IBM--before he found fulfillment in graphs at his venture TigerGraph!

Who's the Guest?

Name: Dr. Yu Xu

💪What they do: Dr. Yu Xu is CEO of TigerGraph

So before starting your week, pause for a coffee break ;) and enjoy this chit-chat with Dr. Yu Xu.

1. What topic areas are you most passionate about?

I’m most passionate about bringing the power of graphs to advanced analytics, Machine Learning (ML), and Artificial Intelligence (AI).

With the native and parallel technology breakthroughs in the industry by TigerGraph, utilizing graph analytics on large data sets to provide unique features that improve traditional ML/AI models is critical to many use cases.

Such use cases include anti-money laundering, supply-chain optimization, real-time fraud prevention, customer journey analytics, personalized recommendation systems, and more.

2. Through your experience, how do you visualize the evolution of data mining and data science? Could you brief them?

Data mining and data science have a long history. Due to the data explosion over the past 10 years and the dramatic cost reduction in data storage and performance improvements in CPU/GPU, deep learning (auto-ML) is fundamentally changing how data mining and sciences are applied across industries today. 

In addition to all the exciting things happening in data mining and data science right now, an up and coming trend to shed light on is how to leverage graph technology to augment ML/AI, whether it’s new features only available through graphs, or explainability for ML models. Graph neural networks are gaining tons of attention in both academia and industries.

TigerGraph has organized an open conference called Graph + AI Summit, which occurs twice a year to facilitate this. The last two virtual conferences have been hugely successful, bringing together over 10,000 data scientists and researchers, Fortune 100 companies, innovative startups, and industry analysts to share best practices for combining graph and AI. 

3. How did you get to become an expert in Data Analytics?

I gained experience in data analytics through my years of experience training in the field. I’ve always been a database guy, as I see it as an integral part of the data analytics infrastructure.

To further my knowledge in this discipline, I got my Ph.D. in Computer Science from the University of California, San Diego. Beyond this, working with customers every day helps me learn what matters to real-world use cases. 

4. 2 Sentences that best summarize how you learned ML and data analytics?

I learned the basics and theories from my M.S. and Ph.D. studies in Computer Science. I also learned a great deal while at Teradata, working with leading customers and their use cases. And while I was at Twitter, I got to work alongside some of the best data scientists and ML engineers in the world. These experiences taught me a lot more than what I learned from my schooling.  

Now, I am learning from leading companies such as UnitedHealth Group, JP Morgan Chase, Visa, Jaguar Land Rover, Duke Energy, Nike, Walmart, and many other innovative companies, specifically using graphs to improve their ML and data analytics. I find this, even more, educational and inspiring.  

5. What’s the main value proposition of Graph Analytics Technology?

The main value propositions for graph analytics technology are highlighted with the phrase, “Connect, Analyze and Learn from Data.”

Graph analytics provides these three core capabilities to drive business outcomes:

Connect all data sets and pipelines: Organizations of all sizes are connecting all of their data, including Spark pipelines, for machine learning and Kafka pipelines for streaming data with a distributed graph database.

Analyze connected data for never-before insights with advanced analytics: After connecting all of the data, the next step is to analyze the data 10-100 times faster with graph-based analytics.

Learn from the connected data with in-database ML: After connecting and analyzing data, organizations are learning from the data with graph-based ML.

6. Can you please brief us on How are graphs used for e-commerce/customer intelligence, anti-fraud detection, supply chain intelligence, and other areas?

For this question, I think it will be most powerful to explain using customer examples aligning with the core capabilities listed in question 5. These include: 

You can connect data sets and pipelines: One customer example would be UnitedHealth Group. They are connecting 200+ sources to deliver a real-time customer 360 to improve the quality of care for 50 million members.  

The graph is also used to analyze connected data, providing never-before insights through advanced analytics: For example, Jaguar Land Rover has accelerated supply chain planning from 3 weeks to 45 minutes, reduced supplier risk by 35%, and added 100 million pounds annually in profits. NewDay, a leading specialist financial services provider and one of the largest issuers of credit cards in the UK, uses advanced graph analytics to prevent and preempt financial fraud. 

There are many opportunities to learn from the connected data with in-database ML: Intuit has built an AI-based customer 360 with in-database ML for entity resolution, personalized recommendations, and fraud detection. Graph-based ML at Intuit has reduced at-risk events by 50% while improving accuracy by 50%, overall reducing false positives. Seven out of the top 10 banks are driving real-time fraud detection and credit risk assessment with in-database ML.

7. Regarding Tiger Graph’s growth, What is the biggest improvement that you introduced in the last 12 months that has considerably improved your workflow?

The #1 challenge for TigerGraph right now is our goal of hiring 300+ talented team members quickly to scale our business.

Before the pandemic hit, TigerGraph was mostly hiring in the San Francisco Bay Area, especially within our engineering teams. In the middle of last year, we decided to drop the location requirement, which is the single biggest improvement that has materially changed our business. 

8. What are the top challenges you currently face as a professional data scientist and CEO? And how do you go about tackling them?

Hiring the right talent is critical to the success of our business. As mentioned earlier, TigerGraph is hiring in many locations now. For example, we’re building an R&D Innovation lab in San Diego and a sales center in Dallas.

Another challenge we’ve faced is how to take our successes in the U.S. and UK, and expand our business to more regions. We’re hiring experienced leaders in various regions who can help the company build local teams quickly. 

9. Which non-paid activities would you be keen to take part in if the opportunity raised your profile or delivered value to your audience?

I would love to volunteer and educational opportunities with student organizations to help students learn how to navigate new tools like graph analytics and UI/BI in data analytics. 

10. What are your passions outside of work?

I enjoy reading books and listening to music. 

11. And the last question, since the series is named “Monday with Entrepreneurs”, What are your Monday Morning rituals?

I start my Monday mornings with a cup of coffee! Following that, I get ready for our TigerGraph executive meeting and look forward to sharing what’s been on my mind over the past couple of days with the team and hearing their updates in return. Monday is one of the most exciting days of the week, in my opinion, so I’m pretty psyched in the morning.

Shahmeer: I'm so glad we had this time together. Thank you for taking a break from your life to share your knowledge with us. I'm sure readers will enjoy and learn.

Thanks, Hackers, for reading :) If you have any suggestions regarding questions to ask in this series or if you want to get featured, I am available on slack, and you can also reach out to me on Linkedin ;)

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