TigerGraph Community: September Updates

This month we’re featuring Deepti Soni, a Consulting Analytics Specialist at Mastech InfoTrellis.

Tell us about yourself: 

1. Where do you work? What do you do? What are your favorite things about your job? I am working as a Senior Data Science and Analytics Advisor in Mastech InfoTrellis. We design and implement AI/ML based algorithms to derive value in our clients’ data when solving their complex business problems. There are many things I love about being a senior data scientist and analytics advisor at Mastech Infotrellis. One of the most important ones is our collaborative environment. We truly have a positive work environment where everybody is there for each other. It’s great to learn from my colleagues, and even more rewarding when I know I’ve helped others in solving challenging first-of-a-kind problems. 

2. How did you learn about TigerGraph? I first read about TigerGraph in one of the articles published by Gartner on graph databases.

3. What are some of the use cases you’re using TigerGraph? For many of Mastech Infotrellis’ clients, I am architecting knowledge graph and ML-based models/solutions using TigerGraph for multiple use cases, such as graph-based MDM, anti-fraud, and money laundering, predictive analysis, credit scoring, listing information mining, unifying data silos for enhanced Customer intelligence, etc.

4. What are your favorite things about TigerGraph? We work with a few graph database providers. TigerGraph is one of the few providers of distributed native graphs. In simple terms, TigerGraph is easy to learn/use, scales up easily as data grows, and supports fast multi-hop traversals which is very beneficial for analytics use cases. I also appreciate that the people behind the TigerGraph DB are very supportive and welcoming. 

5. Can you share some of the most complicated use cases where you’re using TigerGraph? If so, what are they? At Mastech InfoTrellis, when we architect event-driven databases, we have a set of AI accelerators that help in implementing complex business solutions rapidly and systematically. In one of the implementations of anti-money laundering, ML models based on the initial dataset were generating a high number of false positives and negatives. The data was in many silos, in different languages, had a variety of transaction formats and thus making the investigations expensive. With the help of our AI accelerators implemented on TigerGraph, we enriched the data and uncovered previously unknown insights. The connections found through the social links of entities established indirect relationships and thus resolved entities more accurately. TigerGraph provided faster pattern detection on such a massive amount of data of connected people, companies, their transactions and payment chains, etc. in the underlying network. With our solution the client was able to reduce costs associated with fines and with investigating false positives and false negatives.

6. Tell me about your experience in the Developer Community- we really appreciate how much you contribute to others learning TigerGraph and helping to answer their questions. TigerGraph’s developer community is very active and has many channels such as community forums, Discord, and Slack, etc. to reach out for help. I try to help community members by answering their graph modeling, GSQL, and application-related questions. I’ve trained a few resources on TigerGraph in some of our client teams and also at Mastech InfoTrellis. I also shared my learnings/experience in Graph+AI summit 2020.

Thank you, Deepti, for all your contributions to our community. We appreciate your mentorship to so many in our community!