2023-Management-Zing_Data

SABIN THOMAS: Hello, everyone. My name's Sabin Thomas, and I'm the founder of Zing Data. I'm here to talk to you about simple social data analysis. Just as a quick insight into Zing Data-- my co-founder and I are both graduates of MIT. We have fond memories of being here over 10 years ago, and we've had some great experiences here. Zack has been working in multiple product management capacities at really large companies.

He also has ML patents. I myself have co-founded two startups that have exited, F5 and [? Synopsis, ?] and both of us came to the problem of data access being something that needs to be solved. So fundamentally, the problem that Zing Data is trying to solve is one of data access. We believe that most employees at large enterprises or even at small companies don't have easy access to data.

They don't have it where they need it, and moreover, they don't have the tools to make that data actionable. And so in our research we categorized workers into two buckets. This first bucket being the deskless workforce. And so these are folks that are out in the field, working in construction and health care, in different facilities that are not something that has easy access to a desktop, and then your traditional desk-based worker.

Here, you have folks that already have easy access to a desktop, but are doing work on the go. And so for these two modes of work, there's been a problem of data access. And this is where Zing Data set out to solve. In our conversations even with data teams at large enterprises that have significant complex data engineering pipelines, they're getting shoulder tapped almost 40% to 50% of the time to solve really small ad-hoc report requests from their exec team. And this takes time away from their ability to solve more complex data science problems.

And this is another thing that Zing Data is going out to solve. So Zing Data is the platform that enables fast, collaborative business intelligence, and it works anywhere. I stress anywhere because we spent a lot of time and effort in a mobile-first interface, as well as the backend architecture, to support this natively. Mobile being a primary computing interface for the newer generation, as well as in emerging markets, really lowers the barrier for people to be able to access their enterprise data.

This is all available on the web as well, but the mobile interface is what we see a lot of promise on. The Zing Data platform in one of these core features that we've launched pretty recently is the speech-to-data querying. We're the only vendor out there that from your phone can talk natural language or plain English and can run a query on your Databricks or a data warehouse, automatically get those results, sample and categorize those data for you, and apply visualization inference on top of that.

And so this really represents an unlock for most users who are non-technical-- don't have SQL experience-- to be really able to get insights from their own enterprise data. Another cool thing that we also have is notifications. So from your own enterprise data or your own time series data, you can-- within a couple of clicks or taps-- be able to, say, generate notifications when my data changes by x percent up or down. No data engineer required, no data team required.

And so this really brings home the concept of self-service analytics. And with mobile, you get a location, and so we're able to also use spatial results from your enterprise data. And that's already baked in as part of the natural language query. So all these features allow the masses at your enterprise to be really able to engage with your own data. We also have collaboration that brings in the social aspect here.

When you look at a chart or a data, there's usually some kind of action that you're trying to drive towards, and this is where you can do-- with Zing Data-- be able to tag multiple people, and bring them into the conversations about why this is trending up or down. A use case that I'd like to highlight here is when we worked with 7-Eleven Global. 7-Eleven Global is using us-- Zing Data-- to be able to empower their franchise operators. So among all their stores, they're able to give their franchise operators insights into how their sales were.

They can use them for out-of-stock notifications. They also look at distinctions between beverage and fast food purchases, and be able to work with all of that on the go. And they're using this across multiple locations for their franchise operators. And so this really highlights the use case where a non-technical user out in the field is able to get insight from their own data. They're using us connected to their data warehouse, which is powered with Trino and Clickhouse.

We're also used by multiple Fortune 1,000 companies and emerging startups. One company I'd like to highlight here is Casa Goro, which is an Argentinean retail store. Has about eight locations, and the owner of this retail chain has signed up with Zing Data completely mobile. Has not looked to the web interface at all-- completely mobile-- and is using us to be able to get warehouse information directly on the go. And so you can see how this really empowers emerging market use cases because of this ability.

We see a spectrum of use cases between the non-technical as well as very highly advanced use cases. Wisdom Panel is a company that's owned by Mars that does genetic testing for dogs. And their data science team is using us to look at their own model performance all on the go. So our partnership ask here at the conference is to be able to-- we'd love to have a conversation with you if you're in one of our target industries-- manufacturing, retail, logistics, operations.

And we're looking for early adopters as well as channel conversations. So please come see me at the booth over there. Company history. We were founded in 2021, and we are backed by VC companies that are in the space. That's all I have. Thank you.

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