SDO 013 - Business Intelligence on the Edge - Zack Hendlin

Jan 22, 2023

What are your thoughts on 5G?

You may not know this, but one of my dreams is to go into venture capital to embed myself further into my love of startups. Last year, I had the opportunity to delve deeper into my passion for venture capital by interviewing for a senior associate role at a venture firm. I was tasked with creating an investor memo on an early-stage startup, and it was during this process that I was exposed to the potential of streaming data and 5G network infrastructure. My research showed that the implementation of 5G is analogous to the impact broadband had on the early internet through increased speeds. It will have a similar effect on how we handle and utilize data. In short, 5G enables the mass adoption of streaming data workflows and using data-intensive workflows on our edge devices— fundamentally changing how everyday users consume data.

Announcement:

Real a quick shoutout to the Data Teams Summit conference, which will host a LIVE session of the Scaling DataOps Newsletter on January 25th, 2023! For this session, I will ask data leaders at various levels how they handle their data infrastructure when they face scaling issues. I can’t wait to hear my guest’s insights!

You can register for this free virtual event here!

Hear from Zack Hendlin, Founder & CEO of Zing Data

I am beyond excited for you all to hear the insights from Zack Hendlin! We met via LinkedIn, and I knew he would be the perfect person to go into more detail regarding the intersection of streaming data and 5G. He is the Founder and CEO of Zing Data, which focuses on enabling business intelligence on edge devices such as mobile phones. This shift of “everyday users consuming data differently” is already here, and Zack and his team are building it. Zack shares some use cases from the field to give you a glimpse of what this future holds.

The core product of your company is enabling individuals to utilize intense data workflows via mobile devices. How will this new user behavior impact both opportunities and challenges for data practitioners?

Zack: “Yeah. I think the huge opportunity is that way more people at a company are now able to use data. So if you think about folks who are literally driving trucks or working in a retail chain, we don't think of those folks as traditionally like users of BI tools. But this now lets them use that, and we, in signup behavior, see that they actually want to use it.

We had a big company that grows berries, and the person who signed up wasn't like the IT manager, it was someone who works in the field picking berries. Same with a big company that runs events. And so the opportunity is way more people can be getting value from data. We refer to it as the other 90% of the company who's not on a BI team, who's maybe not a PM or an engineer, now can use this.

The challenge that creates is if you're building data pipelines or doing data engineering, you now need to sort of think about a wider set of uses, right? So maybe it's not just, "Hey, am I gonna hit the quarterly numbers? How am I tracking relative to a goal line? Or what's my margin?" It might be, "Hey, how am I tracking on inventory, and what do I need to order more of so that I don't run out by next week?" And that's a little bit more of a kind of in-the-field use case where you want to make sure that you're creating the right aggregates and pre-processing the data in a way that makes it usable because your data user is not necessarily someone who is in R or Python. It's way more likely someone who has used Excel a little bit and who is out in the field, and you don't want them to have to parse JSON to get something useful as a result. So you need to put in a little bit more thought upfront to kind of simplify that.

I would liken it to the way that iMovie on your phone or TikTok on your phone sort of tries to make it easier to edit. You still, though, need to have a good video to edit. You could shoot video on your phone, but if it's really a lot of background noise and wobbly, even if you can edit it in a lightweight way, it's still not gonna come out great.

So I think that the onus is on data engineers and data scientists just to make sure that there are good examples people can learn from and that they're thinking about this broader set of use cases. For data users who might look a bit different from the traditional PM data scientist engineer that they've, created a table that has a hundred columns, and it's like, "Hey, go figure it out. It's all there. Go read a wiki about it." Now I think it shifts it to a little bit more of let's create some good examples that people can click into immediately, get something useful from, and then build on.”

I’ve been fascinated recently about the impact of 5G networks on data. Given that you are in the mobile space, how do you see the rise of 5G changing what’s possible for data products at scale?

Zack: “Great question. 5G in 2020 was about 3% of folks worldwide. It's much higher than that in the US, but only about 3% of people had 5G access, according to Statistica in 2020. By 2030, that's going to be 64%. So more than half of folks globally. And what that means is there's this whole new set of data creation and data consumption use cases that open up.

So by data creation, I mean IoT devices. And we actually have seen this amongst our users so far, where maybe you have an oil field, and you wanna have the devices that are going around, whether those are excavators or other pieces of equipment and know, are they up or down right now, and what are their GPS locations?

And that lets you know in real-time if things are functioning the way they're supposed to. If there are temperature readings or gas readings that are outside of tolerances, you can be firing that information every second or at least every minute. And those types of data creation use cases get opened up.

Which then lets you do really cool stuff on the consumption side. You can then set up things like real-time alerts. So instead of waiting a day or an hour to know that something is broken, you actually can know within the span of a second or less right? Cuz this data is getting created, it's getting uploaded, there are these real-time ingestion engines, Kafka, or there's a bunch of other like versions that make it a little bit more consumable, these pub-sub type models. And then you can consume it. And we built a really lightweight kind of interface where you can say, "Hey, I'm gonna tap on a graph, let me know when it drops by X percent, or goes above X or goes below Y." And you can do that on your phone and get a push notification up to every minute. And so that makes it way easier to consume all this real-time data that's coming in. And so I think the big opportunity is you're gonna go from big data sets that take a long time to create and analyze much more real-time use cases.

An example of that is we have a company that uses us in their retail chain. And what they can do now is say, "let me know every minute I have a product with inventory that drops below a hundred units that are selling really fast," and they can know that within a minute of that happening. And so it changes the whole like nature of, "Hey, I need to plan on a monthly basis."

You still may want to plan, and you still may wanna forecast, but you can respond much better in real-time. Or we had a company in the energy space, and they wanna know when there are outages. Like when your power goes down. And oftentimes, actually, 5G and wireless networks still have battery backups. So you still may have wifi wireless coverage even though 5G coverage, even though the power grid is down. And so they can say, "Hey, what houses are down right now?" And they can go check them in real-time based on the IoT data that's coming off of sensors. And that opens up these whole new use cases that are really exciting.

And then I always think about what does that actually mean? Like why do we care? Why deal with all this? There's more work that goes into streaming all this stuff in real-time, and ultimately it comes down to you can be radically more efficient if you're making a decision before something goes wrong or before you run out of inventory or before your customer queue length builds up, and customers are really upset about something, you actually can address problems before they become big problems.

And basically, you can make stuff just way more efficient. I think 5G is this really interesting intersection of devices now being good enough to do cool stuff on 'em. If you think about data on your phone today, it's still in its nascent stages. I liken it to email 15 years ago.

And 5G is part of making email on your phone better, but so too are more powerful devices— things like autocorrect. Remember when the iPhone first came out, and there was this like "sent from your iPhone" little thing along the bottom, and attachments weren't very full-featured? It was like hard to type.

Now there are dictionaries that are personalized to each user, there's auto-correct if you type something wrong, there's in the background, it will upload attachments. There are much better ways to display it. And so email has gotten way better cuz devices have gotten better, and networks have gotten better.

And that same trend is helpful for data being more usable on its own. So I'm excited cause I think that, and then one other cool thing, which you didn't explicitly ask about, but things like DuckDB and kind of really efficient ways to use data in an offline mode. If you manage those two things together really well, you can create fast, lightweight feeling experiences, even though you may actually have a huge data store underlying it in some data warehouse that's petabytes. So like we work with Databricks, we work with Snowflake, we work with these like really big data stores if we want them to be, but to an end user, we don't want them actually to have to think about like the data size. And we wanna do all these like clever optimizations that mean when they tap something, it's fast, it's responsive, and in an offline mode, DuckDB and other stuff like that we think can even extend that so you get most of that functionality even if you're offline. And then when you're back online with devices being able to store a bunch of stuff, 5G being able to send over relatively large datasets quickly. You actually can create these pretty great experiences of querying, visualizing, interacting with data on a phone that wouldn't have been possible three or five years ago.”

Throughout your career you have led product initiatives at large scale companies, hyper-growth companies, and now you own startup. What are the unique challenges of building successful products at each level of company scale?

Zack: “Yeah. I think starting at the kind of smallest size and then working up. The most important thing is a small company. Is building something that is useful to people because you don't have huge reach yet. You don't have a big budget yet, typically. And so unless it's like substantially better than what's out there, folks are not going to know of you or trust you.

So even if they come to know of you, why should I trust you? Why should I connect my data source to you? Which is a pretty big thing. Or if you're Facebook, why should I give you my personal information? Or LinkedIn, why should I put in the upfront work to fill out my profile?

So build something that people really want is the base aim of any product. And that is particularly true as a startup because it's not like you have 10 products you're offering. It's not like you have a big sales team who can sell it. There are some exceptions for big enterprises like focus companies, but for the most part, if you're product led, it needs to be understandable to someone and valuable to them.

So build something people want is universally true and probably the most important thing for a startup. And then, as you're getting people to use it, especially if there's some social or network component to it, help people understand how it's better together, better with their friends, better with their team. So we built at mentions, we built like shared questions, all that type of stuff that then lets you pull in other folks from your team. So you're like, "Hey, I have this cool analysis. I see that most of our customer support time is spent on. I now want to tag my colleague who works on customer support and have her go create more like billing FAQs so that the time spent on that goes down." And so build ways for people to share it and get more value as the network grows.

And if you look at most big companies like LinkedIn, Figma, and Facebook, that are in like the collaborative space. They all have flavors of this, make it work well in single-player mode. Facebook worked well in single-player mode cuz you could see people's pictures. Figma worked great in single-player mode, but then when you added friends, colleagues, and teammates, it became way more valuable.

So small company, build something people want. As you start growing figure out ways you can make it more valuable as there are more team members there or more friends there. And then when you get to like really big, like Facebook, LinkedIn scale, then there's actually a lot more considerations around like, how does this fit into a product experience that makes sense.

As an example, Facebook people initially went to share what was happening in their lives and stay in touch with friends. Now, over time, as the networks have become really large and maybe folks who knew in high school or college, and you get further from that, that network becomes less call it close.

And so the things that you might have shared when it was 300 people who you were really close to are different from what you might share when like, your uncle and your aunt and your mom are all there. And so what you offer needs to adapt. So things like a marketplace kind of start to make more sense. But there's really they've struggled with that close intimate sharing that used to be there. And so for bigger companies, I think a lot of it boils down to how does this fit into what people use us for. If LinkedIn says, "Hey, we're going to offer viral video," like it hasn't quite done it nearly as well, I don't think, as someone who said, like TikTok, "I wanna make this easy and fun and lightweight." So as you're bigger, you need to think a little bit more about product strategy and how all these products fit together.

And then the second thing you need to think about is, does this scale well? Is this a thing that we should be doing that makes sense, given our network, given our scale to do? A lot of things a startup might do really well, like amazing customer support, big companies struggle with as an example.

So always build something people want. Start out making your product amazing. Over time make it amazing in multiplayer mode. And then as you get bigger and bigger, make sure it fits in as a suite of products or a set of use cases that really make sense when people think about your company.”

Person Profile:

Zack Hendlin is the Founder & CEO of Zing Data. Feel free to connect with him on LinkedIn to learn more about his work.

What are others saying in the DataOps space?

Zing's CEO on the Monday Morning Data Chat with Ternary Data | Zing Data | Mobile-first business intelligence

What: Interview Zack did with Monday Morning Data Chat and Ternary Data on their podcast to talk about why the future of data is increasingly mobile.
Why: Three questions are not enough, and you want to hear a more in-depth interview with Zack.
Who: You are looking to understand further how the ways in which data is consumed are changing.

From dial-up to 5G: a complete guide to logging on to the internet

What: A overview of the history of the internet and its mass adoption.
Why: To gain the historical context of why 5G is so impactful.
Who: It’s not enough to hear future predictions, you want to learn the history to see if it will repeat itself.

The 2022 Stream Processing Market Update Report by Bloor Research

What: A market report of the streaming data space providing a high-level overview. Especially pay attention to the “Market Trends” section as this helped me connect the dots to the impact of 5G.
Why: The report provides a great high-level overview of the factors driving streaming data adoption and the current players in the space.
Who: You are trying to understand the opportunity streaming data has in the market.