- “Data engineering” is a software engineering approach to designing and developing information systems
- Data engineers make it possible to translate massive amounts of data into insights
Large organizations like RingCentral deal with a virtual tsunami of data every day – exponentially more than companies ever have before. That’s…good, right? More information is always better, isn’t it? Sure. Right after we process it, protect it, analyze it, make it accessible and teach ourselves how to actually act on it. We’re tired just thinking about it. Fortunately RingCentral’s new VP of Data Engineering Haleh Tabrizi, is up for the challenge.
A single source of truth
“I lead our data and analytics organization,” says Tabrizi, “which includes everything from where data sits on the platform side, all the way to analytics—generating insights and actions from the data. It all starts with a single source of truth; making sure every decision is based on a unified source of information. Anyone in the data science world knows this is not a task for the faint of heart, so I’m getting to know stakeholders across the company: Sales, Marketing, Finance, Product and so forth, and building solutions for each one.”
Data as a product
Everyone needs something different from their data. Which means a one-size-fits-all approach seems to fit anyone. For Tabrizi, treating data like we would treat a product will make all the difference.
“I’m trying to take us toward what’s called a “data-as-a-product” focus. That means, the company has needs that data can solve, and my team delivers that data in ways that facilitate good decision-making and application building. One of the main challenges I’m hearing across the board regarding data is that it’s not perceived to be complete. My “customers,” the business functions we serve, are telling us the data they’re getting is not getting them all the solutions they’re looking for. Well, we want to deliver that. The data exists, it just has to be served up in the right way. So, understanding what the desired outcome is-and-isn’t will help us serve up that data in just the right way. Which is pretty similar to the approach you might take if you were building a product.”
Converting data into insights is tough. Doing it over and over again, even tougher. But what if we could put AI to work to make it accurate, fast…and self-serve? Tabrizi think’s it’s possible, but not without a lot of work.
“Right now our organization is separated by a data warehouse and a data lake, and we have separate strategies around the architecture and the use cases of each. I’m looking for a senior architect who can help us define an architecture and a unified view across our data platform and tools so we can consistently deliver the right solutions to our stakeholders. The other priority is around automation and self service. Right now, Data Engineering is the bottleneck between the data itself and the people who need it. So I’ll be looking for help with defining the tools and self-service mechanisms people need to get the information they need on their own.”
Custom solutions for every challenge.
Tabrizi ultimately envisions a self-serve data environment with standardized tools that allow users to create custom solutions for the specific challenges they face. But identifying the right infrastructure and tooling work still needs to be figured out — which is where the opportunities lie.
“I’m looking for people who can help me execute on this vision while trying to figure out the details of how we do this well. So they have to be visionary people who can understand the overall landscape, and also have the data architecture skills and experience to identify the specific platforms and tools to make it work. When we put it all together, I think we’ll be able to uncover and act on insights in really powerful ways.”
Originally published Apr 26, 2022, updated Dec 30, 2022