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The NY Enterprise Tech meetup was one of my favorite events to attend in-person prior to the pandemic. While the meetup is now all virtual, the speakers they bring in continue to be top-notch–particularly in the data space. The host of the meetup is Work-Bench, a VC focused on enterprise software companies. Priyanka Somrah is a VC analyst at Work-Bench who speaks with the most innovative startups building data tools on a weekly basis. In this episode, Priyanka talks about how data tools can effectively sell into the enterprise, how the data engineering profession has grown in the last few years, and the most effective Go-to-Market (GTM) strategies for data companies.

Note: There were some recording issues with this episode so apologies in advance for some of the gaps in the conversation as well as the background noise.

Flipping VC on its head

Work-bench is early-stage enterprise VC firm that flips the VC model upside down. Unlike most VCs that go off and try to source the most innovative companies, Work-Bench hosts quarterly corporate roundtables with Fortune 500 companies. From speaking with the end customer of these data infrastructure and engineering tools, Work-Bench finds the pain points these large enterprises face. From there, they go and find the companies that offer a solution to these problems.

This aligns with what I hear a lot in the B2B SaaS space in terms of creating an effective marketing strategy. Paint point selling or solution selling are some buzzwords you might hear in this regard. Instead of selling the features of your product, you focus on the problems your product solves for your target customer. Seems like a reasonable strategy marketing and sales people should adopt, but all the cold spam InMails and connections on LinkedIn might convince you otherwise.

How to become enterprise-ready

One of the main challenges for data infrastructure tools is selling into the enterprise. A company’s data is one of the most important assets the company has, and Priyanka provides some best practices on how data tools can better prepare themselves to be a legitimate enterprise solution:

1. Security – Being able to safeguard the customer’s data is priority number one. This means getting SOC 2 certification or offering a single sign-on feature.
2. Scale – Can the tool reduce the administrative overhead and grow and scale as the customer’s requirements grow and scale
3. Flexibility – This is becoming a table stakes feature. Is the tool modular enough to integrate with a customer’s existing tool stack?

Assuming the data tool meets these enterprise standards, Work-Bench then acts as the matchmaker between the various Fortune 500 companies they are connected with and the startup that provides a relevant solution.

Big data and the rise of data engineering

Big data has really changed the face of the world. As a result, the ETL process and data modeling have changed considerably as well. With tools and processes changing, the skills and expertise required by data professionals needs to adapt. We saw with the last episode about data engineering at Canva, the data engineer is more than just an individual contributor focused on BI tools. The data engineer now needs to have knowledge about the entire data pipeline and how the different aspects of the pipeline interact with each other.

The modern data landscape is merging analytics and engineering.

What are some tools that exemplify the merging of these two professions? One tool Priyanka called out is dbt (data build tool), an open-source data transformation tool that empowers the data analyst with transformation powers. Check out this blog post Priyanka wrote to learn more about some of these tools.

Moving into the world of data operations, Priyanka talked about metadata tools that modernize the data cataloging process. Some of these tools (open-source) include Amundsen from Lyft and Nemo from Facebook.

Metadata tools can programmatically capture the important points about your data as it flows through your pipeline into your data warehouse. The goal is to map out the lineage of the data, understand what’s causing delays in the pipeline, and assist with pipeline debugging. These tools gives you a view of how your data is transformed to make sure the quality is high throughout the lifecycle.

Governance, risk, and compliance

I think this an overlooked area of the data infrastructure space which means it’s ripe for innovation and new entrants. As mentioned earlier, one of the most important factors for a data tool to become enterprise-ready is providing security and privacy for your customer’s data. Priyanka spoke about new regulations such as GDPR and CCPA forcing companies to be more strict with how they handle their users’ data. Priyanka wrote this blog post highlighting the different areas of compliance companies should be aware of along with a B2B staple: a map of all the tools.

Whether it’s evidence collection or providing tools for your auditors, more data startups are creating innovative solutions to help customers with a myriad of compliance needs.

The GTM motion for data infrastructure companies

This was one of the most important topics I wanted to discuss with Priyanka given my own background in this area. Unfortunately, this is one of the questions that got messed up in the recording, so I’ll try my best to summarize Priyanka’s response.

Priyanka first started by saying a key consideration for early-stage companies is asking yourself: do you go directly to the enterprise or start by selling to other startups? Figuring out your “wedge” can be an early differentiator for your brand and sales strategies.

In Work-Bench’s portfolio, the companies that go after SMB/mid-market have products that customers can quickly get started with. These are tools and platforms that most likely offer a generous free tier for the data team to experiment with. Product-led growth is probably also common with these tools since the SMB/mid-market consists of companies of all shapes and sizes. For enterprise-focused GTM, the founders probably have a lot of enterprise experience and are well-versed in selling a solution to multiple buyers at a large enterprise.

Work-Bench has their own GTM playbooks where they take best practices from a GTM “win” from one company and try to find a pattern that other companies can apply to their own GTM processes.

What to look out for in the data management space

Priyanka is most excited about tools for data operations space like dbt and Fivetran, and data warehouse tools like Snowflake and BigQuery. The most important feature of these tools: they give people a self-serve way to query data.

Thinking further down the pipeline, you have the actual consumers of the data analytics. In terms of data democratization, Priyanka thinks a shared interface where data is unlocked for end consumers could be an interesting feature for data tools to adopt. A world where the data engineer and the data analyst can collaborate together on the pipeline instead of each individual focusing on just their part of the story.

Conclusion

You don’t have to work at a VC like Work-Bench to stay on top of all the companies in the data infra space. Priyanka started the Data Source to give people an inside scoop on all things data and data infrastructure. Priyanka is always talking a lot of people in the data space to help hone her theses about the space, and you can follow her newsletter to see Priyanka “learn in public” as she does her research.

I’m always looking to learn. I want to know when I’m wrong.

Other Podcasts & Blog Posts

In the 2nd half of the episode, I talk about some episodes and blogs from other people I found interesting:

• Developer Love ep #12: Platform Success with Ceci Stallsmith and Paige Paquette of Calyx Consulting