Announcing Metaplane’s $13.8M Series A
Tags: machinelearning, dataengineering, dataobservability, announcement
Published: 2024-03-05 16:43:14

**Other links:**

- **PR Newswire**: [Metaplane Announces $13.8M Series A led by Felicis on Heels of Rapid Growth](https://www.prnewswire.com/news-releases/metaplane-announces-13-8m-series-a-led-by-felicis-on-heels-of-rapid-growth-302079081.html)
- **VentureBeat Exclusive**: [Metaplane nets $13M to detect data anomalies with AI](https://venturebeat.com/data-infrastructure/exclusive-metaplane-nets-13m-to-detect-data-anomalies-with-ai/)
- **Metaplane Blog**: [Announcing Metaplane's $13.8M Series A](https://www.metaplane.dev/blog/metaplane-raises-13m-series-a)
- [**CEO & Co-Founder, Kevin Hu's, LinkedIn Announcement**](https://www.linkedin.com/posts/kevinzenghu_dataengineering-dataquality-dataobservability-activity-7170814269066887168-WpFs?utm_source=share&utm_medium=member_desktop)

Today I’m happy to announce our Series A led by Felicis Ventures with participation from existing investors Khosla Ventures, Y Combinator, Flybridge Capital Partners, Stage 2 Capital, along with new investors B37 Ventures. We’re also welcoming Javier Soltero, the SVP & GM of Canva Enterprise, previously the VP & GM of Google Workspace and a two-time founder, to the board.

Following 6x growth in the past year, this Series A investment brings our total amount raised to $22.2M. Most importantly, it’s consistent with our company principle of raising the right amount of money at the right time at the right valuation. This way, we make sure the success of our company is aligned with our customers’ success.

Since our [previous fundraise](https://www.metaplane.dev/blog/the-next-stage-of-metaplane) last year, over 100 companies like Ramp, Bose, Anduril, and Ro have trusted Metaplane to ensure trust in their data. We are the highest rated [Data Observability product on G2](https://www.g2.com/products/metaplane/reviews), with 4.8/5.0 stars across 90+ reviews. And today, I’m happy to share our plans to help 10,000 companies ensure trust in the data that powers their business. But first, we should start from the beginning: why does trust in data matter at all?

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/p9tyqgdn3rqaxzki30v0.png)

## Why This Matters: Untrusted Data is Worse than Useless

In 2024, the most common method for detecting data issues is still CDT, or Customer-Driven Testing. Customers who are looking at the data notice that something looks wrong, then fire off a Slack message to the data team like:

- “Is this dashboard broken?”
- “Why does this number seem off?”
- “Why aren’t these accounts up-to-date?”

Time and trust – the two things that are easy to lose and hard to regain – are lost. The investments of data teams start to get undermined. Companies slowly lose confidence in data, and as a result, lose their ability to build competitive advantage from one of their truly unique and defensible assets. Trusted data is the foundation on which companies progress from business intelligence to automation to generative AI.

The downward spiral happens for two reasons. The first reason is **asymmetry**. While data teams are responsible for hundreds or thousands of data assets across fragmented systems, the consumers of data care most about the number that they’re currently looking at. The second reason is **entropy**. The surface area of data maintained by the data team tends to grow, leading to more opportunities for breakage. These two forces trigger a vicious cycle in which maintaining trust in data feels like a Sisyphean task of pushing a boulder up a hill.

Metaplane was founded to provide leverage in this fight against asymmetry and entropy. Using automations on top of metadata, our hope is that data work in 2034 will feel less like “working in the dark” and more like software engineering work. Issues still happen, but engineering teams have tools like Datadog and Splunk to help prevent, anticipate, and resolve them. Vicious cycles give way to virtuous cycles of increasing trust and reliability. 

We’re already seeing the influence of engineering best practices in the work of moving, transforming, and using data. Now is the time to see maturation in the higher level governance problem.

## What We’re Building: The Next Tier of the Tech Tree

Metaplane is already at the forefront of what is possible in data observability. Last year we said we would extend data quality from detection to prevention, expand integrations, and expand data observability from monitoring quality to usage and spend. We did all of that and more over the course of [50+ product launches](https://www.metaplane.dev/blog/category/changelog) in the past year, which were made possible by working closely with our customers while laying robust foundations.

The end result is that data observability became possible. Now, we want data observability to feel powerful, but in the way that water is powerful. Data observability should mold itself around the needs of your organization. It should be soft when we need light requirements, and heavy when we have critical workloads. And when it works, it’s like it’s hardly even there.

We think this next era of data observability as powerful will rest on three key pillars:

### Observe Everything

Especially when it comes to data, anything that can go wrong, will go wrong. Worse: data is so interconnected that there are cascading effects, such that the root cause of an issue in one corner could be something completely unexpected in another corner.

That’s why every piece of metadata counts (especially if your product is named after the “metadata plane”). Metaplane was already the first data observability tool to launch integrations with transactional databases, ETL tools like Fivetran, and reverse ETL tools like Hightouch and Census.

But what about upstream issues like unsanitized inputs in a CRM or a faulty migration in your application DB? Or dashboards that suddenly go unused, or spiking query times? Every single piece of telemetry emitted by your data systems should be observed, stored, centralized, and monitored with the proper architecture.

### Automated Monitoring Architecture

We define a monitoring architecture as the design decisions that determine what you monitor, alongside how and when you monitor it. Just like data architectures, monitoring architectures should reflect and anticipate the needs and evolution of the business.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/sm11wwmkdxvegqmzf0bl.png)

Specifically, this “inverted pyramid” architecture is frequently the optimal trade-off between signal and noise, coverage and cost, and centralization and context. Ideally, this architecture is expressed on the semantic level through constraints like “give me all tables two layers upstream of a Tableau worksheet used by our VP of Marketing” or “show me all Fivetran landing zones that have been delayed in the past month.”

By combining metadata from your data stack alongside these semantic filters, Metaplane should help you implement and maintain this architecture automatically by default.

### Trust Everywhere

Achieving 100% trustworthy data is a Sisyphean task, like pushing a boulder up a hill. But the goal isn’t to have perfect data. The goal is to get as much value out of data as possible, which means knowing how trustworthy it is given a business goal.

Customer-Driven Testing is not a tenable solution. Neither is continually refreshing an open tab. Given that reality, the trustworthiness of data should be like a label on data where and when it is used. If that data is consumed in a dashboard, there should be a red/yellow/green status check. If it’s consumed in a web application, there should be a Chrome extension. If it’s consumed in an Airflow job, there should be an API to retrieve the status of underlying tables.

By observing everything and maintaining an optimal alerting architecture on top, the last mile is to make sure that data is trusted at the time of consumption.

### Building on Solid Foundations

If you already use Metaplane, you’ve probably used features that are part of those big pillars. Progress is well underway. But rest assured, all progress we make comes hand-in-hand with investing in what got us here:

1. **Metadata extraction** from the tools you use most, including column-level lineage parsing from query history and deep integrations with BI tools.
2. **Domain-specific machine learning** that is tailored to the unique patterns of your business, to ensure every alert is helpful and to avoid dreaded alert fatigue.
3. **Integrated experiences** that are easy to get started with (you can connect to Metaplane in less than 30 minutes without talking to a salesperson), easy to get value from, and deeply blend together metadata so that you get what you want, when you want it.

## How We Get There: Iron sharpens iron

Our Series A comes with expectations for growth. And of course we’re investing in go-to-market to educate the market and provide great human experiences for our customers. But the primary way we invest in growth is by continuing to invest in building the best product.

We build the best product by being the best partners to our customers. The future of data observability is yet to be built, but the problems that it solves exist today.

Learning from legendary partnerships like that between Uber and Twilio, our approach is to partner closely with the companies that live in the future. These forward-thinking companies bring in Metaplane to solve a problem. But because every company is unique, they stretch Metaplane to solve their problem. We look at the ways in which the product is stretched, combine it with feedback, then solidify it into a real product. 

Almost every feature we have has been co-developed with a customer: 

- After ClickUp asked for lineage down to Reverse ETL tools, Todd and Jen built our Hightouch and Census integrations within two days. 
- Colby worked with CarGurus to add visualizations of circular references to our lineage map. 
- Our public-facing API was developed together with Klaviyo, who wanted to version control their monitors using Terraform. 
- We re-architected high cardinality GROUP BY monitors to meet Bluecore’s scale of 1000s of distinct groups.

Customers often view Metaplane, both the product and people, as extensions of their teams. And that’s exactly how we want it to feel.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/epoyh29x3b9zfni1wa6b.png)

As the number of customers depending on Metaplane grows, our commitment is to get even closer to our customer’s needs, building the best possible product for them as quickly as possible. This is the crucible in which the best product is built.

## Making the People Who Believed in Us Look Brilliant

During our fundraise announcement last year, I wrote about a quote from HubSpot co-founder Dharmesh Shah: “Success is making those who believed in you look brilliant.” This year, that is more true than ever. While we’re welcoming new investors, new teammates, new partners, and new customers to the table, we’re devoted to strengthening the relationships that got us here.

So, thank you to our customers, old and new. Helping you is the reason we exist as a company, and we measure our success by how happy you are. Please keep the feedback coming in our shared Slack channels :).

Thank you to our new investors Felicis and B37 for trusting us with your time and resources, along with our existing investors at Y Combinator, Flybridge, and SNR for doubling down.

Thank you to our partners at Snowflake, dbt, Sigma, Brooklyn Data Co, and many other organizations who keep pushing the state-of-the-art in our industry forward.

Thank you to the team for continually raising the bar, and most of all for building what we’d buy ourselves. With you all, there’s no choice but to enjoy the flight :).

And to those of you who want to help your company look brilliant by ensuring trust in data, [we’d love to chat](https://www.metaplane.dev/book-a-demo). Cheers to a bright future where data is the lifeblood of companies without the teams behind that data getting paged at 3am!