.jpg)
In this year’s Enterprise Tech 30, companies in the data space continued to dominate, making up nearly a quarter of the companies on the list. We sat down with four of the leaders of such companies—Hex, Benchling, Grafana Labs, and MonteCarlo—to hear more about the challenges they’re tackling.
Hex—#5, Early Stage, Enterprise Tech 30
Hex is a collaborative data workspace that empowers organizations to drive impact from their data. The platform combines real-time collaborative notebooks, interactive data apps, and knowledge management into one, seamless experience.
Why this problem, now?
“Every organization is trying to be more data driven. They're bringing in people like data scientists and data analysts… who want to be able to ask and answer questions and drive decisions and insights for business.”
“Something we experienced as builders and users of data tools is that a lot of those data tools were very inaccessible. They were hard to use, very siloed, and it was hard to actually collaborate on those workflows and share the results.”
Why Hex?
“With Hex, we wanted to build a platform that would make it very easy for [data professionals] to do really powerful data analytics and data science work; to be able to collaborate together on it as a team, and then—crucially—be able to take that insight and share it broadly within the organization to help make the best decisions.”
Benchling—#3, Late Stage, Enterprise Tech 30
Benchling is a cloud-based platform for biotechnology research and development. More than 200,000 scientists at over 700 companies and 7,000 research institutions globally have adopted Benchling to make breakthrough discoveries and bring the next generation of medicines, food, and materials to market faster than ever before.
Sajith Wickramasekara, Founder and CEO
Why this problem, now?
“Scientists are working on some of the most pressing problems in the world from disease, pandemics, climate change, and more, and biotechnology is one of the most promising solutions to these challenges… but the tools that they're equipped with are really dated and the work has gotten incredibly complex.”
“The scientists working on these problems are building incredible products, and the science behind these products is really complex—orders of magnitude more difficult and challenging than what they were working on decades ago—but these same scientists, they're using paper notebooks and Excel spreadsheets, and we're asking them to do things like cure cancer. It's like asking people to go from building bicycles to jet planes, but the tools are the exact same.”
Why Benchling?
“We ultimately want to build software that helps these folks bring very complex products to market faster, more effectively, and ultimately will save lives or impact lives all over the world.”
Grafana Labs—#9, Late Stage, Enterprise Tech 30
Grafana Labs provides a vendor-neutral observability platform based on Grafana, an open source technology for dashboards and visualization leveraged by 10M+ global users.
Why this problem, now?
“In a way, observability is really just kind of a new word for monitoring, but it's become really important over the last two or three years [given] the extreme transformation, and the swiftness of that transformation, to the digital software world.”
“Open source and open standards and interoperability is really in our DNA. We're all about respecting the customer's journey that they're on, respecting the investments that they've already made, and really interoperating with everything that they already have.”
Why Grafana Labs?
“Grafana Labs is about allowing you to bring all this data from all over the organization, from all different vendors, different databases, and bring it together so you can understand it. We really think that's a more powerful way of delivering insights.”
MonteCarlo—#5, Mid Stage, Enterprise Tech 30
Monte Carlo is a data reliability company that helps teams increase trust in data by identifying, eliminating, and preventing data downtime.
Why this problem, now?
“In the last decade we've developed strong solutions for ingesting, processing, storing, and analyzing data, but we have very little innovation around actually being able to trust the data. As part of that, I think data observability is really gaining significant traction right now.”
Why MonteCarlo?
“Data engineers, data analysts, and data scientists out there deliver data products; it could be dashboards, it could be machine learning models, and helping them make sure that that data is reliable is actually really hard today. We started Monte Carlo with the goal of helping data teams deliver those products in a trusted manner.”
