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DataRobot CEO Dan Wright coming to TC Sessions: SaaS to discuss role of data in machine learning
Just about every company is sitting on vast amounts of data, which they can use to their advantage if they can just learn how to harness it. Data is actually the fuel for machine learning models, and with the proper tools, businesses can learn to process this data and build models to help them compete in a rapidly changing marketplace, to react more quickly to shifting customer requirements and to find insights faster than any human ever possibly could.
Boston-based DataRobot, a late-stage startup that has built a platform to help companies navigate the machine learning model lifecycle, has been raising money by the bushel over the last several years, including $206 million in September 2019 and another $300 million in July. DataRobot CEO Dan Wright will be joining us on a panel to discuss the role of data in business at TC Sessions: SaaS on October 27th.
The company covers the gamut of the machine learning lifecycle, including preparing data, operationalizing it and finally building APIs to make it useful for the organization as it attempts to build a soup-to-nuts platform. DataRobot’s broad platform approach has appealed to investors.
As we wrote at the time of the $206 million round:
The company has been catching the attention of these investors by offering a machine learning platform aimed at analysts, developers and data scientists to help build predictive models much more quickly than it typically takes using traditional methodologies. Once built, the company provides a way to deliver the model in the form of an API, simplifying deployment.
DataRobot has raised a total of $1 billion on $6.3 billion post valuation, according to PitchBook data, and it’s been putting that money to work to add to its platform of services. Most recently the company acquired Algorithmia, which helps manage machine learning models.
As the pandemic has pushed more business online, companies are always looking for an edge, and one way to achieve that is by taking advantage of AI and machine learning. Wright will be joined on the data panel by Monte Carlo co-founder and CEO Barr Moses and AgentSync co-founder and CTO Jenn Knight to discuss the growing role of data in business operations
In addition to our discussion with Wright, the conference will also include Microsoft’s Jared Spataro, Amplitude’s Olivia Rose, as well as investors Kobie Fuller and Laela Sturdy, among others. We hope you’ll join us. It’s going to be a thought-provoking lineup.
Buy your pass now to save up to $100. We can’t wait to see you in October!
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Companies betting on data must value people as much as AI
The Pareto principle, also known as the 80-20 rule, asserts that 80% of consequences come from 20% of causes, rendering the remainder way less impactful.
Those working with data may have heard a different rendition of the 80-20 rule: A data scientist spends 80% of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights. Imagine a 30-minute drive expanded to two-and-a-half hours by traffic jams, and you’ll get the picture.
As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now.
While most data scientists spend more than 20% of their time at work on actual analysis, they still have to waste countless hours turning a trove of messy data into a tidy dataset ready for analysis. This process can include removing duplicate data, making sure all entries are formatted correctly and doing other preparatory work.
On average, this workflow stage takes up about 45% of the total time, a recent Anaconda survey found. An earlier poll by CrowdFlower put the estimate at 60%, and many other surveys cite figures in this range.
None of this is to say data preparation is not important. “Garbage in, garbage out” is a well-known rule in computer science circles, and it applies to data science, too. In the best-case scenario, the script will just return an error, warning that it cannot calculate the average spending per client, because the entry for customer #1527 is formatted as text, not as a numeral. In the worst case, the company will act on insights that have little to do with reality.
The real question to ask here is whether re-formatting the data for customer #1527 is really the best way to use the time of a well-paid expert. The average data scientist is paid between $95,000 and $120,000 per year, according to various estimates. Having the employee on such pay focus on mind-numbing, non-expert tasks is a waste both of their time and the company’s money. Besides, real-world data has a lifespan, and if a dataset for a time-sensitive project takes too long to collect and process, it can be outdated before any analysis is done.
What’s more, companies’ quests for data often include wasting the time of non-data-focused personnel, with employees asked to help fetch or produce data instead of working on their regular responsibilities. More than half of the data being collected by companies is often not used at all, suggesting that the time of everyone involved in the collection has been wasted to produce nothing but operational delay and the associated losses.
The data that has been collected, on the other hand, is often only used by a designated data science team that is too overworked to go through everything that is available.
All for data, and data for all
The issues outlined here all play into the fact that save for the data pioneers like Google and Facebook, companies are still wrapping their heads around how to re-imagine themselves for the data-driven era. Data is pulled into huge databases and data scientists are left with a lot of cleaning to do, while others, whose time was wasted on helping fetch the data, do not benefit from it too often.
The truth is, we are still early when it comes to data transformation. The success of tech giants that put data at the core of their business models set off a spark that is only starting to take off. And even though the results are mixed for now, this is a sign that companies have yet to master thinking with data.
Data holds much value, and businesses are very much aware of it, as showcased by the appetite for AI experts in non-tech companies. Companies just have to do it right, and one of the key tasks in this respect is to start focusing on people as much as we do on AIs.
Data can enhance the operations of virtually any component within the organizational structure of any business. As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now. The goal for any company looking to tap data today comes down to getting it from point A to point B. Point A is the part in the workflow where data is being collected, and point B is the person who needs this data for decision-making.
Importantly, point B does not have to be a data scientist. It could be a manager trying to figure out the optimal workflow design, an engineer looking for flaws in a manufacturing process or a UI designer doing A/B testing on a specific feature. All of these people must have the data they need at hand all the time, ready to be processed for insights.
People can thrive with data just as well as models, especially if the company invests in them and makes sure to equip them with basic analysis skills. In this approach, accessibility must be the name of the game.
Skeptics may claim that big data is nothing but an overused corporate buzzword, but advanced analytics capacities can enhance the bottom line for any company as long as it comes with a clear plan and appropriate expectations. The first step is to focus on making data accessible and easy to use and not on hauling in as much data as possible.
In other words, an all-around data culture is just as important for an enterprise as the data infrastructure.
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Don’t give your weed dealer all your data
Hello and welcome back to Equity, TechCrunch’s venture capital-focused podcast, where we unpack the numbers behind the headlines.
Our beloved Danny was back, joining Natasha and Alex and Grace and Chris to chat through yet another incredibly busy week. As a window into our process, every week we tell one another that the next week we’ll cut the show down to size. Then the week is so interesting that we end up cutting a lot of news, but also keeping a lot of news. The chaotic process is a work in progress, but it means that the end result is always what we decided we can’t not talk about.
Here’s what we got into:
- A little URL to IRL update from Natasha, who just got back from an edtech conference.
- How one VC got hit by ransomware, and why stolen LP data could be a wake-up call for investors.
- The crew chatted through some Cloud 100 numbers, and riffed for a minute on Figma, Gusto and Mailchimp, companies all reportedly worth around the $10 billion mark.
- From the early-stage funding round side of things, we noodled on Surfside’s $4 million raise, and the capital that Pave recently attracted. Felt also raised money to make maps more mainstream, which had us thinking about use cases galore.
- In unicorn-land, Trendyol raised a mountain of cash, while UpGrad became India’s newest unicorn.
- Climate change is going to mean lots more companies need to handle disaster prep, Danny argues. His recent EC-1 here about RapidSOS got into the deep and complex world of three simple numbers: 911.
- And we ended the show with a riff on Salesforce+, which we had fun with but also tried to take seriously because we are journalists after all.
Equity drops every Monday at 7:00 a.m. PDT, Wednesday, and Friday morning at 7:00 a.m. PDT, so subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts.
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Opaque raises $9.5M seed to secure sensitive data in the cloud
Opaque, a new startup born out of Berkeley’s RISELab, announced a $9.5 million seed round today to build a solution to access and work with sensitive data in the cloud in a secure way, even with multiple organizations involved. Intel Capital led today’s investment with participation by Race Capital, The House Fund and FactoryHQ.
The company helps customers work with secure data in the cloud while making sure the data they are working on is not being exposed to cloud providers, other research participants or anyone else, says company president Raluca Ada Popa.
“What we do is we use this very exciting hardware mechanism called Enclave, which [operates] deep down in the processor — it’s a physical black box — and only gets decrypted there. […] So even if somebody has administrative privileges in the cloud, they can only see encrypted data,” she explained.
Company co-founder Ion Stoica, who was a co-founder at Databricks, says the startup’s solution helps resolve two conflicting trends. On one hand, businesses increasingly want to make use of data, but at the same time are seeing a growing trend toward privacy. Opaque is designed to resolve this by giving customers access to their data in a safe and fully encrypted way.
The company describes the solution as “a novel combination of two key technologies layered on top of state-of-the-art cloud security—secure hardware enclaves and cryptographic fortification.” This enables customers to work with data — for example to build machine learning models — without exposing the data to others, yet while generating meaningful results.
Popa says this could be helpful for hospitals working together on cancer research, who want to find better treatment options without exposing a given hospital’s patient data to other hospitals, or banks looking for money laundering without exposing customer data to other banks, as a couple of examples.
Investors were likely attracted to the pedigree of Popa, a computer security and applied crypto professor at UC Berkeley and Stoica, who is also a Berkeley professor and co-founded Databricks. Both helped found RISELabs at Berkeley where they developed the solution and spun it out as a company.
Mark Rostick, vice president and senior managing director at lead investor Intel Capital says his firm has been working with the founders since the startup’s earliest days, recognizing the potential of this solution to help companies find complex solutions even when there are multiple organizations involved sharing sensitive data.
“Enterprises struggle to find value in data across silos due to confidentiality and other concerns. Confidential computing unlocks the full potential of data by allowing organizations to extract insights from sensitive data while also seamlessly moving data to the cloud without compromising security or privacy,” Rostick said in a statement
He added, “Opaque bridges the gap between data security and cloud scale and economics, thus enabling inter-organizational and intra-organizational collaboration.”
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Tinybird turns raw data into realtime API at scale
Meet Tinybird, a new startup that helps developers build data products at scale without having to worry about infrastructure, query time and all those annoying issues that come up once you deal with huge data sets. The company ingests data at scale, lets you transform it using SQL and then exposes that data through API endpoints.
Over the past few years, analytics and business intelligence products have really changed the way we interact with data. Now, many big companies store data in a data warehouse or a data lake. They try to get insights from those data sets.
And yet, extracting and manipulating data can be costly and slow. It works great if you want to make a PowerPoint presentation for your quarterly results. But it doesn’t let you build modern web products and data products in general.
“What we do at Tinybird is we help developers build data products at any scale. And we’re really focused on the realtime aspect,” co-founder and CEO Jorge Gómez Sancha told me.
The team of co-founders originally met at Carto. They were already working on complex data issues. “Every year people would come with an order of magnitude more data,” Gómez Sancha said. That’s how they came up with the idea behind Tinybird.
Image Credits: Tinybird
The product can be divided into three parts. First, you connect your Tinybird account with your data sources. The company will then ingest data constantly from those data sources.
Second, you can transform that data through SQL queries. In addition to the command-line interface, you can also enter your SQL queries in a web interface, divide then into multiple steps and document everything. Every time you write a query, you can see your data filtered and sorted according to your query.
Third, you can create API endpoints based on those queries. After that, it works like a standard JSON-based API. You can use it to fetch data in your own application.
What makes Tinybird special is that it’s so fast that it feels like you’re querying your data in realtime. “Several of our customers are reading over 1.5 trillion rows on average per day via Tinybird and ingesting around 5 billion rows per day, others are making an average of 250 requests per second to our APIs querying several billion row datasets,” Gómez Sancha wrote in an email.
Behind the scene, the startup uses ClickHouse. But you don’t have to worry about that as Tinybird manages all the infrastructure for you.
Right now, Tinybird has identified three promising use cases. Customers can use it to provide in-product analytics. For instance, if you operate a web hosting service and wants to give some analytics to your customers or if you manage online stores and want to surface purchasing data to your customers, Tinybird works well for that.
Some customers also use the product for operational intelligence, such as realtime dashboards that you can share internally within a company. Your teams can react more quickly and always know if everything is running fine.
You can also use Tinybird as the basis for some automation or complex event processing. For instance, you can leverage Tinybird to build a web application firewall that scans your traffic and reacts in realtime.
Tinybird has raised a $3 million seed round led by Crane.vc with several business angels also participating, such as Nat Friedman (GitHub CEO), Nicholas Dessaigne (Algolia co-founder), Guillermo Rauch (Vercel CEO), Jason Warner (GitHub CTO), Adam Gross (former Heroku CEO), Stijn Christiaens (co-founder and CTO of Collibra), Matias Woloski (co-founder and CTO of Auth0) and Carsten Thoma (Hybris co-founder).
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Enterprise AI platform Dataiku launches managed service for smaller companies
Dataiku is going downstream with a new product today called Dataiku Online. As the name suggests, Dataiku Online is a fully managed version of Dataiku. It lets you take advantage of the data science platform without going through a complicated setup process that involves a system administrator and your own infrastructure.
If you’re not familiar with Dataiku, the platform lets you turn raw data into advanced analytics, run some data visualization tasks, create data-backed dashboards and train machine learning models. In particular, Dataiku can be used by data scientists, but also business analysts and less technical people.
The company has been mostly focused on big enterprise clients. Right now, Dataiku has more than 400 customers, such as Unilever, Schlumberger, GE, BNP Paribas, Cisco, Merck and NXP Semiconductors.
There are two ways to use Dataiku. You can install the software solution on your own, on-premise servers. You can also run it on a cloud instance. With Dataiku Online, the startup offers a third option and takes care of setup and infrastructure for you.
“Customers using Dataiku Online get all the same features that our on-premises and cloud instances provide, so everything from data preparation and visualization to advanced data analytics and machine learning capabilities,” co-founder and CEO Florian Douetteau said. “We’re really focused on getting startups and SMBs on the platform — there’s a perception that small or early-stage companies don’t have the resources or technical expertise to get value from AI projects, but that’s simply not true. Even small teams that lack data scientists or specialty ML engineers can use our platform to do a lot of the technical heavy lifting, so they can focus on actually operationalizing AI in their business.”
Customers using Dataiku Online can take advantage of Dataiku’s pre-built connectors. For instance, you can connect your Dataiku instance with a cloud data warehouse, such as Snowflake Data Cloud, Amazon Redshift and Google BigQuery. You can also connect to a SQL database (MySQL, PostgreSQL…), or you can just run it on CSV files stored on Amazon S3.
And if you’re just getting started and you have to work on data ingestion, Dataiku works well with popular data ingestion services. “A typical stack for our Dataiku Online Customers involves leveraging data ingestion tools like FiveTran, Stitch or Alooma, that sync to a cloud data warehouse like Google BigQuery, Amazon Redshift or Snowflake. Dataiku fits nicely within their modern data stacks,” Douetteau said.
Dataiku Online is a nice offering to get started with Dataiku. High-growth startups might start with Dataiku Online as they tend to be short on staff and want to be up and running as quickly as possible. But as you become bigger, you could imagine switching to a cloud or on-premise installation of Dataiku. Employees can keep using the same platform as the company scales.
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Near acquires the location data company formerly known as UberMedia
Data intelligence company Near is announcing the acquisition of another company in the data business — UM.
In some ways, this echoes Near’s acquisition of Teemo last fall. Just as that deal helped Singapore-headquartered Near expand into Europe (with Teemo founder and CEO Benoit Grouchko becoming Near’s chief privacy officer), CEO Anil Mathews said that this new acquisition will help Near build a presence in the United States, turning the company into “a truly global organization,” while also tailoring its product to offer “local flavors” in each country.
The addition of UM’s 60-person team brings Near’s total headcount to around 200, with UM CEO Gladys Kong becoming CEO of Near North America.
At the same time, Mathews suggested that this deal isn’t simply about geography, because the data offered by Near and UM are “very complementary,” allowing both teams to upsell current customers on new offerings. He described Near’s mission as “merging two diverse worlds, the online world and the offline world,” essentially creating a unified profile of consumers for marketers and other businesses. Apparently, UM is particularly strong on the offline side, thanks to its focus on location data.
Near CEO Anil Mathews and UM CEO Gladys Kong. Image Credits: Near
“UM has a very strong understanding of places, they’ve mastered their understanding of footfalls and dwell times,” Mathews added. “As a result, most of the use cases where UM is seeing growth — in tourism, retail, real estate — are in industries struggling due to the pandemic, where they’re using data to figure out, ‘How do we come out of the pandemic?’ ”
TechCrunch readers may be more familiar with UM under its old name, UberMedia, which created social apps like Echofon and UberSocial before pivoting its business to ad attribution and location data. Kong said that contrary to her fears, the company had “an amazing 2020” as businesses realized they needed UM’s data (its customers include RAND Corporation, Hawaii Tourism Authority, Columbia University and Yale University).
And the year was capped by connecting with Near and realizing that the two companies have “a lot of synergies.” In fact, Kong recalled that UM’s rebranding last month was partly at Mathews’ suggestion: “He said, ‘Why do you have media in your name when you don’t do media?’ And we realized that’s probably how the world saw us, so we decided to change [our name] to make it clear what we do.”
Founded in 2010, UM raised a total of $34.6 million in funding, according to Crunchbase. The financial terms of the acquisition were not disclosed.
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Data scientists: Bring the narrative to the forefront
By 2025, 463 exabytes of data will be created each day, according to some estimates. (For perspective, one exabyte of storage could hold 50,000 years of DVD-quality video.) It’s now easier than ever to translate physical and digital actions into data, and businesses of all types have raced to amass as much data as possible in order to gain a competitive edge.
However, in our collective infatuation with data (and obtaining more of it), what’s often overlooked is the role that storytelling plays in extracting real value from data.
The reality is that data by itself is insufficient to really influence human behavior. Whether the goal is to improve a business’ bottom line or convince people to stay home amid a pandemic, it’s the narrative that compels action, rather than the numbers alone. As more data is collected and analyzed, communication and storytelling will become even more integral in the data science discipline because of their role in separating the signal from the noise.
Data alone doesn’t spur innovation — rather, it’s data-driven storytelling that helps uncover hidden trends, powers personalization, and streamlines processes.
Yet this can be an area where data scientists struggle. In Anaconda’s 2020 State of Data Science survey of more than 2,300 data scientists, nearly a quarter of respondents said that their data science or machine learning (ML) teams lacked communication skills. This may be one reason why roughly 40% of respondents said they were able to effectively demonstrate business impact “only sometimes” or “almost never.”
The best data practitioners must be as skilled in storytelling as they are in coding and deploying models — and yes, this extends beyond creating visualizations to accompany reports. Here are some recommendations for how data scientists can situate their results within larger contextual narratives.
Make the abstract more tangible
Ever-growing datasets help machine learning models better understand the scope of a problem space, but more data does not necessarily help with human comprehension. Even for the most left-brain of thinkers, it’s not in our nature to understand large abstract numbers or things like marginal improvements in accuracy. This is why it’s important to include points of reference in your storytelling that make data tangible.
For example, throughout the pandemic, we’ve been bombarded with countless statistics around case counts, death rates, positivity rates, and more. While all of this data is important, tools like interactive maps and conversations around reproduction numbers are more effective than massive data dumps in terms of providing context, conveying risk, and, consequently, helping change behaviors as needed. In working with numbers, data practitioners have a responsibility to provide the necessary structure so that the data can be understood by the intended audience.
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Soda monitors data and helps you fix issues before it’s too late
Meet Soda, a data monitoring platform that is going to help you discover issues with your data processing setup. This way, you can react as quickly as possible and make sure that you keep the full data picture.
If you’re building a digital-first company, you and your customers are likely generating a ton of data. And you may even be leveraging that data to adjust your product itself — think about hotel pricing, finding the right restaurant on a food delivery website, applying for a loan with a fintech company, etc. Those are data-heavy products.
“Companies build a data platform — as they call it — in one of the big three clouds [Amazon Web Services, Google Cloud, Microsoft Azure]. They land their data in there and they make it available for analytics and more,” Soda co-founder and CEO Maarten Masschelein told me.
You can then tap into those data lakes or data warehouses to display analytics, visualize your data, monitor your services, etc. But what happens if there’s an issue in your data workflows?
It might take you a while to realize that there’s some missing data, or that you’re miscounting some stuff. For instance, Facebook miscalculated average video view times for several years. When you spot that issue, an important part of your business might be affected.
Soda wants to catch data issues as quickly as possible by monitoring your data automatically and at scale. “We sit further upstream, closer to the source of data,” Masschelein said.
When you set up Soda with your data platform, you instantly get some alerts. Soda tells you if there’s something off. For example, if your application generated only 6,000 records today while you usually generate 24,000 records in 24 hours, chances are there’s something wrong. Or if you usually get a new entry every minute and there hasn’t been an entry in 15 minutes, your data might not be fresh.
“But that only covers a small part of what is considered data issues. There’s more logic that you want to test and validate,” Masschelein said.
Soda lets you create rules to test and validate your data. Basically, think about test suite in software development. When you build a new version of your app, your code needs to pass several tests to make sure that nothing critical is going to break with the new version.
With Soda, you can check data immediately and get the result. If the test doesn’t pass, you can programmatically react — for instance, you can stop a process and quarantine data.
Today, the startup is also launching Soda Cloud. It’s a collaboration web application that gives you visibility in your data flows across the organization. This way, nontechnical people can easily browse metadata to see whether everything seems to be flowing correctly.
Basically, Soda customers use Soda SQL, a command-line tool that helps someone scan data, along with Soda Cloud, a web application to view Soda SQL results.
Beyond those products, Soda’s vision is that data is becoming an entire category in software products. Development teams now have a ton of dev tools available to automate testing, integration, deployment, versioning, etc. But there’s a lot of potential for tools specifically designed for data teams.
Soda has recently raised a $13.5 million Series A round (€11.5 million) led by Singular, a new Paris-based VC fund that I covered earlier this week. Soda’s seed investors Point Nine Capital, Hummingbird Ventures, DCF and various business angels also participated.
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No-code business intelligence service y42 raises $2.9M seed round
Berlin-based y42 (formerly known as Datos Intelligence), a data warehouse-centric business intelligence service that promises to give businesses access to an enterprise-level data stack that’s as simple to use as a spreadsheet, today announced that it has raised a $2.9 million seed funding round led by La Famiglia VC. Additional investors include the co-founders of Foodspring, Personio and Petlab.
The service, which was founded in 2020, integrates with more than 100 data sources, covering all the standard B2B SaaS tools, from Airtable to Shopify and Zendesk, as well as database services like Google’s BigQuery. Users can then transform and visualize this data, orchestrate their data pipelines and trigger automated workflows based on this data (think sending Slack notifications when revenue drops or emailing customers based on your own custom criteria).
Like similar startups, y42 extends the idea data warehouse, which was traditionally used for analytics, and helps businesses operationalize this data. At the core of the service is a lot of open source and the company, for example, contributes to GitLabs’ Meltano platform for building data pipelines.
y42 founder and CEO Hung Dang. Image Credits: y42
“We’re taking the best of breed open-source software. What we really want to accomplish is to create a tool that is so easy to understand and that enables everyone to work with their data effectively,” Y42 founder and CEO Hung Dang told me. “We’re extremely UX obsessed and I would describe us as a no-code/low-code BI tool — but with the power of an enterprise-level data stack and the simplicity of Google Sheets.”
Before y42, Vietnam-born Dang co-founded a major events company that operated in more than 10 countries and made millions in revenue (but with very thin margins), all while finishing up his studies with a focus on business analytics. And that in turn led him to also found a second company that focused on B2B data analytics.
Image Credits: y42
Even while building his events company, he noted, he was always very product- and data-driven. “I was implementing data pipelines to collect customer feedback and merge it with operational data — and it was really a big pain at that time,” he said. “I was using tools like Tableau and Alteryx, and it was really hard to glue them together — and they were quite expensive. So out of that frustration, I decided to develop an internal tool that was actually quite usable and in 2016, I decided to turn it into an actual company. ”
He then sold this company to a major publicly listed German company. An NDA prevents him from talking about the details of this transaction, but maybe you can draw some conclusions from the fact that he spent time at Eventim before founding y42.
Given his background, it’s maybe no surprise that y42’s focus is on making life easier for data engineers and, at the same time, putting the power of these platforms in the hands of business analysts. Dang noted that y42 typically provides some consulting work when it onboards new clients, but that’s mostly to give them a head start. Given the no-code/low-code nature of the product, most analysts are able to get started pretty quickly — and for more complex queries, customers can opt to drop down from the graphical interface to y42’s low-code level and write queries in the service’s SQL dialect.
The service itself runs on Google Cloud and the 25-people team manages about 50,000 jobs per day for its clients. The company’s customers include the likes of LifeMD, Petlab and Everdrop.
Until raising this round, Dang self-funded the company and had also raised some money from angel investors. But La Famiglia felt like the right fit for y42, especially due to its focus on connecting startups with more traditional enterprise companies.
“When we first saw the product demo, it struck us how on top of analytical excellence, a lot of product development has gone into the y42 platform,” said Judith Dada, general partner at LaFamiglia VC. “More and more work with data today means that data silos within organizations multiply, resulting in chaos or incorrect data. y42 is a powerful single source of truth for data experts and non-data experts alike. As former data scientists and analysts, we wish that we had y42 capabilities back then.”
Dang tells me he could have raised more but decided that he didn’t want to dilute the team’s stake too much at this point. “It’s a small round, but this round forces us to set up the right structure. For the Series A, which we plan to be towards the end of this year, we’re talking about a dimension which is 10x,” he told me.
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