Everybody talks of big data and how it will allow companies to find all kinds of insights. At the same time, executives admit that 75% and more of the data their companies have gathered is useless. How can that be?
Anastasia: The problem with big data is that people can’t reconcile the growth of data with the ways they want to use them. Data grows in size, we all know that, but this is just one dimension. It is also the number of different locations data is stored in that increases, and most importantly, the number of formats it is stored in grows as well. Take the example of a list of employees and their function, neatly arranged in a table with rows and columns. Compare it to the same information displayed on a website, which uses a different protocol to display the data. Combining data from such distinct sources is a lot of work. You need to change the formats to bring them together in the same one. On a bigger scale, for companies with their numerous different systems and with much more data, this means big investments in data warehouses that bring the data together. So, one of the main reasons why businesses don’t make sense of the data they already have is that there is so much effort involved in preparing data before they can even start to work with it.

Lars: All companies are engaged in some sort of digital transformation. But what digital transformation really means is either improving the top line, by understanding customer needs better in order to increase sales or by improving the bottom line, for example by reducing costs. In order to achieve these goals and not to fall behind their competition, they need to make use of data. Take the example of predictive maintenance. Modern machines are equipped with sensors that can be used to predict when they will break down. If you can send someone to fix a machine before this happens and eliminate downtime, this is a major value proposition. It saves cost and becomes a major selling argument as well. But such innovations are difficult to implement if the data is imprisoned in different silos. RAW Labs is removing such barriers in order to enable digital transformation.

Let’s talk about how this actually works later. First I want to know, Anastasia, what led you to co-found a startup?
I’ve been working on the topic of database systems for almost 3 decades now. Throughout my academic career, I’ve looked for ways how to build efficient database systems that get the maximum out of the software and hardware that is available at the moment. Even before my co-founder Miguel Branco joined me, I was working on a project born out of frustration. I was working on scientific data management applications, with datasets coming from fields such as earthquake simulation or neuroscience. My idea was to change the paradigm and find a way that allows scientists to just ask a question without having to spend all their time preparing datasets. The project was called “NoDB” for “no database”. When Miguel (who previously worked at CERN) joined our group, it felt like the right time to give it a shot. There was a lot of encouragement from my network as well. I’ve worked with the biggest database companies and many of them such as Oracle, Microsoft, IBM, and Snowflake use my work. All of the people that learned about the project were very positive about it. This strengthened my resolve to put this tool into the hands of people and give businesses a superhighway to access their data.

Lars, you had an international career with senior sales positions at different software companies. Why abandon it and join a small team with a handful of people?
First of all, because it is a great team. And secondly, because they solve the single most important problem that companies have when they want to accelerate their digital transformation. I instantly realized how powerful Raw Labs’ new approach is because in all projects I’ve worked on, accessing the data was the problem. I’ve seen projects fail because the systems couldn’t handle the amount of data or because it simply took too long to copy or process them. RAW Labs solves all of these problems, that is why I joined.

Your product is already in use at a few first customers, what is the feedback?
Anastasia: The feedback is really good. We’ve spent a long time developing the product. But as in drug development, after a lot of difficult work, the actual product is very easy to use. Our customers don’t need a lot of resources to implement it, the product is available in the cloud and if you know how to use the programming language SQL you’re good to go.

Lars: What we’re focusing on now and in the next 12 months is choosing the right customers.

You can be picky about your customers as a startup?
Lars: We have to, in order to make sure there is a strategic fit with what the customer wants to achieve such as in the case of the large telecom we currently work with. We want to make sure that the first ten enterprise customers that use RAW Labs will be super successful in what they want to do, and then we can accelerate sales. Our product is not industry-specific, but the use cases vary across industries. In manufacturing, the optimization of inventory and the efficiency of supply chains are a topic, for example. In financial services, the goal can be the acceleration of statutory reporting, to name one use case.

The market RAW Labs is active in is dominated by the big database companies we’ve already mentioned, but there are also new success stories such as Snowflake with their recent IPO that doubled the value of the company in a day to more than USD 75 billion. Where does Raw Labs fit in among such giants?
Anastasia: Snowflake is one of the shining examples of how to do it right. They’ve developed their product from the ground up while in stealth mode. Likewise, we’ve been secretly working on our product for the better part of the past 5 years. We developed a unique technology that allows users to ask questions with absolutely no data or other preparation. Nothing like RAW exists in the market because the ability to “just ask” has been built into the core of the product. Many software companies try to achieve a similar goal, but it’s hard because they add features to existing rigid architecture and because they aim to replace the client’s infrastructure. RAW is complementary to the infrastructure of the client and works with the existing software, facilitating communication between humans and data. People aren’t interested in more databases. They want answers, and this is what we provide to them.