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People
Datahunt is a company founded with the goal of creating a platform that fulfills the needs of raw data acquisition, aggregation, and refinement.
To put it more simply, we aim to build a data ecosystem so that all processes for creating artificial intelligence can be handled on one platform. We already have a number of projects underway in the areas of data collection and data processing.
We are taking it a step further and conducting POCs on training and deploying AI models on processed data. Based on this, we are preparing to launch new products that will deliver upgraded value to more customers within this year.
Before starting Datahunt, I worked in private equity, where I was involved in acquiring and running various companies, so I had a lot of opportunities to see different companies in different industries. I think that's when I personally got the idea that platform companies are good companies and valuable companies.
In the modern world, a lot of new information is created and utilized every day, creating various values. If it is impossible to create and utilize all this information directly, the next best thing is to build a platform to trade these values. The possibilities of such a platform company are endless. There are various platforms, but I was convinced that artificial intelligence is the most promising field in terms of future growth potential.
These ideas were well aligned with the business structure and needs of FiscalNote, and I thought that the experience accumulated in the process of growing FiscalNote into a leading company in the AI industry would definitely helpDataHunt, so I was happy to receive the investment, and we have been growing hard until now.
In the end, it's all about people, and I think the biggest obstacle to a company's ability to excel is a small number of people who bring down the atmosphere. I attend every interview no matter how busy I am, and I think a lot that people who bring down the atmosphere not only in interviews but also in everyday life shouldn't be part of Datahunt.
I think it's the most important job of a manager to create an environment where people can work to their fullest potential without being stressed outside of work, and I emphasize this to the point where I live by it.
I think that an organization that works most efficiently is not a passive organization that works because someone tells them to, but an organization that knows that working this much will bring more fulfillment and rewards. We are trying to establish this process as a corporate culture.
First of all, I would say that it is easy to integrate AI and the precision of the entire process.
Existing data-related companies have been appealing to their already established infrastructure to the extent that they use the fact that they have a lot of data labeling workers as a selling point. However, it is not necessarily good to spend more money to speed up the work by utilizing a lot of workers.
As far as I know, DataHunt is the first company in Korea to adopt a'human-in-the-loop' approach to data labeling, which means that we use AI to speed up the work that people do. It's a data processing method that takes the best of both worlds: human work and AI work.
AI can be faster but not as accurate, and human work can be more accurate but not as fast, so we ensured the completeness of the data by having AI preprocess the data that needs to be processed first, and then humans check the AI's preprocessed data for accuracy. This saves time and manpower for the entire process, which of course translates into lower overall costs.
Asa result, we can say that we're able to complete our projects faster while increasing the accuracy of our work, and at the same time, we're saving money, which is the biggest advantage.
I think it's the completeness of the data. For example, if a developer doesn't take the bugs they encounter in their work seriously and these situations are accumulating, even if it seems like a minor issue at first, the cost and time to fix it will increase over time.
Similarly, if you hire a data hunt to work on your data and the resulting data lacks completeness, your credibility will be compromised, so I think it's important to create a culture that doesn't tolerate mistakes.
(Laughs)Everybody makes mistakes, and I'm very sensitive to them, but I can't help but make mistakes, and it's inevitable that people will make mistakes, but I think it's important to have a culture where there's a system in place to cross-check those mistakes so that they don't detract from the overall quality of the work.And it's important to have a culture where you don't ignore or pretend to ignore mistakes that are found.
In our business, a lot of data accumulates quickly, so even if you have a good system, it doesn't mean much if the people who use it don't have the skills.After the Data Hunt team members understand exactly what data the customer wants, and what purpose they need it for, we try to pinpoint any ambiguities through continuous feedback with the customer.
For example, if you give a worker an ivory sample and an apricot sample in a job guide and say, "Label A as ivory and B as apricot," different people might say it's more like A and more like B. To help with this ambiguity, we have a system that measures the RGB values (color values) on the image, and if the tag (color) that the worker selects is outside the acceptable range of the RGB values measured on the image, an alarm goes off.
In short, the team knows exactly what data the client wants, and Datahunt's interface is built to technically assist the data labelers, so the data is more complete.
First-time clients usually start with a small POC, and I don't know of a single company that we've done a POC for that didn't end up winning business. We're very proud of our products and services, and we're confident that we can compete with any company in our space.
I think a lot of really talented people these days want to go to fast-growing companies, and the problem with fast-growing companies is that they're riskier, and I'm a risk averse person. I wouldn't even think of taking on a challenge that no one else has done before, and the reason I was able to start this business is because I had the full support of FiscalNote.
Datahunt is a fast-growing startup, but it's also a subsidiary of a global company called FiscalNote. We have 11 subsidiaries, and the intangible value and know-how that each of those companies have accumulated as they've grown has been transferred to DataHunt, so we've gotten a lot of strategic support, not just monetary investment, but strategic support in a lot of ways. And that's really helping us grow.
There's no risk like there is in other startups that if you don't see the funding metrics for a month or two, you're out of business, or your career is in jeopardy. I can confidently say that we're a company where great people don't have to worry about anything other than their job.
Yes.(Laughs)
When Datahunt first got an office in Gangnam, there were three or four employees, including me, who worked there for more than a year, so I'm very envious of the people who are joining DataHunt now.
WheneverI meet someone who has been in the same industry for over 30 years, I always ask them, "How did you get into this?" and they almost always say,"It was my first job. I got good at it, so I stuck with it." These environmental factors often set the course for your long-term career.
I think a lot of people have to weigh up where they can best utilize their skills and what they can give to the company versus what the company can give to them, and I would say that people who have the opportunity to work at DataHunt are very fortunate.
If you look at recent surveys, the number of organizations that want to incorporate AI into their business is more than a majority of all organizations. Our customers are companies that have never had to deal with AI data before, and that's only going to continue to grow, and I hope you'll join us as we explode.
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