Data itself is not inherently valuable. It is how we arrange and interpret data that provides value, especially in this day and age where data is being created exponentially. When data becomes difficult to interpret to drawing meaninful insights from is where Adaptive Management comes in. It has developed a cloud-based platform, DataMonster, to seamlessly synthesize data into straightforward and useful results. The first platform to undertake analyzing alternative data in this manner, the company is changing the way companies view and process data.
AlleyWatch chatted with founder Brad Schneider about the company, its origin, future plans, and the state of data.
Tell us about the product or service.
There has been a lot of buzz in the news for the past several years on how data is empowering individuals and companies to make better decisions. For example, if you’re in the retail business and you buy aggregated data on how much Americans are spending across different retail categories each month, you can make better informed decisions about your own business. If you see spending compressing within your sector but you haven’t seen a drop in your own sales, you might think differently about how much inventory you’re stockpiling. Having this type of data changes decision-making from gut-based to evidence-based. It also allows you to better understand the probability of different events occurring.
The issue with data is that most companies and individuals don’t know where to find useful data, and the average decision-maker doesn’t know what to do with the data once they find it. There is where Adaptive Management comes in with our DataMonster™ analytics ecosystem.
We developed DataMonster™ to make it easy to quickly find answers from data. DataMonster™ aggregates various sources of data and synthesizes them into answers that the typical business person can use to drive commercial success.
How is it different?
There are a lot of companies in the data ecosystem working on developing solutions to various aspects of the data pipeline from ingestion and management to analytics. We’re building something totally new.
Until now, the various steps in using data have been disaggregated. Finding data, standardizing data, visualizing data and creating predictions based on data have all been performed by different applications or people. Gluing all these pieces together constitutes 95% of the complexity of using data.
Adaptive has developed partnerships with hundreds of the leading data providers and continues to onboard new data sources every day to create a unified ecosystem for leveraging data. We’re not just cleaning up our clients’ data or looking for insights. We’re setting a new standard for how data is discovered, visualized, and used.
Our clients can now monitor all of their data on one platform and combine datasets to build predictions with the click of a few buttons. We provide broad coverage across industries from energy and industrials to healthcare and consumer markets, and beyond.
What market are you attacking and how big is it?
We’re attacking the business to business market for data analytics. This market is already tens of billions of dollars and growing. However, today’s market does not empower the average business person. Most solutions target power users and data scientists. While we address these users as well, there is a much larger market in serving everyone else.
What is the business model?
We use a seat-based subscription model in which our clients purchase access for specific users within their organization. Whether our clients are working with one or a hundred datasets, we charge the same amount. We want to make using data simple, and that philosophy starts with our pricing model.
What inspired the business?
After graduating from MIT, I cofounded a small data analytics company that worked with Fortune 500 companies to help them use their transaction data to better understand their customers and the way in which they were buying our clients’ products. After a few years, I decided I wanted to go in a completely different direction. In 2004, I moved out to Northern California to join a small Hedge Fund as an investment analyst covering the technology sector. I had always been interested in understanding what technology companies were doing and who was winning and losing, so I thought this would be a fascinating job. After a few months, I found myself surprised by how simple the research techniques were. Most of them involved having conversations with people surrounding the businesses such as customers, suppliers and partners. Although these conversations were enlightening, there was an exactness missing that I had grown accustomed to from my days working with a company’s data. In the years that followed I began scraping, purchasing and in some cases creating data to help make myself a better investor. As I brought on more and more data, I quickly became overwhelmed. I lacked the right tools to make sense of it all and make it scalable within the firm I was working at. It was at this moment that I started to create the early version of DataMonster to help me better leverage all of this data. As the software grew in complexity, I later realized that the functionality within it was applicable to anyone looking to create a higher quality, more repeatable decision-making process.
What do you consider alternative data?
Alternative data includes all types of data outside of financial market data that could be useful for predicting outcomes. Some examples are social media sentiment data, satellite data, prescription drug data, shipping port activity data, web traffic data, geolocation data, and much more.
What are the milestones that you plan to achieve within six months?
Since launching DataMonster™ we’ve seen tremendous demand for the product. Over the next six months, we will be focused on scaling the platform to support many more users and use cases. We are especially focused on providing simple to use tools which allow our customers to work on the same data as their competitors and still have an advantage through their better understanding of relevant factors surrounding the data, such as what a company does and how it has changed over time.
What is the one piece of startup advice that you never got?
The one piece of advice no one gave me was what to do when things are working out and the business is scaling quickly. Most of the conversations I had early on focused on how to respond to failures in different parts of the growth process from fundraising to team building to product architecture. When the business started seeing faster than expected product adoption we had to shift gears to address new types of challenges, such as swiftly building out our recruiting and training process, putting in place customer success frameworks and a creating more reliable software development and quality assurance processes.
If you could be put in touch with anyone in the New York community who would it be and why?
I’d love to sit down with entrepreneurs who have gone through the process of building world-class technology companies here in New York. People like Michael Bloomberg and the founders of companies like Etsy, MongoDB and Dataminr.
Why did you launch in New York?
I believe New York is about to experience a renaissance around entrepreneurship. Much of New York City has been built around industries like Finance, Advertising and Publishing. These sectors had attracted some of the brightest minds in the country. As evolving business models start pressuring many of these industries to evolve, it incentivizes these minds to find new and interesting problems to solve. The talent available here is some of the best in the world. On top of that, there aren’t nearly as many software companies to compete with for that talent as there are in other technology hubs.
Where is your favorite bar in the city for an after-work drink?
My favorite bar in the city is Raine’s Law Room in the Flatiron. It’s a speakeasy bar that transports you back to the 1920s. They’ve done an amazing job creating an atmosphere that compliments their creative cocktails perfectly.