Treasure Data
What they do: Provide a cloud-based platform that combines Big Data, machine learning, and digital marketing.
Problem they solve: According to Treasure Data, if your company is not making decisions based on “all of your data and the accurate interpretation of it, your business is doomed to fail.” Okay, that’s a bit extreme, but data is driving business decisions from the largest enterprise on down to solopreneurs.
Yet, one of the most challenging problems is not figuring out which customer data points you need, but gaining access to them. Barriers typically involve a mix of IT capabilities and time, along with restrictive business workflows – which prevent the right people from accessing the right data at the right time.
Additionally, the key data about the “full customer journey” is usually spread across such disparate systems as e-commerce, retail point of sale, customer service, and much more.
#Big50-2017 #startup Treasure Data collects and unifies messy, fragmented data to deliver actionable insights to non-data scientists. #BigData Share on XThe raw data is messy and fragmented, making analysis difficult.
How they solve it: Treasure Data collects and unifies data that is spread out across various systems and applications. Its analytics engine makes sense of this messy data, and then provides nearly anyone in the organization with access to data-based insights through intuitive dashboards.
As a platform, Treasure Data keeps all relevant APIs up to date, relieving the IT team of managing these critical connections to various SaaS systems and applications as they are updated.
The platform also supports a list of nearly 100 integrations with business-critical applications and systems, ranging from Web and mobile analytics to CRM and BI to storage and databases, as well as containers like Docker and Kubernetes.
Tamr notes that its open-source data logging and unification engine, Fluentd (which Treasure Data’s engineers authored and maintain), is “among the top 10 most-used applications on Docker and is included as a best practice for all users of Amazon AWS.”
Headquarters: Mountain View, CA
CEO: Hiro Yoshikawa, who was previously a principal at Mitsui Ventures.
Year Founded: 2011
Funding: $54 million total. The most recent round was in November of 2016, when Treasure Data closed a Series C round for $25 million. The round was led by SBI (formerly known as SoftBank Investment) and INCJ (Innovation Network Corporation of Japan), with participation from existing investors Scale Venture Partners, Sierra Ventures, AME Cloud Ventures, Dentsu, IT-Farm, Bill Tai, and others.
Competitors include: Incumbents, such as Adobe, Salesforce, and Oracle, as well as such startups as mparticle, Tealium, and Segment.
Customers include: Wish.com, WB Games, Mattel, LG, Kapost, and Subaru.
Why they’re in the Big 50-2017: Treasure Data did well in all three phases of the Big50 competition. They finished high in online voting; their fundamentals pop, highlighted by $54M in funding and a solid customer list; and their pitch for the content challenge was a good one.
We’re also bullish about the ecosystem of integrations piling up around the platform. As various Big Data and BI vendors compete to be the universal language of data, Treasure Data is proving that it can keep pace with the key data generated by a wide range of enterprise apps.
