Treasure Data Debuts in Gartner MQ
Treasure Data recently made our debut in the Gartner Magic Quadrant for Data Management Solutions for Analytics (DMSA). This is a significant achievement for our team, who have worked so hard over the past 6+ years to develop a product deserving of this recognition.
To give some perspective, when we started this company back in 2011, one of our top priorities to accomplish was to be in a Gartner Magic Quadrant. Gartner Magic Quadrants have long been the gold standard in the industry. It is gratifying to be recognized and brings another level of validation to our product and puts Treasure Data on the map as a serious “enterprise vendor.”
When a goal like this is accomplished, it is important to take a minute and reflect on the journey and appreciate how far we have come as a company. Our vision from the beginning at Treasure Data was to build a sophisticated cloud-based data management platform and we have never strayed from that core foundation.
One big problem we saw, that was shared across many modern data infrastructure users, and companies in general, was the lack of a unified way to collect big data — of various types from various sources — in real time. The methods were messy, manual, time intensive, and subject to errors. So we built our own open source solution, Fluentd, to help automate and overcome this — and then released it into the tech community. Since then, it’s grown to become a standard technology used by some of the most respected companies in the world including Microsoft, Amazon Web Services and others.
The Treasure Data platform was built on our data collection frameworks represented by Fluentd and our other open source data collector projects, such as Fluent-bit and Embulk, with the addition of important features and capabilities to enable companies to swiftly gather their customer data, analyze it, and make decisions to better run their businesses.
According to Gartner, “The DMSA market is increasingly polarized. On the one hand, there is tremendous hype about new data types, new technologies to store and manage them efficiently, and new roles and skills to use them effectively. On the other, there is a recognition that investment in foundational traditional technologies (employed for the traditional data warehouse use case) will be essential to serve as a platform for the next wave of innovation. Both the traditional and the new are required for a modern DMSA platform.”
I agree with Gartner’s sentiment. Data permeates all areas of business decision-making. But what’s become most sought after, yet increasingly harder to gather, is data generated by people, or about people whom you should know. It’s ironic that this “first-party data” which a company generates itself about its own customer base and owns outright is still so hard to collect — not to mention using it to improve the bottom line.
At Treasure Data, we’ve given an even greater focus to pulling together customer (and potential customer) data, processing, stitching with other types of data and persisting it as raw format forever, (not in summarized fashion).The combination of people’s data with machine generated data is very complex, allowing companies to drive highly personalized marketing — more quickly and at scale. In effect, this both pleases the customer and generates more revenue, overall. It’s a true win-win.
Treasure Data has evolved to where we are today to address companies needs, as the enterprise Customer Data Platform. We continue to stay close to our open source roots, relying on data ingestion, data processing and analytics functionality, including the Fluentd log collector, Plazma columnar database, Apache Hive, the Presto SQL-on-Hadoop engine, as well as workflow orchestration.
We consider this inclusion in the Gartner DMSA Magic Quadrant confirmation of our mission to enable businesses to unlock the power of their data to improve the customer journey. A big thank you to our valued customers, over thirty of which spoke with Gartner directly sharing their unique use cases, for their ongoing support and trust.
Also, see our page on the status of a Gartner CDP Magic Quadrant.
*Gartner Magic Quadrant for Data Management Solutions for Analytics, Adam M. Ronthal, Roxane Edjlali, Rick Greenwald. 13 February 2018
The Gartner document is available upon request from Treasure Data. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.