The Price/Performance Problem
The world's largest organizations struggle to extract the value they need from their largest data assets.
At Craxel, we see the world differently than other technology companies. We see trillions of people, places, and things, each with interconnecting timelines. Organizations struggle to capture insights from even a portion of these underlying events and relationships.
As data volumes grow, performance becomes unaffordable. As long as your performance is coupled to data volume, every insight will take longer and longer and/or cost more and more as the mountain of data grows.
The Challenge Today: As Data Volume and Velocity Increase, the Ability to Extract Value Decreases
More data should equal more insight. Unfortunately, that is not usually the case today. The reason is the performance of traditional algorithms for indexing and querying data are coupled to the size of the data set. As the data size increases, extracting insight gets costlier, slower, or both.
Until now, there are only two approaches for handling massive data volumes: continue increasing computation resources or deal with ever increasing latency to extract value from your data. The former is unsustainable, and the latter loses productivity; neither is acceptable.
Today's Databases Still Use the Algorithms Invented in the 1970s
These algorithms simply can't organize large quantities of data quickly and efficiently in multiple dimensions at scale. This means organizations can't naturally model large scale use cases as events and relationships on interconnected timelines. The ramifications are organizations are not able to rapidly and efficiently extract value from their data.
The performance and cost of traditional data organization methods are coupled to data set size. Because of this coupling, these technologies are not efficient and have poor price/performance at scale. The only way to achieve information advantage at scale is to decouple performance and cost from data set size, a seemingly impossible challenge.
