The Price/Performance Problem: Why Traditional Data Systems Can't Scale with Your Mission
More data should mean more insight—not more cost, delay, and obscurity.
The world's largest organizations are drowning in data but still struggle to extract the value they need from their largest assets.
As data volumes grow, so do the costs and delays. Why? Because the performance of most data platforms is directly coupled to data size. Every new byte makes indexing, querying, and analysis slower and more expensive.
This coupling created a price/performance wall that most legacy systems can't scale past. And it's getting worse by the day.
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.
Performance becomes unaffordable as data volumes increase. 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 were 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.
Why Existing Approaches Fail
Legacy data technologies weren't designed for today's multidimensional, high-velocity data, yet they are still in widespread use today. They work fine for simple key-value lookups. But they fall apart when faced with:
- Petabyte-scale analytics
- Interconnected timelines and events
- Real time decision making
- Complex relationships in data across people, places, and things
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.
There are only two ways most organizations try to cope:
- Throw more compute at the problem—costly and inefficient
- Accept slow insight—kills productivity and mission impact
Neither is acceptable in a world that demands real time decisions and AI-powered automation.
The only way to achieve information advantage at scale is to decouple performance and cost from data set size, a seemingly impossible challenge.
Don't keep pushing the rock uphill.
