Streaming Analytics for every industry
Panopticon’s visual analysis dashboard technology has many different applications and is designed to handle virtually any source of data, including real-time message buses, CEP engines, column-oriented tick databases, OData sources, and standard relational databases.
Streaming analytics adds real-time insight to your decision-making process. It enables better business decisions by making it easy to consume and understand huge amounts of live, streaming data, as well as historical streaming data that has been captured in a database.
Traditional approaches to business analysis rely on batch processing. Data is collected on an on-going basis, but is processed at some interval. In many use cases, even a few seconds delay in spotting an outlier can be costly, so even traditional analytics systems that refresh their every few minutes are not up to the tasks required of them in the real-time marketplace. Many systems require that you load all data to be analyzed into a proprietary repository, which takes a lot of time and makes it impossible to incorporate real-time streaming sources into your decision-making process.
When is streaming analytics the right approach?
Clients find our streaming analytics tools to be especially useful when people need to make timely, informed decisions based on:
- Time sensitive data that is changing rapidly or continuously in real time
- Historic time series data sets for which changes across time and deltas between periods are critical
- Multivariate data when users must make comparisons between multiple variables
- Multiple hierarchies that must be changed dynamically and frequently in order to interpret the data properly
- Multiple data source repositories that must be federated and analyzed in a single information visualization or dashboard
- Significantly large data volumes
The best tool for in depth analysis of complex, fast-changing data
Panopticon doesn’t simply show a single number or KPI indicator. Instead, we have designed our analytics software to provide the user with a broad understanding of the data set as quickly and efficiently as possible.
Panopticon helps users answer questions like:
- What is performing well overall and inside each category within a hierarchy?
- What is performing badly overall and within each category?
- Is this performance uniform across a hierarchy?
- Are there any outliers?
- Is there a trend?
- Is there clustering?