The purest form of Autonomous Data Discovery. Foresee opportunity. Expose risk. Discover value.
Our high-resolution data profiles are created using Topological Mathematics, and through this technique, we produce thousands of metrics that can help machines better classify and understand the data to which they are exploring. Essentially, more data means more differentiation to analyze.
Data gets copied and replicated quite often, especially if the organization is doing data science in a meaningful way. Data redundancy can lead to significant data governance challenges, increase costs, and increase exposure risk if not kept in check. With the high-resolution data profile we can use the results of these profiles to measure for data redundancy across large data environments.
The change statistics produced via analysis enable us to provide volatility indexes for defined periods. These volatility time series are compelling for advanced analytics and data science, as it helps us to understand the relationship between data productivity and business productivity.
Our high-resolution profile produces significantly more statistics that quantify the underlying data values in that given field, as well as the data fields around it, we can use global reference tables and machine learning models to adjust score results in real time. This approach is more scalable and autonomous than other products on the market, resulting in lower costs and less human involvement.
We automatically study the correlation and relationships of data volatility of any or all cohorts to any or all business measures (i.e., key metrics, performance indicators). Using proprietary algorithms, we're able to provide recommendations to the organization about what data is most significant and valuable in their organization, and what data is not highly correlated to business outcomes.
enables you to explore attributes of your data that directly impact the economics of how it should be treated. Metrics such as Redundancy and Sensitivity form the building blocks of valuation and risk models.
Customized sensitivity rules contribute to training sets to auto-identify data patterns that are important to you.
In addition to standard exploratory and diagnostic capabilities within the platform, analytical consumers can quickly connect their BI tool of choice for ed views and insights.
A robust API layer is available, allowing for an easy integration into existing applications.
Our deployment architecture is built on principles of flexibility and scalability.
Black Swan has over 200 sensitivity rules available out of the box that meets North American and European standards. We continuously supplement and update the rules in our sensitivity library, assuring our customers are up to date. Additionally, new business rules can be created.
In addition to native analytics within the platform, data can be accessed for advanced analytical activities via dedicated API calls that support a variety of customizable insights, predictions, and models by integrating with business intelligence and data science platforms.
Fully documented APIs with Swagger allows for rich integration with other tools and platforms, and the provision of a webhook enables for tighter event-based integration where required.
A highly flexible architecture that allows organizations to optimize based on their infrastructure. Black Swan can be deployed on-prem, in the cloud, and hybrid environments. Additionally, because Black Swan allows for location-based queuing, processing can be handled extremely efficiently.
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