A human genome is equivalent to about 1.5 gigabytes. About three genomes fit on a DVD. Genomic medicine attempts to build individualized strategies for diagnoses or therapies that utilize genomic information, but legacy analytics aren’t suited to iterative trials at scale. Legacy tools aren't suited to visual exploration of the genomic data for hundreds of individuals.
OmniSci offers researchers a platform to discover genetic opportunities for trials of new life-saving treatments using genomic data science. When scientists can visualize and cross-filter data with such transformative efficiency, they uncover hidden patterns and cryptic correlations between our genetics and our risks for cancer, heart disease or inherited disorders.
Hospitals around the world look to optimize their operational efficiency by predicting optimal staffing levels without putting lives are at risk during periods of peak demand. Healthcare administrators lack a common visualization platform to track results and extend their machine learning models for precise staffing predictions.
The OmniSci platform lets hospital staffing analysts visualize a model’s predictions and compare them to actual staffing outcomes. Every click on a chart generates SQL queries that complete in milliseconds, then all visual elements instantly refresh, giving you the best clinical pathway for your patients. Likewise, the number one goal of a hospital is to keep readmission rates low. Imagine using all of this data to create ML models that guide staffing decisions and predict readmission and recommend preventative treatment.
Unauthorized intruders infiltrate systems of national governments and global corporations by hiding their tracks across identities, devices and behavior. Teams in Security Operations Centers (SOCs) receive countless incident alerts, the majority of which are false alarms.. When an actual attack has occurred, slow analytics across fragmented data make it impossible for security teams to identify and patch vulnerabilities before more data is compromised.
OmniSci's zero-latency visual capital markets data analytics don't require pre-aggregation., That speed gives financial security analysts immediate insight. They can perform hundreds of queries in quick succession, without losing access to any granular information. SOC teams can investigate far more cyber alerts every day, and when they do find a vulnerability they have the data they need to apply the right patch quickly, maintaining an ongoing patch cadence that deters future attacks.
Location-based metadata can create a wealth of new business opportunity. Yet this stream of data scales too quickly for developers who want to create apps showing relationships between the location of people and equipment to other operational data. Existing analytic engines cannot keep up with the scale and speed of today’s operational data.
The OmniSci Core SQL engine was designed for this new era of location intelligence. It can process billions of rows of structured data in milliseconds and visually render those with the same speed. This dramatically accelerates logistics analytics, marrying vital business datasets to mobile location metadata. OmniSci Core is open-source, in-house or third-party developers can easily create new apps to deliver analytic apps for exploring layers of operational location and logistics data.
How Skyhook uses OmniSci for location intelligence and insights
Using Extreme Analytics to Deliver Competitive Insights in Telecommunications
Artificial intelligence is increasingly used to predict machine failure, to minimize downtime and maintenance costs for automobiles, ships or aircraft. Yet engineers have trouble explaining an AI model’s black-box recommendations, so leaders fall back on human experience and "gut" guesses in favor of predictive analytics in logistics industry to keep field equipment running as intended.
OmniSci’s extreme visual analytics make AI models more accessible to the scientists who create them and to the broader audiences of logistics leaders, military commanders or regulators who must understand those models. With immediate visual exploration of the same underlying elements that trained AI models, anyone can trust their predictions.