Solve the data-decision gap
You’ve got all the data you need to make better decisions.
But data is spread across internal and external systems, and is often poor quality. Single view solutions can’t handle data lake volumes, and quickly become siloed to specific use cases.
Without good data, models under-perform and can lack transparency, so you can’t confidently automate decisions. Your teams are slowed down by manual data gathering and mountains of false positives.
The data-decision gap is draining the value out of your data assets and slowing your digital transition. The only way to solve it? Contextual Decision Intelligence.
What is Contextual Decision Intelligence?
Contextual analysis automatically builds critical data to support a decision. It creates a complete network graph of real world entities and their links to internal and external data. Then finds relevant features and patterns your experts can recognize and contribute to.
Contextual Decision Intelligence combines context with data science models to automate most decisions. Others can be referred to operational teams – intuitively presented with the context to support anything from a quick decision to a detailed investigation.
The result: automated decisions that build on the knowledge of your best people – and augmented decisions helping them be their best. Spotting connections you can’t spot right now.
How our technology underpins CDI
Entity resolution links internal and external data to gain a complete picture of real world entities – people, organizations, addresses, telephone numbers, internet devices – and their transactions and behavior.
Handling data without unique match keys or data which is dirty, incomplete, or deliberately manipulated. It adapts to the fuzziness and strictness of different use cases. Creating a powerful single analytical view.
Network generation automatically finds sets of resolved entities and links that are relevant to a given decision, filtering out low value ones and expanding others.
Allowing automated decisions to use the whole context, and showing users what they need to know – quickly.
Data scientists use context alongside raw data to boost features, rules and models that identify and score opportunity and risk. Experts contribute to transparent contextual models by identifying patterns based on their experience. Scorecards, statistical models, AI and ML optimize automated decisions.
This means models find more opportunity, focus on value, reduce false positives, and are much more transparent.
Visualize and Explore
Analysts and operations teams review referred decisions in their context through interactive visualizations of entities, links, locations and timelines to make a final decision. They can expand to new links, review raw data or search for additional information, without extensive manual work.
So manual decisions are made faster, with more insight, and with fewer errors.
Why take the CDI approach?
Drive automation and deliver greater business value from enterprise data.
Process operational decisions faster and more accurately.
Spot hidden risks and identify high-value growth opportunities.
Automate decisions with confidence, using context-based models.
Overcome poor quality data and inaccurate decision models.
Use accurate, transparent models for regulatory compliance.
How to apply CDI to your organization
Take this approach to data, and transform the way your people make decisions.
See how the Contextual Decision Intelligence platform works and find out more about the underlying technology.
Browse through our solutions to see how Contextual Decision Intelligence helps solve your challenges across multiple use cases.
Find out how Contextual Decision Intelligence applies to your industry, and the specific ways you can use it.
Why use the Quantexa platform
Build once, use many, ingest to create a single view with networks
Apply your data to multiple use cases—without replicating data sets.
more accurate decisions
Use context to improve decision accuracy across the organization, find new opportunities and uncover risk.
Scale to billions of records
in batch or real time
Built on proven, scalable open source technologies like Hadoop, Spark and Elastic.
Future-proofed open architecture
Integrate seamlessly into your existing IT ecosystem, with flexible deployment options: native, or containerized for private and public cloud.
Ensure data transparency
Use explainable data linking, advanced AI and decision models for regulatory compliance.
Keep your data secure
Rely on granular security levels for dynamic control, with all activity audited.
Operationalize your data in a matter of months – not years.
Use entity resolution and data volume to overcome missing or poor quality data.
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