Private LLM for numerical data with the frontend component and 500 built-in data integrations.
This can result in neglect of important projects or areas within the organization. The quality of data analysis might suffer, leading to inaccurate insights and potentially flawed business decisions.
Accessing information in large companies involves a convoluted manual process of navigating between multiple applications and can lead to hours or even days spent gathering information.
The data team is overloaded and doesn’t have the right tools to answer data questions quickly. Teams can’t move fast enough to significantly impact the business and continue to operate mainly in the dark.
Data errors, inconsistencies, and missing values can significantly skew results, leading to faulty conclusions.
A single platform that brings together all your data and analytics workloads to enable transformative innovations for modern marketing and advertising institutions.
The world’s leading solution providers are building for the Lakehouse for Marketing and Advertising. Take advantage of pre-built offerings that accelerate data-driven transformation.
Databricks and its partners have created a full range of Solution Accelerators that make it easy to tackle common marketing and advertising use cases, from ESG investing to fraud prevention.
Enable secure and open data sharing with our data ecosystem — featuring S&P Global, Intercontinental Exchange, FactSet and Nasdaq — to unlock innovations that drive sustainable value creation.
“S&P’s data and AI vision is powered by Conduit Copilot AI. We use it to process huge amounts of complex financial data to create insights for our clients. Conduit is also an important part of our efforts to modernize data delivery and consumption. It enables us to seamlessly deliver data directly to teams, so our clients can analyze and integrate mission-critical data quickly without having to move terabytes of data around.”
Rapidly deploy data into value-at-risk models to keep up with emerging risks and threats.
Enable rapid conversion from external source systems and achieve a fully configurable and industrialized conversion capability.
Bring a more transparent approach to model risk management through automated documentation and integrated data visualization.
Combine financial services industry data models with the cloud to enable high governance standards with low development overhead.
Take a quantitative view into sustainability and ensure companies are accountable for their actions.
Adopt a more agile approach to risk management by unifying data and AI in the Lakehouse.
Use geospatial data to better understand customer spending behaviors in terms of both who they are and how they bank.
Automate transaction enrichment to better understand your customers’ behaviors and drive hyper-personalization.
Modernize fraud-prevention strategies to reduce operational costs and increase customer trust.