Best On-Premises Data Collaboration Platforms

Gigasheet uses AI to turn healthcare price transparency data into actionable market intelligence. The platform processes Transparency in Coverage datasets at scale and benchmarks payer and provider rates to reveal outliers, savings opportunities, and competitive insights. Users can combine transparency data with their own claims, contract, or network information in a spreadsheet-style interface built for large datasets. Gigasheet’s AI agent generates reports, dashboards, and executive summaries that help teams compare pricing, evaluate networks, and make informed contracting decisions without complex setup or external tools.

Coginiti, the AI-enabled enterprise data workspace, empowers everyone to get consistent answers fast to any business question. Accelerating the analytic development lifecycle from development to certification, Coginiti makes it easy for you to search and find approved metrics for your use case. Coginiti integrates all the functionality you need to build, approve, version, and curate analytics across all business domains for reuse, all while adhering to your data governance policy and standards. Data and analytic teams in the insurance, financial services, healthcare, and retail/consumer package goods industries trust Coginiti’s collaborative data workspace to deliver value to their customers.

Einblick is the fastest and most collaborative way to explore data, create predictions, and deploy data apps. Our canvases radically change data science workflows by making it so much easier to explore, clean, and manipulate data on a novel interface. We are the only platform that let you collaborate in real-time with your whole team. Decision-making is a group activity, so let’s get everyone involved. Don’t waste time hand-tuning models. Our AutoML is focused on helping you create explainable predictions and identify key drivers without fuss. Einblick packages common analytics functionality into easy-to-use operators that let you abstract repetitive tasks and get to answers faster. From Snowflake to S3 buckets to CSV files, connect your data source and start getting to answers within minutes. Take a list of churned and current customers and join in everything you know about them. Uncover the key factors that led to churn, and identify how at-risk every customer is.

MLReef enables domain experts and data scientists to securely collaborate via a hybrid of pro-code & no-code development approaches. 75% increase in productivity due to distributed workloads. This enables teams to complete more ML projects faster. Domain experts and data scientists collaborate on the same platform reducing 100% of unnecessary communication ping-pong. MLReef works on your premises and uniquely enables 100% reproducibility and continuity. Rebuild all work at any time. You can use already well-known and established git repositories to create explorable, interoperable, and versioned AI modules. AI Modules created by your data scientists become drag-and-drop elements. These are adjustable by parameters, versioned, interoperable, and explorable within your entire organization. Data handling often requires expert knowledge that a single data scientist often lacks. MLReef enables your field experts to relieve your data processing task, reducing complexities.

Easily create rich, interactive dashboards that accelerate analytical insights across your enterprise. Cloudera Data Visualization enables data engineers, business analysts, and data scientists to quickly and easily explore data, collaborate, and share insights across the data lifecycle—from data ingest to data insights and beyond. Delivered natively as part of Cloudera, Data Visualization delivers a consistent and easy-to-use data visualization experience with intuitive and accessible drag-and-drop dashboards and custom application creation. Data Visualization is fully secured by SDX, enabling augmented data workflows across all your data and analytic workflows. Build predictive applications from ML models served in Cloudera Machine Learning, or leverage your data warehouse to power fast intelligent reporting without moving data or using third-party tools.

Roseman Labs enables you to encrypt, link, and analyze multiple data sets while safeguarding the privacy and commercial sensitivity of the actual data. This allows you to combine data sets from several parties, analyze them, and get the insights you need to optimize your processes. Tap into the unused potential of your data. With Roseman Labs, you have the power of cryptography at your fingertips through the simplicity of Python. Encrypting sensitive data allows you to analyze it while safeguarding privacy, protecting commercial sensitivity, and adhering to GDPR regulations. Generate insights from personal or commercially sensitive information, with enhanced GDPR compliance. Ensure data privacy with state-of-the-art encryption. Roseman Labs allows you to link data sets from several parties. By analyzing the combined data, you'll be able to discover which records appear in several data sets, allowing for new patterns to emerge.

ZinkML is a zero-code data science platform designed to address the challenges faced by organizations in leveraging data effectively. By providing a visual and intuitive interface, it eliminates the need for extensive coding expertise, making data science accessible to a broader range of users. ZinkML streamlines the entire data science lifecycle, from data ingestion and preparation to model building, deployment, and monitoring. Users can drag-and-drop components to create complex data pipelines, explore data visually, and build predictive models without writing a single line of code. The platform also offers automated feature engineering, model selection, and hyperparameter tuning, accelerating the model development process. Moreover, ZinkML provides robust collaboration features, enabling teams to work together seamlessly on data science projects. By democratizing data science, we empower companies to extract maximum value from their data and drive better decision-making.