EDA
EDA

Predictive analytics made easy

Empower your users to analyze any data set using sophisticated techniques and tools with ease. Successfully deliver insights and predictions in hours, not days. Deploy fast and have a lower total cost of ownership by centralizing enterprise data and tools without having to worry about faulty SaaS integrations, hybrid IT infrastructure challenges, access control, monitoring challenges, and unexpected breaks in process flows—which often hinders automation and productivity.

Solving complex problems
with Analance

Explore how our advanced analytics solutions are enhancing operations and unlocking new possibilities in various industries and departments.

On demand predictive insights

On-demand
predictive insights

You can connect, prep, and train data in a platform with an intuitive GUI and step-by-step process workflows. Build and deploy models in minutes and get on-demand predictive insights to solve your toughest business challenges.

Code free and code friendly platform

Code free and
code-friendly platform

Whether you are a data scientist or a citizen user, you are expected to get intelligent insights based on richer logic and execute decisions in greater numbers and more sophisticated ways. Use out-of-the-box ML algorithms to jumpstart analysis or bring in custom ML scripts whether in R or Python.

Governance and Compliance

Governance
and compliance

A multi-tenant hierarchy isolates data from each tenant and offers dedicated workspaces that allow users to create their own entity. But you can easily share predictive models and dashboard insights across the organization.

Predict the future of your business in 5 steps

Analance Dashboard 7

Step 1: Grab your data

Pull your data from multiple data sources through 20+ data connectors. Combine separate data sets by setting relationships with each dataset before starting an exploratory data analysis.

Step 2: Data analysis

Prep a part of your data for modeling and leave the rest for model validation. Analyze your data to understand the inter-relationship between them with 21 different statistical functions for univariate and/or bivariate analysis. Understanding of the data helps to eliminate outliers and anomalies and identify key predictors to consider for model building.

Analance Dashboard 7

Step 3: Data modeling

Users can work with 40+ out-of-the-box ML algorithms that are categorized under 9 different types of analysis or build custom algorithms. Test different types of models to find the best fit for the data you are working with. Run one or multiple algorithms at the same time through ensemble to identify the best performing algorithms. Fine tune the model on accuracy, sensitivity, and specificity to improve confidence on data output from every model.

Step 4: Model validation

To validate the trained model, compare the actual numbers versus what is predicted. Use the remaining data records from your historical dataset to validate the accuracy of your model. This is the stage where you can further optimize the model to improve accuracy before exposing it to a live data set for predictive analytics.

Step 5: Get your predictions

Expose the best performing model to new incoming data. Visualize your predictions on a live, interactive dashboard to make smarter decisions and achieve exceptional outcomes to optimize and automate business operations. Eliminate the guesswork and make decisions based on accurate, data-driven recommendations.

Want to learn more?

Connect with our rep to learn how Analance can empower your team with accurate and reliable customer insights.

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Enhance your data and start a pilot project.

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