Knowledge Leaps:

User Guide

Welcome.

This site will help you get the most out of the Knowledge Leaps platform.

Learn about its features and discover how to use its powerful data engineering tools.

Discover use cases, how tos or dive in right here.

What Is Knowledge Leaps

Knowledge Leaps is an application that makes your data useful. Start by putting your data into KL, then discover the insights contained within. Build a database. Create a test. Visualize your data. All can be done in seconds.

Run a data business? Use data in your business? Let Knowledge Leaps become the analytical spine of your firm.

Build A Data Story - Efficiently and Quickly.

The KL platform is here to help you tell a story using data. It's goal is to identify the best set of ingredients for the data to reveal its insights. Using powerful search algorithms it discovers the correlations that are the building blocks of an evidence-based data story.

Using a predictive algorithm is a rigorous method of searching data. For 99.9% of all uses, the best algorithm is the simplest one. For narrative discovery simple means as bias-free** as is possible.

Foundation + Solutions = Magic.

On top of the core data platform, Knowledge Leaps comprises a suite of useful tools. A set of analytics solutions that you can use to extract value from data without needing 3 PhDs on staff.

The current solutions can: calculate optimum prices on 100,000 products; help design test and control experiments; Identify and track custom audiences; Build classification models to explore relationships in data.

What's in the App?

The Knowledge Leaps (KL) platform comprises four key components.

Where next?

Start with a tour or a list of how tos or get familiar with the terms we use.

Need further help?

If you can't find an answer to your question then please submit it using this link or the link in the footer.

June 2020.

Sign me up!

**Bias is the enemy of an insight. In analytics bias means you are over-fitting your data to a theory. What ever the model tells you about the data isn't necessarily applicable in a broader realm. It's important to remove bias when doing data analysis.

Report abuse