"Next gen tool for Visual analytics"


Slicd screenshot


Mac OS dmg (Mchip)
Windows 64bit

Coming soon !

About Slicd

Slicd is an analytics application that enables users to slice, combine and transform data using block diagrams - just like a flow chart. Using this approach a complex data analysis problem can be broken down into smaller components that can be analyzed and tested with any of the thousands of data analysis functions available in the Slicd block library. The blocks can then be connected to each other to synthesize and answer questions posed by the original problem statement. Users, looking for cues to understand underlying data, can also use Slicd to explore data freely in an ad hoc manner.

Analytics without coding

Slicd liberates users from writing complex monolithic SQL like code by using intuitive visual blocks to perform analytics tasks. Each block represents a specific analytics function such as data selection, grouping, merging, mathematical transformation etc. To perform analytics tasks, blocks can be connected to each other on a diagram using input/output ports. Documentation and examples enable users with no knowledge of analytics or coding to get started quickly. A rich and powerful block library covering several analytics functions allows any user to explore and derive insights from data without writing a single line of code.

Drag and Drop Features

Sclid brings easy to use drag n drop block diagram features to analytics. Blocks representing specific analytics function can be dragged from a library and dropped onto a visual diagram interface. By connecting these blocks, users can build, assemble and test analytical models in a modular way. This also allows users to track data flows between blocks quickly and easily - a capability almost missing in other popular tools such as R/Python/SQL/Excel. Accompanying the visual diagram interface is a set of intuitive GUI elements including property panes, data explorer tabs, help pages and numerous examples. In Slicd, users will find it very easy to explore data.

Less Error Prone

Using standardized blocks to perform analytics tasks, Slicd reduces errors in data modeling. The analytics block are themselves built on standard Pandas/R blocks that have been thoroughly tested. Each block can also be run independently in the diagram so that any connection or user errors can be caught upfront before running the entire model.

Save Tons of Time

With no coding requirements, Slicd saves you tons of time. It also saves you months or even years required to learn a coding language. Also a rich set of GUI features such as intuitive drag and drop, visual diagrams, individual block run etc. reduce the time taken to not only build but also debug data analytics models

All in One

Some analytics tools are good at visualization while others are good at complex data analysis. As a result, users often switch between tools to complete their analysis. By rolling data modeling, visualization and exploration into one convenient package, Slicd makes analytics very convenient for users.

Why Slicd?

Existing industry standard solutions focus mostly on data visualization and reporting. Complex, ad hoc or exploratory types of analyses either push these tools beyond their limits or require a steep learning curve. They lack the depth and richness of analysis functions available in open source solutions like R or Python which have found widespread applications including machine learning. However, using these solutions requires advanced programming skills in Python/R and it is not easy to visualize data flows from one analysis component to another. Slicd combines the power of libraries like Python/Pandas and R with the ease of use of flow charts. Slicd abstracts Pandas/R functions and encapsulates underlying code into a block. Users only need to know the inputs, outputs and functionality of a block without worrying about the underlying code. Using a flow chart like approach users can connect easily connect blocks to analyze data. Visualization blocks allow users to chart and graph data very easily.

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