Concepts

Creating and managing a complex sequence of analytic steps can be an onerous task. The sequence often includes a mix of types of analytic objects, such as datasets, insights, dashboards, machine learning models, and even optimization scenarios. These can be interconnected in an endless number of ways. LityxIQ lets you design, manage, and execute these complex processes very easily in a simple visual environment called a "Process Flow". An example of a process flow is below: Some key co...

The Data Manager is a solution embedded into LityxIQ. It includes a very powerful set of functionality used to import, clean, manage, and manipulate data. Once you create data with the Data Manager, it becomes available consistently in all other areas of LityxIQ, including Insight, Predict, and Optimize.

A Derived Dataset in LityxIQ is a dataset created from one or more other datasets. It is defined by specifying up to seven steps which are processed in sequence. Each step is optional except the first. - Select an incoming dataset or datasets. These will be the source data that is goes through further processing steps. Multiple datasets can be selected, in which case they are stacked on top of each other (analogous a UNIION query operation as would be familiar to SQL users) to start the data...

In LityxIQ, data is stored in what is referred to as Datasets. Just like most analytic applications, LityxIQ stores data in its own special format which is fully maintained on the backend. In the case of LityxIQ, this format is a high speed data structure that supports big data and fast parallel operations. As in other applications, a LityxIQ dataset can be thought of as a table with rows and columns, where rows generally represent different observations (e.g., customers, transactions, products)...

The default view when going to the LityxIQ Data Manager is the Manage Data area. This provides a list of all datasets that are available to you, organized by dataset library. After clicking the Manage Data, you will see a list of available datasets. Managing and working with the list is described below. - Dataset Library - This drop down box displays a list of all of the dataset libraries to which you have access. Select the library you wish to view. - Create New Dataset button - C...

LityxIQ provides a good amount of flexibility in naming variables in datasets. This is very beneficial for the sake of easily understanding what is represented in the data because you can give variables descriptive names. However, there are still some restrictions on how variable names are created, as described below. Important Note: Variable names are case sensitive. This means that the names ABC and Abc (for example) are considered different variables. This is important in particular when ref...

Every variable in a Lityx dataset can hold data of a single data type. The dataset's dictionary is where formats are determined and available for viewing. The data types supported by LityxIQ are described below. Data Types for Holding Numbers Integer - integer data types hold data in the form of whole numbers. There are four sub-types (also called Formats in LityxIQ) that are differentiated based on the range of possible integer values that can be stored. From a technical perspective, this ...

The NULL (or written Null or null) value in LityxIQ can be thought of as an unknown or missing data value. It is analogous to NULL values you may be familiar with in SQL tables, or NA values in other applications. Any data value for any variable in a dataset can have the value NULL. NULL values should not be confused with zero values or empty strings. They are different, and represent an unknown data value. NULL values in datasets are not uncommon, so it is important to understand how they are c...