Intelligent Data Visualization and:

Profiling
Data profiling, also called data discovery or data auditing, is specifically about discovering the data available in your organization and the characteristics of that data. Data profiling is a critical diagnostic phase that arms you with information about the quality of your data. This information is essential in helping you determine not only what data is available in your organization, but how valid and usable that data is.

Analysis
Data analysis is a business perspective on enterprise data in order to identify patterns and establish relationships. Similar to "data mining," data analysis techniques are useful for virtually any business to gain greater insight into the trends within their business, their industry, and their customer base.

Modeling
The analysis of data objects and their relationships to other data objects. Data modeling is often the first step in database design and object-oriented programming as the designers first create a conceptual model of how data items relate to each other. Data modeling involves a progression from conceptual model to logical model to physical schema.

Architecture
A framework for organizing the interrelationships of data, (based on an organization's missions, functions, goals, objectives, and strategies), providing the basis for incremental, ordered design and development of systems based on successively more detailed levels of data modeling.

Cleansing
Also referred to as data scrubbing, the act of detecting and removing and/or correcting a database's dirty data, that is, data that is incorrect, out-of-date, redundant, incomplete, or formatted incorrectly. The goal of data cleansing is not just to clean up the data in a database but also to bring consistency to different sets of data that have been merged from separate databases.

Augmentation - Enrichment
Application of methodologies and techniques for adding new data to source data that is required but is either partially represented or completely missing. Commonly achieved through the correlation of industry specific key data or the employment of computational algorithms which derive relationships through data composition and matching. The approaches for matching between data elements have a basis in statistics and probability. Augmentation typically utilizes data sources outside of the immediate scope for department or divisional data sources being operated on for a given data initiative.

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