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Home SolutionsBusiness IntelligenceIntelligent Data Visualization
Intelligent Data Visualization
Profiling
Data profiling, also called data discovery or data auditing, is specifically about
discovering the data available at client organization and the characteristics of that
data. Data profiling is a critical diagnostic phase that arms client with information
about the quality of available data. This information is essential in helping to
determine not only what data is available in client 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correcting a database's dirty data, that is, data that is incorrect, out-of-date,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.
Metadata
Metadata involves the capture and presentation of the meaning and context behind
the data in an organization. Metadata can be descriptions and/or definitions about
the data and is key to transforming it into information that is useful to your business.
Many technological based business initiatives within an organization fail because the
data that is behind the initiative is not well understood. Metadata can be of critical
success in the delivery of a variety of initiatives including data warehouses, data
integration, service oriented architecture, data migration and customer relationship
management (CRM).
The problems are generally not technological in nature but semantic whereby the
meaning or context of the information is not perfectly understood. Metadata provides
the context that allows the business to better interpret its data.
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