Data Architecture

Data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.Data is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture. A data architecture should[neutrality is disputed] set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems. A data architecture, in part, describes the data structures used by a business and its computer applications software. Data architectures address data in storage and data in motion; descriptions of data stores, data groups and data items; and mappings of those data artifacts to data qualities, applications, locations etc.

Sample Architecture

Source Structured Content

The term structured data generally refers to data that has a defined length and format for big data. Examples of structured data include numbers, dates, and groups of words and numbers called strings. Most experts agree that this kind of data accounts for about 20 percent of the data that is out there. Structured data is the data you're probably used to dealing with. It's usually stored in a database.
Although this might seem like business as usual, in reality, structured data is taking on a new role in the world of big data. The evolution of technology provides newer sources of structured data being produced - often in real time and in large volumes. The sources of data are divided into two categories:
Computer or machine-generated: Machine-generated data generally refers to data that is created by a machine without human intervention.
Human-generated: This is data that humans, in interaction with computers, supply.

Data Extraction

Data extraction is the act or process of retrieving data out of data sources for further data processing or data storage. The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow.

Data Quality & MDM

The importance of data quality and master data management is very clear: people can only make the right data-driven decisions if the data they use is correct. Without sufficient data quality, data is practically useless and sometimes even dangerous. But despite its importance, the reality in many of today's organizations looks quite gloomy: data quality has been voted among the top three problems for BI software users every year since the first issue of The BI Survey back in 2002.

Data Integration

Data integration is the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information. A complete data integration solution delivers trusted data from various sources.

Search Analytics

Search analytics is the use of search data to investigate particular interactions among Web searchers, the search engine, or the content during searching episodes. The resulting analysis and aggregation of search engine statistics can be used in search engine marketing and search engine optimization.

Data Visualisation

Data visualization is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data. To communicate information clearly and efficiently, data visualization uses statistical graphics, plots, information graphics and other tools.