Big Data management is the organization, management and governance of a large volume of both structured and unstructured data.
The role of Big Data management is to ensure a high level of data quality and accessibility to Business Intelligence applications and Big Data analysis. Companies, governments and other organizations are implementing Big Data management strategies to help them cope with the growing amount of data. Data that is counted in terabytes or even petabytes and are stored in a multitude of formats.
Effective Big Data management allows companies to identify and locate key information in a mass of unstructured and semi-structured data from a variety of sources, such as call recording systems, logs systems or social networks.
Most Big Data environments extend beyond relational databases and traditional data warehouses. They integrate technologies capable to process and store non-transactional data formats.
This trend around the collection and analysis of Big Data has given rise to new platforms that combine a traditional data warehouse technology with Big Data systems in a logical architecture. For example, the system decides which data should be retained for compliance purposes, which other data should be deleted, and which data should be retained and analyzed to improve business processes and provide a competitive advantage to the enterprise.
These processes require careful classification of data so that small datasets can be analyzed quickly and productively.
The role of Big Data management is to ensure a high level of data quality and accessibility to Business Intelligence applications and Big Data analysis. Companies, governments and other organizations are implementing Big Data management strategies to help them cope with the growing amount of data. Data that is counted in terabytes or even petabytes and are stored in a multitude of formats.
Effective Big Data management allows companies to identify and locate key information in a mass of unstructured and semi-structured data from a variety of sources, such as call recording systems, logs systems or social networks.
Most Big Data environments extend beyond relational databases and traditional data warehouses. They integrate technologies capable to process and store non-transactional data formats.
This trend around the collection and analysis of Big Data has given rise to new platforms that combine a traditional data warehouse technology with Big Data systems in a logical architecture. For example, the system decides which data should be retained for compliance purposes, which other data should be deleted, and which data should be retained and analyzed to improve business processes and provide a competitive advantage to the enterprise.
These processes require careful classification of data so that small datasets can be analyzed quickly and productively.
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