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Big Data Blizzard

Much like the snow storm taking place right now, organizations are facing what feels like a blizzard of complicated data challenges.

Much like the snow storm taking place right now, organizations are facing what feels like a blizzard of complicated data challenges. They need to handle more and more complex product and customer requirements, as well as a rapidly evolving regulation landscape. Data is often siloed and organizations do not communicate. Profit margins are shrinking and expenses are being squeezed. Traditional methods of data management and analysis are expensive, time consuming, and lack agility. Data is modeled, then re-modeled in a never ending round of data warehouse building and maintenance. There is a proliferation of data warehouses which are expensive to build and maintain, and often do not supply the desired answers. The concept of “Big Data” was introduced as part of “Web 3.0” in order to manage the vast amounts of both structured and non-structured data that exist within the internet. Web 3.0 introduces new methods for accessing, combining, using, and sharing data from disparate information sources, regardless of variations in underlying data structures. The creation of a formal structured framework for conceptual descriptions, modeling and organizing data is known as an ontology. It is fundamental to the establishment of definitions used in Resource Description Frameworks (“RDFs”), which are general methods to describe implemented resources using a common syntax. Data access is virtualized so that different data sources or data formats are accessible and interoperable without persisting intermediate copies of the data. Information can be integrated in a way that preserves the context-specific meaning of the original data source. Queries are then able to be run across multiple disparate systems. The tools to assist us processing “big data” are also increasing both in terms of numbers and sophistication. There are specialized databases such as Hadoop which have been created for this purpose and specialized RDF stores such as Mulgara which are emerging. Additionally, techniques such as parallelized data masking are employed to ensure that data analysis is not derailed by privacy violations. As the deluge of data continues to increase, there are more tools and techniques that are being created so that we can tame the beast that is our data.