Big Data Analytics and Business Intelligence tools for Enterprises
Big data defined
A clear big data definition can be difficult to pin down because big data can cover a multitude of use cases. But in general the term refers to sets of data that are so large in volume and so complex that traditional data processing software products are not capable of capturing, managing, and processing the data within a reasonable amount of time.
These big data sets can include structured, unstructured, and semistructured data, each of which can be mined for insights.
How much data actually constitutes “big” is open to debate, but it can typically be in multiples of petabytes—and for the largest projects in the exabytes range.
Often, big data is characterized by the three Vs:
The data that constitutes big data stores can come from sources that include web sites, social media, desktop and mobile apps, scientific experiments, and—increasingly—sensors and other devices in the internet of things (IoT).
The concept of big data comes with a set of related components that enable organizations to put the data to practical use and solve a number of business problems. These include the IT infrastructure needed to support big data technologies, the analytics applied to the data; the big data platforms needed for projects, related skill sets, and the actual use cases that make sense for big data.
What is data analytics?
What really delivers value from all the big data organizations are gathering is the analytics applied to the data. Without analytics, which involves examining the data to discover patterns, correlations, insights, and trends, the data is just a bunch of ones and zeros with limited business use.
By applying analytics to big data, companies can see benefits such as increased sales, improved customer service, greater efficiency, and an overall boost in competitiveness.
Data analytics involves examining data sets to gain insights or draw conclusions about what they contain, such as trends and predictions about future activity.
By analyzing information using big data analysis tools, organizations can make better-informed business decisions such as when and where to run a marketing campaign or introduce a new product or service.
Analytics can refer to basic business intelligence applications or more advanced, predictive analytics such as those used by scientific organizations. Among the most advanced type of data analytics is data mining, where analysts evaluate large data sets to identify relationships, patterns, and trends.
Data analytics can include exploratory data analysis (to identify patterns and relationships in data) and confirmatory data analysis (applying statistical techniques to find out whether an assumption about a particular data set is true.
Another distinction is quantitative data analysis (or analysis of numerical data that has quantifiable variables that can be compared statistically) vs. qualitative data analysis (which focuses on nonnumerical data such as video, images, and text).
A clear big data definition can be difficult to pin down because big data can cover a multitude of use cases. But in general the term refers to sets of data that are so large in volume and so complex that traditional data processing software products are not capable of capturing, managing, and processing the data within a reasonable amount of time.
These big data sets can include structured, unstructured, and semistructured data, each of which can be mined for insights.
How much data actually constitutes “big” is open to debate, but it can typically be in multiples of petabytes—and for the largest projects in the exabytes range.
Often, big data is characterized by the three Vs:
- an extreme volume of data
- a broad variety of types of data
- the velocity at which the data needs to be processed and analyzed
The data that constitutes big data stores can come from sources that include web sites, social media, desktop and mobile apps, scientific experiments, and—increasingly—sensors and other devices in the internet of things (IoT).
The concept of big data comes with a set of related components that enable organizations to put the data to practical use and solve a number of business problems. These include the IT infrastructure needed to support big data technologies, the analytics applied to the data; the big data platforms needed for projects, related skill sets, and the actual use cases that make sense for big data.
What is data analytics?
What really delivers value from all the big data organizations are gathering is the analytics applied to the data. Without analytics, which involves examining the data to discover patterns, correlations, insights, and trends, the data is just a bunch of ones and zeros with limited business use.
By applying analytics to big data, companies can see benefits such as increased sales, improved customer service, greater efficiency, and an overall boost in competitiveness.
Data analytics involves examining data sets to gain insights or draw conclusions about what they contain, such as trends and predictions about future activity.
By analyzing information using big data analysis tools, organizations can make better-informed business decisions such as when and where to run a marketing campaign or introduce a new product or service.
Analytics can refer to basic business intelligence applications or more advanced, predictive analytics such as those used by scientific organizations. Among the most advanced type of data analytics is data mining, where analysts evaluate large data sets to identify relationships, patterns, and trends.
Data analytics can include exploratory data analysis (to identify patterns and relationships in data) and confirmatory data analysis (applying statistical techniques to find out whether an assumption about a particular data set is true.
Another distinction is quantitative data analysis (or analysis of numerical data that has quantifiable variables that can be compared statistically) vs. qualitative data analysis (which focuses on nonnumerical data such as video, images, and text).
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