Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
IBM identifies 5 high value use cases that can be your first step into big data.
Data Warehouse Modernization is about building on an existing data warehouse infrastructure, leveraging big data technologies to 'augment' its capabilities.
There are three key types of Data Warehouse Modernizations:
Pre-Processing - using big data capabilities as a “landing zone” before determining what data should be moved to the data warehouse
Offloading - moving infrequently accessed data from data warehouses into enterprise-grade Hadoop
Exploration - using big data capabilities to explore and discover new high value data from massive amounts of raw data and free up the data warehouse for more structured, deep analytics.
The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable
When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
Excerpts from: http://www.sas.com/en_us/insights/big-data/what-is-big-data.html, http://www-01.ibm.com/software/data/bigdata/use-cases.html
Category: Big Data