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.
Big Data Exploration - it enables you to explore and mine big data to find, visualize, and understand all your data to improve decision making. By creating a unified view of information across all data sources - both inside and outside of your organization - you gain enhanced value and new insights.
Enhanced 360º View of the Customer - it is a holistic approach that takes into account all available and meaningful information about the customer, to drive better engagement, more revenue and longterm loyalty. It combines data exploration, data governance, data access, data integration and analytics in a solution that harnesses the volume, velocity and variety.
Security Intelligence - To meet the security challenge, businesses need to augment and enhance cyber security and intelligence analysis platforms with big data technologies to process and analyze new data types (e.g. social media, emails, sensors, Telco). Analyzing data in-motion and at rest can help find new associations or uncover patterns and facts to significantly improve intelligence, security and law enforcement insight.
Operations Analysis focuses on analyzing machine data, which can include anything from IT machines to sensors, meters and GPS devices. By using big data for operations analysis, organizations can gain real-time visibility into operations, customer experience, transactions and behavior.
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.
Why Is Big Data Important?
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
- Cost reductions,
- Time reductions,
- New product development and optimized offerings, and
- Smart decision making.
When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
- Determining root causes of failures, issues and defects in near-real time.
- Generating coupons at the point of sale based on the customer’s buying habits.
- Recalculating entire risk portfolios in minutes.
Detecting fraudulent behavior before it affects your organization.
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