What is Big Data and where it was originated from. What are its applications, how it can be applied to our systems. All such question arise in pour curious minds. Here is the answer to all of that . Here i am sharing a little article on what this big data actually is and what are its major applications.
Big data defined
As far back as 2001, industry analyst Doug Laney (currently with Gartner) articulated the now mainstream definition of big data as the three Vs of big data: volume, velocity and variety1.
- Volume. Many factors contribute to the increase in data volume. Transaction-based data stored through the years. Unstructured data streaming in from social media. Increasing amounts of sensor and machine-to-machine data being collected. In the past, excessive data volume was a storage issue. But with decreasing storage costs, other issues emerge, including how to determine relevance within large data volumes and how to use analytics to create value from relevant data.
- Velocity. Data is streaming in at unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time. Reacting quickly enough to deal with data velocity is a challenge for most organizations.
- Variety. Data today comes in all types of formats. Structured, numeric data in traditional databases. Information created from line-of-business applications. Unstructured text documents, email, video, audio, stock ticker data and financial transactions. Managing, merging and governing different varieties of data is something many organizations still grapple with.
At SAS, we consider two additional dimensions when thinking about big data:
- Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data involved.
- Complexity. Today's data comes from multiple sources. And it is still an undertaking to link, match, cleanse and transform data across systems. However, it is necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.
Why big data should matter to you
The real issue is not that you are acquiring large amounts of data. It's what you do with the data that counts. The hopeful vision is that organizations will be able to take data from any source, harness relevant data and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smarter business decision making. For instance, by combining big data and high-powered analytics, it is possible to:
- Determine root causes of failures, issues and defects in near-real time, potentially saving billions of dollars annually.
- Optimize routes for many thousands of package delivery vehicles while they are on the road.
- Analyze millions of SKUs to determine prices that maximize profit and clear inventory.
- Generate retail coupons at the point of sale based on the customer's current and past purchases.
- Send tailored recommendations to mobile devices while customers are in the right area to take advantage of offers.
- Recalculate entire risk portfolios in minutes.
- Quickly identify customers who matter the most.
- Use clickstream analysis and data mining to detect fraudulent behavior.
Challenges to Consider
Many organizations are concerned that the amount of amassed data is becoming so large that it is difficult to find the most valuable pieces of information.- What if your data volume gets so large and varied you don't know how to deal with it?
- Do you store all your data?
- Do you analyze it all?
- How can you find out which data points are really important?
- How can you use it to your best advantage?
Enabling Technologies
A number of recent technology advancements enable organizations to make the most of big data and big data analytics:- Cheap, abundant storage.
- Faster processors.
- Affordable open source, distributed big data platforms, such as Hadoop.
- Parallel processing, clustering, MPP, virtualization, large grid environments, high connectivity and high throughputs.
- Cloud computing and other flexible resource allocation arrangements.
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