Businesses are feeling increasingly the need to store, manage ever-increasing amounts of data. It is too difficult to estimate the growth of the volume of data generated and even more for the coming years, the fact is that the volume will grow conspicuously. There is a real necessity to expand the architecture for data management. If it is not addressed yet, will be soon on the table of many IT companies. But what exactly is Big Data?
An interesting view of what are the big data has been highlighted by Alexander Jaimes, a researcher at Yahoo, he said that “we are the data”.
The widespread nowadays of the electronic device, generates a lot of information that is often indirect, and which may go to increase large database. But the size is not enough to talk about Big Data. It is important to distinguee data unstructured from a Big Data.
According to many analysts, if the information has the characteristics of Variation, Velocity and Volume then you are in front of a real Big Data.
The analyst firm Gartner use frequently the following definition to describe Big Data.
“Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision-making”.
Therefore, big data is the capability to manage a huge volume of different data, at the right speed, and within the right time frame to allow real-time analysis and response.
Even though is convenient to simplify Big Data into the three Vs, it can be confusing and too basic.
For example, you may be managing a relatively small amount of very different, complex data or you may be processing a huge amount of very simple data. Therefore become more important to include also the fourth V that is veracity. Veracity means how accurate is that data in predicting business value. The results of a Big Data analysis should make sense in order to correspond at the real necessity of the Business.
The present-day innovative business may want to be able to analyze massive amounts of data in real time to immediately assess the value of their customer and the potential they can obtain to provide additional offers to that customer in order to increase their business. It is essential to identify the correct amount and correct types of data that can be analyzed to impact business outcomes.
The combination of the those V’s cannot makes the Data be processed using traditional technologies, processing methods, algorithms, or any commercial off-the-shelf solutions.
Data defined as Big Data includes technology platform that generated data that can include sensor networks, nuclear plants, X-ray and scanning devices, and airplane engines, and consumer-driven data from social media.
Big Data technologies might prove to be beneficial to an organization, as follow:
- Accelerate the growth of data volumes to be processed;
- To blend structured and unstructured data;
- Facilitate high-performance analytics;
- Reducing operational costs;
- Simplifying the execution of programs.
Due the fact that Data has become the fuel of growth and innovation for Business, it is important to have architecture to maintain growing requirements.
Firstly it is important to take into account the functional requirements for big data.
That data must first be captured then organized and integrated. When this phase is successfully implemented, Data can be analyzed based on the result being addressed. Finally, management takes action and decision based on the outcome of that analysis. For example, booking.com might recommend a hotel based on a past search or a customer might receive a code for a discount for a future booking of a related place to one that was just purchased.
To conclude, the author and statistician Nate Silver states the importance of the use of Big Data, “Data-driven predictions can succeed—and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves”.
- Big Data for Dummies by Judith Hurwitz, Alan Nugent, Dr. Fern Halper, and Marcia Kaufman. (2013)
- Data Warehousing in the Age of Big Data by Krish Krishnan, Morgan Kaufmann (2013)
- Too Big to Ignore—The Business Case for Big Data by Phil Simon (2013)
- http://www.gartner.com/it-glossary/big-data/ (Last accessed 30/04/2015)
Sabrina Titi – DBS – 10190537