What Are the Three Distinct Features That Define Big Data?

what are the three distinct characteristics that distinguish big data from traditional data

What Are the Three Distinct Features That Define Big Data?

In this new age of innovation and financial activity, the need for data that is available in real time cannot be denied. It has become very important for decision-makers in finance, commerce and other industries to make timely analysis of the data available. With the widespread use of mobile devices, internet and social networks, information is not kept just within the office walls any more. It is now at the fingertips of anyone with access to a computer. It is also at the fingertips of banks and financial institutions everywhere.

There are many questions that arise when considering how to manage data over time. The first question is: “How do you deal with increasing volume?” The second question is: “How do you deal with diversity?” And the third question: “What are the three distinct characteristics of big data that distinguish it from traditional data management?” In order to answer these questions, it is necessary to understand how it works, what its limitations are and what its three distinctive characteristics are.

First, big data is a source of unstructured, unprocessed, unverifiable and potentially sensitive data. It can include all manner of financial transactions including ATM usage, real estate sales, cell phone usage, social network activity and much more. Large volume, speed and verification are what distinguish traditional data management from big data. Because it is a complex system with potentially sensitive and potentially unprocessed data, traditional data management systems are designed around complex transaction processing protocols that require considerable training to implement correctly and can take significant time to implement.

Second, big data is inherently transactional dense. Traditional data management systems manage one or few bulk transfers at a time per day. With terabytes and petabytes of data in storage, even the largest of IT departments can only perform one simultaneous bulk transfer per day, if they are able to get their hands on the necessary equipment. With traditional data isolation, data is stored on individual machines and connected via network adapters. Big data requires the isolation of individual machines-even individual servers-which means there is a need for data isolation which is not possible with traditional data management systems.

Third, big data is less constrained by physical device constraints than traditional data management systems. With terabytes and petabytes in storage, even the biggest of IT departments may not be able to dedicate all their time and resources to managing a single database server. The time and resources needed to optimize and secure the physical security of a server can become an important consideration when designing a database architecture. This is because big data is very sensitive to attack and data integrity is a necessity in any network environment. The inability to assure physical integrity of data storage can significantly decrease the level of data security.

Fourth, big data increases exponentially with the ability to connect to new sources. As networks increase in capacity and become more critical to business operations, the rate at which data is transferred and transformed into action depends heavily on the speed of new technologies. This is why managing data is so difficult over time; because new sources are rapidly introduced. Additionally, new sources will invariably require new storage mechanisms. As a result, the rate of expansion of the storage market is one of the main reasons IT administrators face challenges in managing data volume.

Fifth, data isolation and data integrity/consistency are intrinsically linked. While it is impossible to eliminate all sources of data, IT administrators can reduce the threat posed by threats originating from outside sources. Specifically, data isolation and data integrity/consistency are tied to the availability of storage media and network topologies.

Finally, the last characteristic is the need for continuous evaluation and monitoring. Traditional CRM approaches rely primarily on historical data and do little to consider potential outages or service interruptions due to weather or other outages. IT managers need to understand the risk of maintaining a data management function without monitoring it on a regular basis. By considering each of these key characteristics IT administrators can better understand how they are able to manage the data that will be stored within their organizations.