What Are the Three Distinct Qualities That Distinguish Big Data From Traditional Data?
What are the three distinct characteristics that distinguish big data from traditional data? Those will be three points on the spectrum that differentiate the advantages and disadvantages of Big Data from traditional data. As we all become more familiar with big data, we are likely to ask those questions. If you have not, you should know that by the time you finish reading this article you will understand what those three distinct characteristics are.
First, traditional data has a lot of information tied together in relational databases. It is often the case that business enterprises store customer data in a relational database. Traditional data typically includes things like addresses, phone numbers, product descriptions, sales order histories, and so forth. In some cases, traditional data can even uniquely identify individuals.
Second, big data does not have as many restrictions on what it contains as traditional data does. Traditional data usually only includes things that have been in circulation for a couple of years. We are familiar with social security numbers, driver’s license numbers, credit card numbers, and so forth. By contrast, big data typically contains information from unique sources, such as unstructured data, web services, and the likes.
Third, traditionally, all data has been relational in nature. Traditional data has required organizations to store and retrieve information using traditional database management systems. This is very problematic and sometimes results in data corruption, data loss, and so forth. The three distinct characteristics of big data make it difficult for traditional databases to operate effectively and creates challenges for data-users.
In order to understand what are the three distinct characteristics that distinguish big data from traditional data, it helps to understand how traditional databases work. First, organizations must use indexes, stored within their own data repository or database server, in order to access and manipulate the data within those files. By doing this, it is possible to access all pieces of data in a timely fashion. If something is deleted, for example, it can be recovered from a previously accessed portion of the database.
Secondly, data is rarely used in isolation, meaning it will not be stored in its entirety. Instead, it is often combined with other types of data, such as financial data, travel data, or sales and service data. It is also frequently stored in forms such as rows and columns, making it difficult to determine which data belongs where. Lastly, the organization’s frequently needs to update or delete large amounts of data in a short period of time.
Finally, traditional data models, while being relatively efficient for the most part, often suffer from the inability to represent time. Big data, on the other hand, solves this problem by representing time as a function of sources. Each piece of information is assigned a cost per unit, so as users gain more information, their costs increase proportionally. As well, because the amount of information changes rapidly and users frequently access it, data is often automatically re-organized, refreshed, and deleted, which further reduces the amount of time it takes for users to access it.
As technology increases in the future, so too will these three distinct characteristics of data, making it much easier for businesses to manage their data and applications. Organizations that embrace data modeling, including Hadoop, can achieve much higher levels of productivity. Big data will help to transform the way business users experience enterprise data management. Organizations that properly embrace big data will reap the rewards, while those that do not will slowly and painfully suffer.
Today, almost every organization needs an enterprise data management system to effectively deal with their data. Without a data management platform, managing data becomes extremely difficult. The availability of big data has created a new class of solutions that help organizations to extract value from massive amounts of data. This ability to extract value allows companies to create business decisions based on real time information and reduce operation cost.
Big data is poised to change the way organizations manage their data, the way in which they analyze it, and how they use it. Organizations are realizing that to compete in today’s marketplace, they need to have an edge on traditional data modeling techniques. To achieve this, enterprise data management must incorporate four primary attributes: collection, analysis, integration, and sharing. Big data provides organizations with the capacity to quickly evaluate, understand, and act upon the information that is available to them. By combining these four fundamental Big Data Attributes with the established discipline of strategic decision making, organizations can rapidly develop value. Organizations that meet these basic attributes will be at the forefront of the emerging era of big data.
Organizations that meet the above characteristics will gain an edge over competitors in today’s marketplace. These organizations will also be able to provide the benefits associated with data portability, flexible, comprehensive, and scalable capabilities. When selecting an enterprise data management provider, the selection process must be guided by the specific requirements of your company. Only then will you be able to identify and select a provider that is able to deliver the appropriate solution to your business’ unique requirements.