What are Big Data and Its Implications for Business?
What is Big Data? If you are not yet familiar with this term, then allow me to explain it briefly. Big Data is a discipline which treats with respect ways to analyze, collect, or otherwise deal efficiently with large data sets which are too vast or complicated to be handled by more traditional data processing software. Today’s sophisticated hardware and software allow us to collect, analyze and represent large amounts of data. Thus we can leverage the power of large-scale computing for better decision making. We also use big data to make inferences and predictions.
The two main types of big data are unstructured and structured. Unstructured data refers to those resources that have not been categorically partitioned or processed into smaller parts. In other words, unstructured data often comes in the form of raw real-time data. Often, unstructured data is more sensitive and less compact than structured data. On the other hand, structured data typically contains metadata (information regarding the original data) as well as fully resolved data.
Structured big data usually comes in the form of structured data. Usually, when someone asks for or requests information on a particular structured resource, he is requesting this information in a fixed format. This may be in the form of a table, column, or file. However, sometimes people want to request the information in a different structured format. This is where “unstructured” data comes into play.
As mentioned above, big data has two main characteristics: it is usually highly unstructured and it is usually growing exponentially. So far, I have explained what these characteristics are. Now, let’s discuss how each characteristic of big data can best be utilized to help companies improve their businesses. The first characteristic – the most important one in my opinion – is the characteristic that it can be handled by any machine. By this I mean that machine can process big data with little or no human intervention.
Unstructured data is very coarse in nature. In other words, it is devoid of any structure; it is not well-ordered and it has no well-defined boundaries. Think about all the trucks you see idling down the highway. Trucking is one of the largest users of unstructured big data, but only because it is cost-effective. When we talk about big data’s impact on marketing, however, we come to realize that it will make our lives much easier in terms of speeding up decision-making. When companies can more efficiently manage and distribute customer information, they can generate higher ROI and improve profitability.
The second quality refers to what is called “gathering.” Gathering refers to the process of evaluating and collecting qualitative or quantitative data in an automated way. What is happening with data mining these days is that companies are starting to automate the gathering process – taking raw unstructured data and transforming it into usable, actionable intelligence that can then be used and shared by a company.
The third quality refers to what is being termed as “synthetic or artificial intelligence.” In other words, it refers to the ability for a machine to “think” like a human and apply its newly-collected big data to previously unstructured data in order to create new, relevant, and actionable insights. Examples include using Twitter’s hash mark functionality (i.e., using pre-existing lists of keywords) to discover fresh set of keywords and using Facebook’s fan page functionality (i.e., using information about the fan page’s engagement level to rank the brand among different social media users) to discover new connections. These examples demonstrate that big data’s impact on marketing is not only growing exponentially, it is also redefining the way in which we gather and analyze information and applying it to previously unstructured data sets to provide businesses with entirely new dimensions of understanding.
Of all the four qualities mentioned above, “semi-structured” represents the largest percentage of the data set – that is, the majority of what is available. Semi-structured data sets have been described as having a fixed formatting and a specified set of attributes that cannot be changed, such as format, color scale, headers, footers, and margins; however, these attributes may be modified at any time by the user. Hence, instead of writing new code for each piece of information that is now being stored in a semi-structured form, a business simply needs to adapt its existing software in order to accept the new format. This is one of the biggest advantages of big data: because it requires little customization, almost all businesses can now convert their semi-structured forms of information to fully-structured forms without too much additional cost.