What are Big Data Analytics and Its Significance For Businesses?
What is big data? It is an umbrella term, which encompasses several important ideas and technology areas, which have been developed over the past few years. By “big data” we mean information that is too massive in nature to be effectively handled by traditional information Processing applications. In other words, anything and everything are potentially big data. With this in mind, the concept of what is big data has taken a rather drastic new look lately, and this talk of giant and monster data may very well be true.
Traditional Information Science approaches to data mining, also known as “stream processing”, involves identifying previously unknown insightful data points (or “insights”) and then attempting to extract meaning from these observations. The traditional key concept here is to find previously unknown but insightful insights. The challenge here is not only storing and accessing all this enormous amount of data, but also analyzing it in order to discover the relationships among various data points, i.e., what actions can we take to make the most of what we have found and what lessons we can draw from all the insights that we have accumulated thus far. Traditional tools such as mathematical algorithms, visualization tools, and visualization techniques such as probabilistic regression, karyronics, decision trees etc. are used for this purpose.
With big data analytics, the challenge is to not only find descriptive analytics, but to discover predictive analytics as well. This requires exploiting the full power of big data sources and exploiting all the various dimensions of those data sources. This is actually easier said than done. The first step in building up a comprehensive predictive analytics system is to collect relevant data sources. The challenge here lies in identifying and managing the different sources and dimensions.
One challenge is when the domain of interest is vast and consists of large amounts of unlabeled, unprocessed data. In such cases, what is needed is specialized platforms to help analyze such domains. When such domain is too large to be represented by any existing analytics platform, what is needed is to create new prototypes. The challenge is to come up with ways in which new prototypes can be managed without disturbing the existing domains. One example of such a problem is when there are multiple dimensions to represent the domain of interest.
Another challenge is identifying the correct domain/data source. For instance, if the domain is the customer buying behavior in a particular market segment, what is the correct metric? When we look at what is big data analytics, the first thing we look at is the architecture of the analytics itself. As mentioned earlier, these systems are complex because of the use case and the size of the data. Also, there are cases where the domain is very large and traditional techniques would fail to provide insight into the domain.
To address these challenges, some developers look to extract insights from social media data sets. There are many companies that provide tools that facilitate the extraction of insights from social media data sets. While there are many potential ways for data mining, the use of social media itself has limited the scope of potential solutions. For instance, many developers have tried to use Facebook’s data to understand consumer buying habits on a long-term basis. However, this data only tells us about the buying habits of Facebook users over a short period of time.
Such information is not useful for forecasting or designing future products or services. What is needed instead is to make predictions in the context of a larger, longer-term use case. Such information can be used to generate charts and graphs, along with the appropriate descriptive metrics, to support strategic decisions regarding product development or deployment.
What is big data analytics also help in addressing the issue of poor data quality. Since this is the core challenge of big data analytics, vendors are looking for ways to make their analytic tools more robust and reliable. There is an increasing need to validate data in order to make meaningful insights and recommendations. The potential use of such insights to solve problems goes beyond predicting market trends. Rather, it aims at detecting and preventing fraudulent activities before they take place.