What is Big Data Analytics?
What is Big Data? According to Wikipedia, “a data set is a collection of unorganized or structured information in a real-time or near real-time situation.” According to IT executives, “Big Data” is the “language of the business.” According to Webster, the term “big data” means “increasingly complex and usually unmanageable data sets produced by research and statistical methods.” Big Data is a rapidly growing field which takes into account various techniques to analyze, collect, and sometimes manipulate large data sets which are either too large or complicated to be processed by existing data processing software.
Big Data analytics covers a wide array of technologies, tools, and processes that are utilized for the analysis, gathering, and mining of large-scale databases. Examples include graph and charting tools like visualization and visual recognition tools, data mining tools, and data warehousing systems. The analysis of data warehouses and our recent progress in the field of social networks have given rise to the use of sophisticated analytical tools like supervised online learning and artificial intelligence. Another important trend in the field of big data analytics is the development and use of tools like Hadoop and Spark. These tools accelerate the delivery time of applications by dramatically reducing load on servers and users’ machines.
There are several benefits of using big data analytics for businesses: first, it accelerates decision making by providing insights that previous analysis was incomplete. Second, the insights generated by big data analytics can be used to make more informed decisions in the future, since current trends are continuously changing. Third, the insights generated by this technology can help managers take action on projects before they become problematic. Fourth, big data analytics provides business insight that would not have been possible using traditional database management.
There are a number of ways in which to use this technology to derive valuable insights from huge databases. Data is often analyzed using traditional database management methods: either manually or through complex programs. However, these traditional databases are inefficient at delivering insights because of the large amount of data they contain and their inability to provide insights based on the unique characteristics of each piece of data. Furthermore, traditional databases require continuous maintenance and updating to keep up with changes in the market, making them impractical for some applications.
On the other hand, predictive analytics makes it easy to derive valuable insights from large-scale databases by efficiently partitioning the data set into smaller subsets. The smaller the subsets, the larger is the range of attributes that can be studied. Predictive analytics takes data mining a step further by combining traditional database management techniques with the power of big data analytics. Traditional databases can be used to efficiently detect relationships among variables, but this method fails to take into account the unique features of individual attributes. This approach also tends to focus only on a small range of attributes, leaving out attributes that may be of interest to a business. Thus, predictive analytics relies on a combination of traditional database management techniques, machine learning, and other tools to effectively analyze and make insights from large and complex data sets.
With all these tools at your disposal, it is now easier than ever before to rapidly and accurately gain insights from large-scale data analysis. What is even better is that you don’t have to go through a stringent curriculum. While graduate programs in computer science and engineering are still widely available, you don’t need any prior background in these fields to learn how to use modern tools like R and Python. In fact, a simple knowledge of basic data analysis concepts like linear and logistic regression and machine learning, can give you the necessary skills to quickly start working with predictive analytics tools. Once you get to work, you will be amazed at the speed with which you can make use of and build on these tools and at how quickly insights from large-scale data analysis comes to you.
Another interesting piece of what is big data analytics is the rising importance of data warehouses. Data warehousing is the term used to describe the set of standardized formats for keeping large amounts of information in a single location. This can be both horizontal and vertical, with the data in each warehouse organized in rows and columns of equal size and spaced appropriately. Data warehouse tools to help you manage your analytics data by allowing you to easily access your information and to manage your databases and data warehouses from a single interface. This is highly useful in making your life easier, as you can use one tool for managing and organizing large sets of diverse data and another tool for analyzing small subsets of your data. Data warehousing also allows you to make the most of your analytics capabilities, as you can easily combine different types of analytical processing, such as neural networks or supervised learning, into a single algorithm.
In addition to what is already described, machine learning and unstructured data mining are also other topics that come up time and again when you talk about big data. Machine learning refers to the process of feeding natural language processing or computer programs with huge amounts of data in order to create new, predictive classifications and labeling methods. Unstructured data mining is also related to machine learning and involves using complex algorithms and databases to mine specific pieces of unstructured data, such as text, images, audio and video signals, and user generated content from the web. These tools allow you to analyze new data in an entirely new way, taking this into account and ensuring that your results are as accurate as possible. This can enable you to make more informed decisions in your business, as you can predict customer behavior and even manufacture products more efficiently, all of which has the potential to save you money and increase profit.