What is Big Data? Big Data is a rapidly growing field which aims to address the need for reliable, flexible, expandable and predictive computing tools, especially in situations where traditionally straightforward data analysis techniques are insufficient or unsuitable. Today’s modern software applications are designed to deal with ever-increasing amounts of data that are generated on an everyday basis by all manner of devices such as mobile phones, webcams, tablets, personal digital assistants (PDAs), and other portable devices. Big Data is therefore a generic term which covers any methodologies, platforms or technologies used to extract and process large amounts of data. With the increase in processing power, memory, storage and bandwidth of modern computers, the amount of data which can be processed in a short period of time has increased dramatically, making traditional data analysis methods quite unsuitable for certain applications.
Data analytics is thus a collective discipline which studies how to use statistical or machine learning techniques to extract and analyze real world data sets. It covers a wide range of approaches to analytics, ranging from traditional techniques like data mining, visual analytics, pattern identification and natural language processing to newer ones like quantitative methods, domain knowledge and crowdsourcing. In recent years, there has been a focus on establishing algorithms or artificial intelligence to support these newer techniques. Some of the newer approaches to analytics even involve experiments on actual environments to test different algorithms or identify patterns and anomalies. This has the potential to reduce bias and save money in the long run.
Data Analytics uses both unstructured and structured data. In unstructured data sets, the data is usually collected in a simple, uniform way and may not have a specific pattern. Examples include marketing surveys, real-time dashboards, online questionnaires, audio and video recordings and physical items in storage. On the other hand, structured data comes from a controlled, standardized format and usually includes structured business information like customer accounts, sales reports, loan documents and financial statements.
There are many potential benefits from using big data analytics. One of the big benefits is that you can get quantitative insights into what is happening in the marketplace just by collecting a few pieces of data. By combining diverse sources of data, you can create new understandings and predictions. This ability to make quick, accurate and insightful assessments of customer behavior has the potential to revolutionize how businesses operate. With the help of advanced computer software, behavioral experts can predict consumer behavior before events occur, and financial experts can determine the viability of mergers and acquisitions.
Another big data analytics tools is machine learning, also known as deep learning. Machine learning can be applied to almost any domain, including online search, content analysis, product prediction and advertising campaigns. Compared to traditional data mining, the process of using machine learning to extract insights from large amounts of unstructured data is much more efficient and time-efficient. However, it does require deep expertise and considerable training. Deep learning has the potential to completely revolutionize the way industries operate, particularly those that generate goods and services on which people depend for their livelihood.
Predictive analytics works by using numbers to make inferences. For instance, if you are looking to hire an attorney, a good piece of analytic software will allow you to easily identify the skill sets that your prospective lawyers possess. The same software will enable you to categorize the common characteristics that all lawyers have, such as aggressive or passive manner, flexibility or firmness. Once you have identified the common traits, you can train the machine to understand these traits and build predictive analysis models to address particular problem situations. Unlike traditional analytics, predictive analytics works on a data-driven approach, hence its accuracy rate is very high.
Image processing and visual processing are another area where predictive analytics excels. By ingesting large sets of images from the internet, machine learning systems can detect patterns in images and categorize them into different groupings. The accuracy rate of these systems may vary depending on the size of the data sets and the complexity of the classification problem. This is why, when you are considering how does image processing work, you should take into account the importance of accuracy for image processing jobs and how well it complements other forms of analytics.
Although big data analytics offers plenty of benefits for companies, they can also be quite time consuming. Therefore, some analysts who are looking for ways to speed up the analytical process try to use technologies that help them analyze large amounts of data in little time. One popular technology used for this purpose is data mining. With this method, a researcher builds a database of different information and then uses it to create new insights and solutions to a wide variety of problems. In addition to reducing processing time, it also reduces the costs incurred due to labor and resource utilization.