What is Big Data? If you have a business or are managing a business, you should know what is Big Data. Without this knowledge, you will not be fully prepared to capitalize on new technologies and solutions that will take your business to the next level.
Big data is a very complex field, which takes various forms. It can be in the form of text, images, audio, video, structured data, and unstructured data. Text and image examples include text messages, emails, and images found in websites, web pages, images found in images, and other data which can be retrieved using specialized software. Audio and video examples include voice recordings, streaming video, streaming audio, and video streams.
What is Big Data? Data visualization refers to the ability to visualize and understand the data sets produced by the various Big Data tools. While there are many tools which can be used to achieve this end, three different approaches to data visualization are emerging. These include visual graphs, visual charts, and the integration of structured and unstructured data sets.
How does one compare these three approaches to data analysis? To answer this question, it is necessary to understand what each one is and what they are best used for. The first category, Structured Data Sets, are those which are produced using common statistical and computing techniques. Examples include surveys, experiments, and experiments in scientific research.
Visualization tools used in this setting include data mining, artificial intelligence, and genomics. What is more, it is not uncommon to find social media data mining and news stories in this category as well. The second category, Unstructured Data Sets, includes sources such as public domain, e-commerce, and Wikipedia.
Visual graphs and visual charts can be applied in a variety of settings including science, business, and government. A data analysis project using this method will usually begin by displaying data sources in an unstructured manner. Next, the visual tool will be able to organize and present this data according to topic, perspective, or approach. Finally, it is important to consider the implications of using this approach and what types of analytics can be extracted from the resulting data sources.
The third category, Quantitative Analytics, represents tools that will be useful for more complex applications involving aggregated and predictive analytics. This category may also include natural language processing (NLP), event chain detection (ECD), and decision trees. In this setting, what is important is how the data sets are visualized, analyzed, and ultimately used. Three types of predictive analytics that may be applied are Latent Semantic Analysis (LSDA), Event Chain Detection (ECD), and Hierarchical Meta-spective Tree Structure (HMT).
All three forms of big analytics provide insight into data sources and allow for the identification, organization, and evaluation of risk. While each form of analytics has different applications in different fields, they all share a common field goal: to provide businesses with quantitative insight into data sets associated with fraudulent activities. Each of the forms of big analytics presented here may be applied to a particular industry or application, but all have the potential to greatly impact how companies respond to fraud and other fraudulent activity. With the help of technology, analytics can give management the data necessary to manage these events in the best possible way.
The first type of big analytics that most companies use is Real-time Costing & Analytics (RCTPA). RCTPA applies the principle of Costing Analytics, where the analysis of costs (by the end-user) is used to improve customer service. With RCTPA, real-time data is used to make decisions about inventory, staffing, packaging, and more. This approach makes it possible for customer service representatives to make informed decisions when they need to, without requiring managers to manually collect data or rely on estimates from stakeholders. With this technology, companies have access to real-time analytics at the point-in-time that managers need it most.
The second type of big analytics that is commonly applied is the event-based or predictive analytics approach. This approach targets specific business problems like workflow issues, quality issues, or the satisfaction of customers. Companies involved in this type of big data use case must apply advanced technologies and software to analyze events and then implement solutions that are designed to improve customer satisfaction, streamline workflow, reduce cost, and boost productivity. Although this approach requires more time than the event-based and the predictive types, it allows companies to solve complex problems more quickly.
The last form of big analytics that most companies use today is the structured research and event-based analytics approach. This form applies statistical techniques and software to large-scale data sets in order to provide a solution to specific business problems. Although the approaches of these forms of big data analytics vary, all of them share a common goal: to improve service, improve product, and help companies streamline their operations to achieve maximum return on investment. However, even though these approaches may use different forms of big analytics, all of them are designed to work together to solve problems and make a company more profitable in the future.