What is Big Data?
What is Big Data? It has been coined as a metaphor for the idea of information overload. The frustration many people have with computers and their inability to process and present information quickly and easily has reached a tipping point. Computers are now being used in every aspect of our lives, from scheduling appointments, to weather forecasts, to currency exchange rates. Computers have also become an important part of our health care system, with medical imaging systems using data to diagnose and treat patients. In essence, computers are taking over the healthcare industry by force.
Although all three use different forms of analytics, each platform shares one core ability: data mining. Data mining occurs when the analyst extracts previously structured data to solve a problem, or if the analyst is trying to mine unstructured data for new information. The problem arises when both structured and unstructured data are presented to a single analyst.
In a healthcare environment, data mining can be used to gather and analyze customer experience data sets. These data sets will provide insight into how current processes are impacting the customer experience. Data mining can also be used in product development, wherein data analysis will determine whether new products meeting customer expectations can meet or exceed their competitors. This allows a company to develop products that consumers are actually buying. The problem then is not finding the data sets, but rather analyzing them in order to determine what the key metrics are, and how to measure them.
Data mining and data analysis are not the same thing, though they are frequently used together. The primary difference between the two is that while data mining relies on structured data, analysis relies on unstructured data. Data mining begins with identifying large data sets which can then be mined for information. Structured data sets include prior research, surveys, case studies, etc. Unstructured data sets come from a wide variety of sources such as magazines, newspapers, and Internet.
Analysis utilizes these large and complex data sets in order to provide insights into how consumers are shopping, what they want, and what they don’t. Analysis helps companies understand their customers better so they can create products that solve problems, address needs, and exceed customer expectations. Data mining is often performed without the assistance of outside specialists and is typically only executed by a data scientist who has previous engineering or business experience. Data scientists perform many different tasks such as identifying relationships among customers, understanding behavior, modeling, creating visual graphs, and leveraging large databases and software systems to run an effective data analysis. While there are many types of algorithms which can be used to perform this job, there are two main types: mathematical algorithms and decision trees.
A mathematical algorithm is one which is formulated by a computer program to efficiently collect, sort, analyze and extract the needed information from the complex data sets. The primary objective is to minimize the amount of time and space taken in performing the necessary operations. A mathematical algorithm will most likely be a spreadsheet application and most likely require programming skills of some level. Decisions based on mathematically calculated algorithms can be applied to unstructured and structured data sets and both types of data can be analyzed using the same methods.
Another form of what is big data is the recommendation engine. Recommendation engines leverages the full power of computers by providing recommendations based on mathematical formulas and statistics. In most cases, this type of algorithm will generate a report with the numbers being compared to a certain criterion. This report can then be used to create or modify choices in the product mix, marketing campaign mix, etc. These types of decisions can be made quickly and accurately without the aid of sales, marketing, technical or other specialists, which can be time consuming and expensive.
Lastly, the last type of what is big data is called diagnostic analytics. Diagnostic analytics uses data mining techniques to search for patterns, anomalies in large data sets. It is used in the following applications: health care, retail, travel, supply chain management, industrial and other businesses that need to make quick analysis and predictions of future trends or behaviors in their customer base.