Data Analytics has become one of the most important areas in many industries today. These statistics help companies decide how to improve their product and services.
To go into more detail about these statistics, data analysis is used for quantitative analysis, which analyzes the world of business by looking at any input data, which can then be processed to give a quantitative value. For example, consider the following example. Company X collects certain information about its customers, such as what they purchased, where they came from, who visited their business location and what time they called, among other information.
Customer Service data has been collected and analyzed. This includes: Where did they come from? What were their demographics, what was their age group? The answer to this question will lead the company to a better understanding of how to improve their customer service.
During the analytical process, it is important that the data collected are accurate. There should be a data integrity process that is followed to ensure that the data gathered from the customers is used properly and that it can be used by a company’s data analysts.
There are many types of analytical processes that are available for a company to use. These analytical processes may be referred to as Data Science, Data Mining and GIS or Geo-spatial analysis.
Companies can conduct data mining on a variety of topics. Examples of topics include: Travel Behavior: What customers are doing on business trips?
Geospatial Analysis: How are the employees clustered together? An Example would be: Network Associates: How does your company’s traffic flow through different geographic areas?
In terms of implementing Data Science and Data Mining techniques, companies can deploy analytical resources to create Big Data. They can perform several types of activities to gather the big data, such as: A Quick Case Study: To apply Big Data activities to real world scenarios.
Virtual Field Studies: Information is also gathered from different surveys, which allows users to look at it in a visual format. Interactive Studio: It uses data visualizations to give you a sense of what the potential is, based on geography, demographics, location, geographic regions, industry, geographic region, department or project.
Software analytics (GUI): Information is collected from software applications to enable users to use it, change it or view it. Quick Studio: For users to see the data from applications in a variety of formats, depending on user preferences.
Statistical sampling and demographic modeling: Data sets are sampled and displayed to allow users to have a sense of the data. Analytical Segmentation: Is the data to be analyzed properly or could the data only be used for certain purposes?
These are just a few examples of the many areas which can be considered during an analytical process. Data Analytics can be performed at the following levels: Customer behavior, Customer purchase behavior, Pricing data, Product data, Customer survey data, Customer preferences, Marketing data, Employee demographics, Customer purchase, Data segmentation, Residual segmentation, Expected return customer, System change data, Business process analysis, Datalogine data, GIS data, Financial data, Investment data, Sales data, CRM data, Market data, Customer relationship management data, Advanced statistics, Non-profit organization and NLP data.