Data Analytics

Data Analytics:

Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information.

Data Analytics involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.

It is used in a number of industries to allow the organizations and companies to make better decisions as well as verify and disprove existing theories or models.

The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.

Applications of Data Science:

  • Internet search: Search engines make use of data science algorithms to deliver best results for search queries in a fraction of seconds.

  • Digital Advertisements: The entire digital marketing spectrum uses the data science algorithms – from display banners to digital billboards. This is the mean reason for digital ads getting higher CTR than traditional advertisements.

  • Recommender systems: The recommender systems not only make it easy to find relevant products from billions of products available but also adds a lot to user-experience. A lot of companies use this system to promote their products and suggestions in accordance with the user’s demands and relevance of information. The recommendations are based on the user’s previous search results.

Types of Big Data Analysis

  1. Prescriptive Analysis: Prescriptive analysis is the most valuable analysis because it informs the business what steps should be taken to improve the situation. The use of this data can be the beginning of change for the organization. Even though it is considered the most valuable, it is the least used. Companies could use prescriptive analysis to give specific issues to isolated problems, such as in the health care industry and the problem of diabetes and obesity. Big data could identify the obese patients with both diabetes and high cholesterol, three contributing factors in the development of heart disease. These patients could be targeted immediately to initiate a four-fold attack on the risk factors through diet, diabetes education, exercise encouragement and cholesterol monitoring. At present, the issues are addressed by different specialists, if they are even addressed at all. Combining treatment strategies would be much more effective and the combination of information could be compiled through prescriptive analysis.
  2. Predictive Analysis: The prediction of possible scenarios derived from the analysis of the information. Predictive analysis is usually used for business forecasting, looking at the past to foretell the future. For example, the business might look at the previous summer sales to predict the next summer potential sales for a particular product. Predictive analysis is especially useful in marketing and sales departments to mimic previous campaigns that were successful. Some businesses are using predictive analysis to examine the sales process, from customer introduction, communications with the customer, the lead to the customer, the sale to the customer, the closing of the sale, and the follow-up communications.
  3. Diagnostic Analysis: Diagnostic analysis focuses on past predicaments to discern the who, what and why of a situation. This analysis can use an analytic dashboard, or widgets that help the reader see at a glance the information at hand. A good example of diagnostic analysis is Facebook’s Page Manager dashboard. It displays information like the number of posts, the number of visitors, the quantity of comments and likes, the page views and the feedback from the customer. Seeing these analytics at a glance instead of paging through reports brings a faster grasp of the salient points of the data. Utilizing diagnostic analytics will explain the failure of a marketing campaign to increase sales of a specific product.
  4. Descriptive Analytics: Descriptive Analytics gives real-time data on the current situation. Instead of giving last week or even yesterday’s data, this information is happening now. An example of the usage of descriptive analytics is pulling the current credit report for a customer desiring to purchase a new car. Examining the past behavior to assess the current credit risk and predict the future credit profile would help the sales manager determine if the potential customer can or will fulfill the credit contract.

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