Webcloudsoft

Data Analysis

Project Description

DATA ANALYSIS

Data analytics is important because it helps businesses optimize their performances. Implementing it into your business model means your company can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. Your company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services.

Types of Data Analytics

Data analytics is broken down into four basic types.

  1. Descriptive analytics: This describes what has happened over a given period. Has the number of views gone up? Are sales stronger this month than last?
  2. Diagnostic analytics: This focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?
  3. Predictive analytics: This moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?
  4. Prescriptive analytics: This suggests a course of action. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output.

Data Analysis Steps

The process involved in data analysis involves several different steps:

  1. The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or divided by category.
  2. The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.
  3. Once the data is collected, it must be organized so it can be analyzed. This may take place on a spreadsheet or other form of software that can take statistical data.
  4. The data is then cleaned up before analysis. This means it is scrubbed and checked to ensure there is no duplication or error, and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed.

In a world increasingly becoming reliant on information and gathering statistics, data analytics helps individuals and organizations make sure of their data. Using a variety of tools and techniques, a set of raw numbers can be transformed into informative, educational insights that drive decision-making and thoughtful management.