Question

1. What is business/data analytics?

2. What are the three broad classifications of data analytics?

3. How do businesses use data analytics?

4. Find a picture and/or diagram of the information pyramid and post it. Describe it in your own words.

5. What are three reasons why we study business analytics?

Answer 1

1. Business analytics as a subpart of Business Intelligence it is implemented with goal identifying and allocation of data. in this process, it follows collating, sorting, processing, and studying business data, and using statistical models and iterative methodologies to transform data into business insights. The goal of business analytics is to determine which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue. here data can be analyzed, patterns recognized, and models developed to clarify past events, create predictions for future events, and recommend actions to maximize ideal outcomes.

2. three broad classifications of Business analytic are:

• Descriptive Analytics

  Descriptive analytics describes or summarizes a business’s performance of past and present. it identifies strengths and weaknesses and provides insight into customer behavior.

• Diagnostic Analytics

  Diagnostic analytics shifts from the “what” happened in the past to how and why to change current events. it finds out the flaw and tries to rectify. it uses probabilities, likelihoods, and the distribution of outcomes to understand why events may occur and employ techniques including attribute importance, sensitivity analysis, and training algorithms for classification and regression.

• Predictive Analytics

  Predictive analytics forecasts the possibility of future events using statistical models and machine learning techniques. A common application of predictive analytics is sentiment analysis. Existing data can be collected to provide a comprehensive picture of opinions held by a user. Prescriptive analytics goes a step beyond predictive analytics, providing recommendations for next best actions, and allowing potential manipulation of events to drive better outcomes.

3. Business uses this data analytic in different ways and most popularly,

• Transportation.
• Manage Risk.
• Delivery Logistics.
• Proper Spending.
• Energy Management.
• Internet/Web Search.

4.

In this analytic value pyramid, the first step we need to complete is the data collection, without proper data it is very difficult to proceed further for analysis. the second step is to report, we need to prepare a systematic and short report to analyze. mostly here we use statistical tools to accomplish this work. the third step is to analyze the data this is the most crucial point of data analysis. this is also known as data interpretation and significance level determination. the nest step is opportunities, where we are looking for the best opportunities where we can utilize this information to optimize output or goal. once we decide our target or goal among all the alternatives we will proceed to act on that. the successful implementation of analysis will present our vision.

5. in this technology-oriented age data analytic is one of the most demanding areas due to its target orientation and successful operation. it will strengthen your analytical skill.  It empowers professionals with data management technologies like Hadoop, R, Flume, Sqoop, Machine learning, Mahout Etc. The knowledge and expertise of the skills is an added advantage for a better and competitive career.