Question

In view of the burden on the urban planning brought by soaring population, the Melbourne city council would like to find some demographic facts about Melbourne population, such as the average age, the population composition, education status, employment rates and more factors.

(a) Describe the forms of data analysis.
(b) Outline the key steps that should be followed to conduct this analysis.

Answer 1

There are four forms of data Analysis:

• Descriptive Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

1. Descriptive analysis answers the “what happened” by summarizing past data, usually in the form of dashboards. Its biggest use is track the key performance indicators.

Business applications of descriptive analysis include:

• KPI dashboards
• Monthly revenue reports
• Sales leads overview

2. Diagnostic Analysis

After asking the main question of “what happened”, the next step is to dive deeper and ask why did it happen? This is where diagnostic analysis comes in.Diagnostic analysis takes the insights found from descriptive analytics and drills down to find the causes of those outcomes. Organizations make use of this type of analytics as it creates more connections between data and identifies patterns of behavior.A critical aspect of diagnostic analysis is creating detailed information. When new problems arise, it is possible you have already collected certain data pertaining to the issue. By already having the data at your disposal, it ends having to repeat work and makes all problems interconnected.

Business applications of diagnostic analysis include:

• A freight company investigating the cause of slow shipments in a certain region
• A SaaS company drilling down to determine which marketing activities increased trials

3. Predictive Analysis

Predictive analysis attempts to answer the question “what is likely to happen”. This type of analytics utilizes previous data to make predictions about future outcomes. This type of analysis is another step up from the descriptive and diagnostic analyses. Predictive analysis uses the data we have summarized to make logical predictions of the outcomes of events. This analysis relies on statistical modeling, which requires added technology and manpower to forecast. It is also important to understand that forecasting is only an estimate; the accuracy of predictions relies on quality and detailed data. While descriptive and diagnostic analysis are common practices in business, predictive analysis is where many organizations begin show signs of difficulty. Some companies do not have the manpower to implement predictive analysis in every place they desire. Others are not yet willing to invest in analysis teams across every department or not prepared to educate current teams.

Business applications of predictive analysis include:

• Risk Assessment
• Sales Forecasting
• Using customer segmentation to determine which leads have the best chance of converting
• Predictive analytics in customer success teams

4. Prescriptive Analysis

The final type of data analysis is the most sought after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision.Prescriptive analysis utilizes state of the art technology and data practices. It is a huge organizational commitment and companies must be sure that they are ready and willing to put forth the effort and resources.

Artificial Intelligence (AI) is a perfect example of prescriptive analytics. AI systems consume a large amount of data to continuously learn and use this information to make informed decisions. Well-designed AI systems are capable of communicating these decisions and even putting those decisions into action. Business processes can be performed and optimized daily without a human doing anything with artificial intelligence.

B)

Step 1: Decide on the objectives or Pose a Question

The first step of the data analysis pipeline is to decide on objectives. These objectives may usually require significant data collection and analysis.

Step 2: What to Measure and How to Measures

Measurement generally refers to the assigning of numbers to indicate different values of variables. Suppose, through your research you are trying to find if there was a relationship between height and weight of human, it would make sense to measure the height and weight of dogs using a scale.

Step 3: Data Collection

Once you know what types of data you need for your statistical study then you can determine whether your data can be gathered from existing sources/databases or not. If data is not sufficient the you have to collect new data. Even if you have existing data, it is very important to know how the data was collected? This will helps you to understand you ca determine the limitations of the generalizability of results and conduct a proper analysis.

The more data you have, the more better correlations, building better models and finding more actionable insights is easy for you. Especially data from more diverse sources helps to do this job easier way.

Step 4: Data Cleaning

This is another crucial step in data analysis pipeline is to improve data quality for your existing data. Too often Data scientists correct spelling mistakes, handle missing values and remove useless information. This is the most critical step because junk data may generate inappropriate results and mislead the business.

Step 5: Summarizing and Visualizing Data

Exploratory data analysis helps to understand the data better. Because a picture is really worth a thousand words as many people understand pictures better than a lecture. Likewise, Measures of Variance indicate the distribution of the data around the center. Correlation refers to the degree to which two variable move in sync with one another.

Step 6: Data Modeling

Build models that correlate the data with your business outcomes and make recommendations. This is where the unique expertise of data scientists becomes important to business success. Correlating the data and building models that predict business outcomes

Step 7: Optimize and Repeat

The data analysis is a repeatable process and sometime leads to continuous improvements, both to the business and to the data value chain itself.