What is data analysis process? 

Data analysis process is a process of applying logical techniques/ statistical techniques systematically to describe data. Also illustrate data, recap data and evaluate data. In other word, the data analysis process is a process of collect and organize data to perform helpful conclusions.

Many business organizations use data analysis/ data analytics process to help them make better decisions in their business.

 How many types of data analysis process are there? 

A process of data analysis is not just collecting the data step. The data collected is raw data that with no meaning and cannot help you in its raw form. The business is how to get useful patterns, trends and insights from these raw data. And use the results to make better business decisions. Results which come up from data analysis process depends on the type of data analysis process. In addition to that each types of data analysis concern with a main question:

• First data analysis/ analytics type: Descriptive analytics type (what happened?)

Descriptive analytics type search for past/ old data and answer to the question what happened? And explain if the action happened is positive or negative. 

• Second data analysis/ analytics type: Diagnostic analytics type (why did it happen?)

Diagnostic analytics type explains the reason that make the past action happened. Which answer the question of why did it happen?

• Third data analysis/ analytics type: Predictive analytics type (What is likely to happen in the future?)

Predictive analytics type as it names seems is predict with what can happen in the future? And derived trends and insights from past data to use these trends and insights to make future prediction.

• Fourth data analysis/ analytics type: Prescriptive analytics type (What’s the best course of action?)

Prescriptive analytics type answers the question What’s the best course of action? Which means that the organization can make the better business decisions. That’s done by according to the analyzing results from the above three types of data analysis process.

Now, you know just simple things about each type of data analysis process. You will know more details about the four data analysis type in the below paragraphs.

• First data analysis/analytics type: Descriptive analytics 

First type of data analysis/ analytics process is the Descriptive analytics/ analysis. In the Descriptive analysis/ analytics concern with what happened in the past. And the analyst has to describe what happened in the past? And his job is to compress a large amount of data. Then present it in a simple way like a pie graph or other.

In descriptive data analysis/analytics there are two major techniques used which are data aggregation and data mining.  Data aggregation is a process of collect data and present it in summarizing format. But data mining is the process of explore data to uncover pattern and trends.

Descriptive analysis/ analytics is a simple type of data analysis process. But it is important for make more deeply analyzing with the other data analysis/ analytics types.

• Second data analysis/ analytics type: Diagnostic analytics

For the second data analysis/ analytics type, the diagnostic analysis which answers the question of why did something happen? That’s mean the diagnostic analysis/ analytics concerns with the reason that make something happens. So, the main goal of diagnostic data analysis is to understand why something happen? and explore and determine if the data contains any anomalies and fix these anomalies cases. You can also use diagnostic data analysis to see what can you perform to drive positive conclusions.

While diagnostic data analysis depends on research for reasons of what happen? more and more of these researches will help you to find why does make data anomaly?

Diagnostic data analysis/ analytics can be done by using many functions like regression analysis, probability theory and many other techniques.

• Third data analysis/ analytics type: Predictive analytics

It is good for your business to predict what can happen in the future? and the future action can affect on your business/ organization, that’s what the third type of data analysis is about. Predictive data analysis depends and bases on the past patterns and trends. In order to predict what is likely to happen in the future? And from the past trends and patterns the predictive data analysis can make predictive models. Models which help analytics to estimate the probability of a future action.

These predictive models working by determining the relationship between a group of variables that relevant to a specific action/ event. These predictive models are human-led predictive analysis.  Predictive models are a branch of predictive data analysis. The other type is machine learning models which is predictive models led by machine.

Machine learning models are predictive models designed to recognize patterns and trends in data and make automatically accurate predictions.

As the predictive analysis predicts what can happen in the future. Then it eliminates a lot of guesswork you can do to predict.

Finally, here predictive data analysis builds basically on what happened in the past ? Why did it happened to make more accurate predictions?

• Fourth data analysis/ analytics type: Prescriptive analytics

Final and fourth type of data analysis/ analytics is the prescriptive data analysis. Which depends on the results of the previous three types of data analysis/ analytics. Prescriptive data analysis based on what happened? Why did it happen? And what is the likely to happen in the future? To determine what should to do in the future? and what is the best decision you can make according to the prediction results?

Perspective data analysis play a great role in commercial business. Because it helps organizations to know what is the best chance of success. However, perspective data analysis is important, it is the complicated type can be done for analyze your data.

Complexity of perspective data analysis/ analytics is from it use algorithms, statistical methods, computational methods and machine learning methods.

 Data analysis process steps and phrases 

The process of data analysis is whole not about collect data by a proper tool or application. That’s to define trends and patterns in data and then take the best decision of your business to success. This process flow in many steps which are

1. Collecting data step: the first step in data analysis process is collecting data step. Collecting data step can done by collect data from websites, databases and other sources. And you should collect data in this step according to your measurement parameters.
2. Cleaning data step: the second step in data analysis process is cleaning data step. Cleaning data step means to ensure that your data does not contain any useless patterns. After this step, your data should be cleaned to get the best results in the other following steps.
3. Analyzing data step: the third step in data analysis process is analyze data step. Here for this step, you can use data analysis tool to analyze your data according to your parameters.
4. Interpreting data step: the fourth step in data analysis process is interpreting data step. In this step review you are considering all parameters to make suitable decisions. Also, if there are preventing factors for implementing decisions.
5. Visualizing data step: the fifth and final step in data analysis process is visualizing data. Use any suitable visualization techniques like charts, graphs to visualize data at this step of data analysis process. 

 Video; 4 Types of Analytics 

With greetings: Al - Manara Consulting to help researchers and graduate students