Data analysis refers to as the process involved in inspecting, cleansing, transforming and modeling data with the aim to discover any useful information, conclusions as well as supporting decision making processes. Data analysis may be a complex affair to some students. They are sometimes required to look for someone to do their data analysis assignments.
The main purpose of Data Analysis is to extract all useful information from data and making a decision depending on the data analysis.
A simple example of Data Analysis is the decisions we make from our day to day lives. We make decisions after analyzing our past experiences or thinking about what will happen in the account of a that similar decision. This is simply analyzing our past or future and coming up with a decision based on the analysis.
Data analysis works the same way, only that specialists called data analysts handle it for business purposes.

Why Do We Need Data Analysis?

Analysis is important for any type of growth. Be it in business or your personal life. Strategic management involves taking up Data Analysis to ensure the growth of your business. You will need to look back and analyze your past mistakes, derive a plan avoiding repetition of the same mistakes in order to move forward.
Data Analysis does not necessarily come in handy if your business is failing. If it is doing well, then you need to make decisions to keep moving forward and experience more growth. All you require is analyze business data and processes.

Tools of Data Analysis

The importance of Data Analysis tools is that it makes it easier to process and manipulate data for any user, analyze the relationships between data sets as well as helps to identify the patterns and trends used for interpretation.
The following is a list of the tools you require to carry out a successful Data Analysis you require for your research.

Types of Data Analysis: Techniques and Methods

Based on your business and technology, there are different types of Data Analysis techniques that can be used.
The following are the major Data Analysis methods that you can use:

  • Text Analysis
  • Statistical Analysis
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis

Text Analysis

Text Analysis refers to as Data Mining. It is a Data Analysis Method that is used to discover a pattern in large sets of data using data bases or instead data mining tools.
It is used by analysts to transform raw data into useful business information. Strategic business decisions are made by Business Intelligence tools which are present in today’s market.
It generally offers a way to extract and examine data from a system and finally interpret it in to useful business information.

Statistical Analysis

Various industries are using data analysis to show ‘what happens’ by using any past data in the form of dashboards. Statistical Analysis includes the following;

  • Collection of data.
  • Analyzing data.
  • Interpretation of data.
  • Presentation of data.
  • Modelling of data.

Statistical data is used to analyze a set or a sample of data. There are two main categories of this type of Analysis. They are;

  • Descriptive analysis
  • Inferential Analysis.

Descriptive Statistical Analysis

It refers to as Analysis that analyzes complete data or a sample of numerical data that is summarized.
It states the mean as well as the deviation for continuous data whereas percentage as well as frequency for categorical data.

Inferential Statistics Analysis

Inferential statistics is a type of statistics that uses a small number of population to determine or predict if the data can work. It is used to compare samples from past research. Inferential Statistical Analysis helps you arrive at different conclusions from the same set of data by selecting different samples from it.

Diagnostic Analysis

Diagnostic Analysis is a type of Data Analysis that shows the reason as to why something happened. It does this by finding the cause from the insight found as a result of Statistical Analysis. It is a useful Analysis method when it comes to identifying behavior patterns of particular set of data.
In case a new problem in your business arises, you can take a look into Diagnostic Analysis to find similar patterns of that particular problem. If they are similar, then you can take similar measures to eliminate the new problems.

Predictive Analysis

Predictive Analysis is a type of Data Analysis that seeks to explain what is likely to happen by the use of previous set of data. This type of Analysis helps you to make predictions about some outcomes in the future based on the current set of data or past data. The accuracy of Predictive Data Analysis is based on how much detailed information you have access to and how much you dig in to it.
An example of Predictive Analysis includes;
If last year I bought two pairs of shoes based on my savings and if this year my salary increases by a double chance, then I can purchase four pairs of shoes this year. As easy as it seems, you need to think about other issues that may arise within that same period of time that your salary was likely to be increased. You might find that the pairs of shoes you would wish to purchase are out of stock or their price has also increased. You would also wish to buy something else like a dress or a pair of skates.
Predictive Analysis therefore helps to make these future predictions based on what you currently know.

Prescriptive Analysis

Prescriptive Data Analysis works to combine the insight from all previous types of Analysis to determine the best action to take in a current problem or decision that needs to be made.  Most companies that depend on data to be driven are utilizing Prescriptive type of Data Analysis because the above two (Predictive and Descriptive Analysis) are not enough to improve the data performance.
Based on situations and problems affecting a company currently, they are able to analyze the data and make effective decisions to eliminate the problems.

Data Analysis Process

What is a Data Analysis Process? It refers to as gathering the information by the use of a proper application or tool that allows you to navigate a set of data and find a pattern in it. Based on the information and data you collect, then you will be able to make a decision or get into useful ultimate conclusions.
Data Analysis is a wide area consisting of the following phases;

  • Data Requirement Gathering
  • Collection
  • Data Cleaning
  • Analysis of data and Interpretation
  • Data Visualization

We will discuss each of the above in detail as follows;

Data Requirement Gathering

The first question you should ask yourself is why you wish to do this analysis. All you need to find out is the goal you are looking forward towards in order to the Analysis of data. After that, you need to decide which type of Data Analysis you wish to base on the problem or aim of your analysis.
In this phase, it is where you make the decision of what to analyze and how to do the measurement. You must understand why you are investigating to avoid causing more damages to your business, or tampering with your data if your organization depends on data to run. In Data Requirement Gathering phase, you also need to understand what measures you need to take to do the Analysis.

 Data Collection

Immediately after Data Requirement Gathering phase, you will enter another phase called Data Collection. While at this stage, you will at least have an idea about the things you are required to look in to and measure and the results you should have as your findings.
Having that rough idea in your mind, it is now time for Data Collection. This should be based on the requirements you have. Collect your data, and remember to process and organize it for Data Analysis.
Collecting the data, you might have required to do so from different sources. Therefore, you need to keep a log marking in it a collection date as well as the source of data.

Data Cleaning

Data Cleaning refers to as the process of detecting and correcting inaccurate records from a set of data. After you are done with Data Collection, then you are required to clean it. The data that you collected might be inaccurate of irrelevant to your aim and must therefore be cleaned. This is to avoid a repetition of pattern in your set of data that causes the rise of errors.
This phase is important before doing an Analysis to avoid getting the wrong diagnosis which might lead to greater problems. After Data Cleaning phase is done, your output will be a step closer to the results you are aiming for.

Data Analysis

Once you ensure that your data is collected, completely cleaned, and processed, then it is ready for the next phase called Data Analysis. As you go through your set of data, you might realize that you have the exact information you require, or you might wish to collect more data for your Analysis.
Based on the requirements, you can use the following two ways to understand, interpret and arrive at conclusions based on your requirement;

  • Data Analysis Tools
  • Software

 Data Interpretation

This is the next step after Data Analysis phase. Data Interpretation refers to as the process of reviewing data by the use of predefined processes which will help you give some meaning to the data and arrive at a needed relevant conclusion.
It is a process that involves taking the results you have from data analysis, and using them to make a conclusion.
Therefore, after analyzing your data, it is finally time to interpret it for your results. You can present your results in two ways depending on your preference. The two ways are;

  • Simple words presentation.
  • Using a table or chart for presentation.

After you have made an interpretation of your data, then you can use the results you arrived at to decide you best move in order to move forward.

Data Visualization

This is a common process that you can encounter in your day-to-day life. It is often appears in the form of charts and graphs. In simpler words, data visualization is important to make it understandable to the human brain. It is easy for it to visualize the data when it is presented in chart or graph forms. In this way, it will be able to process the information being fed to it.
Data Visualization was often discover unknown trends by observing the relationships and comparing sets of data. Doing this can help you get meaningful information from a set of data.