Data analysis can seem misleading. It can imply that one step is required to analyze data. Data analysis is an iterative process. Data analysis process involves defining the problem, collecting and cleaning data, exploring and analyzing the data using statistical methods, interpreting the results, and communicating the findings through reports or visualizations to stakeholders. This is something that any data analyst can see, but it’s also important for those interested in a career as a data analyst.
Do you want to know more about data analysis and how it is used? You’re in the right spot. We’ll explain the data analysis process in detail, including the steps involved, how it is done, and the best way to do it.
What is Data Analysis?
Data analysis begins with identifying the problem that can be solved using data. Once you have identified the problem, you can gather, clean, process and analyze data. Analyzing this data serves to identify patterns and provide meaningful insights with the ultimate goal to solve the problem.
What is the Best Process for Data Analysis?
Data analysis is a precise process. Let’s say you want to make the best pizza dough recipe. Your problem could be framed as a lack of knowledge, i.e. not having enough pizza dough recipes.
What data might help you solve this problem? You could start by looking through all the online recipes. This data could be sorted, by filtering recipes with low reviews and comments pointing out flaws. Once you have compiled the top recipes, you can start to analyze them. What commonalities do you see? Perhaps you discover that the best pizza recipe is dependent on the type of pizza you make. In this case, it might be a good idea to combine certain recipes. Although the data analysis process will not create the perfect pizza dough recipe, it can help you get started.
Also read: What Is Metadata: Definition, Examples, and Types
The Data Analysis Process
Let’s get a deeper look at the data analysis process.
Establish the purpose of the process
This is the most important step as it can help you set yourself up for success. The purpose can be described as a business question or problem statement that is related to the organization’s goals. Examples include:
- Are customers likely to respond positively when X product is launched?
- How can you reduce employee turnover?
- Can AI tools be used to reduce production costs?
Once you have defined the problem you can begin collecting data. There are three types of data. The type of problem you have will determine which of the three categories you choose. Data analysis problems often require a combination.
First-party data refers to data generated by your organization. This data often includes information about customer interactions and can be used to predict the future behavior of your customers.
Second-party data, which is data that has been generated from external sources but is specific to your company, could also be used. This could include reviews and customer comments on review sites or social media.
Third-party data is gathered from think tanks and government sources. It is more interested in the customer base than any specific interaction a customer had with your company.
Some data may not be accurate or useful. You will need to get rid of data points that are duplicated, inconsistent, outdated, or irrelevant.
This is data cleaning. You’ll most likely end up with duplicates or outliers when you combine multiple data sources. When you have millions of data points to deal with, which is often the case in data analysis, You can’t go through every piece of data by yourself to find duplicates and outliers. According to data analysts, the time it takes to clean data accounts for 70-90% of data analysis.
You can also perform an exploratory analysis at this stage. This is an initial and brief data analysis. Exploratory analysis can also help you identify other data points that may be needed.
Once you have all of the data you need, you can start to process it. This involves organizing and classifying the data into the appropriate categories. The data are now ready for analysis.
There are many ways to analyze data. There are many ways to analyze data. One is using algorithms and mathematical models to manipulate variables. This helps to extract pertinent information and valuable insights that relate to the problem.
Different Types of Data Analysis
Let’s take a look at various data analysis methods, which can all be combined depending on the problem.
Descriptive analysis, as the name implies, summarizes or describes the data and its characteristics. It does not just describe what has occurred. This type of data analysis is used to tell a story about what has happened. Analysis and descriptive statistics combine disparate data to create digestible points. This can be done at the exploratory data analysis stage.
Diagnostic analysis focuses on the “why” and helps you to diagnose why it is happening. This stage is not about making predictions or finding solutions. Understanding the causes of the problem is the goal. This technique is used to identify issues.
This is where you can start to generate forecasts based on your data. When data analysts want to predict the future, they perform predictive analytics. This helps business stakeholders to gauge their performance.
Also read: Top Predictive Analytics Models and Their Uses
This type of analysis combines all data analysis techniques in order to make recommendations. These are the foundation of data-driven decision-making.
This technique allows you to draw conclusions based on the data you’ve collected and analyzed. For example, “lack of employee training is a reason for employee attrition” and “employees attrition affects customers satisfaction”
Data Visualization and Presentation
Data visualization is a vital skill, essential to present your findings to non-technical audiences. You can share your insights with stakeholders or other target audiences by using data visualization software. Data-driven decisions require statistical analysis that is easy to understand and use. Interactive dashboards and visual representations will be helpful.
Biases and Pitfalls To Avoid in the Data Analysis Process
These biases should be considered during the data analysis process.
When you collect data and clean it up, selection bias can occur. There are many types of data analysis.
- Attrition bias. Participants who leave the research study share similar characteristics which can lead to a biased participant pool.
- Sampling bias. If your study is based only on data from a specific group of people and excludes others. This results in data and analysis that are not representative. There are many types of sampling bias.
Self-selection bias.If the study offers the sample the option to participate in the research. People who don’t want to answer the questionnaire or survey because they aren’t interested in it will most likely be in similar groups. This will impact the inclusion of the study.
- Survivorship bias. If the survey or study results are biased toward their purposes.
- Undercoverage bias. If the study excludes whole target groups.
- Non-response bias. People who haven’t answered the questionnaires correctly, are forgetfulness, or simply refused to answer are excluded from the study.
Confirmation bias refers to when data is used to support a predetermined conclusion rather than looking at the data. By covering all sides of an argument or problem, confirmation bias can be avoided. Each perspective should be given equal importance.
Outlier bias is when organizations overlook anomalies in data in order to present a clearer picture. Revenue projections that are based on an average number of factors with high-performing variables concealing failures are the most obvious example of outlier bias.
These biases can result from poor data analysis or other unavoidable errors. These include:
- Data Quality Not Use
- Inadequate data cleaning
- Not siloing data appropriately
These pitfalls can be avoided by creating a clear strategy that is based on solid statistical analysis and data collection. It is also a great way to avoid unwanted surprises by knowing the state of your organization’s data readiness. Your analysis should always be linked to a core business question.
Top Data Analysis Tools
These are the best data analysis tools. These tools will allow you to collect, clean, and mine data for effective analysis.
1. Microsoft Excel
Excel’s advanced features will allow you to clean up and visualize your data. You can use conditional formatting and charts to identify patterns and trends. These activities can be performed with Excel
- Regression analysis
- Statistic analysis
- Inferential statistics
- Statistics descriptive
- Analyzing exploratory data
This tool is used primarily for data mining, as the name implies. You can use it to create summaries or conclusions using other statistical techniques such as descriptive statistics and inferential stats.
Tableau is a data visualization platform that allows you to share insights and collaborate on data analysis tasks. You can also share reports with stakeholders. Tableau offers robust analytical features such as unlimited what-if analysis and allows you to calculate with as many variables as you want.
4. Apache Spark
Apache Spark allows you to analyze large datasets by performing large-scale data engineering, regression analysis, and exploratory analysis.
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