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How to Solve Data Analysis Problems in the Real-World

When it comes to data analysis, there are many tools and techniques available to help you analyze your data. However, in a real-world data analysis project, there are a few extra things you need to do that you won’t necessarily learn in a textbook or a course. In this post, we will go over some of the most recurring hidden problems in real-world data analysis projects and my tips for you to deal with them.

Tip #1: Understand the Business Problem First, Then Frame it as a Data Analysis Problem

Before you start analyzing your data, it’s important to understand the business problem you’re trying to solve. Ask yourself:

What is the business outcome that management wants to improve?
Is there any solution currently working to solve this?
What insights are decision-makers looking for?

My advice:

I always ask questions at the beginning of every project:

What is the business outcome that management wants to improve? It is crucial you talk with all relevant stakeholders at the beginning of the project. They have more business context than you and can help you understand what is the target you need to shoot at.
Is there any solution currently working in production to solve this, like some rule-based heuristics? If there is one, this is the benchmark you have to beat in order to have a business impact. Otherwise, you can have a quick win by implementing a non-ML solution.
Is the model going to be used as a black box or as a tool to assist humans to make better decisions? Creating black-box solutions is easier than explainable ones. If you work in healthcare, for example, you need explainability. If you work in financial trading, you don’t.

If you can answer these questions, it means you know WHAT is the Data Analysis problem you need to solve. And that is a fantastic starting point for the project. Having a clear understanding of the business problem you’re trying to solve will help you focus your analysis and ensure that the insights you provide are actionable.

Tip #2: Focus on Data Quality and Quantity

The quality and quantity of your data are critical to the success of your data analysis project. Make sure your data is clean, accurate, and complete. Also, consider how much data you have and whether it’s enough to draw meaningful conclusions.

My advice:

Take the time to clean your data before you start analyzing it.
- Here’s a strategy for cleaning your data:
  1. Identify missing data: Missing data is a common issue in datasets, and it can affect the accuracy of your analysis. Identify which variables have missing data and decide how to handle them. You can either remove the observations with missing data or impute values using methods such as mean, median or mode.
  2. Handle outliers: Outliers can skew your results and affect the accuracy of your analysis. Identify and handle outliers by either removing them or transforming the data to reduce the impact of outliers.
  3. Check for errors: Errors such as typos, duplicates, and inconsistencies can occur in datasets. Identify and correct these errors to ensure accurate results.
  4. Standardize variables: If you have variables measured on different scales, it can be difficult to compare them. Standardize variables by scaling them to have a mean of 0 and a standard deviation of 1.
  5. Check for multicollinearity: Multicollinearity occurs when two or more variables in your dataset are highly correlated. This can affect the accuracy of your analysis, so identify and handle multicollinearity by removing one of the variables or transforming them.
Collect as much data as possible, but don’t sacrifice quality for quantity.
Consider using data augmentation techniques to generate more data if needed.

Tip #3: Use the Right Tools and Techniques

There are many tools and techniques available to help you analyze your data. However, not all tools are created equal, and not all techniques are appropriate for every data analysis project. When selecting tools and techniques, consider:

The size and complexity of your data
The type of analysis you need to perform
Your expertise with the tools and techniques

My advice:

Choose tools and techniques that are appropriate for your data and analysis needs.
Consider using open-source software to save costs and avoid vendor lock-in.
Don’t be afraid to experiment with new tools and techniques, but be sure to test them thoroughly before using them on your project.

Tip #4: Communicate Your Findings Effectively

Communicating your findings effectively is critical to the success of your data analysis project. Make sure your insights are clear, concise, and actionable. Consider who your audience is and tailor your communication accordingly.

My advice:

Use data visualization techniques to help convey your findings.
- Here are some common data visualization techniques:
  1. Line Charts – used to display trends over time, such as stock prices or temperature changes
  2. Bar Charts – used to compare values of different categories, such as sales revenue by product or region
  3. Pie Charts – used to show proportions of a whole, such as market share or percentage of a budget
  4. Scatter Plots – used to display the relationship between two variables, such as age and income or height and weight
  5. Heatmaps – used to show the density or concentration of data, such as website traffic by time of day or user location
  6. Geographic Maps – used to display data based on geographical location, such as sales by region or population density
Consider creating a dashboard to present your findings in a clear and concise manner.
Test your communication with a small group of stakeholders before presenting to a larger audience.

Tip #5: Continuously Monitor and Improve Your Analysis

Data analysis is an iterative process. Once you’ve completed your analysis, it’s important to monitor the results and continuously improve your analysis. This will help ensure that your insights remain relevant and actionable over time.

My advice:

Set up a monitoring system to track the results of your analysis.
Continuously review and update your analysis as new data becomes available.
Consider using machine learning techniques to automate some of your analysis tasks and improve the accuracy of your insights.

By following these tips, you can overcome some of the most recurring hidden problems in real-world data analysis projects. Remember, data analysis is an iterative process, so don’t be afraid to experiment, monitor, and continuously improve your analysis.