Data analysis is essential for business growth and academic purposes because the next steps and decisions depend on its result. One little mistake can turn the course of your action in the wrong direction. Or, you can make a poor decision due to errors in your data metrics.
However, mistakes in data analysis happen all the time, and seasoned data analysts review their findings multiple times to ensure accuracy. Plus, they also get a second opinion from an independent analyst when they have to decide serious actions on data analytics results.
Whether you have to perform data analysis for business purposes or academic practice, you must know the common data traps and errors. So, you can ensure maximum accuracy. Check out the seven common data analysis errors and ways to prevent them in this post.
How Can Mistake in Data Analysis Cost You?
Suppose you work for a company that needs to decide on its marketing budget for the next quarter. You have to present your target audience's favorite digital platforms, insights, and the best way to sell your products and services. If you make mistakes in your data analysis, you can estimate the wrong number of people who can be buyers. Or, you can present the wrong metrics that'll lead to allotting the wrong budget for marketing campaigns. As a result, time, money, and resources can go to waste due to some errors in your data analysis.
Similarly, every growth decision in every company requires top-notch accuracy in data analysis. Otherwise, growth strategies can turn into wasted efforts because you often get wrong numbers due to data analysis mistakes. So, the first order of business is to ensure accuracy in data analysis.
Since the last decade, the demand for data analysts has been rising, and companies are offering high salaries. An intermediate-level data analyst can easily command a 6-figure salary. That's why choosing Data Analytics Master's Program Online as a career change option will surely get you a high-ticket job after two years. Also, you can even work as a freelance data analyst while handling your current responsibilities.
7 Common Data Analysis Errors and Their Prevention
Here are the common traps that should never get into during data analysis.
1. Sampling bias
It happens when you take non-representative samples. For example, suppose you have to know the popularity of a political party by taking a survey. In that case, you have to be diverse in choosing people for opinions. Sampling bias will happen if you take a survey from a neighborhood where people are affiliated with one particular party. As a result, your survey will never give you the actual popularity.
To avoid sampling bias, you have to inspect the data source. And if some factors can collude with the final results, you have to change your samples and take data from the right sources.
Cherry-picking is the practice of making a hypothesis and finding data to support it. Cherry-picking is an intentional error rather than a mistake, and it happens quite frequently. Also, cherry-picking is unethical and can have serious consequences in several disciplines, such as public policy, health, engineering, and more.
To avoid cherry-picking, you have to find out all the known biases in your data. So, you can filter out the wrong stacks of data to ensure accuracy.
3. Disclosing metrics
When a subject knows the values of a specific metric, then the metric becomes useless. It's called disclosing metrics. For example, suppose a teacher knows all standardized test questions and teaches students only the required questions. In that case, the students will score higher in test results. But the quality of education will be lost. In the early days of the internet, websites employed keyword stuffing to deceive search engines because Google and other search engines made their ranking criteria public. As a result, websites created useless content that had no value for the user.
To avoid disclosing metrics tracks, you have to screen the data and find problems that create wrong practices.
It is a mistake during your analysis process. When you get the data and create a graph, the graph's curves can fit any model. That's why you can fit the success and failure of that model with your data. As a result, you anticipate the wrong future and make error-full predictions.
To avoid an overfitting trap, the person checking everybody's data must not have any previously known model in mind. The findings of data metrics should be independent of historical models for accurate future suggestions.
5. Focusing only on the numbers
Focusing only on numbers can create turbulent real-world consequences. For example, suppose a lending company approves a loan for a business due based on deposits in the last three months. In that case, it'll be a wrong decision. It's because the deposits can be due to any factor. That's why the lending company has to consider the nature of business and the demand for products and services to approve the loan. Otherwise, anyone can fool the lenders by making fake deposits and creating an image of a thriving business.
To avoid such a mistake, you have to analyze the results and verify their integrity. Also, you have to consider different angles and situations that can produce the same result.
6. Solution bias
It is similar to cherry-picking. However, it will be wrong practice and will lead to failure. To avoid such a mistake, you must consider the solution's moral, ethical, social, and economic implications. Next, you have to examine your solution closely before starting it.
7. Communicating poorly
It is a problematic error because performing data analysis is one thing and conveying your findings to the concerned people is another thing. Presenting and showing your analysis should be done with elegant charts and infographics. So, everything can be communicated clearly without any ambiguity.
Now, you know the seven most common data analysis traps and ways to avoid them. So, be careful while performing data analysis and try to review your findings multiple times. So, you can have maximum accuracy and produce the best solution for your problem.