Analysing Data: 9 Common Errors and How to Fix Them for Improved Reporting

Understanding the customers and their behavior towards your brand is the key to improving sales, customer targeting, reducing costs, and building problem-solving strategies. But this productive process of assessing important data to leverage the best potential out of it might go wrong at times, even by professionals.

These mistakes act as traps that are obstacles for businesses and turn out the entire implementation strategy to set on the wrong approach. Imagine assessing incomplete data to determine the target audience’s behavior, which might later turn out to be false. It will make your marketing campaigns approach in the wrong direction and end up reaping less or no productivity.

Therefore, there are nine common mistakes in data analysis that even professionals slip out on. In addition to elaborating on the mistakes, you must also refer to the tips mentioned in this article to avoid them and ensure better reporting of business data analytics.

Understanding the Types of Data Analysis

Before you get along with the mistakes in data analysis, you must know their types. It is because mistakes while conducting analysis, irrespective of any type, will result in a degradation of sales.

There are four types of data analysis, which include descriptive, diagnostic, prescriptive and predictive analysis. Each of them contributes towards helping your business properly utilise insightful data and seeking an increased customer base, better sales and enhanced revenue.

• Descriptive Data Analysis:  Such an analysis describes what has already occurred within your business operations, such as the growth of revenue since the past year and the number of sales your existing campaign generated.
• Diagnostic Data Analysis: Diagnostic analytics is about helping you answer certain things about your business that will help you know why it happened. For instance, you will understand why this marketing campaign became more successful than the others you initiated earlier this year.
• Predictive Data Analysis: Predictive analytics, as the name suggests, is about predicting the business’s further actions by taking note of the things that happened in the past. For instance, if customers responded well to one product in the past, and a new model is launching this year, a predictive analysis will help determine if it will receive a good response.
• Prescriptive Data Analysis: Prescriptive data analysis is the most important type, as it determines the course of action for your business to thrive. It assesses the previous data and helps specify what needs to be done to improve certain products or operations within the business.

9 Practical Data Analysis Mistakes

The importance of data analysis is inevitable as it adds simplicity to studying or breaking down data into actionable insights. Apart from helping businesses understand their customers, data analysis also helps with increased sales, reducing operational costs and developing problem-solving strategies.

Keeping the importance in mind, some common and avoidable data analysis mistakes can trigger unwanted repercussions. So, to ensure that you don’t have to bear them, let’s get you the knowledge on mistakes that are commonly incurred and how it disrupts the productivity of your business:

1. Waiting for a Long Time Before Acting

Your business data analysis outcomes need immediate action with rectified reciprocation. Most professionals who execute data analysis often take a prolonged time to act, adversely impacting the positive impact that could have been made with immediate action.

It mostly happens with marketers or data analysts because they expect the data to update for the business. Bad results might turn into good results only with necessary actions and not by timely analysis of data.

2. Biased Sampling

You need to think from the human perspective to break down your target audience and direct your business goals with a widened sample. A bias in your sample will lead you to test your products or services on only one audience group. In contrast, the other relevant group of your target audience is being left out, and the necessary data is not being generated for the overall growth of your business.

For example, if your sample is not big enough and collects data only from middle-aged people. But as your product is ideal for middle-aged and elderly audiences, you are missing out on critical data from the other group.

3. Confusing Correlation with Causation

Correlation doesn’t result in causation, but as the data trends seem similar, professionals get confused by thinking they are related. They aren’t related, as when two things are happening simultaneously, it doesn’t conclude that one is happening due to the other. This confusion makes data analysts make mistakes while deciding on operational or marketing actions.

4. Not Considering Anything Beyond Numbers

Data analysts and marketing experts often stick to the numbers they obtain through analysis. Trusting numbers without context is a mistake. Analysts miss out on asking why this number is obtained, a standard error that leads marketers to make mistakes while running campaigns.

5. Responding on a Very Less Data

As waiting too long to act is a major mistake, not waiting long enough to get sufficient data for building an actionable insight is also a blunder. One of the most common traps for data analysts or marketers is that they hurry to act on very little data. Imagine launching a marketing campaign with minimal observational data; then, there is a rare chance it would work as expected.

6. Using Inaccurate Comparison Benchmarks

During data analysis, it becomes critical for you to compare the results with a benchmark in mind. You can compare it based on your previous month, a different product or organisational parameters. Still, it should be the right one to give you accurate results to act upon. The wrong benchmark might give you incorrect metrics.

7. Using Inaccurate or Unreliable Information

Data is considered accurate or reliable only if it is complete, consistent, timely and valid. If there is any missing data, incorrect values, or errors, or if the data is out-of-date, it is a mistake to count on them. Most professionals do this mistake by not validating the source of data, its correctness or completeness before including it in the report.

8. Wrong Visualization of the Data

You can either view your data in a graphical or chart form. Irrespective of how you view it, you must have an idea to understand the patterns or relationships proficiently. Most analysts make minute mistakes in assessing the data relationships while being visualized messily or incorrectly. Hence, the actionable insights are problematic due to those minute observant errors.

9. Setting up Unwanted Objectives

At times data analysts and marketers set up campaigns without any targets. As a result, there is very poor data collection, inaccurate findings and a baseless report. Running random campaigns without a vision will only churn down your energy and will multiply the work without any objectives. Define your parameters before you set any of your campaigns.

Tips on How to Avoid Common Data Analysis Mistakes for Better Reporting

If you are a data analyst or a marketer, you will agree to the mistakes listed above. You might have made a couple of them in the past or might be continuing with a few of them right now. But it’s time to end them all, and follow these tips below to ensure you don’t repeat these expensive mistakes ever again:

• Know the source of your data.
• Check if there are any biases in your data.
• If there is a possibility of screening the data for problems, then go ahead and do it at all times.
• Get a team ready to re-analyse the data to verify its integrity and whether it is outdated.
• Look out for any moral, social, ethical or economic implications that you must consider before starting with your data analysis.
• Keep a large sample with yourself to work with, as a smaller one will limit you on valuable insight.
• Ensure that all analysts follow the specific procedures for assessing data to come to accurate conclusions.

Conclusion

Here is everything you should know to improve your data analysis for the organization and derive productive outcomes from it. These mistakes might seem very minute, but the repercussions can delay your business in maintaining a pace to keep up with the competition. You don’t want to be left behind because of the wrong data analysis approach. So, make sure every bit of data accounts for your profit.