By Eleanor Bennett

How To Guides

4 min read

Data analysis is incredibly useful for all kinds of businesses and also has academic and hobbyist applications.

Nonetheless, it’s still possible to fall into numerous traps when trying to accurately interpret your data.

That’s why we’re giving you a list of the top 8 common data analysis mistakes to avoid at all costs.

Our first expert Jitin Narang, CMO at TechAHead contributed the following five top data mistakes to avoid:

1. Unstructured Data Analysis

“Analyzing data needs to be a structured process that begins with well-defined objectives followed by hypotheses.”

“While doing the analysis, sometimes analysts often jump into the data without even thinking about the core problem.”

“The analysis needs to be carried out in a structured manner starting from making the initial hypothesis to getting the required metrics in the result.”

“All the analysis carried out must be able to answer both the ‘what’ and the ‘why’ questions about the things that need to be learnt from the data.”

2. Overfitting & Focussing on the wrong metrics

“This is probably the most common mistake that is seen in different kinds of analysis.”

“Overfitting actually describes a model that fits exactly on any given data. The problem here is that the model does a good job of explaining the current set of data on hand but struggles to predict future patterns.”

3. Handling Correlated Variables

“Usually, there are a number of correlated variables like salary & no. of years in a company.”

“The more the number of variables, the more complex it will make the dataset for analysis. Therefore, it increases the complexity of the model and as a result increases the chances of overfitting.”

“Therefore, while performing the preliminary analysis, any variables with large correlation need to be removed as they do not contribute anything in the prediction power of the model.”

4. Splitting Time-Based Datasets

“While performing data analysis, any datasets need to be split into training & testing dataset where the model is trained on the training data & the required metrics are being checked on a completely new kind of data i.e. the testing data.”

“Usually, the splitting process is random in nature where randomly the points within the dataset are put into the training or testing set.” “However, carrying out the same process for a dataset consisting of a date-time variable is an incorrect practice.”

“For splitting such datasets, a threshold date needs to be chosen after analyzing the data. All data points before that threshold date go into training data and all data points after that threshold data go into testing data.”

5. Handling Imbalanced Datasets

“These are those datasets where the data points for the class to be predicted are present in a very scarce amount.”

“In this scenario, just building a prediction model and running it on the dataset will not help in gathering any new information.”

“The data will need to be resampled first either by upsampling or downsampling in order to make it more balanced for the model to learn.”

6. Insufficient process documentation

“While working as a business analyst and later implementing analytical solutions for various clients as a consultant, I noticed one aspect of work that stood out. It was the documentation of the processes.”

said Michael Sena, Founder of Senacea.

“When engaging in deep work, such as preparing a detailed report or dashboard, some things might seem obvious to us. The data source, periods covered or even some operations related to data cleaning and manipulation can appear not worthy of mentioning in the procedure manuals.”

“It might take us a few minutes to modify client-declared name data to avoid duplication when some clients included an additional middle name in submitted data. But if we don't reflect that precisely in the process descriptions, a colleague covering for us while we are sick, might not be aware of that spreadsheet task.”

“This may snowball the mere data cleansing work to incorrect reports prompting inaccurate data and in turn, losing us a valued client.”

“It pays off to document procedures accurately and automate tasks that present such an opportunity to avoid such events.”

Our third expert Eric Niloff, founder of EverPresent, contributed the next two types of mistakes to avoid when performing data analysis.

7. Ignoring outliers when analyzing trends

“This most commonly occurs when sales go up due to an unusually large client or set of clients.”

“If a growth moment is not based on data representing a phenomenon unlikely to repeat, then it's not a trend.”

8. Overrating small increases or decreases

“Especially for new or growing businesses, there is likely no difference between a 3% increase or 3% decrease on any metric. It's just different versions of flat.”

“But the language you use around these small differences in data can corrupt company culture and breed an oversensitivity to data.”

“Too many people are so anxious for data to say something that they turn nothing into something in their data.”

Here at, our hosted ELK solution uses the latest versions of Elasticsearch, Logstash and Kibana to make data analysis as easy and intuitive as possible.

If you can avoid the mistakes our experts have listed here, you will find it easy to interpret your data meaningfully and reach significant, relevant conclusions.

If you liked this article on data analysis mistakes to avoid at all costs then why not check out our blog on SDLC or our article on the best ways to start learning Java?

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