Top 5 Mistakes To Avoid When Working With the Data

It goes without saying that data is the cornerstone of every business decision that a company is making. By wisely using the data, a company can not only get meaningful insights on its current state, market trends, and user behavior, but draw accurate forecasts, adjust its development strategy, and tweak the processes with an aim to maximize profits.

Today, such buzzwords as Big Data, Data Science, and Machine Learning are becoming more of a standard than a breathtaking innovation. Despite that, many companies still make critical mistakes that reset their data value to zero and cause financial and time losses. In this article, we are here with the top-5 mistakes to avoid when working with the data and the corresponding solutions.

1. No Defined Metrics And No Clear Goals

One of the biggest mistakes that companies make when approaching their data is skipping the part where you need to define your metrics and goals. Here is why it’s so important.

The ultimate goal behind data processing and analysis is to obtain insights that can be applied to a certain business problem. However, it can only be possible if you have well-defined metrics and goals – otherwise, your collected data will be useless.

When working with the data, you will be comparing different metrics to see their differences, relationships, or dependencies. Without first defining these metrics, you will be comparing a set of random data to another set of random data and that will lead to simply wasting your time.

The same applies to defining your goal: by clearly understanding what exactly you want to learn from the data, it will become much easier for you to analyze and manage it.

A good example of such a metric can be data completeness (measured by a percentage of missing values) or data connectivity (measuring by a percentage of data intersection between two datasets).

As well, clearly defined metrics help when you have a big vague question: they allow you to break a big question into smaller and more concrete ones and to initiate a clear line of enquiry that’s easier to work on.

2. Poor Data Visualization

You have most probably come across data visualization at some point in your work: it might have been a pie chart or a graph in a presentation. However, not all companies understand how important data visualization is.

Data visualization, as the name suggests, is the representation of collected and analyzed data in a suitable visual format. It might be an above-mentioned pie chart, a graph, a heat map, etc. The choice of the data visualization method will depend on the data and your purpose.

So how can you do wrong with data visualization? There are several most common mistakes:

  • Confusing and/or uninteresting visualizations that don’t tell a story and are hard to follow;
  • Wrong visualization method that does not fit your purpose and data;
  • No indication of value (with the help of colors or sizes): all data looks the same and the main points are not highlighted;
  • Complex visualizations that cannot be understood.

When talking about the data, many specialists tend to place the primary focus on its collection, processing, and analysis while completely overlooking the visualization part. However, the whole point of working with the data is to understand it and how can you do so without a proper visualization?

3. Low Quality Data

Even though data quality is a must when it comes to data processing, there are still many mistakes being made in regards to its quality. The thing is, if a company has available data, it’s not always 100% suitable for use. In most cases, there are many inconsistencies that need to be taken care of before a data scientist can work with the data. 

Some of the most common issues with the data fields which can ruin final analytics:

  • Extra space;
  • Blank cells;
  • Duplicates;
  • Different formats;
  • Different cases (i.e. lower and upper mixed).

The first thing anyone working with the data has to do is “cleaning” the data and preparing it for analysis and processing. If your data is inconsistent and contains errors, the ML model will be unable to learn and therefore will not deliver the expected results.

4. Assigning Wrong People

Another big mistake that companies make when working with data is assigning the wrong people to the tasks. The thing is, not everyone understands the difference between a data scientist and a data engineer and even fewer people understand that most data-related problems can be solved by a skilled business analyst. 

While data scientists and data engineers focus on building ML models and data systems, analyzing and interpreting data, business analysts are responsible for helping companies make business decisions based on the collected data.

Therefore, instead of hiring data scientists, in most cases, a company can efficiently solve its issues simply by assigning a business analyst to a task. Of course, it doesn’t mean that companies do not need to work with data scientists – but in most cases, they need a business analyst instead.

5. Too Much Focus On Algorithms (And Their Over-Complicacy)

It can be tempting for data scientists to use complex fancy algorithms to “make things work better” – but in reality, a simple and robust ML model with high-quality data can be just as effective (and even better).

The primary things to focus on when working with data are 1) domain knowledge and 2) data quality. If a data scientist uses complex algorithms but has zero or very little domain knowledge, the algorithms won’t help much as the model will deliver random results.

On the other hand, good knowledge of the industry and the use of relevant simple techniques (including logistic regression or linear regression) will deliver much better and more accurate results since a data scientist will know exactly what they are doing and why.


Work with the data is not as complex as it might seem: one just has to carefully analyze why you need this data and what problems it can help resolve. And before hiring data scientists and data engineers, first, try to approach the problem by using available resources and see whether they will deliver the expected results.

Leave a Comment