Article | April 26, 2022

Top 5 Common Mistakes of Data Analytics & How to Avoid Them

Data analytics is a hot topic in the business world and more companies are investing in this are in order to gain more insights into their customers and employees.  However, there are some common mistakes that can be made with data analytics which we need to pay attention to and these are discussed below.

1.     Not understanding data sources and how they work together (mixing data sources).

Data is one of the most valuable resources in the digital age.  However, not understanding how data is collected and processed can have a negative impact on the rest of your marketing efforts.

The most common mistakes made in data analysis are:  the mixing data sources (i.e. combining customer data from different channels) and not understanding how to filter or separate the good data from the bad data (i.e. using ineffective filters or separating irrelevant information).

2.      Not analyzing the data correctly because of insufficient understanding of statistical concepts.

Statistics is an important field of study for any data scientist or business analyst.  It’s necessary to know how to analyze data correctly so as not to misinterpret the results.

I will discuss two common mistakes that people make when analyzing data and the ways to avoid them.  The first mistake is not understanding statistical concepts such as a p-value means or a confidence interval.

The second mistake is misinterpreting the results.  For example, if you are trying to determine whether a company’s marketing campaign led more people to buy their product and the target cities in question are unknown, then the analysis will be misinterpreted because you are analyzing the wrong population.

3.      Too much focus on inputs and not enough on output.

Data is only useful if it is utilized in a way that has an impact on the outcome.  There needs to be a balance between using data and creating data that can be useful.

Data without proper analysis or interpretation will not result in any value and instead will just serve as expensive noise.  Data can be used for many different things; it may even be able to generate more data points that we would have never been able to produce ourselves.

4.      Ignoring visualization techniques (e.g., dashboards) for presenting insights to clients, stakeholders – those who have the power to adopt change.

Many people might not know that data visualization is the process of transforming information into something visual that can be understood at a glance.  Presenting insights in the form of visuals helps people better understand the data.

As data becomes more and more voluminous, it becomes difficult for humans to comprehend it all without the use of some type of visualization technique.  Data visualization techniques can help us detect patterns and trends in data and generate insights that we would otherwise never notice.

The most common types of visualizations include: charts; graphs; diagrams; maps; infographics; and illustrations.  These are quite effective tools as they relay more information in less time and with fewer words than if they were written on paper or spoken aloud verbally.

5.      Gaining insights from only one perspective or angle of analysis.

The only data perspective that people are aware of is their own.  By looking at the world through only their lens, individuals cannot develop an accurate understanding of the whole picture.  There are many ways in which people can gain a better understanding about a topic.  They can read other people’s perspectives; they can be exposed to new experiences and they can be given access to different types of data.  This is why there is a need for more perspectives when it comes to data analytics.