Nobody is perfect, even Data Analysts. In many industries, data entry remains a vital component of day-to-day operations. Those who work with data impact a variety of aspects of a business, such as sales numbers, customer information, and financial data. Regardless of how much employees double-check their work, mistakes are bound to happen, generally pertaining to transcription or transposition. Even one error can cause significant problems for a company.
This article will explore seven of the most common mistakes that Data Analysts make, as well as some ways to spot and avoid these errors.
7 Most Common Errors in Data Analytics
Although Data Analysts work carefully to be accurate with their work, errors arise in the data-handling process. Here are seven of the most common mistakes Data Analysts make:
- Cherry-Picking: This problem occurs when information is selected to support a specific position or hypothesis. This ethically wrought concern can involve acts such as ignoring evidence that contradicts the hypothesis or pushing analysis in a given direction. Cherry-picking can have serious repercussions in such fields as health or public policy.
- Bias: Sampling bias is one of the most common errors made by Data Analysts. It occurs when the sample isn't representative of the whole. Sampling bias creates problems in that it can indicate incorrect overrepresentations of certain groups, or weigh the analysis too much in one direction. Solution bias also should be avoided when working with data. This form of bias entails falling for a solution that may feel perfect for the problem at hand, but still may not be correct.
- Not looking beyond the numbers: Numbers don’t always tell the entire story. When Data Analysts look only at numbers without considering their larger context, this can have real-world consequences. One example would be of a lending company that creates a model that considers geographic bias, but in doing so relies on data from biased sources. This can subsequently lead to numbers that at first look clean but do not truly represent the geographic reality of the sample. It is imperative for those working with quantitative data to understand the numbers in their contexts. They should ask “why” instead of “what” in order to be true to the entire picture.
- Selecting the wrong graphs for visualizations: It takes experience to know which visual representation of data is best suited for the needs of a specific audience. In some instances, those working with data visualizations choose a less effective visual depiction to convey data, such as selecting a pie chart to depict information that does not need to be compared in a parts-to-whole manner.
- Overfitting data: Ideally, it is possible to make predictions by training a model with prior data and applying it to a different dataset with a similar distribution. However, errors occur when a complicated model is made to closely fit a limited set of data points. This can lead to including noise in the model rather than eliminating it.
- Improper data cleansing: The process of data cleansing can take up the majority of the time a Data Analyst devotes to working with data. However, it is an important step, and those who skip it may not know what data is missing or incorrect, and may overlook the limitations for interpreting the analysis. Failure to perform adequate data cleansing can lead to polluted analysis and incorrect insights.
- Focusing on the algorithm over the problem at hand: One of the most common errors made by Data Analysts pertains to creating data models or algorithms without considering the overall purpose of said model or algorithm. This shortsightedness can lead Data Scientists or Data Analysts to rely too heavily on powerful machine learning models in which it’s possible to manipulate the algorithm to fit the existing data too tightly. Rather than overfitting data to work in a given model, Data Analysts should strive to create a model that not only fits historical data but can also be generalized.
How to Avoid Errors in Data Analytics
There’s good news for Data Analysts who wish to avoid some of the most prevalent mistakes when working with data. Measures can be taken not only to efficiently cut back on errors but also to enhance data integrity and even automate workflows to ensure that data entry isn’t neglected.
The following are a few tips for Data Analysts to adhere to in order to avoid mistakes:
- Fostering a good work environment can help those working with data avoid common hurdles such as fatigue, eyestrain, or discomfort, which can negatively impact the ability to perform accurate work. A healthy workspace equipped with ergonomic chairs, as well as regular breaks, are two measures that can help to ensure a healthy and productive work environment.
- Promoting accuracy over speed is important for reducing data analysis errors. Although it’s necessary to maintain a given speed in order to keep workflow going, providing employees with realistic goals that emphasize accuracy over speed helps to ensure that the job will be done correctly.
- Pinpointing the main sources of inaccuracies is a crucial step toward eliminating points that can cause slow-downs. This process can entail reviewing data entry errors, as well as patterns and statistics, to locate the main internal and external data error sources.
- A great way to improve the consistency and accuracy of data collection and entry is to standardize the processes. Standardization not only allows employees to perform accurate, quick work but is an essential prerequisite to automating the data-entry process.
- Enabling automation not only cuts down on the costs of resources and labor but also reduces the human errors that can occur in the monotonous, manual data entry process.
Learning more about the best practices in data analytics is a sure way to spot errors, and to learn how to avoid them altogether when working with data.
Hands-On Data Analytics Classes
For those who are interested in studying data analytics, Noble Desktop offers a variety of data analytics classes. Courses are offered in New York City, as well as in the live online format in topics like Python, Excel, and SQL.
Other great data analytics classes are also available from top providers. More than 130 live online data analytics courses are currently listed, in topics like FinTech, Excel for Business, and Tableau, among others. Courses range from three hours to six months and cost from $219 to $27,500.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse approximately 400 data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.