A Guide to Data Cleaning For Better Analytics Results

A Guide to Data Cleaning For Better Analytics Results

Synergy Codes

10 min
A Guide to Data Cleaning For Better Analytics Results

When you’re working with data to get a better understanding of how your business operates, the analysis results you’re going to produce will be only as good as the various data pieces you process. Thus, you need to make sure you’re not dealing with dirty data and take steps to clear your stacks. Now, take a look at our guide to data cleaning to improve the quality of insights you’re generating and get better business results.

Regardless of what type of data you’re dealing with, be it strictly numerical, or mixed, falling into several categories, if you’re looking to analyze and optimize your operations, you have to make sure you have reliable data available.

In this post, we’ll explain what is data cleaning, its importance, and its benefits.

What is data cleaning?

Data cleaning is the process of making data ready for analysis through deleting or modifying data points considered irrelevant, duplicate, incorrect, incomplete, or poorly formatted.

This bad data isn’t useful for analytics because it may interfere with the process or produce errors and inconsistencies when it comes to results. Several data cleansing methods are depending on how the information is stored and what answers you’re looking to obtain.

The data cleansing process isn’t merely about deleting information but more so figuring out a way to maximize the efficiency of a dataset.

This is reflected in the fact that data cleaning involves operations such as fixing spelling mistakes, standardizing data sets, filling in any missing values or empty fields, and spotting any duplicate data points.

Data scrubbing is an important part of broader data science, as it affects the process of data analysis and leads to obtaining more reliable answers.

Data cleaning vs. data transformation

A quick note concerning the difference between the two notions. Data cleansing refers to the process of removing any pieces of information that don’t fit a given data set. Transformation, on the other hand, also called data wrangling, involves the conversion of data from one format to another for warehousing and analysis.

Why do I need clean data?

You may be wondering why data cleaning is necessary?

Well, regardless of what you wish to use data for, be it some sort of analysis or creating a visualization, having high-quality data at your disposal is crucial for obtaining accurate answers. Raw data flowing in from various sources, including manual inputs, carries the risk of containing all sorts of mistakes.

Inaccurate data will make it harder for any BI tool or data scientist to produce meaningful results based on it.

Thus, it’s important to understand the benefits of data cleaning, which include:

  • Elimination of errors when several data sources are involved.
  • Fewer errors lead to an overall better user experience and less frustration among team members.
  • Opportunity to detail the different functions and what your data is supposed to do.
  • Chance to track errors to see where they’re coming from, allowing you to make the necessary adjustments.
  • Utilizing data cleansing tools will create better business practices and lead to more efficient decision-making.

Actionable data cleaning steps to improve data quality

The specific data cleaning techniques will first and foremost depend on what sort of figures and information you’re trying to improve the quality of. Not every data type will be subject to the same scrubbing method. Still, what follows is a general data cleaning process explained that you can base your efforts on.

Delete duplicate or irrelevant entries

Step one is to get rid of any redundant entries from your dataset, including duplicates and other irrelevant data. The former usually make their way into your set during the collection process. Some of the other instances when doubles may be created involve merging data from multiple sources, scraping data, obtaining data from clients, or other departments in the organization. Deleting these repetitive items is a crucial step in reaching better data quality.

Irrelevant entries are the ones that don’t fit the particular problem you’re trying to tackle. For instance, if you’re attempting to run data analysis about New York City, you should remove items concerning Los Angeles and Chicago from your dataset. This will clear the unnecessary noise and lead to more accurate results.

Fix structural errors

Structural flaws include things like unusual naming conventions, typos, or improper capitalizations. These inconsistent data items can lead to mislabeled categories or classes. For instance, you may see both “N/A” and “Not Applicable” appear, while they should be treated as a single category.

Sift unwanted outliers

You may frequently come across singular entries, or unique values, which seem to not fit the dataset you’re trying to analyze. If you feel like you have a valid reason to remove this unusual piece of information, doing so will create more accurate data for you to work with.

Keep in mind though that the mere existence of an outlier doesn’t automatically render it incorrect. Feel free to remove it only if it’s irrelevant to your data analytics task or an actual mistake.

Take care of missing data

Many algorithms won’t work properly if they’ll find that some specific data is missing. There are certain ways to deal with this, albeit imperfect.

  • You can disregard the parts of the data that are missing, however, this will lead to a loss of information, in the end, so keep that in mind.
  • You can manually input data based on other items but again, this is tricky because you may be making incorrect assumptions.
  • You can change how the data is used to navigate through the missing pieces of data.

Validate and ask questions

Toward the end of the data cleaning process you should be able to answer the following questions:

  • Does the data make sense?
  • Does the data follow the rules for its field?
  • Does it prove or disprove your theory, or bring any insights to the surface?
  • Can you find trends in the data to help you come up with your next theory?
  • If not, is that because of a data quality issue?

Incorrect conclusions caused by poor data quality can negatively affect reporting and business decision-making. Before embarking on data cleanup, it’s important to develop a culture of quality data at your organization.


The main goal of data cleaning is to raise the overall quality of the data, as well as make it more uniform and easily accessible to any specialists and software working with data.

Because businesses collect information flowing in from a myriad of sources, you should aim to standardize your data to make it more manageable and useful.

It’s best to develop practices that will ensure inputs stay consistent and correct but at some point, data cleansing effort may be required anyway, as incorrect data can be coming from sources you don’t have much control over, such as customers.

Analyzing data will yield the most accurate and useful results when performed on well-structured, error-free sets of information. Once you’re sure your data is clean, you can confidently crunch it and move forward with the insights you produce.