Most people who regularly work with data agree that your analysis and insights are only as good as the data available to you. Trash data can only produce ineffective analysis. Also referred to as data cleansing and data scrubbing, data cleaning comprises one of your organization's essential steps if you wish to establish a premise of quality data decision-making. This article will discuss data cleaning, its benefits, and how to create and use clean data.
What is Data Cleaning?
Data cleaning is the process of removing or fixing corrupted, inaccurate, improperly formatted, incomplete, or duplicate data in a dataset.
When multiple data sources are combined, many margins of error for data occur. If the data is not accurate, algorithms and outcomes can be unreliable, even though they may appear correct on the surface.
While there isn't one universal set of steps in the data cleaning process since the processes may be different depending on the dataset, it is essential to create a template for your data cleaning process to know you're doing it the correct way every single time.
Data Cleaning vs. Data Transformation
Data cleaning comprises the process of removing data that does not belong in your dataset. Transforming data from one structure or format into another structure or format is known as data transformation.
Data transformation processes are also commonly known as data munging or data wrangling, which refers to transforming and mapping data from a more 'raw' data format into another format suitable for analyzing and warehousing. Below, we walk you through the main processes of how data is cleaned.
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How is Data Cleaned?
Even though data cleaning employs various techniques that may differ depending on how your company stores data, these necessary steps are a great start for you to formulate a framework to suit your organization.
Step 1: Take Out Duplicate or Irrelevant Observations
Getting rid of unnecessary observations from your dataset, such as irrelevant observations or duplicate observations, is the first step in cleaning your data up.
Data collection is the phase where most duplicate observations occur. When data from multiple places is combined or data is received from various departments or clients, there are many chances for duplications in the data. One of the most significant parts of cleaning data is deduplication.
When you notice observations that do not fit into the specific issue you're trying to analyze, it is known as an 'irrelevant observation.' If you want to analyze data about millennial customers, for example, but your dataset holds information about older generations, these are irrelevant observations you may want to get rid of.
Taking out irrelevant observations makes data analysis more efficient and reduces distraction from your primary target- along with building a more performant and manageable dataset.
Step 2: Deal With Structural Problems
Structural errors happen when you transfer or measure data and identify weird naming conventions, incorrect capitalization, or typos. These inconsistencies can result in mislabeled classes or categories. Both "N/A" and "Not Applicable" may appear, for example, but they should be analyzed in the same category.
Step 3: Pinpoint And Remove Unwanted Outliers
You will often find one-off observations that do not immediately appear to fit within the set of data you are working with. If there is a real reason to take out an outlier (such as improperly entered data), taking it out will make the data you are working with perform better.
However, in certain situations, the outlier proves the rule. The key here is to keep in mind that not all outliers are incorrect, so further analysis is necessary to figure out the validity of the number. If you can prove an outlier is irrelevant for analysis or is a mistake, you may want to take it out.
Step 4: Deal With Missing Data
Missing data cannot be ignored because most algorithms won't accept datasets with missing values. There are several ways you can handle missing data. While all of these options should be a last resort, all of them can be considered:
- First, you can take out any observations that have missing values. However, be mindful that doing this will drop or lose information.
- Second, you can put in missing values based on other observations; there is the chance you are losing the integrity of the data because you may be working off assumptions rather than concrete observations.
- Third, you can try changing the way you are using the data to navigate null values.
Validate & QA
When you've completed the data cleaning process, run through these questions for basic validation:
- Does the data fall in line with the proper rules for its field?
- Does the data make sense?
- Can you find trends in the data to help you formulate your next theory?
- Does it bring any insight to light or prove or disprove your working theory?
- If you answered no to any of these questions, is it due to a data quality problem?
False conclusions resulting from 'dirty' or incorrect data can result in less-than-optimal business decision-making and strategy. Erroneous conclusions may produce an embarrassing moment in a reporting meeting when your data doesn't stand up to scrutiny.
To avoid this situation, it's critical to create a culture of quality data in your organization. Emphasizing quality data in your organization means documenting the tools you need to create this culture and making it clear what data quality means to you.
What Are The Benefits of Data Cleaning?
Ultimately, clean data will make your organization more productive overall and allow you to make decisions based on the highest quality information. Benefits of data cleaning include:
- Getting rid of errors when multiple sources of data are combined
- Fewer errors mean less frustration for employees and happier clients
- Being able to accurately map the different functions so that your data does what it's supposed to
- Monitoring errors and better reporting to see where errors come from makes it easier to correct corrupt or erroneous data in the future
- Data cleaning tools can help with faster decision-making and more efficient business practices.
Having a thorough comprehension of data quality and creating, managing, and transforming data in your organization is a vital step in making more effective and efficient business decisions. We hope this article has helped!