What is data cleaning?

Data cleaning, often referred to as data cleansing or data scrubbing, is the process of repairing or removing incorrect, corrupted, repeated, or incomplete data within a dataset. If data is inaccurate, conclusions and outcomes are unreliable, which can affect efficiency, productivity, and profitability.

Benefits of data cleaning 

Some benefits of data cleaning include:

• Removal of errors, resulting in a boost in efficiency and productivity

• Higher quality data, ensuring higher standards for customer care

• Easier to identify and resolve future errors

• Prevention of bottlenecks and delays in service delivery

Data cleaning steps

Data cleaning procedures need to be tailored to specific datasets, but a type of generalized procedure is described by following these steps:

Step 1: Identify and remove duplicate or unnecessary data

During data collection and transfer, there are many opportunities to accidentally introduce duplicate or irrelevant data points. It’s important to identify what data is beneficial and what data isn’t useful to decide whether it may be better off unincluded. 

Step 2: Repair syntax and format

Syntax and formatting are critically important to maintaining data sets. Various errors ranging from typos to improper naming conventions can lead to introducing more errors into the data set, lowering its performance. 

Step 3: Remove outliers as appropriate

Outlier data can often be the first step towards discovery, but it can also often be an outlier because of some error. Removing such outliers may enhance the performance of your data sets function. 

Step 4: Address missing data

Missing data can pose a range of risks to the performance of any given data set, ranging from potentially compromising data integrity, and making certain algorithms obsolete. Missing data can occur when data isn’t stored for certain variables or participants, which can happen due to incomplete entry, equipment malfunctions, lost files, and a myriad of other reasons. To address missing data, the absent information can either be accepted, removed, or recreated, and this choice will depend on the reason why the data is missing. 

Step 5: Understand and confirm data quality

The data set should be clear and organized, concentrated with only the information that is necessary. Excess data makes analysis and use of data more difficult, affecting productivity and performance. Data sets should be clear in their purpose and not questionably relevant.

Explore how Dotmatics can help improve data clearing and lab data management, contact us today to get the conversation started.