Modern businesses count on business data, and maintaining a high degree of accuracy and quality in data is critical. Suppose a business’s data is raw and messy and contains errors and duplicates that can lead to errant insights and irrelevant decision-making. However, businesses may fail to maintain it in the desired quality. Businesses can tackle such quality concerns through data cleansing. But what is data cleansing? Through data cleansing, experts identify and remove errors and irrelevant information from the given dataset. In particular, data cleansing is performed to ensure that the data available to you is reliable, error-free, and clean with no inaccuracies. To ensure the reliability of your business data, data cleansing experts incorporate data validation techniques in their process. In this blog, let’s explore some of the major techniques for efficient data cleansing.
Top Techniques for Effective Data Cleansing
- Removal of Duplicate Data – Duplication is a major data entry issue. In this case, the same data will be present multiple times in a data set. Data duplication can negatively impact the quality of data and, therefore, cause faults in decision-making. By analyzing the entire data set, specialists can detect duplicate entries and remove them to ensure better accuracy. Furthermore, incorporating automated data cleansing tools can eliminate duplicate entries in easy steps.
- Fill Missing Values – Missing entries are one of the major manual data entry errors. When data entry specialists leave sections blank, it can result in inconsistent or unreliable data sets. One thing we can do to avoid this inconsistency is to check whether the section can be filled. If the data is not available, removing the related record will be ideal.
- Use of Standard Data Formats – Using a standard format is one of the top techniques for effective data cleansing. Data entered will be from different sources and may contain different formats. This may cause confusion and inconsistencies. For example, dates in the data sets can be entered in DD/MM/YYYY and MM/DD/YYYY formats. But, to ensure consistency, the data must be rewritten in the standard format. Additionally, keeping details such as name, address, and phone number in a standard format allows you to keep your work standards high.
- Validation of Data Correctness – Invalid data can affect data quality negatively. There are chances to enter invalid or incorrect data. Incorrect data refers to mismatching details to the actual context. For example, an email account without ‘@’ or a phone number with a digit less than the actual one. To tackle this data quality issue, we can implement thorough data validation techniques.
- Removal of Irrelevant Data – One thing every business must know is that not all data is relevant to your operations. There can be irrelevant information in your dataset that will not align with the organization’s business goals. Moreover, this irrelevant data can affect the quality of your decision-making. So, decluttering is the only way to eliminate this irrelevant data. Reviewing the data set thoroughly can remove irrelevant data.
- Correction of Data Entry Errors – We know that manual data entry comes with errors. This results in wrongly entered information and typos. To be clear, it can include all types of data entry errors, such as misspelled words, unintended numerals, and incorrect field values. Using automated data cleansing tools can be an effective way to tackle this data quality concern. Additionally, analyzing and reviewing the data set multiple times can help.
- Elimination of Outliers – Outlier elimination is one of the top techniques for effective data cleansing. Outliers are variations in a data set. They may be caused by entry errors or natural variations. Identifying them is essential as it can affect the quality of the data. In fact, identifying outliers is quite easy as they show dissimilarity from other values. There are several techniques to remove outliers, depending on the nature of the data. Once you find the outliers, you can decide whether to remove them or not.
- Data Cleansing Tools – There are manual and automated data cleansing tools for effective preparation of data. Moreover, data validation tools aid in refining the given dataset. These tools help detect inaccuracies, validate data, and standardize the input format of the required fields. Additionally, automated cleansing tools help automate repetitive tasks in data cleansing and improve the overall quality of datasets.
Why is Data Cleansing Important?
Incorrect data is always a bane in data management. As data is crucial for decision-making, having quality data is of paramount importance. Incorrect insights can cost your business heavily in terms of money and resources. That means with quality data by your side, you can take your business to the next level and reap better results than ever. Outsourcing to a professional data cleansing team can help you tackle issues related to data quality.
Conclusion
Data cleansing is a pivotal process that ensures accuracy and consistency in data management. By using these top techniques, service providers can leverage effective data cleansing and better results in the long run. Additionally, utilizing tools and continuous monitoring can enhance data quality and lead to better decision-making.
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