Data complexity: Complex data structures or formats may require more time for cleansing. For example, unstructured or semi-structured data may require additional effort for parsing and transformation. Larson, Jennifer (2001). Greek Nymphs: Myth, Cult, Lore. Oxford University Press. p.88. ISBN 978-0-19-512294-7.

When undertaking data cleansing, businesses should consider several factors to ensure a successful and effective process. Here are some key considerations: In addition, the ancient Greek philosopher, Porphyry (233 to c. 304 AD) wrote of the priestesses of Demeter, known as Melissae ("bees"), who were initiates of the chthonian goddess. [13] The story surrounding Melissae tells of an elderly priestess of Demeter, named Melissa, initiated into her mysteries by the goddess herself. [14] When Melissa's neighbors tried to make her reveal the secrets of her initiation, she remained silent, never letting a word pass from her lips. In anger, the women tore her to pieces, but Demeter sent a plague upon them, causing bees to be born from Melissa's dead body. From Porphyry's writings, scholars have also learned that Melissa was the name of the moon goddess Artemis and the goddess who took suffering away from mothers giving birth. Souls were symbolized by bees and it was Melissa who drew souls down to be born. She was connected with the idea of a periodic regeneration. Data standardisation: Converting the data into a consistent format or structure, such as correcting spelling mistakes or abbreviations. Improved Data Accuracy: Data cleansing helps identify and rectify inaccuracies, errors, and inconsistencies in the dataset. By removing duplicate entries, correcting misspellings, and standardising formats, data cleansing enhances data accuracy. Clean and accurate data ensures that the organisation's analyses and decisions are based on reliable information.Correcting Errors: Errors like spelling mistakes or inconsistent values are corrected based on domain knowledge or external reference sources. While the specific steps may vary depending on the context and the nature of the data, here are five general steps involved in data cleansing: Melissa became a popular name in the United States during the 1950s. The name was very popular from the 1960s to the 1990s, today Melissa is a relatively uncommon baby name; in 2010, fewer than 2,500 girls were given the name, compared with around 10,000 in 1993 and well over 30,000 at the name's peak popularity in 1979. [17] In 2007, Melissa was the 137th most popular name for girls born in the United States, dropping steadily from its peak of second place in 1977. It was among the top ten most popular names for girls from 1967 to 1984. [18] In popular culture [ edit ] Data Audit: The first step is to perform a thorough audit of the dataset to identify potential issues. This involves examining the data for missing values, duplicate records, inconsistent formats, outliers, and other anomalies. It's essential to understand the quality and structure of the data before proceeding further.

Better Decision-Making: Data cleansing contributes to better decision-making. Clean data provides a more accurate and comprehensive view of the business operations, customer behaviour, market trends, and other critical factors. This enables organisations to make data-driven decisions with greater confidence, leading to improved outcomes. The time required for data cleansing can vary widely depending on several factors, including the size and complexity of the dataset, the quality of the initial data, the specific data cleansing tasks involved, and the tools and resources available. Here are some factors that can influence the duration of the data cleansing process:Data Governance Policies: Organisations that have established data governance policies and frameworks may define the frequency of data cleansing as part of their data management practices. These policies can help guide the regular assessment and cleansing of data to maintain quality standards. Enhanced Customer Insights: Clean data allows for a more accurate analysis of customer information, leading to better insights. It enables organisations to understand customer preferences, behaviour patterns, and segmentation more effectively. Clean data supports targeted marketing campaigns, personalised customer experiences, and improved customer satisfaction. Let us celebrate the hive of Venus, who rose from the sea: that hive of many names: the mighty fountain, from whence all kings are descended; from whence all the winged and immortal Loves were again produced. [15]

Data Cleansing Tools and Resources: Evaluate and select appropriate data cleansing tools that align with your requirements. Consider factors such as scalability, ease of use, compatibility with data sources, and automation capabilities. Ensure that you have the necessary resources, including skilled data analysts or engineers, to execute the cleansing tasks effectively.Removal of duplicate records: Duplicates can occur in datasets due to data entry errors, system glitches, or merging of data from different sources. Data cleansing identifies and removes duplicate records, ensuring that each entity or observation is represented only once. This prevents data redundancy, reduces storage requirements, and improves data integrity. Cost Savings: Data cleansing can lead to cost savings in various ways. By identifying and removing duplicate records, organisations can reduce storage costs. It also minimises the time and effort spent on manual data troubleshooting and error correction. Moreover, clean data supports more efficient business processes, optimising resource allocation and reducing operational costs.