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Handling Missing Data: Missing values can be imputed using methods like mean imputation, regression imputation, or deletion of incomplete records. Data cleansing is typically performed by our data experts, who are responsible for ensuring that the data used in an organisation is accurate, consistent, and reliable. They perform various tasks to cleanse the data, such as: Melissa is a common variant form, with others being Malissa, Melesa, Melessa, Meliza, Mellisa, Melosa, and Molissa. [5]

Data Cleansing Strategy: Develop a data cleansing strategy that outlines the approach, methodologies, and tools to be used. Determine the sequence of cleansing tasks, establish rules and criteria for data validation, standardisation, and deduplication. Consider the resources, budget, and timelines required for the cleansing process. Data cleansing, or data cleaning, offers several benefits for organisations that rely on data for decision-making, analysis, and operations. Here are some key benefits of data cleansing: Data normalisation: Transforming the data into a standardized format, eliminating anomalies or outliers. Dataset size: The larger the dataset, the more time-consuming the data cleansing process can be. Cleaning and processing a small dataset may take a few hours or even minutes, while cleansing a large dataset with millions of records can take days or weeks. According to Greek mythology, perhaps reflecting Minoan culture, making her the daughter of a Cretan king Melisseus, whose -issos ending is Pre-Greek, [6] Melissa was a nymph who discovered and taught the use of honey and from whom bees were believed to have received their name. [7] She was one of the nymph nurses of Zeus, sister to Amaltheia, but rather than feeding the baby milk, Melissa, appropriately for her name, fed him honey. Or, alternatively, the bees brought honey straight to his mouth. Because of her, Melissa became the name of all the nymphs who cared for the patriarch god as a baby. [8] Melissa can also be spelled Mellissa, Mellisa, Melisa, Malissa, Malisa, Mallissa, Mallisa, Milisa, and Milissa.Forbes Irving, Paul M. C. (1990). Metamorphosis in Greek Myths. Clarendon Press. p.314. ISBN 0-19-814730-9. Cost savings: Data cleansing can lead to cost savings by reducing unnecessary storage costs, optimising data processing and analysis, and minimising the risk of errors and inefficiencies caused by inaccurate data. Clean data streamlines business operations and supports more efficient resource allocation.

Consistency and standardisation: Data cleansing helps in standardising data across different sources and systems. It involves normalising data formats, removing inconsistencies in naming conventions, and standardising units of measurement. This consistency enables data integration, comparison, and meaningful analysis across multiple datasets. Business Needs and Criticality: Consider the impact of data accuracy on your organisation's operations, decision-making, and customer experience. If data integrity is critical for your business, such as those in the financial or healthcare industries, more frequent data cleansing is necessary to ensure reliable insights and compliance with regulations.Data cleansing is suitable for a wide range of organisations and industries that work with data. Here are some examples of who can benefit from data cleansing: 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. 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: Seasonality and Events: Some industries or businesses experience seasonal fluctuations or events that impact data quality. For example, retail businesses may require more frequent data cleansing during peak shopping seasons, while tax-related data may need to be cleansed before specific deadlines. Consider such events or patterns that may require additional data cleansing efforts.

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. Correcting Errors: Errors like spelling mistakes or inconsistent values are corrected based on domain knowledge or external reference sources. Data Governance: Consider the data governance policies and procedures in place within your organisation. Data cleansing should align with the overall data governance framework, ensuring that data quality standards, ownership, and responsibilities are clearly defined and followed. The Melissa is the title of a beekeeper priestess in Starhawk's 1993 novel, The Fifth Sacred Thing.

Enhanced Data Quality: Data cleansing improves the overall quality of the dataset. By eliminating errors, inconsistencies, and outliers, it ensures that the data is consistent, complete, and reliable. Clean data enhances the effectiveness of data analytics, reporting, and data-driven processes. 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. Enhancing data quality: Data quality is a measure of how well data meets the requirements of its intended use. By identifying and correcting errors, such as missing values, duplicate records, or inconsistent formats, data cleansing improves data quality. High-quality data leads to better decision-making, improved operational efficiency, and increased customer satisfaction.