Data cleaning
Data cleaning, also known as data cleansing, is a crucial process for organizations that manage large volumes of data, such as inventory records, customer information, and payroll data. This process involves meticulously checking, correcting, and reorganizing data to ensure accuracy and relevance, ultimately resulting in a well-organized dataset that enhances operational efficiency. Common issues addressed during data cleaning include outdated information, typographical errors, and the removal of irrelevant data, which can otherwise clutter data storage and impede decision-making.
Organizations often utilize both specialized software and human expertise in data cleaning. While software can efficiently identify and report errors according to established rules, human judgment is essential for making subjective decisions about data relevance and consistency. Data cleaning not only helps improve data management practices but also supports effective data analytics, which can lead to better insights and increased profitability. Overall, the data cleaning process is vital for businesses aiming to maintain accurate records and adapt swiftly to changing organizational needs or customer requirements.
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Data cleaning
Data cleaning, or data cleansing, is a method of managing the data accumulated by businesses and organizations. This data includes inventory records, payroll data, attendance records, customer information, and many other sets of information. Though this data provides a valuable resource to companies, allowing them to critically examine their efforts to find flaws and inefficiencies, it often requires careful management. Data becomes obsolete, typos are made, and flaws are entered into the system.
To improve the way organizations utilize their data, many use data cleaning. During this process, the well of information collected by an organization is carefully checked, corrected, and reorganized. At the completion of the process, the organization is presented with a current, accurate, and well-organized data set. This data may then be utilized to improve the efficiency of the organization's operations. Some data cleaning operations utilize software to help analyze large amounts of data. The software automatically finds many errors and inconsistencies. However, most data cleaning is completed by people who directly understand the needs of the organization. This insight allows them to know what data is still relevant to its operations.
Background
Data cleaning is an important part of data management. Data management refers to the many data-related tasks carried out by businesses and other organizations. It includes document storage, record management, data security, quality management, contact data, and data destruction. Any business or organization that takes in customer information, tracks its shipments, or manages finances carries out data management.
Data records are valuable resources. They provide businesses with insights into their finances, customers, successes, and shortcomings. Successful data analytics can help companies maximize their profits and avoid future financial difficulties. However, for many businesses, managing the large amounts of data taken in during financial transactions, record keeping, and other operations is a difficult task. Some companies hire specialized data management firms, while others manage their data internally.
Organizations have good reason to focus on properly managing data. For customer-centric businesses, losing large amounts of customer contact data could be devastating. Warehouses and product distributors rely on data storage to organize, store, and locate products. Sales-focused businesses rely on data storage to record and fulfill customer orders and track billing and payments. In any of these situations, suddenly losing a significant amount of data could stop a business from functioning, and thus profiting, until the data is recovered or replaced. Any digital business that stores large amounts of data on a central server is at risk of data loss.
Well-conducted data management allows organizations to quickly and efficiently change to suit their members’ and customers’ needs. For example, a warehouse company with efficient data management can quickly alter the layout of its inventory to accommodate new products or remove older products. It can also improve productivity by ensuring that employees have quick and simple access to any information relevant to their tasks. Inventory management through digital data can also save service industry businesses such as restaurants and retail locations' money through reductions in unnecessary types of inventory. Data management can allow business owners to easily check what items are in stock, what items are selling, and what items need to be reordered.
Overview
Data cleaning is the process of maintaining the wealth of data collected by businesses or organizations. Many organizations collect a substantial amount of data during day-to-day operations. If stored physically, this data takes up valuable space. When stored digitally, the data costs money in the use of server space and electronic maintenance to keep.
Much of the data collected by organizations is only relevant for a short period of time. Customers’ personal information, including addresses and phone numbers, changes regularly. The old prices of goods are stored within outdated spreadsheets. Errors are introduced in one data set, then repeated when that data set is copied. To combat these and other issues, organizations engage in data cleaning.
When undergoing data cleaning, an expert first organizes all available data. They then carefully comb through the data, flagging any incomplete, irrelevant, incorrect, or outdated information. Much of this information can be deleted, freeing up space and resources for the company. However, other information may have to be updated or corrected. For example, clients’ personal information, such as addresses and phone numbers, may need to be checked to ensure consistency and accuracy.
Data cleaning specialists will check with an organization, verifying any specific rules to which the data should adhere. They check the ranges within which all numbers in given fields should fall, allowing them to identify any outliers that might be the result of input errors or data corruption. They check expression patterns, meaning the format within which data must be represented to be properly read by computers or users. For example, organizations may have particular requirements for the formatting of dates and phone numbers. Specialists will also ensure that data remains consistent across all fields. For example, the date that a patient was admitted to a hospital should be the same across all the patient’s various records.
Some specialized types of software can be used to assist organizations with data cleaning. This software will analyze any data entered into it, cataloging the data and checking it against any rules set by the organization. It then provides a report of any places where the rules were broken, allowing the organization to inspect and correct the errors.
Cleaning software cannot make many of the subjective decisions inherent in data cleaning, however. It cannot tell when a set of data has become outdated or irrelevant to the ongoing work of a business. For this reason, the best data cleaning solutions often involve both software analysis and human observation.
Bibliography
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