4 Reasons Why Data Quality Management Relies on Data Cleansing
One of the most important assets a company has is its data. As corporations find themselves with increasing amounts of customer, employee, and product information, they should be investing time, money, and resources into managing and organizing this intangible asset. Data quality management encompasses the union of business and technology departments working to establish policies and practices for acquiring, maintaining, and disseminating valuable data within an organization. Data cleansing is a vital practice for effective data quality management. It involves detecting, removing, and correcting errors and inconsistencies from databases as a means for improving the quality and usefulness of data. The following four benefits of effective data cleansing show why maintaining accurate data relies on this practice.
Removing duplicate and inaccurate data
Data quality issues generally arise from mistakes made during data entry. These human errors result in duplicate or inaccurate data that affects how information is used and analyzed. This, in turn, leads to misinformed decision-making and missed business opportunities. Misspellings, word transpositions, missing values, and incorrect information can all cause costly redundancies and inconsistencies in data. There are specific cleansing needs depending on whether the data storage of an organization comes from a single source or multiple sources. Each require cleansing for inaccuracies, though in instances of data integrated from multiple sources, there are other aspects to consider. Often multiple sources contain overlapping data that is only partially redundant, requiring special attention to effectively eliminate duplicates while keeping relevant and valuable data from each source. Maintaining data quality ensures organizations that the information they use to run their business is accurate, reliable, and trustworthy.
High quality data plays a significant role in a company’s decision-making process. The speed of today’s business world forces corporations to take advantage of available methods for accurate analysis and fact-based decision making. Data cleansing is a crucial aspect of data preparation, which allows for more efficient analysis. If the data that companies use when considering business strategies is riddled with inaccuracies, they may be unknowingly making ineffective and unprofitable choices. Maintaining updated and useable customer data is another important aspect of data management. The amount of incoming customer data that an organization receives increases daily. While that information may start off clean, there are many opportunities for errors that threaten data quality. Prioritizing data cleansing benefits business when making decisions regarding effective marketing campaigns, customer acquisition strategies, and in building stronger customer relationships.
Streamlining business practices
All organizations in competitive markets strive to cut costs, increase efficiency, and improve productivity. If an organization keeps reliable sales information, they can use that data to analyze the performance of the products or services they are selling. Clean data provides information that helps companies focus resources on what is connecting most with the consumer and tells them if it is necessary to reconfigure their approach to take advantage of the demand. In addition to how companies target their products to consumers, streamlining manufacturing and employee work practices can serve to reduce overhead while maintaining high productivity. A company database consisting of clean, quality data can help organizations evaluate whether or not employees are maximizing efficiency during working hours and avoiding mistakes related to misinformation, such as invalid vender contacts and outdated customer files.
Companies are using business analytics as a way to synthesize the vast amounts of data they acquire on a consistent basis, and they often aim to turn this insight into increased sales and profits. Clean data is necessary to carry out complex analysis, which can benefit companies in many ways and help them achieve their long-term goals. Business analytics based on accurate and consistent data allows companies to evaluate current business strategies, eliminate wasted time and costs, and increase overall efficiency. Predictive modeling is another valuable tool that businesses can employ to increase revenue by predicting customer preferences, forecasting sales, optimizing the manufacturing of goods and highlighting marketing techniques that resonate most with consumers. However, predictive modeling is only effective if the data used is free of errors and redundancies. All of these improvements have a direct effect on the bottom line by increasing revenue through targeted marketing and sales techniques and increasing profits through minimizing overhead and other business costs.
Effective data quality management relies on consistency and accuracy. The way a company maintains and utilizes data correlates to their profitability and success. Data cleansing is a necessary practice that business professionals and information technology managers should incorporate into their regular management practices to increase clear and efficient use of data.
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