7 Data Cleansing Activities Companies Should Perform

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jakaria
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Joined: Tue Jan 25, 2022 4:26 am

7 Data Cleansing Activities Companies Should Perform

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Bad data can cost your business dearly. Keeping your data clean is essential. Only 29% of businesses think their current customer or prospect data is inaccurate in some way. It's actually higher than that - it's just the number of companies that think their data is bad. Why is bad data a problem? Poor quality data leads to a lot of waste in organizations across all industries. In fact, a recent study showed that bad data can cost businesses in the United States up to $3 trillion a year. data-cleansing-activities Did you know that around 20% of the average contact database is dirty? For example, if your business averages only 5,000 contacts, bad data could cost you almost $100,000 a year! Any company that collects a decent amount of customer data should perform regular audits to assess all of this information. Without it, you could have huge amounts of waste in your marketing and sales budgets and never know it. What is Data Cleansing? Data cleansing, also known as data scrubbing, involves first identifying and then removing or correcting inaccurate records from a database, dataset, or table. It starts with recognizing incomplete, unreliable, inaccurate, or irrelevant parts of the data, and then restoring, remodeling, or deleting the changed data. You can perform data cleansing techniques as a batch process through scripts or interactively with data cleansing tools. cleaning-gif Once complete, your dataset should be consistent with other related datasets in your operation.

Discrepancies identified or eliminated may have been caused by user input errors, corruption in storage or transmission, or by various data dictionary descriptions of similar items in various stores. Benefits of Data Cleansing Clean data leads to several health benefits (for the business). Here are some things you can expect after a good data cleaning: You'll remove major errors and inconsistencies that are unavoidable when multiple data sources are brought together in a dataset. Using tools to clean your data will increase efficiency because your team can quickly get what they need from a clean database. Fewer mistakes means happier customers and fewer frustrated employees. You can map the different functions and what your data is intended to do, and where your data comes from. Data cleansing activities your business should perform Want clean data? It's more than just an annual audit. To company email list truly reap the benefits of data cleansing, your organization must perform certain data cleansing activities consistently. Some good guidelines to follow are to focus on your main metrics. What is the overall goal of your business and what is each member trying to get out of it? A good way to start is to involve all interested parties in a brainstorming session. When you're ready to move forward, follow some best practices when it comes to creating a data cleansing process and learn the difference between good data and bad data. good-data-vs-bad-data 1. Watch for errors Accurate record keeping is essential. Keep a record and look at trends where most errors come from within your business. This will make it much easier to identify and fix incorrect or corrupt data. Error monitoring is vital if you're integrating other solutions with your business software, so that errors don't clutter the work of other departments. 2. Standardize your data entry processes What if you started with accurate, clean data? You can when you standardize the entry point and check the importance of it.

By standardizing your data process, you'll ensure a good entry point and reduce the risk of duplication, bad records, and other dirty data issues. 3. Validate and maintain data accuracy Once you've cleaned up your existing database, validate the accuracy of your data. Research and invest in data tools that allow you to clean your data in real time. Some tools now even use AI or machine learning to better test accuracy. 4. Scrub Duplicate Data Identify duplicates, as it will save you time when analyzing the data. You can avoid this by researching and investing in different data cleansing tools that can analyze raw data in bulk and automate the process for you. 5. Analyze the results Once your data has been standardized, validated, and cleaned for duplicates, use third-party sources to add it. Trusted third-party sources can capture information directly from proprietary sites, then cleanse and compile the data to provide a more complete picture for business intelligence and analysis. 6. Communicate with your team As with any project, communication is key. Be sure to communicate the new standardized cleaning process to your team. Now that you've cleaned up your data, it's important to keep it clean. This will help you develop and strengthen your customer segmentation and send more targeted information to customers and prospects, so you want to make sure your team is compliant. 7. Include a feedback process Your team would also benefit from a feedback process. Each organization should create a process that controls where incorrect information is reported from and then updated in the database. For example, you can establish a feedback mechanism for emails sent but not delivered due to an incorrect address, so that they are flagged and the invalid email address is removed from customer data. . Keep your data clean These data cleansing activities, if done consistently, can allow you to turn your raw, dirty data into useful, useful, and manageable information. cleaning-minion-gif Although the process can be difficult, it is beneficial. That's why your business shouldn't ignore this master data management function. All the data cleaning activities mentioned above will provide you with clearer customer data which will play a vital role in helping the business grow. Data cleansing not only gives you good quality data but also brings uniformity in data sets that are merged from different sources. Moreover, your job of maintaining and storing good quality data is not just limited to data cleaning. You must take great care of incoming data so that it is consistent with similar data sets that are used by the organization.
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