Data Cleaning: Everything You Need to Know

How to clean data in 5 steps

The data cleanup process includes following and practicing five steps to remove all unnecessary, irrelevant, or harmful data from datasets before moving on to the analysis part. The data cleaning steps include:

1. Profiling

The first step in data cleaning is understanding the current state of your data or finding where the messes exist so that you know what needs cleaning. Data profiling evaluates data accuracy and completeness and identifies inconsistencies, duplicates, and whether your data conforms to any standards or patterns.

The exercise of profiling forces you to question if your data is housed in the right spot, robust enough for your needs, easily analyzed or reported on, and current. Profiling is a crucial first step because it sets you up with what to look for and improve upon while data cleaning. Once you complete profiling, you’ll better understand your data.

2. Standardization

Standardization consists of converting data to a common format so users can process and analyze it. It’s also a great place to start fixing what you found in profiling.

For example, a United States postal code of 02110 could appear as 02110-1000, or 021 10 with a space in the middle. This not only makes it difficult to query and report on, but it also throws off every other process that relies on that data. Resolve those issues and keep formatting consistent across all data and systems to make human or computer analysis much more feasible and accurate.

3. Deduplication

Duplicates are inevitable in any database but are especially prevalent in customer relationship management (CRM) systems, where customer-facing teams add and change data daily. Data deduplication refers to the removal or deletion of redundant records. It eliminates excess copies of information in a database so only one piece of data remains in the master data, making your dataset more accurate.

Redundant data can harm your business strategy and processes. In a study we completed in 2022, we found that 48 percent of businesses have issues fully leveraging their CRM system due to duplicate data. We also discovered that 60 percent of businesses report that duplicate data is their marketing departments’ most significant hurdle when pulling campaign lists, and 73 percent say it’s their biggest issue when evaluating campaign performance.

4. Verification and enrichment

It’s easy to get excited about all the data you can add and verify. Evaluating what is most important for your business and customer relationship needs is imperative, as this comes at an additional cost.

Focusing on verifying data such as email addresses, phone numbers, and physical addresses will help you stay in contact with your customers and prospects, making it an excellent investment. Next, consider verifying or enriching data points that help you create frictionless customer experiences or are key indicators in your industry.

5. Automation and monitoring

In the best-case scenario, your company has already implemented cutting-edge prevention strategies to help reduce problems before they occur. Even in that case, you won’t completely eliminate potential data issues.

Successful monitoring of errors involves screening for these five major types of problems and automating the screening and clean up processes wherever possible:

1. Missing or excess data: Empty fields, missing values, or non-relevant information.
2. Incorrect data: Data that has been entered inaccurately, such as misspelled names.
3. Misformatted data: Data is in the wrong field or doesn’t follow standard structures.
4. Duplicate data: A single piece of information is mistakenly recorded more than once in the system.
5. Unanticipated results or analysis: A resulting analysis based on data goes against common knowledge or logic.

Data cleaning challenges and how to overcome them

Data errors may appear straightforward at first glance, but sometimes, they differ from what you expect. Some common challenges that can reduce clean data include:

• Unknown weak points: Your data contains errors, but you don’t know how or where they occurred in the data process.
• Deleted data: Information needed to fill data gaps cannot be found in the data warehouse due to deletion.
• Multiple data sources: Many businesses collect data from a multitude of sources, which often follow different structures or formats. If data is not standardized during data entry, this can increase errors and create unusable data.
• “Clean” data needs to replace “dirty” data: Erroneous data that has been identified and fixed needs to be replaced instead of added to the system.
• Consistent, costly maintenance: Data needs cleaning on a regular schedule so your team has continuously good quality information, but it can be time-consuming and expensive. Using an automated platform to aid in your data cleanup processes can save your team time and allow them to complete their tasks more efficiently.

Navigating the complexities of customer data cleansing

Earlier, we discussed the five major types of data errors to watch while cleaning data. Below, we’ll cover a more expansive list of the potential mistakes that can hurt your data quality.

• Irrelevant data: Data that isn’t important or relevant to your business and its goals is considered irrelevant data. Your company should identify exactly what data is essential and stop gathering unnecessary information that can cause future problems.
• Type conversion: Data types should be standardized across a dataset. If a value is numeric, then all corresponding data values should also be numeric. If not, there will be a categorical value error. Use specific field types instead of free-form text fields to ensure accurate data entry.
• Syntax errors: These errors occur when there is a coding issue that affects how the data is processed. Some solutions to this problem include removing white spaces, padding strings, and fixing any typos in the code.
• Standardize data: Make sure that each dataset follows the same format. For example, if the data uses “units,” all entries should be in that format.
• In-record and cross-dataset errors: In-record and cross-dataset errors happen when two or more values in a dataset contradict each other’s information. An example of this is if your total doesn’t accurately match the sum of your data’s values.
• Unused fields or field redundancy across objects: Data should be captured in one spot and then shared across related records. Capturing the same data point in multiple spots leads to incomplete and inconsistent data capture.

Benefits of cleaning data

Data is the backbone of successful business strategy and gleaning valuable insights. However, not all data is equal, so data cleaning is integral to helping your organization accelerate and grow. Clean data leads to benefits for your business, including:

Improved decision-making

Quantity doesn’t equal quality, and data is the best example of that. With clean data, your teams can make better decisions because they use the highest-quality and most relevant information needed to do their jobs well.

Accurate data also helps to build trust within your organization. Employees who aren’t worried or frustrated about working with incomplete or inaccurate information are more likely to create innovative solutions and strategies to help grow your business. With clean data, they can spend more time crafting solutions rather than putting more energy into cleaning the data first.

Reduced costs

Efficient and effective data cleaning can reduce the costs associated with solving a disorganized or erroneous database. For example, if your data system can’t access or provide accurate payment data, your team may be on the hook for lost or incorrect payment information.

Cleaning can also help you use past data that your team may need for future processes and strategies—consider it an investment in your future data.

Increased productivity

Data cleaning helps maintain organized data, which ultimately maximizes efficiency. Your collected data will be more accessible to the teams that require it when they need it most, especially since they won’t have to spend unnecessary time looking for or collecting data that should be easy to find in your CRM.

Following data cleaning methods will, in the long run, save your team time and help them stay productive.

Positive reputation with customers

When you collect customer data, you must apply that information and engage with each customer correctly (in relation to the data). If you use dirty data, you could work with invalid information, increasing the chances of negative customer interactions.

For example, if your team reaches out to a customer with irrelevant information or unwanted communications, you risk damaging your brand’s reputation. The customer may find your business solicitous and try to avoid other communications from your company. Meanwhile, clean data will help build trust between you and your customers. Improved accuracy enables you to engage positively and consistently, showing customers you understand and can meet their needs.

Competitive edge

Cleaning data doesn’t just help you meet industry goals and standards—it can also help you stay ahead of the competition. Accurate, well-organized, and accessible data gives your company various advantages, including better marketing results and return on investment (ROI).

Gathering customer data helps you deliver a targeted message that feels more personalized, which will create better audience engagement and loyalty to your company rather than your competitors. Customer data cleansing is pivotal in developing marketing strategies to gain more leads and conversions. With clean data, you have more opportunities to personalize sales and marketing outreach.

Data cleaning solutions

So, how do you start or improve your data cleaning processes?

First, determine the type of process that best fits your data needs. Manual data cleaning should function at some levels of your data handling process, but it’s not the most effective or error-preventative method in today’s age of consistently changing and growing big data.

If you store a large quantity of data, we recommend you opt for an automated data cleaning tool like Validity DemandTools®. Companies should invest in a data management platform that can handle large amounts of CRM data securely and efficiently without compromising accuracy, and DemandTools is your solution.

The data cleaning platform helps your team clean Salesforce data faster, resulting in accurate and complete datasets ready for the next step in your data analysis process. DemandTools has a scheduler function, giving your team the power to automate a variety of data cleaning scenarios on a schedule or kick off multiple cleaning jobs with a click, all based on your company’s specific needs.

You can manage the data in your CRM in minutes with the diverse functions DemandTools offers, including:

• Deduplication and duplicate prevention
• Bulk importing
• Standardizing
• Mass modification
• Data quality assessments

Elevate your data management strategy.

Clean data has a significant impact on your business’s success and growth. Have a data cleanup strategy that enhances your data analysis processes so your business can make more informed decisions and maintain positive customer relationships. Dig deeper into Validity’s data cleaning solutions and see DemandTools in action. Learn more about data cleaning and how Validity can help when you contact us.

Dig deeper into Validity’s data cleaning solutions and see DemandTools in action.