Effective Data Cleansing Techniques

Effective Data Cleansing Techniques

The health of your database has a direct impact on your company’s success. High-quality data drives progress by improving marketing activities, increasing lead conversion, strengthening customer relationships, and enriching stakeholder reporting.

On the other hand, insufficient data makes it impossible for any business to function effectively. Quality issues such as missing values, duplicate records, incorrect contact information, and inaccurate analysis are just some of the hurdles that faulty data can create. When the intel you rely on leads to ill-informed decisions and increases your organization’s workload, it’s time to clean things up.   

What Is Data Cleansing?

Data cleansing refers to the process of fixing or deleting information that is incomplete, inaccurate, duplicated, or incorrectly formatted. Bad data can infiltrate your database in many ways, including simple human error, sourcing data from multiple channels, and insufficient user training. 

Inadequate data does not resolve itself. You and your team must perform manual data cleansing steps to reap the full benefits of an efficient database.

5 Standard Techniques for More Effective Data Cleansing

The data cleaning process can feel overwhelming, especially with a large database. It might be challenging to know where to start and where to concentrate your efforts. Fortunately, regardless of the size or sector of your organization, you can take some fundamental actions to make the cleaning method more manageable and successful.

Before beginning, you should assess some of the critical information pertaining to your organization. Meet with leaders from different departments to gather feedback on what data is vital to their work, where they notice data quality issues, and how they hope to use a cleaner database moving forward.

Next, consider your objectives and goals as a company. When you envision a perfect dataset, how do you see it pushing you toward those goals? This evaluation can give you a clear starting point for your data cleanup.

Get Rid of Duplicate Values

Duplicate numeric values are unavoidable when working with a database. Most commonly, users accidentally import the same information more than once when they transfer data from multiple channels. Duplicate observations can also occur when staff from different departments work in the same database without using a uniform imputation method.

Ensuring your team uses consistent imputation data techniques will remove any possibility for grey-area when team members record inferred data.

Duplicate records generate enormous problems for your team and can be a huge waste of time and money for your business. You could send multiples of the same communication to the same individual, muddy data analytics visualizations, report incorrect information to your customers, and develop a reputation for poor business strategy.  

Handle Outliers

Outliers are unavoidable when exporting a large swath of information from a database. Outliers are easy to spot since they appear inconsistent with the rest of the data at first glance.

While outliers will appear in even the cleanest of datasets, they almost always warrant further investigation. If the anomaly results from incorrect data input, you should pull it from your exported file and address it within the database.

Because some outliers are valid values while others are not, you should always double-check their validity when they emerge. 

Correct Spelling and Other Structural Errors

While spelling errors may seem minor and inconsequential, they can have significant implications. 

For example, if your sales reps have repeatedly tried to reach out to a prospective client without success, they may consider the prospect a lost cause and remove them from their contact list. Now, what if someone simply typed an incorrect email address or phone number? 

Imagine this happening to multiple prospects across your database. The amount of money you could be leaving on the table is astonishing.

Spelling and structural errors could also result in duplicate entries. If a staff member wants to enter a new contact into the database, they will check briefly to ensure they do not already have a record. If the person is already in the database but their name is spelled incorrectly, the staff member will add them and mistakenly create a duplicate record. 

Ensure You Have a Uniform Format

Having a clear, straightforward protocol with easy-to-follow data cleansing tools is vital to maintaining consistency throughout your business. Explicit business rules and guidelines will simplify data entry and eliminate confusion when your departments and customers exchange information.

Data uniformity in values is crucial for reports, statistical analysis, and visualizations. To activate filters or criteria, information such as job titles, dates, addresses, etc., must be input consistently. 

Suppose you want to pull a mailing list including all your customers who hold CEO positions, but some records designate them as “C.E.O.,” “Chief Exec. Officer,” or any other format. In that case, your export will be missing a lot of results. 

Run Quality Checks Regularly

An essential, ongoing step in your manual data cleansing techniques should be performing routine checks to maintain data quality. You and your team have spent a considerable amount of time collecting, storing, and cleansing your data, and you do not want to waste those efforts by neglecting to maintain the quality of your data. Make these audits a formal part of your business processes and train new hires on your data cleansing techniques list.

Remember to include records you have previously checked, corrected, or merged in your examinations. As contact information is always subject to change, you must revisit this data in your records and update it accordingly. You can create dashboards, reports, and lists to quickly identify any issues and fix them before they become bigger problems.

What This Means for Your Data Quality

Don’t waste your valuable time and money dealing with a messy database. Set your team up for success with the help of Astreca Consulting. Our Bulk Merge App is one of the most effective and efficient cleaning tools available. 

Having clean information will allow your team to focus less on rectifying avoidable data issues and more on completing tasks relevant to their work. Contact Astreca Consulting today and learn more about Bulk Merge.

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