Don’t pretend all your data is good. It doesn’t matter how careful you are, the data you have collected over the years is never going to be ‘all good’ unless you’ve used a tool to polish it.
In this article, we’ll talk about bad data and what you can do to manage it.
What is Bad Data?
Bad data can be defined as ‘inaccurate’ data.
Business uses data to understand the market, study customers, know about competitors, and make decisions. This data has to be correct and reliable for a business to be able to count on it.
Misspells, poor format, spelling variations, and typos are some small problems that can lead to major trouble. In addition to this, missing information or incorrect information can also result in bad data.
How to Manage Bad Data
#1 Accept That You Have Bad Data
The first step to managing bad data is to accept that you have bad data. You will not be able to correct a problem if you do not identify it as one.
The issue with data is that it’s not always clear if your data is good or bad. This is why it is important that you learn more about data and how to identify bad data.
A few signs include:
- Duplicate records
- Missing information
- A lack of uniformity
- Data lacking heterogeneity
- Repeated fields
A lot of analysts make the mistake of basing their judgments on poor data. Some realize the importance of data checking too late, which results in a wastage of time and resources.
The best way to be sure is to run your data through a Data Cleaning software that will not only identify problems but also solve issues.
Our data cleaning and matching software is used by all kinds of businesses including small and big corporations. You can get your hands on it for free to see how it can benefit your business. The award-winning tool is designed to save time and money and is among the easiest clean and match tools out there.
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#2 Revise or Update Bad Data
Once you realize you have bad data, it’s time to make changes to it. Our data management tool can handle a lot of issues, for example, duplicate and missing information. But in some cases, a software may not be enough.
For example, about 31 percent of users change email addresses every year. A data matching software can correct small typos and match email addresses against databases, but it cannot always find the new email address of a user.
This is why it is important that businesses revise and update data at least once a year. Your email list, address list will be useless in a few years if you fail to update it.
Updating data is said to be easier than collecting data. Users who shared their email or other personal details with you the first time are more likely to share it for a second time for as long as they have trust in you and your data management practices.
In addition to this, you can also use other techniques to revise and update data. Phone numbers, for example, can be verified through databases or sending bulk messages. The method might be costly but it typically works well.
Related Reading: Master Data Management Guide – What Is It? Why It’s Important And Best Practices
#3 Introduce a Data Quality Program
It can be hard to manage bad data without the presence of a data quality program.
The goal of a data quality program is to reduce the risk of errors and to establish common and reliable processes to support the use and production of data. The best way to manage bad data is to prevent errors at their sources, but that can be very difficult since you may already have bad data to deal with.
The presence of a data quality program will reduce the amount of bad data you will have to deal with. Since data has to go through different stages, it’s important to create a plan that takes into consideration all risk factors and carefully defines roles.
#4 Improve Data Collection Techniques
A lot of times, poor data is due to bad data collection techniques. Believe it or not, customers are known to deliberately provide incorrect or missing information because:
- They are not comfortable sharing personal details
- They don’t feel you need their data
- They do not trust your data management techniques
- They’re not motivated enough to write things down
- Your data collection technique is too tiring for them
Take these problems into consideration and use a technique that motivates customers and potential customers to willingly provide the information that you need.
Some tricks you can use include:
- Do not request personal information unless it’s absolutely necessary
- Explain how you intend to use this data and how it can benefit them
- Highlight the steps you take to ensure data security
- Offer something in return, i.e.: discounts or free eBooks
- Avoid lengthy data collection forms
#5 Teach Employees How to Handle Data
Bad data can be managed better if your employees know what to do with it. Remember that just because a collection of data contains some ‘bad’ clusters, doesn’t mean it’s useless.
It might be salvageable. You will need a reliable data matching tool to extract good data out of it.
Teach your employees how to collect, handle, dispose, and manage data so that you can save time.
5 Things You Can Do to Manage Bad Data: Conclusion
We hope these tips will help you manage bad data. The key lies in improving data management techniques and using data cleaning tools like WinPure to correct issues.
