Just as high-quality data can propel your company to greater financial success, low-quality data can take a significant toll on your bottom line. According to a recent Experian report on data quality, 95% of companies say they use data to improve business operations, while 91% believe that bad contact data results in wasted revenue. When you realize the cost of poor data quality, the need for advanced entity matching tools that can track interactions with clients, customers, or patients becomes apparent. The following information can help you determine whether your company would benefit from software that is dedicated to improving data quality.
The Cost of Poor Data Quality
Because the cost of bad data is so significant, a great deal of research has been conducted on its large-scale economic effects. Industry experts estimate that between 25% and 30% of a company’s quarterly revenue can be lost due to bad data quality. These costs add up to a staggering $3 trillion in annual losses for U.S. companies, with $314 billion of that figure being attributed to the health care industry alone. The first step toward avoiding these unnecessary costs is recognizing the types of bad data that may be affecting your company so that you can develop a strategy for improving data quality.
The Different Types of Bad Data
Many companies do not realize they even have bad data in their records, which can obscure the true costs. It is only after a company has invested in entity matching tools that it can begin improving data quality data quality in all its forms. Below are five types of bad data that may be affecting your organization:
- Duplicate data: Data listed more than once in your records takes up unnecessary space in your storage system, which increases overall costs. Duplicate data also slows your processes and may lead to unintended interactions with important contacts. For example, you may accidentally send the same message to the one contact multiple times if you are automating an email list and relying on bad data.
- Incomplete data: Data that is not fully rendered or missing key inputs cannot be accurately categorized. This can cause your company to lose money through sales funnel leakage and lower quality customer profiles.
- Invalid data: Data that includes four-digit zip codes or phone numbers with letters in them may cause errors in your software. This can, in turn, cause your systems to run slower or lead you to miss important contacts.
- Conflicting data: Conflicting entries, such as one name with two different addresses, poses obvious problems if you cannot identify the most accurate information. Reducing conflicting data can immediately reduce the cost of your marketing efforts by reducing the number of contacts you are attempting to reach.
- Unsynchronized data: If your data is not properly distributed between individual components of your master data management system, the resulting unsynchronized data may cause conflicts. This affects your ability to provide an optimal customer service experience and may also lead to lost financial and personal records.
While the cost of poor data quality is significant, enterprise data management solutions can help your company cut expenses and improve operations. By assessing the current state of your data quality, you can begin to explore entity matching tools that will lead to more accurate, actionable data.
How Does Entity Resolution Solve These 5 Data Issues?
By linking all records in your data silos through entity resolution, all five of these data quality problems can be addressed. The best entity matching tools not only solve problems in your data, but also turn “bad” records into potential assets.
- Duplicate data becomes a single view of your customer. Every record referring to the same person is brought together, confirming what you know about the person. Instead of discarding duplicate data, linking the data brings value to the overall data quality of your organization.
- Incomplete data, such as orphaned or fragmented records, is not discarded and is kept until it can be reconciled with existing records. For example, you may have a record that simply has a person’s first name, last name, phone number, and email, but it lacks all other personally identifiable information for that record. Through the entity resolution process, you are then able to link the name and phone number of that record to another record that has many instances of the phone number but completely lacks an email address. Now an otherwise incomplete record has been joined and value has brought back to the whole.
- Invalid data, when recognized as such, can provide keen insight into the entry points in your system and give clues about where you need to focus your data quality efforts in your current IT ecosystem. For example, default values are just one example in which you can learn more about your incoming data sources through data evaluation and entity resolution. Having an invalid address of “entered online” or a strange default date of birth like “1, 1, 1900” gives insight into upstream data sources and processes.
- Conflicting data can often help you build a 360-degree, longitudinal view of your customers over time. If enough raw source data is captured and reconciled, a record with two different email addresses should not be seen or analyzed as a conflict. With time stamps and information about the data sources, this helps build a view of the record.
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- Unsynchronized data is no longer a problem once entity resolution and entity information life cycle management processes have been updated in your IT system stack.
If you are interested in record linkage software that can improve your data quality, contact us today!