Causes of Poor Data Quality

TL;DR: Discover the 5 major causes of poor data quality in CRM systems: data entry errors, incomplete data, duplicate data, outdated data, and lack of data standards.

Five Common Causes of Poor Data Quality

Main reasons for bad data quality

• Data Entry Errors
• Incomplete Data
• Duplicate Data
• Outdated Data
• Lack of Data Standards

Of course, we can further sub-divide these categories in different ways. For instance, data entry errors could be the result of human error. One might think the natural state of data is data accurate, free of duplicates, or without errors. That would be ideal, and in such a case you probably wouldn’t be reading this. In fact, it turns out that data tends to be a rather disorganized creature unless there are checks and balances to keep it in place.

When it comes to business and CRM platforms, like Salesforce, data is usually aggregated in various forms. For instance, a company might collect information about customers from trade shows, events, online webinars, and so on. There is no obligation to provide accurate information, there is no obligation to even provide real information. Some of it might be true, some of it might be false. Some of it might be half true and contain inaccuracies. Later, when that data is entered back into the company CRM, it becomes a problem to sort out the true data from the false.

This is just one example of potential causes for bad data, but there are many avenues that bad data takes to enter an organization. In this article, we will explore some of the root causes of bad data, from data entry errors, incomplete data, duplicate data, outdated data, as well as the lack of data standards. Let’s look at them.

1. Data entry errors

Duplication

Errors during data entry can result in the creation of duplicate records in CRM systems, introducing redundancy and affecting data quality. For instance, if a customer's information is accidentally entered twice due to data entry errors or oversight, it leads to duplicate records. This duplication can cause confusion, inaccuracies in reporting, and wastage of resources. For example, if two sales representatives are unaware of each other's records for the same customer, it may result in duplicated efforts, inconsistent customer interactions, and inefficient use of resources.

Inconsistent practices

Inconsistent data entry practices, such as varied abbreviations, spellings, and formats, can negatively impact data quality in CRM systems. For example, consider the recording of customer addresses. If one employee enters "Street" as "St," while another employee uses "Str," and yet another employee spells it out as "Street," it creates inconsistency and confusion. When conducting targeted marketing or generating customer reports based on address data, this inconsistency can lead to inaccurate segmentation or incomplete analysis. Consistent practices, such as using standardized abbreviations and formats, are essential for maintaining high-quality data in CRM systems.

Human error

Good old simple human error can also lead to data entry errors, such as typos, formatting mistakes, and transposition errors. Over time this can have a significant impact on data quality in CRM systems. For example, imagine a customer's email address is entered as "john.doe@gmail.cmo" instead of "john.doe@gmail.com" due to a typo. This error can prevent effective communication with the customer, resulting in missed opportunities or ineffective marketing campaigns.

Lack of validation

Insufficient validation checks in CRM systems can allow errors to go unnoticed, leading to inaccurate data. For instance, if a CRM system lacks proper validation for email addresses, it may accept invalid or misspelled email addresses without raising any warnings. This can result in a database filled with incorrect email addresses, making it difficult to reach customers effectively. Without validation checks, other types of errors like incorrect phone numbers, addresses, or payment information can also be stored in the CRM system, compromising its overall data quality.

2

Incomplete data

As the saying goes; “You don’t know what you don’t know”. And that’s also true about the data in your CRM. Incomplete data can have knock-on effects in several ways. For instance, with the absence of important data points, doubts can arise about the reliability and validity of the information being used. This undermines the credibility of any outcomes derived from the data, as it becomes challenging to trust the accuracy and completeness of the findings.

2. Incomplete data

As the saying goes; “You don’t know what you don’t know”. And that’s also true about the data in your CRM. Incomplete data can have knock-on effects in several ways. For instance, with the absence of important data points, doubts can arise about the reliability and validity of the information being used. This undermines the credibility of any outcomes derived from the data, as it becomes challenging to trust the accuracy and completeness of the findings.

Missing information may introduce biases or distort the overall picture. For example, if customer demographic information is incomplete, marketing campaigns may fail to accurately target specific segments, resulting in less effective or irrelevant attempts at communication. Irrelevant messaging affects the quality of customer experiences in other ways too. For instance, incomplete data hampers the ability to personalize interactions and tailor offerings to customers' preferences. With misguided perspectives, you also lose the open yourself up making a poor decision. When critical information is missing, assessing risks, identifying trends, and developing actionable strategies becomes a greater challenge.

Incomplete data is like a blind spot, and for a business's direction and goals, it can result in taking the wrong steps. To address the impact of incomplete data on data quality, organizations should prioritize data completeness through proper data collection processes, validation checks, and ongoing data maintenance efforts. Implementing strategies such as data audits, quality control measures, and data validation protocols can help identify and rectify incomplete data, ensuring higher accuracy, reliability, and integrity of the data. By striving for complete data, organizations can enhance their ability to derive meaningful insights, make informed decisions, and provide better experiences to their customers.

3. Duplicate Data

Duplicate data has nothing to do with backing up your database, which is actually a good thing. Duplicate data refers to multiple instances of the same or very similar information within a dataset or database and is one of the biggest problems in data quality. Duplicates happen when identical or nearly identical records present, leading to unnecessary redundancy. Duplicate data can exist within a single dataset, or across different datasets or systems.

When you have duplicate data, not only are you wasting a bunch of storage space, but it can lead to several challenges and drawbacks

It can result in inconsistencies and inaccuracies when performing data analysis, reporting, or decision-making. Duplicate data is a key ingredient for confusion in any organization, especially for sales and marketing teams. For instance, two different two representatives of your company might reach out to the same customer, leading to frustration for both parties.

When you have duplicate data, not only are you wasting a bunch of storage space, but it can lead to several challenges and drawbacks. It can result in inconsistencies and inaccuracies when performing data analysis, reporting, or decision-making. Duplicate data is a key ingredient for confusion in any organization, especially for sales and marketing teams. For instance, two different two representatives of your company might reach out to the same customer, leading to frustration for both parties.

To combat these issues in your Salesforce org using native functionality isn’t always that straightforward. Because of the limitations in Salesforce’s native abilities, most organizations consider the use of a 3rd party application, such as Plauti Duplicate Check. Let’s look at how a 3rd party application like Plauti Duplicate Check can help maintain a healthy set of records in a Salesforce org.

You might already be thinking to yourself “3rd party application? That sounds like a risk”. While it’s true that some 3rd party applications can introduce a potential risk because they run outside of Salesforces’ environment, Plauti solutions are native to Salesforce. This means it runs within the protect Force.com environment developed by Salesforce, your security risk is minimized and generally allows for easier deployment.

One of the key capabilities of Duplicate Check is its ability to find duplicates in your org

Using intelligent matching algorithms, this feature allows you to identify duplicate records across various objects within Salesforce, including Leads, Contacts, and Accounts. It even extends its reach to custom objects, employing both exact and fuzzy matching techniques optimized for specific fields like email, phone, and person names.

One of the key capabilities of Duplicate Check is its ability to find duplicates in your org. Using intelligent matching algorithms, this feature allows you to identify duplicate records across various objects within Salesforce, including Leads, Contacts, and Accounts. It even extends its reach to custom objects, employing both exact and fuzzy matching techniques optimized for specific fields like email, phone, and person names.

Detecting and eliminating duplicates is one great step for a clean dataset, but to keep on top of quality data you need to be able to take pre-emptive measures to combat the chance of it occurring in the first place. To prevent the recurrence of duplicates, Duplicate Check offers robust duplicate prevention functionality. For instance, the real-time alert system notifies users when they attempt to manually enter a record that already exists, eliminating the chances of duplicate data creation. Instead of blocking the creation outright, Duplicate Check provides options for direct merging or listing the potential duplicate for manual verification.

Duplicate Check also excels in handling large data volumes. While it is natively capable of processing millions of records, it offers flexible execution methods to suit various job requirements. With Duplicate Check Local, you can leverage the power of your local machine and keep data within your environment. Alternatively, Plauti Cloud provides even faster processing and the ability to handle larger jobs without the need to maintain a local machine. The Cloud option also introduces collaborative capabilities, enabling teams to work together on jobs seamlessly.

Duplicate Check comes with many more features, such as automation capabilities that enhance efficiency and save valuable time. In all in all, the features and abilities you make the most use of depend on the types of data you handle and the needs of your organization. That said, when it comes to managing duplicates in a Salesforce org, Plauti Duplicate check excels.

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4. Outdated data

Just like your fruit and vegetables, data goes bad, albeit not quite as fast. People change jobs, emails, phone numbers more than you expect, and with that, your data becomes obsolete. Outdated data is even worse than duplicate data, because while the information might seem legitimate, it might be using up time and resources without you realizing.

Customer data in a Salesforce CRM can become outdated due to various factors, and the impact of outdated data on businesses can be significant

According to a study by Salesforce, approximately 70% of customer data becomes outdated within a year. Here are some of the reasons for this:

Customer data in a Salesforce CRM can become outdated due to various factors, and the impact of outdated data on businesses can be significant. According to a study by Salesforce, approximately 70% of customer data becomes outdated within a year. Here are some of the reasons for this:

Contact information changes

Changes in phone numbers, email addresses, or physical addresses can lead to failed communication attempts and missed sales opportunities.

Job changes

LinkedIn reports that professionals change jobs approximately every 4.2 years on average. When customers switch companies or job positions, their contact information and decision-making authority may change as well.

Preferences and interests

Customer preferences and interests are dynamic and can change over time. You might take preference for a certain brand today, but in a couple years, you’re interested in something new. Without regular updates, the CRM may store outdated information, leading to personalized marketing and sales efforts that are no longer relevant.

5

Lack of data standards

When there is a lack of data standards within an organization it can further aggravate the problem of data quality. From simple procedures like a formatting standard for dates & addresses, to overall data governance, standards need to be put into practice. Here are some examples of how a lack of data standards can affect this can happen, the impact it can have on a business, and what can be done to address it:

5. Lack of data standards

When there is a lack of data standards within an organization it can further aggravate the problem of data quality. From simple procedures like a formatting standard for dates & addresses, to overall data governance, standards need to be put into practice. Here are some examples of how a lack of data standards can affect this can happen, the impact it can have on a business, and what can be done to address it:

Inconsistent data entry

Without clear guidelines and standards for data entry, different individuals within the organization may enter data in varying formats, with different abbreviations, or using different conventions. For instance, one person might use "St." for street abbreviations, while another uses "Street" in full. This inconsistency can lead to duplicate records, inaccurate search results, and difficulty in analyzing and reporting on the data.

*TIP – Your team doesn’t have to remember all these formats when you use a tool to help with entry automation, such as Plauti Record Validation.

*TIP – Your team doesn’t have to remember all these formats when you use a tool to help with entry automation, such as Plauti Record Validation.

Lack of data governance

Data governance refers to the overall management and control of data within an organization. When there is a lack of data governance, there may be no clear ownership or accountability for data quality and management. Different departments or individuals may have their own processes, tools, or databases, leading to fragmented and inconsistent data across the organization.

Inconsistent data classification and categorization

When there are no standardized classification or categorization schemes for data in the CRM system, data may be labelled differently by different users or departments. For example, one team may categorize customers as "corporate clients," while another team may use the term "business customers." This inconsistency makes it difficult to aggregate data, perform accurate analysis, or generate meaningful insights.

In this article we’ve looked at five common causes of poor data quality in a CRM system: data entry errors, incomplete data, duplicate data, outdated data, and the lack of data standards

We also looked at the impact of each cause on a business and also mentioned some methods one can adopt to prevent the issues associated with bad data quality. In the next article, we are going to dive deeper into the strategies and solutions employed to fight the battle of poor data quality.

In this article we’ve looked at five common causes of poor data quality in a CRM system: data entry errors, incomplete data, duplicate data, outdated data, and the lack of data standards. We also looked at the impact of each cause on a business and also mentioned some methods one can adopt to prevent the issues associated with bad data quality. In the next article, we are going to dive deeper into the strategies and solutions employed to fight the battle of poor data quality.