The 1-2-3 Guide to Data Quality Monitoring

Your business, like all businesses, relies on vast amounts of data to manage data-to-day operations and develop long-term strategies. The need to maintain high data quality reinforces the need for constant and consistent data quality monitoring. You need to be alerted if your data quality slips so that you can fix the problem and resume the stream of high-quality, highly accurate data that your business needs to survive. 

Quick Takeaways

  • Monitoring data quality ensures that your organization receives accurate data you can use in day-to-day operations.
  • Robust data quality monitoring can dramatically reduce costs and errors.
  • The data quality monitoring process consists of three primary steps—data ingestion, issue identification, and data cleaning.

Why You Need to Monitor Data Quality

Data quality measures the condition of a set of data, in terms of accuracy, completeness, and other key metrics

Organizations depend on high-quality data to help manage their data-to-day operations and to inform their longer-term strategic decisions. If data isn’t accurate, complete, or consistent, it becomes less dependable and therefore, less useful. Poor-quality data can contribute to poor-quality decisions and have a dramatic impact on the success of any business. High-quality data, on the other hand, helps management make better decisions. 

According to our own internal research, robust data quality monitoring can:

  • Reduce costs by 50%
  • Reduce errors by 70%
  • Boost productivity by 80%

Unfortunately, poor data quality abounds, for a variety of reasons. According to businesses surveyed by the Harvard Business Review, only 3% had acceptable data quality, while 47% of recently created records had at least one critical error. This explains why 55% of business leaders don’t trust their company’s own data

The consequences of poor-quality data can be many. The potential downsides include wasting resources, damaging the customer experience, hindering regulatory compliance, and hurting your company’s reputation. 

the impact of poor quality data

Data Quality Monitoring in Three Easy Steps

How can your organization ensure higher-quality data? The answer is simple: You need to monitor your firm’s data quality. The best way to do this is with a robust data quality monitoring solution. 

Most data quality monitoring platforms utilize a three-step process for monitoring and managing data quality. These solutions make data quality monitoring as simple as 1-2-3. 

The three-step data quality monitoring process.

1. Ingest the Data

The first step in monitoring data quality is ingesting the data to monitor. This is the process of importing data, often from multiple sources, to a destination data repository. Data can be ingested either in a batch or in real time and come from a variety of sources, including:

  • Customer relationship management (CRM) platforms
  • Enterprise resource planning (ERP) platforms
  • Billing platforms
  • Other internal or external databases and data lakes

The data ingestion process also transforms data from disparate sources into a single format. From there, the data can be monitored, cleaned, and made ready for use throughout the organization.

(The following video describes the data ingestion process in more detail.)

2. Identify Issues

The second step in the data monitoring process is to identify any data quality issues. In particular, a data quality monitoring solution looks for the following issues:

  • Accuracy – Is the data correct?
  • Completeness – Are all fields populated?
  • Consistency – Is similar data from multiple databases the same, and does it stay the same over time?
  • Timeliness – How recent is the data?
  • Uniqueness – Is there any duplicated data?
  • Validity – Is all data of the proper type (i.e., all dates are in proper date format)?

These issues can be identified in several different ways. Traditional data quality monitoring systems work with a set of manually created rules designed to catch identified errors, although this approach is time consuming and resource intensive. Newer solutions employ artificial intelligence (AI) and machine learning (ML) to automate both the rule-creating and error-identification processes, thus getting more consistent and accurate results while improving efficiency and lowering costs. 

3. Clean the Data

When data errors are found, they must be dealt with. The process of cleaning poor-quality data can take several forms. 

Replace Inaccurate Data

Inaccurate data can be replaced with accurate data from known sources. For example, inaccurate customer addresses can be compared to the USPS database and replaced with accurate current addresses. Other customer contact information can be compared to contact information already residing in a company’s database. If accurate information is not readily available, the records in question may need to be deleted to ensure the overall quality of the database.

Complete Records

In many cases, incomplete records can be completed by simply filling in empty fields with known data. For example, if a given record is missing the customer’s ZIP code, that data can be found in the USPS database and inserted into the record. If an empty field cannot be easily completed, it may be left empty (if the information is inessential) or the record may need to be deleted. 

Compare Records

Inconsistent data can often be reconciled by comparing the records from different data sources. This requires establishing the one best source and using that version of the data. If differences cannot be easily reconciled, one or both of the records in question may need to be deleted. 

Archive Old Data

Older data is often still good but no longer useful. In this instance, data created before a given date may be moved to a separate data archive where it can still be accessed but not used daily. If storage space is at a premium or if older data is no longer useful, it may be deleted. 

Merge Duplicate Data

Duplicated data can often be merged if the data formats are similar enough and if one record contains information not in the other records. For example, one database might contain customer contact information while a second contains similar information plus some demographic info. In this instance, the additional information in the second record is included in the merged record. If the duplicate records are identical in every way, duplicates can be deleted. 

Reformat Invalid Data

Invalid data can often be reformatted to conform to proper field formats. In many instances, free form data is entered when it should be in a more strictly formatted field. Reentering the data into the correct format is often easy and doesn’t result in the loss of important information. If invalid data cannot easily be reformatted, it may be set aside for additional manual work or just deleted.

When the data cleaning process is complete, the data quality is ensured, and the data is ready to be used in your organization’s daily operations. 

Turn to DataBuck for Efficient and Effective Data Quality Monitoring

Data quality monitoring can improve the quality of your firm’s data, and FirstEigen’s DataBuck solution can make the process easy. DataBuck is an autonomous data quality management platform that automates more than 70% of the data monitoring process. You don’t have to create any manual data quality rules – our AI-based system does the work for you and ensures that your company’s data will be of the highest possible quality. 

Contact FirstEigen today to learn about improving data quality with DataBuck.

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