Data Quality Assessment- Metrics & Steps to Know

Modern businesses have access to large volumes of data on a daily basis. However, this does not mean that all data is relevant or accurate.

With a data quality assessment, businesses can determine if their information is reliable and valid. If the data fails the assessment, management can improve the quality of their internal processes to enhance accuracy, timeliness, and consistency. This allows companies to become data-driven, improving decision-making.

What is Data Quality Assessment?

A data quality assessment (DQA) is a series of scientific and statistic evaluations that determine whether data meets a company’s quality standard. The standard may require a certain quantity, type, or data format to conduct certain projects or processes. It can also involve a set of guidelines and tactics used to mine, clean, and apply information.

The objective of performing a DQA is to utilize business data to expose inefficiencies and issues within a process. While empty data fields, inconsistent structures, and incorrect defaults are typically easy to pinpoint, DQA aims to discover the causes of more complex issues.

This enables companies to adapt their strategies to improve operational efficiency, maintain the integrity of internal systems, and remain compliant.

4 Metrics of Data Quality

Regardless of what a company’s standards are, DQA processes ensure the four essential elements of data quality-

• Completeness

Completeness ensures all relevant and required information is available in a dataset. For example, if a retailer collects its customers’ data, they may require both first and last names to complete a dataset. Therefore, any document or request that includes one name without the other is considered to be incomplete.

Assessing the data’s completeness generates a percentage that determines how many records are considered complete. This gives management the opportunity to fill in empty fields and improve the metric.

• Validity

Information is considered valid if it abides by the set of guidelines enforced for its type of dataset. The rules commonly include specific formatting, types, and range.

For example, a telephone number that contains letters would not be considered valid. Therefore, numbers, such as extensions, would need to be specially formatted to enter the system.

Again, validity is another metric shown as a percentage to ascertain how many records in the system remain invalid.

Completeness ensures all relevant and required information is available in a dataset. For example, if a retailer collects its customers’ data, they may require both first and last names to complete a dataset. Therefore, any document or request that includes one name without the other is considered to be incomplete.Assessing the data’s completeness generates a percentage that determines how many records are considered complete. This gives management the opportunity to fill in empty fields and improve the metric.Information is considered valid if it abides by the set of guidelines enforced for its type of dataset. The rules commonly include specific formatting, types, and range.For example, a telephone number that contains letters would not be considered valid. Therefore, numbers, such as extensions, would need to be specially formatted to enter the system.Again, validity is another metric shown as a percentage to ascertain how many records in the system remain invalid.

• Timeliness

Timeliness refers to the most accurate, up-to-date information for a specific task. In other words, it determines if the required data is available when requested.

For example, if a customer submits a change of address form, but the new address has still not been submitted into the system by the time invoices are sent out, this data set fails the timeliness assessment.
The timeliness metric measures the difference between when data is needed and when it becomes readily available.

• Consistency

A piece of data is considered to be consistent if all representations of the information are exactly the same across all systems and databases.

For example, if dates are inputted using various formats throughout an organization, it is known as inconsistent data.

Timeliness refers to the most accurate, up-to-date information for a specific task. In other words, it determines if the required data is available when requested.For example, if a customer submits a change of address form, but the new address has still not been submitted into the system by the time invoices are sent out, this data set fails the timeliness assessment.The timeliness metric measures the difference between when data is needed and when it becomes readily available.A piece of data is considered to be consistent if all representations of the information are exactly the same across all systems and databases.For example, if dates are inputted using various formats throughout an organization, it is known as inconsistent data.

6 Steps of a Data Quality Assessment

A DQA can be tailored to fit any company’s set of standards. However, the assessment framework consists of six comprehensive steps-

1. Define Business Goals

First, businesses must set their short and long-term goals for data quality improvement, internal processes, and guidelines.

For example, companies aiming to ensure that all of their customer records contain accurate and consistent information should set personal information verification and reference rules.

In this scenario, the stakeholders would be the finance, marketing, and manufacturing departments, which need access to accurate customer data. This impacts internal processes such as order fulfillment, billing, and requests.

2. Assess Established Data Sources
Next, management needs to assess their existing data sets against the rules they established in the first step. This includes evaluating information across all internal systems to determine accuracy, completeness, consistency, validity, and timeliness.

Depending on the type of business and volume of data, management might need to perform qualitative and quantitative assessments using various analytical tools. While the financial department may only handle quantitative data, such as billing, the marketing department may process reviews, ratings, and other qualitative information.

Businesses should also use the steps to assess their existing policies, such as data security, industry standards, and data access.

3. Analyze the Assessment Results

Once the assessment has been conducted, managers need to analyze the results on several fronts. One way to perform the analysis is to measure the gap between the existing data and set business goals. Managers can also analyze the information by pinpointing the root causes of low-quality data sets.

For example, if customer addresses are inconsistent across different systems, this may be the result of human error, poor integration, or inaccurate data.

4. Develop Improvement Methods

Based on the analysis, companies need to design and develop a plan of action to improve their data quality. These plans should outline time frames, resources, and any expenses required.

Some businesses may find their software and applications need reprogramming, while others may only need to manually change a few datasets.

5. Implement Solutions

When the improvement plans are set, businesses can begin implementing the solutions. Management should be sure to make a note of any technical changes to internal processes or procedures.

This includes developing an updated standard operating procedure (SOP) to ensure that all employees are properly trained and have reference resources.

6. Monitor Data

After the improvements have been implemented, management must perform periodic checks to ensure data meets quality standards and works towards business goals.