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What are the most common data quality benchmarking challenges?

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Data quality benchmarking is the process of measuring and comparing the quality of data against a set of standards or best practices. It can help data architects to identify and prioritize data quality issues, improve data governance, and demonstrate the value of data quality initiatives. However, data quality benchmarking also poses some common challenges that need to be addressed. In this article, we will discuss four of these challenges and how to overcome them.

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1 Challenge 1: Defining data quality dimensions and metrics

One of the first steps in data quality benchmarking is to define the data quality dimensions and metrics that are relevant and meaningful for the data domain and the business objectives. Data quality dimensions are the aspects of data quality that can be measured, such as accuracy, completeness, consistency, timeliness, and validity. Data quality metrics are the quantitative or qualitative indicators of data quality performance, such as error rate, completeness percentage, or customer satisfaction score. However, defining data quality dimensions and metrics is not a straightforward task, as different data sources, stakeholders, and use cases may have different data quality expectations and requirements. Therefore, data architects need to collaborate with data owners, users, and consumers to establish a common data quality framework and vocabulary, and to align the data quality dimensions and metrics with the business goals and the data value chain.

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Sagar Navroop

? Architect | ??????????-?????????????? | Technologist
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The most common data quality benchmarking challenges are: 1. Establishing uniform definitions and metrics can be a challenge, considering different stakeholders' perspectives. 2. Diverse source integration can often lead to varying levels of accuracy 3. Adoption of new sources/technologies 4. Lack of Industry/organization-level standardization 5. The volume of data and processing speed needs pose a challenge 6. Human error and bias 7. Heterogeneous technology stack adoption could lead to inconsistencies 8. Allocating a budget and managing complex benchmarking efforts around legacy infrastructure can be a challenge 9. Changing/dynamic business goals necessitate constant adjustments - impact benchmarking results

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Jash J.

Data Analyst at Insight Global Health
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- I had the privilege of working with enterprise Data Architects. A thorough understanding of technical processes was one of the notable skills I noticed. This provides an opportunity for mitigation, as data quality is a persistent phenomenon. - Concepts of RDBMS and relationship model are practical during root cause analysis. I was able to assist the Senior QA analysts with efficient bug tracking.

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2 Challenge 2: Collecting and analyzing data quality data

Another challenge in data quality benchmarking is to collect and analyze the data quality data that is needed to calculate the data quality metrics and compare them against the benchmarks. Data quality data can be obtained from various sources, such as data profiling tools, data validation rules, data quality audits, data quality surveys, or business performance indicators. However, collecting and analyzing data quality data can be time-consuming, resource-intensive, and complex, as data architects need to deal with large volumes of data, diverse data formats, multiple data systems, and dynamic data environments. Therefore, data architects need to leverage automation, standardization, and integration techniques to streamline and simplify the data quality data collection and analysis process, and to ensure the reliability, consistency, and comparability of the data quality data.

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Jash J.

Data Analyst at Insight Global Health
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- Designing a protocol for data standardization is critical if your department deals with data requests. Fortunately, the healthcare sector has an edge over finance as the data collected is significantly more. E.g., Enhancement in HL7 Health standards. - There is potential in blockchain technology as the standardization is in-built. However, we have yet to witness what developers and innovators would craft.

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3 Challenge 3: Setting and communicating data quality benchmarks

A third challenge in data quality benchmarking is to set and communicate the data quality benchmarks that are used to evaluate and compare the data quality performance. Data quality benchmarks are the reference points or targets that represent the desired or acceptable level of data quality for a given dimension or metric. Data quality benchmarks can be derived from various sources, such as industry standards, best practices, regulations, customer expectations, or historical data. However, setting and communicating data quality benchmarks can be challenging, as data architects need to balance the trade-offs between data quality and other factors, such as cost, time, and feasibility, and to justify and explain the rationale and the implications of the data quality benchmarks to the data stakeholders. Therefore, data architects need to use a systematic and transparent approach to set and communicate the data quality benchmarks, and to involve the data stakeholders in the process to ensure their buy-in and commitment.

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Merrill Albert

Enterprise Data Leader, Data Governance Officer, Data Thought Leader, Chief Data Officer, Fractional Governance, Data Evangelist, LinkedIn Top Data Governance Voice, creator of #CrimesAgainstData
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For data quality benchmarks, you need to focus on what the business says is acceptable. It's fine to share what other companies do, but ultimately, the business has to decide if they want to do the same or something different. It's their business and they're the ones who suffer the consequences if something is wrong. While the data doesn't always have to be 100% perfect, you have to understand the implications if it's not 100%. Any decisions you make on less than perfect data will be impacted. Some decisions are fine because they just need a ballpark number and perfection isn't necessary. Others could be a bigger issue and you need to know the difference. You also have to clearly communicate the status of the data that's impacted.

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4 Challenge 4: Monitoring and improving data quality performance

A fourth challenge in data quality benchmarking is to monitor and improve the data quality performance based on the data quality metrics and benchmarks. Data quality performance is the degree to which the data quality meets or exceeds the data quality benchmarks. Data quality performance can be monitored and improved by using various methods, such as data quality dashboards, data quality reports, data quality alerts, data quality feedback, or data quality improvement plans. However, monitoring and improving data quality performance can be difficult, as data architects need to track and manage the changes and trends in the data quality data, the data quality benchmarks, and the data quality performance over time, and to identify and implement the appropriate data quality improvement actions and strategies. Therefore, data architects need to use a continuous and iterative process to monitor and improve the data quality performance, and to measure and demonstrate the impact and the benefits of the data quality improvement efforts.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most common data quality benchmarking challenges?

1

2

3

4

5

1 Challenge 1: Defining data quality dimensions and metrics

2 Challenge 2: Collecting and analyzing data quality data

3 Challenge 3: Setting and communicating data quality benchmarks

4 Challenge 4: Monitoring and improving data quality performance

5 Here’s what else to consider

Data Architecture

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What are the most common data quality benchmarking challenges?

1

2

3

4

5

1 Challenge 1: Defining data quality dimensions and metrics

2 Challenge 2: Collecting and analyzing data quality data

3 Challenge 3: Setting and communicating data quality benchmarks

4 Challenge 4: Monitoring and improving data quality performance

5 Here’s what else to consider

Data Architecture

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