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You’re asked about your experience with data quality. What’s the best way to answer?

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If you're a data architect, you know how important data quality is for designing, building, and maintaining reliable and efficient data systems. But how do you showcase your experience and skills in data quality when you're asked about it in a job interview or a performance review? In this article, we'll share some tips on how to answer this common question in a way that demonstrates your value and expertise.

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Naresh Mahale

Director IT at FedEx Services
Solomon Malebye

Data Management & Analytics | Data Governance & Quality | MIS Management Reporting | Product & Pricing Management |
Andrew Fisher CDMP AIGP

Data Governance | AI Governance | Program Management | Responsible AI | Strategic data use

1 Explain data quality

Data quality refers to the accuracy, completeness, consistency, timeliness, and validity of the data that is stored, processed, and used by an organization. Data quality affects the performance, reliability, and usability of data systems, as well as the decision-making, analytics, and compliance of the business. Poor data quality can lead to errors, inefficiencies, losses, and reputational damage. Therefore, data architects need to ensure that data quality is maintained throughout the data lifecycle, from collection to consumption.

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Naresh Mahale

Director IT at FedEx Services
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All good points. DQ is a critical part of data posture at any company. Especially with data exploding over last few years, it’s important to have trusted good quality of data for decision making. DQ should not be an after thought but needs to be planned at inception to all the way to consumption. It should be defined, managed and measured in-line with data pipelines and ETLs using automated tools that also publish profiles and alert when quality metrics are not met. Similar to full stack developers, data engineers and architects should choose right platforms that are capable of implementing DQ.

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Solomon Malebye

Data Management & Analytics | Data Governance & Quality | MIS Management Reporting | Product & Pricing Management |
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Data Quality is keeping data in the exact character it was acquired/submitted from/by the data subject through maintaining its integrity by being complimentary to its rules of compliance

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Tomas Karas

Data management leader in Technology Consulting, PwC Czech Republic
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Crucial is answer sincerely: - Share how long/many times you were working in data quality area - Give examples 3 to 5 examples where you have done anything that is related to data quality in work, school or at home e.g. data cleansing, data standardization, data quality reporting and monitoring, fixing data quality issues, enhance processes for collection of data to prevent data quality issues, establish new validations, reference data management, etc.) - For each example, share actions that you did and key outcome and include some numbers i.e. number of record that you have fixed, number of issues that you have found, number of user for you DQ report. etc. - Summarize skills and tools that you have in DQ area thanks to these examples

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Shrey Gupta

Sr Manager Data Architect at SATS
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DQ measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose, and it is critical to all data governance initiatives within an organization.

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Tlotlo Pilane

Programmer | Cyber Enthusiast | Applications Specialist
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Data Quality is the heart of Science because for you to prove any theory you need facts. However for that we need to achieve it through CIA or confidentiality integrity and availability. There is a technological trend termed the FAIR digital objects this is to ensure that machine and people have access to quality data. For instance Climate Data, that is to say both scientist and machines should be able to make useful insights in long trends using quality data.

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2 Describe your approach

To answer the question about your experience with data quality, you need to explain the steps and processes you follow to ensure and improve data quality in your projects. As a reference, you can use the DQAF (Data Quality Assessment Framework) or the DMBOK (Data Management Body of Knowledge). This includes defining data quality requirements and standards based on business needs and expectations, assessing and measuring data quality using various tools and techniques, such as data profiling, data cleansing, data validation, and data auditing. Additionally, you should identify and resolve data quality issues using root cause analysis, data correction, data enrichment, and data governance. Lastly, you should monitor and report data quality using metrics, dashboards, and alerts, as well as implement and enforce data quality policies and procedures using data quality management systems, data stewardship, and data quality control.

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3 Give examples

Demonstrating your experience and skills in data quality requires providing specific and relevant examples from past or current projects. The STAR (Situation, Task, Action, Result) method is a great way to structure these examples and showcase how you used your approach and methodology to achieve data quality goals. For instance, you may have been working on a data migration project for a client who wanted to consolidate data from multiple sources into a new data warehouse. Your task was to guarantee the data quality of the migrated data and the compatibility of the data models and formats. To do this, you employed data profiling tools to assess the quality of the source data and detect any gaps, inconsistencies, or errors. You also used data cleansing tools to correct and standardize the data according to the target data warehouse specifications. Additionally, you utilized data validation tools to verify the accuracy and completeness of the migrated data, as well as data auditing tools to track and document any data quality issues and resolutions. Finally, you employed data governance tools to establish and enforce the necessary rules and roles. Ultimately, you achieved success in migrating the data with minimal errors and high quality, improving the client's integration, accessibility, and usability. As a result, you received positive feedback from both the client and stakeholders.

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Andrew Fisher CDMP AIGP

Data Governance | AI Governance | Program Management | Responsible AI | Strategic data use
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A persuasive approach to discuss your experience with data quality is to start with a business problem or opportunity. Drawing a picture of how tracking or improving DQ supported or achieved a key business outcome will demonstrate your understanding of why it’s important to any business.

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4 Highlight your value

Finally, as a data architect who cares about data quality, you need to emphasize the benefits and outcomes of your efforts. Doing so can enhance the performance and scalability of data systems, increase the trust and confidence of data users and consumers, support the decision-making and compliance of the business, reduce costs and risks associated with poor data quality, and drive innovation, growth, and competitiveness of the organization. By following these tips, you can answer questions about your experience with data quality in a way that showcases your knowledge, skills, and value as a data architect.

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Vijay Antony

Data & AI Governance(Certified) |SME for Informatica IDMC CDGC | Master Data Management(Certified) | Cloud Integration DI/AI (Certified) | Data Quality | Data Engineering | Data Stewardship | Privacy & Security
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You can complete some data quality certification which will help you stand out with your skill set. Trying to provide advise current industry problem

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Ram Yermal

Transformative Data & AI Leader | Expertise in Data Strategy, AI, & Data Architecture | Champion of Data Literacy & Innovation | Speaker & Mentor |Gartner Peer Community Ambassador, Data & Analytics
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When discussing my experience with data quality, I emphasize my track record of driving tangible business outcomes through data excellence. My unique value proposition lies in my holistic approach, where I not only ensure data accuracy and consistency but also leverage data quality initiatives to unlock strategic insights and drive innovation. By integrating data quality practices seamlessly into the current business processes, I empower organizations to make informed decisions and enhance customer experiences. My ability to bridge the gap between technical data management and business objectives allows me to deliver measurable value by turning data into a strategic asset that fuels growth and success.

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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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You’re asked about your experience with data quality. What’s the best way to answer?

1

2

3

4

5

1 Explain data quality

2 Describe your approach

3 Give examples

4 Highlight your value

5 Here’s what else to consider

Data Architecture

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You’re asked about your experience with data quality. What’s the best way to answer?

1

2

3

4

5

1 Explain data quality

2 Describe your approach

3 Give examples

4 Highlight your value

5 Here’s what else to consider

Data Architecture

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