What is your experience with data quality?

Start with the “Why”! Before proceeding with the mentioned first step, execute the “Step Zero” - to establish the initial prioritized business cases wherein improving data quality would deliver business impact, and for which the business stakeholders provide their sponsorship. Showcase initial success - first proof of value (PoV) - for those prioritized use cases. Ask the business stakeholders to confirm value realization and communicate that across the organization. That would enable to secure senior management attention and capital allocation (for people, processes and technology) to move ahead with a strong foundation, to realize further wins!

My experience with data quality has been multifaceted, encompassing both challenges and rewards. Initially, I encountered the messy reality of real-world data, riddled with inconsistencies, inaccuracies, and biases. This challenged me to develop critical thinking skills and a keen eye for detail, and meticulously scrutinize the source. Over time, I learned to appreciate the transformative power of data quality. Witnessing the impact of clean, reliable data on decision-making, from scientific breakthroughs to efficient business operations, fueled my passion for data integrity. It's a continuous journey, but the satisfaction of transforming messy data into actionable insights keeps me striving for a world where quality data empowers humans.

Data quality assessment is vital for reliable data management. It involves data profiling to understand its structure, content, relationships, and distributions. Data cleansing corrects errors, removes duplicates, and standardizes data. Data validation checks compliance with rules and standards. Data auditing monitors and reviews quality processes. Implementing data governance policies and roles ensures ongoing quality maintenance. This continuous process ensures data remains accurate, complete, and reliable for informed decision-making. We could write a complete article only with this theme.

Data quality assessment is one of the essential step that enables you to assess your state of the data! In my experience, I have observed that the data onboarding considers the engineering aspects to design and integrate data sources where the data quality sits back and wait to get this setup.Most of the time, data quality comes into picture when data consumption or analytics or BI processes starts consuming it. In my opinion, this is the most critical design aspect where you design quality checks, auditing, reporting and rectifying the bad records before it lands into the consumer layer. Data quality is also one of the pillar of data governance! With AI adoption, this becomes the first step to the sucess.

Improving the quality of your data starts with essentially knowing where you’re and what is important to you. Know all data isn’t equal. The quality attributes vary based on the domain of data and its importance.

Data quality is very important because before data become reliable for management decisions making hence it has to be tested for its accuracy, completeness,validity etc. Poor data accessibility could lead to high risk in my field of business and the likes.

When I joined my company, our database was in disarray with inconsistent and outdated data. We initiated a transformation with ETL processes to clean and restructure the data. Beyond technical fixes, we revamped our business processes and nurtured a culture that values data accuracy. This comprehensive approach improved decision-making and emphasized the importance of blending technology with a supportive culture and efficient processes. The journey was challenging but ultimately rewarding, leading to a more efficient and data-driven organization.

Data Quality é a medida da condição dos dados com base em fatores como precisão, integridade, consistência, confiabilidade e se estão atualizados. A medição dos níveis de qualidade dos dados pode ajudar as empresas a identificar erros de dados que precisam ser resolvidos e avaliar se os dados em seus sistemas são adequados para atender à finalidade pretendida. O trabalho de Data Quality é estabelecido por meio de várias etapas e práticas, incluindo a identificação dos dados mais relevantes, limpeza e refinamento dos dados, monitoramento da qualidade dos dados e gerenciamento de qualidade de dados (DQM). O DQM é o processo de pipeline que verifica nos dados os valores necessários, tipos de dados válidos e códigos válidos.

Data quality assessment is a crucial element in establishing superior data quality. It helps in identifying data issues such as inaccuracies, inconsistencies, duplicates, and missing data. Regular assessment provides a current snapshot of the state of data quality. It helps organizations understand what they're doing well and where they need to improve. Data quality assessments can help ensure that a company's data complies with the required regulations. A thorough assessment serves as a baseline that can measure future improvement against, and helps create a roadmap for continuous data quality improvement.

My experience with data quality is more-so experience with metadata quality. I previously held a position wherein I was responsible for the upkeep of a knowledge base for a call center. The upkeep was less about the data in our system itself and more about how agents were to correctly perform their jobs/process the data received. The quality of this data proved to be of exponential importance, as B2B client commitments changed often with customer experience at the forefront.

In my opinion, this is the first step towards your data quality implementation. You need to identify the data stewards or owners who owns the data, understand and help not only to define the data standards but also define the data classifications. Once standards are defined, you can integrate them with engineering pipelines to measure, validate, report, audit and share the DQ reports to the source as well as application teams. DQ report helps source teams to identify the issues and rectify the pipelines to generate the most accurate data feeds for the consumer applications. You can also define the automated frameworks to take care of data quality checks based on the metrics defined, marking data quality issues, reporting them for audits.

Data standardization is a cornerstone for data management. It transcends mere uniformity and creates a common language for data. It provides clarity, consistency, and effective data communication across an organization. By implementing consistent rules and formats, standardization ensures that data from diverse sources is interoperable and meaningful. This uniformity is critical for internal analytics and decision-making and integration with external systems and partners. Recognizing the strategic value of data standardization can transform it from a technical task to a key component in achieving broader objectives like customer satisfaction, operational efficiency, and regulatory compliance.

Experience revealed to me that you must: First focus on identifying relentlessly Key Business Priorities and then assess their related data usage in the areas of decision-making, customer experience, operational performance or risk management. Second is to evaluate how poor quality data impact those business priorities. Third, identify 3 or 4 max relevant data quality dimensions and quantify issues and outcome. Next action steps come easily with support from business owners. Actions such as : - Prioritization of sustainable DQ initiatives - Establishing SMART DQ goals - Continuous relentless evaluation and change Tip : Switch from "Data Quality" to "Quality Data" ;-)

From my experience, working towards data quality has been iterative and often involves many more teams, individuals, initiatives, and even technologies. Data governance quickly becomes a top goal and if you don't have data governance measures in place already, this can take some time to figure out and fully deploy. While identifying and dealing with duplicates, missing values and the like is critical for data quality; talking with the end consumers of the data can be very enlightening. Maybe there aren't glaring issues, but there are changes that can be made to the data to enhance its quality for end users. Different teams may use the data differently and their use case should be considered when assessing and improving data quality.

Even this article repeats the mistake: we have to start from explaining why data quality matters. Companies do not take it seriously because it is difficult (but not impossible) to translate to business case.

In my experience, we need to take a holistic view of Data Quality across the Data value chain - measure and track how the KQIs (Key Quality Indicators across the dimensions of Completeness, Accuracy, Timeliness, etc.) perform across the data value chain. This would enable us to understand which steps, processes, and underlying systems are impacting the KQIs positively and negatively. Action plans to improve the KQIs need to be made based on such a holistic view. A data management tool that includes both data quality and data cataloging (that delivers business and technical data lineage) capabilities would enable automation of developing the holistic view.