Your data governance team is struggling to stay on top of data quality. What skills are missing?

Communication, communication, communication. From my experience, the best way to stay on top of Data Quality is by communicating with the appointed Data Owners and Data Stewards regularly on how they are tracking in terms of Data Quality. This creates the sense of urgency to keep improving within your area and adds a bit of competition or gamification to the field of governance and data quality.

I recommend a targeted training program focusing on practical data literacy. Drawing from my experience, we implemented a training module using real-world company related examples directly relevant to the team's challenges. For instance, we showcased scenarios where inaccuracies in customer data led to errors in financial reporting for a financial services team. This approach effectively highlighted the tangible impact of data quality on financial operations. Hands-on exercises were tailored to the team's datasets, incorporating data profiling tools to identify and rectify anomalies. This tailored training significantly improved the team's data literacy, empowering them to proactively manage data quality issues.

War on talent is real especially when it comes to candidates with data skills. Everyone is looking for talents who understand, interpret, and communicate data effectively. This is the key for success for any Data Governance team to manage data quality risk. One way to address this is to create jobs with specific data skill requirements and provide on the job training as well.

If a data governance team can’t stay on top of data quality, several key skills may be missing: - Data profiling and analysis - Data cleaning and transformation - Technical proficiency - Communication - Collaboration - Problem-solving

As a data management specialist, enhancing data literacy is key to addressing our data governance challenges. I'm committed to advancing my skills through online courses on data analysis, engaging with data communities for shared insights, and mastering data visualisation tools. This will empower me to better identify and articulate data quality issues, develop effective standards, and communicate the importance of data integrity to stakeholders. My goal is to elevate our data governance practices, ensuring a data-driven approach becomes central to our organisational strategy, ultimately leading to improved decision-making and operational efficiency.

As a data management specialist focusing on data engineering, it's crucial to refine my ability to design, build, and maintain robust data pipelines and systems. This skill is essential for implementing data quality checks, ensuring data security, and maintaining system scalability. To elevate my data engineering capabilities, I plan to deepen my knowledge in key programming languages like Python and SQL and master frameworks and tools such as Spark, Hadoop, and cloud services like AWS. This will enable me to enhance the infrastructure that supports data governance, ensuring our data is not only high-quality but also secure and readily accessible for decision-making processes.

As a data management specialist, enhancing my data stewardship skills is crucial for effective data governance. This involves managing and documenting data assets, defining ownership and roles, and setting quality standards. I aim to adopt best practices like DAMA-DMBOK and ISO 8000 and utilise tools like Alation and Informatica for data cataloguing and profiling. This will enable me to monitor data quality, resolve discrepancies, and ensure our data is managed with integrity, supporting informed decision-making across the organisation.

As a data management specialist, I recognise the importance of data ethics in ensuring privacy, compliance, and trustworthiness in our data practices. Ethical considerations are paramount in avoiding bias, discrimination, and harm while promoting fairness, transparency, and accountability. To enhance my data ethics skills, I'm committed to deepening my understanding of regulations and frameworks like GDPR and FAIR principles. Engaging in discussions and trainings on data ethics will further equip me to apply ethical principles effectively across all data activities, ensuring our organization's data use aligns with the highest ethical standards.

Estabelecer uma esteira de DataOps em que aspectos de qualidade são validados antes da implementação pode ser uma boa estratégia. Evitamos que problemas novos aconteçam através da federação de responsabilidade para os desenvolvedores. Implantar uma boa observabilidade de dados também pode ser muito bom. Desta maneira, alertamos os proprietários e os curadores sempre que um problema é identificado. Dica extra: Reaproveitar as regras do DataOps para Observabilidade. Sempre bom lembrar que as dimensões de qualidade de dados geralmente são aplicadas depois que muita coisa ruim já está construída. Então será necessário paciência para tratar o legado e para influenciar as correções sistêmica que forem encontradas.

It may not be a lack of skills but a capacity issue causing DQ to struggle. If that is the case, look at the more modern data quality tools leveraging AI/ML to detect anomalies and alert you on potential DQ issues. This approach can be done without building and maintaining rules over time or managing drift. With these tools, stewards can focus on remediation when automating the definition and orchestration aspects of DQ. Some examples of these tools in this area are Anomalo, Telemai, Deeque, Monte Carlo, and DQLabs.

𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗦𝗸𝗶𝗹𝗹 𝗚𝗮𝗽𝘀: ↳ Conduct a skills assessment to evaluate your team's existing technical and non-technical skills. ↳ Identify knowledge and proficiency gaps to pinpoint specific areas where team members might require additional upskilling. 𝗧𝗮𝗿𝗴𝗲𝘁𝗲𝗱 𝗦𝗸𝗶𝗹𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: ↳ Develop training programs focused on improving data quality. ↳ Promote cross-training and knowledge sharing initiatives. 𝗙𝗼𝘀𝘁𝗲𝗿 𝗮 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗖𝘂𝗹𝘁𝘂𝗿𝗲: ↳ Emphasize the importance of data quality through success stories highlighting the benefits of data quality improvements. ↳ Empower data stewards with authority to ensure data quality within their specific areas of ownership.