How can you ensure data quality when working with different intradisciplinary relationships?

As a Full Stack Data Scientist and Machine Learning Engineer, I start by establishing clear data quality criteria. This involves identifying key data attributes essential for our multidisciplinary projects – like accuracy, completeness, consistency, and relevance. For instance, in a healthcare analytics project, accuracy is vital for patient data, while in a marketing analytics project, completeness might be more crucial. Setting these criteria helps in aligning the data quality goals across different disciplines.

Just follow 5 steps to ensure data quality when working across disciplines – - Define data quality criteria and metrics through collaboration with different domain experts - Be transparent about limitations, use plain language when communicating issues - Develop common data tools and standards for consistency - Do regular monitoring and reviews to identify issues over time - Continuously improve processes as new insights emerge on evolving needs Summary - focus should be on establishing consensus, communicating clearly, standardising systematically, validating rigorously, and continuously improving.

Settings criteria for your data cleaning process is essential to maintain data quality. Have a list of data issues that must be treated in all datasets. Like removing duplicates, trimming data after splitting into columns, deleting it replacing errors etc

Defining acceptable data accuracy thresholds and basic sanity testing is always helpful- 1. Define the outliers and the associated ways to segregate them- relevant for real-time operations data capture like daily active users 2. Set 2/3 reconciliation criteria to cross check numbers from multiple angles- useful for financial metrics, sales, inventory 3. Check directional trends in terms of % movements - example for measuring any traffic the abs. numbers are bound to deviate depending on how it is captured, what tools are used but the YoY, MoM, QoQ changes should be within acceptable limits 4. Perform statistical analysis to measure any sampling biases, confidence intervals etc. useful for large population data

Garantizar la calidad de los datos en entornos con diferentes relaciones intradisciplinarias puede ser un desafío, pero existen algunas prácticas y estrategias que puedes implementar para mejorar la precisión y confiabilidad de los datos: Establecer estándares claros de calidad. Comprender las interconexiones disciplinarias. Validación cruzada para mayor fiabilidad. Normalización y coherencia de datos. Controles de calidad en la entrada de datos. Documentación detallada para replicabilidad. Capacitar al personal y actualizar las prácticas. Gestión de cambios y auditorías periódicas. Fomentar la colaboración interdisciplinaria. Impacto en resultados confiables y relevantes.

There is an important point about communication which is to develop a Data & Intelligence Culture in the company and involve employees. I call it building an internal intelligence network (IIR) or Rede de Inteligência Interna (RII) in Portuguese. My experience as a senior consultant says that if we don't start developing the basis of data culture, there is no point in the other technical steps. It simply doesn't work.

After defining the criteria, it's crucial to assess the current state of data quality. This involves using automated tools and algorithms to detect anomalies and inconsistencies. For example, when working with financial and operational data, I use statistical methods to identify outliers or missing values. This step is iterative and helps in understanding the gaps in data quality from an interdisciplinary perspective.

Em um projeto que atuei, nós recebíamos dados de 5 fontes diferentes e mesmo com critérios de qualidade de dados e a sua padronização, na prática, os dados dificilmente os atendiam, e por algumas vezes precisavam ser reenviados devido ao distanciamento do escopo do projeto. É essencial a utilização de ferramentas para a avaliação dos dados, adquirir uma descrição característica geral e exploratória dos dados, documentando de forma explícita que tipo de dados estão sendo inseridos no pipeline.

This is interesting subject, but it goes to one word "tolerance" Using tools is ok but to which end ? We have to define first the accepted tolerance on each aspect of data quality then use the necessary tools to measure whether the data is below the tolerance level or above it.

Escolher o método e a ferramenta apropriados depende das características específicas do seu projeto, como o volume de dados, a complexidade das relações intradisciplinares e os requisitos de qualidade. Além disso, a automação de processos pode ser valiosa, especialmente em projetos com grandes conjuntos de dados, permitindo uma avaliação mais eficiente e consistente.

Effective communication is key. As a data expert working with diverse teams, I ensure that data quality issues are communicated in an understandable manner. This includes creating dashboards or reports that highlight data quality issues, tailored to the audience. For instance, technical details are crucial for IT staff, while business impacts are more relevant for executive teams. This fosters a collaborative environment where data quality is everyone's responsibility.

Clear communication is crucial for data quality in interdisciplinary relationships as it helps in establishing common understanding and expectations. Clear communication ensures that all team members have a shared understanding of data requirements, definitions, and standards. This reduces the likelihood of misinterpretation or errors.

Ensuring data quality in interdisciplinary relationships involves clear communication, standardized data formats, and collaborative validation processes. Establishing data governance frameworks helps identify and address issues promptly, fostering a shared understanding among team members about data accuracy and reliability. Regularly scheduled data quality assessments and transparent reporting mechanisms can further enhance interdisciplinary collaboration and maintain data integrity.

Communicating data quality issues to third parties involves transparency and clarity. Provide a detailed report outlining specific issues, their impact, and potential solutions. Use a structured format and offer channels for discussion to address concerns collaboratively.

Effectively communicating data quality issues is essential for maintaining the integrity and reliability of information within an organization. It involves transparently articulating any discrepancies, inaccuracies, or anomalies present in the data to relevant stakeholders. Clear and concise communication ensures that decision-makers are informed about the limitations and potential risks associated with the data, allowing them to make informed choices. This process involves not only pointing out the issues but also providing context, impact assessments, and potential solutions to address the data quality challenges. Clear, consistent and compassionate communication is essential.

Collaboration is essential for improving data quality. I work closely with different departments to identify the root causes of data issues and develop solutions. This might involve refining data collection processes, enhancing data cleansing routines, or updating data models. For example, in a supply chain project, collaborating with the logistics team helped in improving the accuracy of inventory data.

Lembrando que a melhoria da qualidade dos dados é um esforço conjunto que envolve pessoas, processos e tecnologia. A colaboração eficaz entre diferentes partes interessadas é essencial para alcançar e manter padrões elevados de qualidade de dados ao longo do tempo.

Foster collaboration and communication: Encourage open communication and collaboration among different interdisciplinary teams. This helps in addressing data quality concerns, sharing best practices, and resolving any data-related issues collectively.

Collaborate on cleaning, validating, and improving data together, leveraging all perspectives. 👉🏽 Encourage interdisciplinary teams to analyze data together to identify potential biases, inconsistencies, or missing information. This leverages diverse perspectives and insights for better detection and resolution of issues. 👉🏽 Standardize data cleaning and validation practices to be uniformly adopted by all teams and individuals engaged in data management. This ensures consistency and minimizes errors. 👉🏽 Regularly evaluate the effectiveness of your data quality measures and adapt them as needed. Feedback from team members across disciplines can be invaluable in identifying areas for improvement and refining your approach.

Promoting data quality requires ongoing effort and collaboration. Data quality improvement - be it through standardization, integration, transformation, enrichment, or governance - addresses quality issues, refining data. Collaborating with intradisciplinary partners allows you to identify and prioritize appropriate improvement actions. Remember, enhancing data quality isn't an isolated task, but a continuous, joined force effort to optimize the accuracy and usefulness of your data for any project.

Data quality is not a one-time effort; it's an ongoing process. Regular reviews and updates to the data quality strategy are necessary. This involves revisiting the defined criteria, assessing if they still align with business goals, and making adjustments as needed. For instance, as new technologies emerge, like AI and IoT, I ensure that our data quality strategies are updated to incorporate these advancements.

Ao adotar uma abordagem dinâmica e proativa para a qualidade dos dados, você garante que seus dados permaneçam relevantes, precisos e úteis ao longo da vida do projeto. A flexibilidade para se adaptar às mudanças é crucial para garantir que os dados continuem atendendo às expectativas das partes interessadas e se alinhem aos objetivos em evolução do projeto.

Define and monitor key data quality metrics. Regularly assess and report on metrics such as accuracy, completeness, consistency, and timeliness. This provides visibility into the overall health of the data.

Provide training and support: Offer training programs and resources to educate team members on data quality standards, data entry protocols, and best practices. This ensures that everyone understands their role in maintaining data accuracy.

In general, the organization who is investing money on data analysis will have many stakeholders directly interested. I have seen the Client changed the criteria of data cleaning which might pull you back. Hence, periodic update on the progress is very important to lead such project and achieve the ultimate objective.

In addition to these steps, it's important to stay updated with the latest trends and technologies in data management. Continuous learning and adapting to new methodologies are crucial. Also, fostering a culture that values data quality across the organization is essential. This includes training sessions for non-technical staff and encouraging a mindset where data is seen as a valuable asset.

Eu destacaria a dinâmica da qualidade dos dados ao longo do tempo. A natureza evolutiva dos projetos e dos dados demanda uma abordagem contínua para garantir que a qualidade seja mantida.

This what method I use 1. Set Standards : Use the same formats, units, and terms for all data to keep it consistent. 2. Use Templates : Collect data using standard templates to make sure it's complete and uniform. 3. Clear Policies : Have clear rules and procedures for managing data 4. Communicate : Keep communication open between different teams to understand each other's data needs 5. Use Tools : Use tools that help integrate and harmonize data from different sources. 6. Check Data: Regularly check data for errors using both automated tools and manual reviews.

Utilize AI algorithms and insights from the LinkedIn community to enhance data quality. AI can help automate data validation, while community input can provide valuable context and domain knowledge.

Data quality (DQ) requires many factors to ensure the best outcomes including closely followed data input controls. For example, a system should only allow a valid value (pre-defined) to be entered or the transaction should reject and require review. DQ reporting is also critical to apply to data sets whereby administrators can monitor the level of quality (valid values) that exist in all critical data elements. Further a well defined data dictionary should be in place to ensure one version of the truth. And don’t forget about the chain of custody of data. Some users may legitimately manipulate / transform certain data elements for a specific purpose but if that data then feeds another user, it may no longer match the data dictionary.