What are the best practices for data validation during communication?

Ensuring data validity is crucial in communication to maintain credibility and trustworthiness. Here are some best practices for data validation during communication: 1- Source Verification 2- Cross-Reference Data 3- Data Triangulation 4- Peer Review 5- Error Checking 6- Transparency 7- Cite Your Data 8- Data Cleaning 9- Understand the Data 10- Presentation Clarity 11- Regular Updates 12- Accessibility 13- Data Security 14- Feedback Loop By following these 14 best practices, you can enhance the accuracy and integrity of the data you communicate, fostering a more informed and constructive dialogue.

When we talk about data, the following is important: 1- Data sources depend on the context of your project. If you don't have an ideal data source, you can create one using surveys or a combination of multiple data sources. 2- consider reliable and relevant sources to meet the needs of the project. 3- define the scope of the project specifying the necessary data and objectives. 4- separate the data into relevant and less relevant categories; You never know what initially less relevant data may become crucial. 5- Data and its quality can vary over time, and the mechanisms you establish to validate the data will help maintain control over these variations.

Data validation during communication involves: Defining data sources, scope, and assumptions to set clear boundaries. Employing descriptive statistics and visualizations to analyze and understand data. Applying data quality rules and tests to ensure accuracy and completeness. Documenting and reporting validation findings to keep stakeholders informed. These practices promote reliable data transmission. Additionally, prioritize data security with encryption, authentication, and error handling to safeguard information during communication.

The primary goal of data validation is to establish trust in the analysis performed. 1.In communicating insights, always use the production data sets. Proactively call out the sources, submit how you ensured they represent factual findings, the recency of data source 2. Always call out the insight confidence indicators when communicating insights. Confidence is high if the data is latest, granular, widely used across the organization, and governed regularly. 3.When assumptions are used in analysis, position the insights as directional insights and not actionable insights. Have a separate session with the data owners to validate assumptions beforehand. 4. Use a standard data maturity certification framework for communication.

Build a solid foundation for the data validation process by clearly identifying and documenting all data sources. Define the scope of your analysis, specify what data is included or excluded, and the rationale for those decisions. Clearly state any assumptions you make about the data. This step is important for transparency and allows others to understand the context of your validation efforts. Consider factors such as data format, update frequency, and potential bias in your sources.

Las estadísticas descriptivas, como la media, la mediana y la desviación estándar, ayudan a comprender la distribución y las tendencias de los datos. Las visualizaciones permiten una comprensión intuitiva de patrones y relaciones en los datos. Algunas de las mejores visualizaciones son: Histogramas: Muestran la distribución de una variable. Diagramas de dispersión: Revelan relaciones entre dos variables. Diagramas de caja (box plots): Proporcionan información sobre la dispersión y los valores atípicos. Gráficos de líneas: útiles para mostrar tendencias a lo largo del tiempo. Estas herramientas ayudan a identificar patrones, anomalías y comprender la estructura subyacente de los datos, fundamental para la toma de decisiones.

Finding missing values, outliers and feature correlations can help us to have a better vision on our data and by that, we will gudge on data validation

Use statistical metrics and visual representations to gain a deeper understanding of the characteristics of your data. Calculate key metrics such as mean, median, mode, and standard deviation to understand central tendencies and variance. Create a variety of visualizations such as histograms, box plots, scatter plots, and heat maps to identify patterns, contours, and relationships within the data. This exploratory analysis helps identify hidden trends and potential issues that may require further investigation or confirmation.

By merging descriptive statistics with captivating visualizations, we can unlock hidden patterns, identify outliers, and trace trends within the data landscape. This visual pattern not only deepens our understanding but also empowers us to communicate findings with captivating clarity. It unveils subtle insights and nuances that might otherwise remain hidden within the raw data, paving the way for a comprehensive and informed analysis.

Je préciserais également qu’il ne faut pas confondre valeurs aberrantes et atypiques. Les premières sont des données non issues de la population (erreur de mesure, d’enregistrement…) tandis que les deuxièmes n’ont rien d’anormales et sont simplement des valeurs rares.

Data quality testing could become one of the next pillars in data management considering the upcoming developments in AI. The real strength of a data team, in fact, could shift from "having good data developers" to "having good data testers" to verify the quality and consistency of an autogenerated AI algorithm outputs.

Verifying and testing data in data science is crucial. - Ensure data follows the expected format, detect outliers, and validate consistency between sources. - Check data completeness, ensure accuracy, maintain referential integrity, and manage temporal data effectively. - Prioritize security and privacy, document detailed processes, and automate tasks for consistency and efficiency. These practices enhance confidence in data quality and improve analysis validity.

1. Focus on impact Prioritize rules/profiling for data that impact directly to the end insights. 2. Risk-based scrutiny Tailor checks to potential damage of bad data. 3. Evolve rulesets with data Considering dynamic nature of data adapt the rules to changing nature and needs. 4. Human + Automation While Automation is really important, but keep humans in the loop for end result judgment.

Perform a robust set of quality checks to ensure the integrity and reliability of your data. Develop and implement a policy to verify the validity, accuracy, consistency, completeness and timeliness of data. Use techniques such as profiling, data cleansing, and data transformation to identify and resolve quality issues. Perform cross-checks to ensure consistency between different data sources or time periods. Automate these quality checks where possible to maintain ongoing data integrity.

In data communication, maintaining high-quality data involves crucial practices. First, validate inputs to prevent errors or malicious data. Second, check data formats like dates and phone numbers. Third, ensure numeric values fall within acceptable ranges. Fourth, maintain data consistency across fields or messages. Lastly, employ cross-field validation to confirm relationships between different data elements. These practices collectively enhance the reliability and accuracy of communicated data, contributing to effective data management and decision-making.

Documentation is a key aspect of data validation as it enables one to help explain their findings better to cross function teams. A detailed report needs to have bare minimum of the following things: - Data sources and a quality check on those sources. - Scope and applicability of the data - Any statistical tests done to validate and analyze that data and corresponding results. - Assumptions made and verified with other to ensure maximum correlation with ideal data. - Data visualisation strategies applied and key findings from those analysis. The key point here is to maintain a generic tone of documentation so that it can be easily understood by other teams to ensure compliance with the original report.

Why should we share our validation findings ? - The analysis done was based on the initial set of data and with basic business knowledge. - The business team will correct any insights that we have differently. - This goes as a handshake between dev team and business team, as they typically agree or disagree to the findings. - In case of disagreement, either we correct the understanding or the business team provides us with more data/information. - This will have a serious impact in your modelling exercise as we are accountable for the models we build. - A feature which seems to add less value must be checked in terms of business angles too.

Create complete documentation of your audit process and findings. Prepare a detailed report describing data sources, methodology and quality control. Clearly state any problems found during the inspection and explain how they were resolved. Include visualizations and summary statistics to support your findings. Offer suggestions to improve data quality or refine the review process in the future. These documents serve as a reference for stakeholders and provide transparency in your data verification efforts.

The data validation report is the compass of transparency, encapsulating data sources, scope, and assumptions. Descriptive stats and visualizations weave patterns, while data quality rules act as gatekeepers, rectifying issues. It's a testament to our commitment to reliability. Shared with stakeholders, this report isn't just findings; it's a narrative of resolution and improvement. Recommendations for enhanced data quality and future validation processes underscore our dedication to informed decision-making. In this concise document, we showcase not just data, but a culture of openness and continual refinement.

By documenting and reporting your findings, you can transform them into a collaborative quest for knowledge, paving the way for reliable conclusions, impactful communication, and ultimately, more informed decisions. So, map your journey, and share the treasures of your data validation endeavors with the world It builds transparency & trust, collaboration & improvement, and communication & impact.

1. Data Precision: Rigorously scrutinize data with an expert's eye, ensuring flawless accuracy. 2. Time Standardization: Embrace ISO standards for date/time formats, guaranteeing universal consistency. 3. Robust Input Validation: Implement stringent entry checks, akin to high-security access protocols. 4. Educative Error Messages: Craft error feedback that instructs and guides, enhancing user understanding. 5. Periodic Data Audits: Regularly review and validate data, maintaining its integrity like a meticulous archivist.

Implement additional best practices to improve the background check process. Set clear quality thresholds and avoid perfectionism, which can hinder progress. Make review an ongoing, iterative process rather than a one-time event. Leverage subject matter experts to provide valuable context and insight. Implement automated validations where possible to ensure consistency and efficiency. Compare new data to previously validated gold standard datasets to maintain quality over time. Educate stakeholders on the importance of background checks and their impact on decision making.

In my experience, it is very important not to allow perfection become the enemy of great. Aligning on thresholds around quality of data is very critical. It is very easy to go down a rabbit hole of chasing perfection. Every issue needs to be viewed in isolation and in combination with other issues to understand the overall impact on the business outcome that we seek to achieve with that dataset.

One of most important yet really simple thing I learned for data communication is that always try to tie it back to a previously reported data which is of gold standard or widely used. Having a clear understanding on how previously reported data reconciles with new data at any level (overall, mean, median, trend) gives a good summary of the quality and builds confidence in the numbers for the users.

The foundation of any data validation process is dependent on how coherently the data sources are defined, considering any underlying assumptions that could be driven purely by domain. It is always advisable to keep a domain expert involved during the phase, but also get your hands dirty. This not only brings more confidence in the data but also ensures no loopholes in the validation process. Once the groundwork is done, the validation process can be completely automated and can be done iteratively to capture the dynamic nature of the data. Additionally, adding a comparative analysis with the previous period accommodates any unforeseen changes in the standardization defined.