Process for Curating Trustworthy Data

Can you truly trust your data if you don’t have robust data quality controls, systematic audits, and regular cleanup practices in place? 🤔 The answer is a resounding no! Without these critical processes, even the most sophisticated systems can misguide you, making your insights unreliable and potentially harmful to decision-making. Data quality controls are your first line of defense, ensuring that the information entering your system meets predefined standards and criteria. These controls prevent the corruption of your database from the first step, filtering out inaccuracies and inconsistencies. 🛡️ Systematic audits take this a step further by periodically scrutinizing your data for anomalies that might have slipped through initial checks. This is crucial because errors can sometimes be introduced through system updates or integration points with other data systems. Regular audits help you catch these issues before they become entrenched problems. Cleanup practices are the routine maintenance tasks that keep your data environment tidy and functional. They involve removing outdated, redundant, or incorrect information that can skew analytics and lead to poor business decisions. 🧹 Finally, implementing audit dashboards can provide a real-time snapshot of data health across platforms, offering visibility into ongoing data quality and highlighting areas needing attention. This proactive approach not only maintains the integrity of your data but also builds trust among users who rely on this information to make critical business decisions. Without these measures, trusting your data is like driving a car without ever servicing it—you’re heading for a breakdown. So, if you want to ensure your data is a reliable asset, invest in these essential data hygiene practices. 🚀 #DataQuality #RevOps #DataGovernance

We need to fix the root causes of data quality issues, not just clean incorrect data. The cost of reappearing data quality issues will accumulate over time. If a data engineer needs to spend one day reviewing and fixing the same or a very similar data quality issue every month, that will result in 12 days of work after a year and two months after five years. Those recurring issues only indicate that there is a process issue in data collection or data management. If we identify the root cause of these issues, we can enhance the data platform's reliability and gain user trust. Some tasks cannot be automated, such as no tool can compel data stakeholders to engage in the process. However, we can present them with reports to confirm that the issue is real and feasible to be fixed. The root cause process for DQ issues is straightforward: 🔸Identify the problem 🔸Engage experts who can confirm it 🔸Collect all information that will describe the problem in detail, such as data samples 🔸Discuss the possible causes and pick the most reasonable one 🔸Implement a solution 🔸Confirm that the issue is solved by triggering data quality checks The most crucial step is the first one - you need to identify the problem enough to show it to the business users. They will be willing to invest in data quality if they see a value. #dataquality #datagovernance #dataengineering

Data quality is fundamental to achieving reliable, impactful program outcomes, especially within the complex landscape of humanitarian and public health interventions. This document, Data Quality and Quality Improvement Training by USAID, provides an in-depth approach to data quality assessment, introducing critical tools like the Data Quality Assessment (DQA) and Routine Data Quality Assessment (RDQA) frameworks. These tools are designed to help organizations evaluate, maintain, and enhance the accuracy, consistency, and timeliness of their data, empowering them to make informed, data-driven decisions. This guide is essential for M&E professionals and program managers who are responsible for data integrity across service sites and reporting systems. It outlines step-by-step processes for verifying data at multiple levels, from on-site service data checks to system-wide evaluations, ensuring that data collection and reporting are aligned with high standards of quality. Practical tools, including Excel-based dashboards and real-time monitoring checklists, support these assessments, allowing for immediate insights into areas that need improvement. Beyond verification, the document emphasizes the value of building data quality into everyday processes, from staff training to cross-referencing data sources, and includes strategies for continuous quality improvement. This resource is indispensable for anyone committed to enhancing program accountability, data reliability, and ultimately, the effectiveness of humanitarian interventions.

You want to deliver actionable insights? It all begins with thorough data validation. Follow these steps to avoid "garbage in, garbage out": 1. 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲:    Understand how your data was gathered to assess its reliability. Ask yourself if you truly know where your data comes from.     2. 𝗖𝗵𝗲𝗰𝗸 𝗳𝗼𝗿 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆:    Verify that data formats, labels, and measurement units are aligned. Look for inconsistencies, such as varying date formats.     3. 𝗘𝗻𝘀𝘂𝗿𝗲 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀𝘀 & 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲:    Confirm that your data is up-to-date and fits your analytical goals.     4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗮𝗻𝗱 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗚𝗮𝗽𝘀:    Look for missing values that could skew your findings. Investigate why gaps exist and fix them through additional data collection or statistical methods.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗮 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲:    Cross-check your data against business logic. Ensure that figures make sense in context, for example, by avoiding impossible values such as negative stock levels. Clarify any discrepancies with stakeholders. Your aim is to generate insights that can be trusted. What are your steps to ensure data quality? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you know the importance of data validation. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #datacleaning #datavalidation #careergrowth

How does your organisation govern data as a strategic asset? Navigating the complexities of data governance can feel like an uphill battle. Yet, the proper framework transforms data chaos into a strategic asset. Here’s a deep dive into the essential components that can streamline your data management efforts: 🔘 𝐃𝐚𝐭𝐚 𝐒𝐭𝐞𝐰𝐚𝐫𝐝𝐬𝐡𝐢𝐩 ↳ Act as the guardian of your data’s integrity. Managing and overseeing data ensures its quality, consistency, and compliance with regulations, much like a diligent curator in a museum. 🔘 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 ↳ Accurate and timely data is the backbone of any sound decision. Monitoring and improving data accuracy, completeness, consistency, and timeliness ensures your data remains reliable and trustworthy. 🔘 𝐃𝐚𝐭𝐚 𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 ↳ Empower specific individuals or teams with responsibility and accountability for data assets. Clear ownership is the foundation of effective data management and governance. 🔘 𝐃𝐚𝐭𝐚 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 ↳ Organise data based on its sensitivity, value, or risk. This classification aids in implementing necessary security and compliance measures, protecting your most critical information. 🔘 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 & 𝐀𝐫𝐜𝐡𝐢𝐯𝐢𝐧𝐠 ↳ Set clear policies for storing, storing, and disposing of data based on legal, regulatory, and business requirements. This practice not only ensures compliance but also optimises data storage. 🔘 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 ↳ Stay ahead of privacy laws and regulations like GDPR or CCPA. Adhering to these standards is crucial to protect individuals’ data and maintain trust within your user base. 🔘 𝐋𝐢𝐧𝐞𝐚𝐠𝐞 & 𝐏𝐫𝐨𝐯𝐞𝐧𝐚𝐧𝐜𝐞 ↳ Trace your data's journey through systems and processes. Understanding your data's origin and transformations ensures its accuracy and compliance, much like a historian tracing the lineage of artefacts. 🔘 𝐂𝐚𝐭𝐚𝐥𝐨𝐠𝐢𝐧𝐠 & 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 ↳ Maintain a searchable inventory of data assets in a centralised repository. This enables easy data access and utilisation, making it a powerful resource for your organisation. 🔘 𝐑𝐢𝐬𝐤 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 ↳ Proactively identify, assess, and mitigate data-related risks, such as breaches or regulatory non-compliance. Effective risk management is essential to safeguard your data. 💡 𝐏𝐫𝐨 𝐓𝐢𝐩: Implementing these practices secures your data and enhances its value, making it a strategic asset for your organisation. 📣 What steps has your organisation taken for effective data governance? #DataGovernance #DataStewardship #QualityManagement #DataOwnership #DataClassification #RetentionArchiving #PrivacyCompliance #DataLineage #DataCataloging #RiskManagement