In today's data-driven world, maintaining data health is crucial for organizations to get the most value out of their data. Large-scale data systems are often complex and require a comprehensive approach to ensure data reliability. To make data-informed decisions, the data itself needs to be trustworthy. In this article, we will discuss five ideas to help you maintain the health of your big data systems.
Five Ideas for Maintaining Reliable Big Data
1. Ensure Data Reliably at the Source
It is critical to collect data reliably at the source. This involves type checking and quality checking the data as it is collected. Digital platforms should have robust instrumentation to prevent data corruption during collection. The data reception and storage system should be reliable and durable, minimizing the risk of data loss due to system failures.
2. Minimize Manual Data Entry
Manual data entry introduces inconsistencies and errors. To maintain data health, it is essential to minimize or eliminate manual data entry as much as possible. This can be achieved through automation and data validation. One such automation tool is Smart Paper which automates the collection of paper assessment data. Automated paper grading eliminates manual checking and data entry of grades. When manual data entry is unavoidable, it is required to have a well-defined process for managing and validating the entered data to ensure consistency and accuracy.
3. Maintain a Consistent ID System for Entities
In large-scale data systems, it is imperative to have a consistent ID system for entities to avoid creating an entity resolution problem. For example, if there are several IDs for one user or one customer, it creates a big issue when reporting the data or analyzing customers as a whole. Split data can lead to confusion and inaccuracies in the reporting. To get around this problem, establish a unique identifier system for all entities and if there are any external IDs, link them with the internal IDs. Once data systems get big, resolving the consistent ID problem becomes more challenging. So this problem needs to be dealt with as early on as possible.
4. Synchronize Clocks and Use Consistent Time Zones
Time-stamped data is critical for many big data systems. Collecting timestamp data in Coordinated Universal Time (UTC) and displaying it to users based on their local time zone ensures that timestamps are accurate and consistent across the system. Reliable timestamps enable more applications of the data and allow us to see user behavior in a nuanced way.
5. Implement Data Health Checks & Monitoring Dashboards
Data health checks and monitoring dashboards are great tools for maintaining data health in large data systems. Regular health checks help identify potential issues such as nulls, inconsistencies, and anomalies in the dataset. Monitoring dashboards provide real-time insights into key data quality indicators. These tools are especially critical when deploying updates to the data system, as they help ensure that the new update does not introduce errors or adversely affect data health.
Conclusion
Maintaining data health in large-scale data systems is an ongoing process that requires constant attention and effort. Organizations that keep their data integrated are able to get compounding returns on their data's values. Integrated datasets allow companies to understand their customers and users better, and improve the efficacy of the product. If you need help auditing and improving your data systems, reach out to us at Playpower Labs. We have helped large-scale education organizations solve challenging data problems in the past.
By implementing these five ideas: ensuring data reliability at the source, minimizing manual data entry, maintaining a consistent ID system for entities, synchronizing clocks and using consistent time zones, and implementing data health checks & monitoring dashboards, you can significantly improve the health of your big data systems. Look after your data health, and it will enable you to unlock the full potential of your data!