Data operations has been an essential component of data analytics since the advent of data warehousing systems. However, it has gained more importance after the digital revolution due to the exponential growth of data, the need for integrating data from various sources outside of on-premises sources, the requirement for quick insights for business decisions, regulatory compliance, and support for artificial intelligence activities.
This blog aims to provide a comprehensive guide for implementing data operations effectively. By following the best practices for data operations, businesses can gain better insights from their data, make informed decisions, and stay ahead of the competition.
Building a Robust Data Operations Framework:
According to a survey, 59% of companies reported that managing data operations is a significant challenge.
Data operations is a crucial function in any organization that deals with large volumes of data. It involves managing data throughout its lifecycle, from ingestion to consumption, and ensuring its quality and availability to end-users
The quote “Hope is not a Strategy” from the Google SRE book resonated with me deeply. It emphasizes the importance of comprehensive planning in Data Operations to establish trust among end users. Rather than relying on hope that all operational tasks will stabilize over time, it necessitates a thorough and strategic approach to consistently deliver reliable data on time. This, in turn, strengthens trust, enhances data reliability, and ultimately supports informed decision-making.
Here are the 10 steps for building a robust Data Operations Team:
Robust Onboarding Applications Checklist
Application Onboarding is the process of validating the code readiness of new applications for deployment. A robust onboarding checklist should be developed and revisited periodically to ensure its effectiveness. The checklist should include items such as performance, capacity planning, job dependencies, communication to user groups, SLAs, data validation scripts, recovery procedures, audit logs, and reconciliation mechanisms. Clear roles and responsibilities for stakeholders should also be documented.
Ensuring Data Quality and Trust
Ensuring data quality is crucial for building trust in the data used for generating insights and reports. Organizations can either choose to implement data quality in-house build framework or use external tools such as Talend Data Quality, Informatica Data Quality etc. This will ensure to validate the reliability, completeness, and accuracy of the data.
Establishing data lineage is crucial for impact analysis and tracking the sources of data used for a column or table. This helps to streamline the RCA process and identify the impact of any changes made to the data. To achieve this, organizations can either develop an in-house metadata-driven solution or use external tools such as Collibra to establish and maintain data lineage.
Protecting sensitive information is crucial, and role-based access control is an effective way to limit data access to specific roles. This will reduce the risk of unauthorized access or misuse of data.
The success of data projects heavily depends on the adoption by end-users after deployment. It is a proven fact that some user groups tend to stick to their old ways of accessing data, which can hinder the adoption of new data tools and processes. To address this, it is crucial to provide thorough user training, ongoing usage monitoring, and periodic re-training to ensure continued adoption and effective use of the data tools and processes.
When jobs fail, data is missing or data mismatches occur, it can create incidents that disrupt operations. To minimize the occurrence of such incidents, several measures can be taken, including:
- Conducting a Root Cause Analysis (RCA) to identify the underlying causes of the issue.
- Documenting the RCA findings and the steps taken to resolve the issue, which can help prevent similar incidents in the future.
- Having regular discussions with the development team to identify and address any repeating issues.
- Adding the identified repeating issues to the onboarding checklist to ensure proper validation and prevention.
Monitoring Tools and Operations KPI
As the number of applications and tech stacks supported by the Data Operations Team increases, centralizing monitoring becomes crucial. Tools such as Grafana, Splunk, and Data Dog can aid in centralizing monitoring. Additionally, data observability tools like Monte Carlo can be employed to proactively diagnose data breaks and pattern changes.
Key Performance Indicators (KPIs) related to Data Operations such as:
a) Data accuracy b) Data availability (Percentage of Time data is available for use) c) Incident resolution time d) Application onboarding time e) SLA compliance f) Data processing time g) Customer satisfaction needs to be collected & reviewed regularly and accordingly adjust the team performance.
Housekeeping tasks in data operations involve regular maintenance and cleaning of the data environment to ensure optimal performance, reliability, and availability of data. Automation of some of the housekeeping works help to save time and reduce manual intervention.
Some of the common housekeeping tasks include:
- Data Purging/ backups/Archiving
- Indexing/Collecting Stats/optimizing databases
- Cleaning up logs/backup tables
- Managing user accounts
- Performing system updates (Patches/upgrades etc)
- Remove unused Tables/reports/Extracts
- License renewals
These tasks can help data operations teams to ensure the optimal functioning of the data environment.
Automation and Cost Optimization
To ensure efficient and effective Data Operations, it is essential to automate manual activities to save time, set up validations, alerts, and notifications. Since many data pipelines run in the cloud, optimizing performance and scheduling is critical to reducing processing costs.
This can be achieved by regularly monitoring and analyzing pipeline performance, identifying bottlenecks, and making necessary adjustments to improve efficiency. Additionally, scheduling jobs during off-peak hours can help reduce processing costs while still ensuring timely delivery of data
Staffing and Skill Development
Building a skilled and well-balanced team is crucial for successful Data Operations Team. Cross-training team members is also important to ensure that they are equipped to handle different tech stacks and to prevent knowledge silos. To maintain a motivated team, it’s important to provide proper rewards and recognition. This can be done through performance-based incentives or other forms of acknowledgment. Additionally, investing in the team’s professional development can help to ensure that their skills stay relevant and up-to-date.
Organizations can establish these solid Data Operations framework by implementing the above-mentioned steps. This framework will enable them to maintain high data quality, availability, and trust, while also improving performance and minimizing incidents.