- AI and machine learning enhance DataOps tasks like data prep, quality checks, and pipeline management.
- They boost efficiency and accuracy, leading to improved data delivery.
- The AI in DataOps market will likely hit $1.5 Billion by 2027.
Data wrangling, DataOps, data prep, data integration-whatever you call it in your organization, managing the operations to integrate and cleanse data is time-consuming. Many firms struggle with efficiently integrating new data sets, improving data quality, centralizing master data records, and creating cleansed consumer data profiles.
While DataOps challenges aren’t new, they’ve become more critical as businesses aim to become data-driven and gain a competitive edge through analytics. Additionally, digital leaders are extending DataOps to unstructured data sources to develop AI search capabilities and prepare data for use in extensive language models.
Here are five ways AI and machine learning can be applied in DataOps.
Leveraging AI and ML for Data Transformation
DataOps is evolving to become more efficient, capable of delivering higher-quality results and handling massive data volumes and velocities. It must also work seamlessly with various disparate data sources and enhance the reliability of data pipelines.
According to Rajan Nagina, Head of AI at Newgen Software, “DataOps integrates people, technology, and workflows to ensure efficient data handling, focusing on improving data quality, accessibility, and reliability. It revolutionizes data management and maximizes its value through efficient processes and automation.”
Automation tools for data pipelines are advancing, with many incorporating machine learning and artificial intelligence (AI) capabilities. AI and machine learning-driven DataOps techniques shift data operations from manual and rule-based methods toward intelligent automation.
Sunil Senan, SVP and Global Head of Data, Analytics, and AI at Infosys, highlights several competitive advantages when enterprises adopt machine learning and AI in DataOps. AI can be used for quick data discovery, cataloging, and rapid data profiling, while ML can detect anomalies, identify inconsistencies, and enrich data. AI, ML, and automation can enhance data quality, harmonize master data, and lay the foundation for developing data products and effective data teams.
Here are five areas where DataOps teams can extend automation and leverage machine learning and AI for transformative capabilities:
- Simplify Data Preparation for New Data Sets
DataOps teams should focus on reducing the time it takes to process new data sets from discovery to loading, cleaning, joining, and cataloging in the organization’s data lake.
Automation and monitoring should be employed to detect and adapt to changes in data formats, improving the speed of incorporating new data sources and recovering from pipeline issues.
- Observability of Scalable Data and Ongoing Monitoring
DataOps engineers should implement monitoring, alarms, and automation to prevent broken data pipelines for swift issue identification and resolution.
Data observability involves logging data integration events and monitoring data pipelines proactively to ensure consistent and reliable pipelines. This helps DataOps teams manage service-level objectives effectively.
- Increase the Accuracy of Data Analysis and Classification
AI and machine learning can be used to examine and classify data as it moves through pipelines. This includes identifying personally identifiable information (PII) and sensitive data in datasets not initially labeled as such.
Automation rules can be developed to categorize and apply business rules based on data classification, enhancing data compliance and security.
- Provide Faster Access to Cleansed Data
Business teams often require real-time access to cleansed data, especially for marketing, sales, and customer care purposes. DataOps can centralize customer data through customer data profiles (CDPs) or master data management (MDM) systems.
Expect generative AI capabilities to augment customer records with information from unstructured data sources, improving data accessibility.
- Reduce the Cost and Enhance Data Cleaning Benefits
DataOps can shift its focus from routine data cleansing and pipeline maintenance to value-added services like data enrichment.
Machine learning can contribute to ongoing data quality improvements by learning from data patterns, reducing costs, and increasing the advantages of data cleansing.
Conclusion
AI and machine learning are essential for DataOps teams to work more efficiently. They help teams manage data health and monitoring while reducing operational costs. Using automated machine learning (AutoML), no-code, and low-code tools, teams can quickly apply machine learning to business processes, ensuring data quality and saving time and resources.
AI and machine learning also help streamline data preparation, improve data quality, and ensure data pipelines run smoothly.
This automation reduces manual work, speeds up new data integration, enhances customer records, and strengthens data governance. In a data-driven sector, these technologies are crucial for staying competitive and delivering business value.
