DataOps

Trend 7: DataSecOps enhances data efficiency

Companies are exploring and adopting DataOps across various data tools and data stakeholders to deliver faster business value. We also see digitized data governance and containerization through automation and self-service tools for greater focus on value delivery. As part of digitized data governance, components like data lineage, data security, data quality, and environment abstraction will integrate with data and logic tests. This will support in selfservice and higher-quality data services delivery.

Enterprises must organize their entire data estate into DataOps pipelines, break the silos of data teams and data products, and create a fully integrated data factory view. They must also evaluate and standardize tools and processes across the entire data estate by building pipeline-as-code with end-to-end integration across the data fabric. Modern technologies will help integrate DataOps and accelerate this journey.

A large food and beverage company wanted to become data-driven and agile. However, long delivery cycles and dependence on engineering for data discovery challenged its vision. The company partnered with Infosys to successfully adopt agile processes and enrich data governance through a phased approach on DataSecOps. The client reduced data discovery time by 90% and achieved 10-times-faster user acceptance of features deployed.

DataOps

Trend 8: AI and ML products integrate with DevSecOps

The AI/ML model’s life cycle involves various stages — from data collection, data analysis, feature engineering, and algorithm selection to model building, tuning, testing, deployment, management, monitoring, and feedback loops. To improve DevOps maturity, AI/ML models are being integrated into DevSecOps pipelines to be standardized, fully managed, and controlled. Configuration management tools, data security, and data privacy tools and services for AI/ML models have gained momentum. Other products, such as Amazon Macie, Pachyderm, and TensorFlow, are also being explored and tested.

A supply chain solutions company deployed AI and ML models on diverse platforms using an open-source software stack. This approach saved 80% of deployment time and enabled elastic and containerized execution, delivering better solutions to customers.

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