Data management is incredibly complex, and continued reliance on manual processes, custom integrations, and inadequate built-in automation mean data becomes more unwieldy as it grows in type and volume. Data operations (DataOps) is the orchestration of people, processes and technology to deliver trusted, quickly, high-quality data to data.
The potential benefits of DataOps include significant productivity gains in delivering information and data to individuals and improving processes for efficiency and optimization. Automated data operations that include AI data-led initiatives can help drive the following outcomes:
- Deliver integrated business-ready data that drives analytics and AI at scale
- Achieve operational efficiency
- Enable data privacy and compliance
Objectives of DataOps
- Fuel continuous and fast innovation for the business by enabling self-service access to trusted, high-quality data for all data citizens.
- Enable continuous data delivery by automating data governance integration while safeguarding regulatory concerns.
- Provide a feedback loop for continuous learning from all data citizens by monitoring and optimizing the data pipeline.
- Correct the misalignment of people and goals by fostering closer links between IT system support, operations and the business.
- Accelerate the delivery of changes and improve delivery quality by introducing automation throughout the data delivery cycle.
- Improve insight into the real value of metadata and data by using results to drive optimization.
ISmile Technologies putting the Ops in DataOps
DataOps supports highly productive teams with automation technology to help deliver efficiency gains in project outputs and the time taken. However, to experience the benefits, the internal culture needs to evolve to truly be data-driven. With more business segments requiring and wanting to manage data to drive contextual insights, the time is right to do the following:
– Increase the quality and speed of data flowing to the organization.
– Obtain commitment from leadership to support and sustain a data-driven vision across the business.
At the core of DataOps is an organization’s information architecture. Do you know your data? Do you trust your data? Are you able to quickly detect errors? Can you make changes incrementally without “breaking” your entire data pipeline?
To answer these questions, the first step is to take inventory of your data governance and integration tools and practices. Tooling is necessary to support any practice that relies on automation. When considering tooling to support a DataOps practise within an organization, think about how automation in these five critical areas can transform a data pipeline:
1. Data curation services
2. Metadata management
3. Data governance
4. Master data management
5. Self-service interaction
The provision of business-ready data includes all of these aspects, and any DataOps practice must include a holistic approach incorporating all five aspects. Organizations that focus on one element of the data pipeline at the expense of others are unlikely to realize the benefits of implementing DataOps practices.
DataOps can seem daunting when organizations are still struggling with basic issues like defining data stewards’ roles or creating data validation rules. However, the DataOps practise offers solutions to many failures that organizations have experienced in their digital transformation initiatives. Learn more about DataOps Managed Services with market-leading technology at ISmile Technologies.