The demand for usable data has skyrocketed, putting pressure on organizations to enhance their data engineering practices. To meet this challenge, data and Analytics (D&A) leaders can adopt several key strategies to foster collaboration, prove business value early, automate processes, manage data as a product, and eliminate operational overhead.
By 2026, data engineering teams that adopt DataOps practices and tools will be ten times more productive than those that do not. To enhance data engineering, D&A leaders should embrace the following best practices:
Cross-Functional Collaboration
- Establish small cross-functional teams comprising data, business, and technical personnel. This structure ensures that all relevant skills are available within the team, promoting effective and efficient data solutions.
- Assign a product owner/manager to drive stakeholder engagement and guide feature priorities. This role is crucial for focusing on business objectives and ensuring data products deliver maximum value.
- Measure performance using progress metrics (e.g., cycle time, code quality) and impact metrics (e.g., incremental revenue improvements). These metrics help track improvements in data delivery processes and their alignment with business goals.
Value-First Model
- Prioritize business value before embarking on significant data engineering initiatives. By focusing on value from the outset, organizations can avoid wasted efforts and ensure that projects contribute meaningfully to business objectives.
- Conduct feasibility and value tests in controlled sandbox environments to prototype solutions. This approach allows teams to experiment and validate ideas without risking disruption to core operations.
- Launch engineering efforts only for initiatives that pass both tests, ensuring efficient resource use. This disciplined approach helps maintain focus on high-impact projects that deliver measurable benefits.
Recurring Manual Tasks
- Encourage a culture that celebrates automation to increase release velocity and business value. Automation should be seen as an investment in long-term efficiency and productivity.
- Upskill teams with software engineering practices, particularly in infrastructure and test automation. Providing training and resources helps teams develop the skills needed to implement effective automation solutions.
- Automate individual tasks and orchestrate these into release pipelines to minimize manual intervention and improve efficiency. Comprehensive automation reduces the likelihood of errors and accelerates the delivery of data products.
Data as Products
- Treat data as products to enhance operational value through increased use and reuse. Viewing data as a product encourages a focus on quality, usability, and customer satisfaction.
- Break down monolithic data systems into modular products that can be developed and deployed independently. This approach allows for more flexible and scalable data management.
- Ensure data products are consumption-ready, up-to-date, approved, and have quantifiable costs and measurable business value. These criteria help maintain high standards and ensure data products meet users' needs.
Operations Overhead
- Offload operational tasks from non-IT citizen users to prevent operational overhead. This strategy frees up valuable time for business users to focus on strategic activities rather than routine maintenance tasks.
- Monitor usage metadata to identify business-critical processes and operationalize them through a gatekeeping process. Organizations can improve overall data operations by understanding how data is used and ensuring critical processes are managed efficiently.
- Promote successful exploratory use cases to production to drive experimentation and measure business outcomes effectively. Encouraging innovation while maintaining control ensures that data initiatives deliver real business value.
