Tips for Making Your Data Business-Ready Faster

The data engineering space is more complicated than ever, with more data from more sources and additional data formats, structures, and approaches. Meanwhile, budgets are not keeping up with the additional work, recruitment, and staffing that this requires, and infrastructure and software costs are only rising. 

According to TDWI Q1 research data, organizations are less than confident in their success in using data to enable users to gain faster insights. Only 18% of respondents said they were successful today and confident they could meet future needs. While 42% say they are somewhat successful today, they state concerns with their ability to respond to future challenges. 

Despite these challenges, teams and organizations must be equipped to be productive with their data. Tom Ridings, Senior Product Director at Matillion, provides some areas and tips your team can focus on so that you can make your data business ready today, not tomorrow. 

Do More with the Same or Less

High-quality pipeline documentation is the basis of efficient data discovery. Matillion has long championed pipeline documentation, embedding core principles of self-documenting pipelines directly within its platform. This approach ensures pipelines are understandable and readable, fostering knowledge sharing and enabling rapid onboarding for new team members.

  • Simplifying Complexity: The complexity of data systems increases the learning curve and introduces risks to data engineering practices. While some complexity is inevitable, optimizing architecture and rationalizing tooling can simplify pipelines and reduce the time required for understanding and maintenance.
  • Utilizing Column-Level Lineage Tools: Column-level lineage tools are crucial in accelerating discovery and learning by providing a visual map of data assets and transformations. This enables a quick understanding of data origins and transformation paths, significantly reducing the time needed for comprehensive learning.

AI has a clear place in creating effective documentation, particularly in automating documentation tasks. Matillion's recent feature leverages AI to auto-document pipelines, translating complex queries like regular expressions into human-readable descriptions. This innovation simplifies documentation processes, ensuring pipelines remain understandable and accessible even to those unfamiliar with intricate coding techniques.

Making Development Productive

Organizations must adopt strategies that focus on building unique business logic while leveraging off-the-shelf solutions and embracing a blend of low-code and high-code development approaches to optimize development productivity and efficiency.

  • Leverage Off-the-Shelf Solutions: Avoid reinventing the wheel by identifying and utilizing off-the-shelf solutions for non-unique components like connectors and API integrations. This approach reduces complexity, accelerates development, and minimizes maintenance overhead, ensuring resources are allocated efficiently to build what truly adds value to the business.
  • Embrace Low-Code and High-Code Duality: Embrace the duality of low-code and high-code development to maximize flexibility and efficiency in data pipeline development. Modern tools like Matillion enable seamless transitions between low-code and high-code approaches within a single platform. This allows developers to leverage the simplicity of low code for most tasks while incorporating high code for complex transformations, accelerating pipeline development without compromising scalability or manageability.
  • Implement Guardrails for Development: Establish frameworks and guardrails that facilitate collaboration and contribution from individuals across different skill sets, including business analysts and data analysts. By providing a safe environment with clear guidelines and test frameworks, organizations empower diverse contributors to participate in data engineering tasks, streamlining development efforts and fostering a collaborative data ecosystem.
  • Increase Productivity and Development with AI Copilot: Boost data engineer productivity using natural language to build data pipelines. The AI co-pilot interprets natural language queries to generate data pipelines automatically, reducing development time and lowering barriers to entry for those doing data engineering across varying skill levels. With a simple user interface, this copilot leverages the scalable infrastructure and sophisticated metadata handling behind the scenes, enabling rapid pipeline creation and knowledge sharing within the organization.

By implementing these strategies, organizations can maximize development productivity, minimize unnecessary complexity, and empower diverse contributors to contribute effectively to data engineering tasks. This will ultimately accelerate data pipeline delivery and efficiently meet evolving business demands.

Optimize for Management

Organizations must prioritize optimizing pipeline management to enhance productivity and efficiency in data engineering, which consumes a significant portion of data engineering time. Implementing software engineering practices and embracing automation can drive substantial gains in productivity and streamline operations.

  • Version Control and Documentation: Adopt strongly versioned systems where pipelines are maintained as code. This approach facilitates efficient debugging by providing a clear history of changes, enabling teams to pinpoint issues quickly and understand the context behind pipeline modifications. Documentation and versioning ensure transparency and accountability in pipeline management.
  • Automation of Testing and Quality Assurance: Integrate automated testing into the deployment process to validate pipeline functionality and data quality. Automated testing is essential for ensuring pipelines perform as expected before deployment to production. By mandating and streamlining automated testing processes, organizations can mitigate risks associated with faulty pipelines and enhance overall pipeline reliability.
  • Embrace a DataOps Mentality for Continuous Improvement: Cultivate a DataOps mindset focused on continuous improvement and proactive problem-solving. Allocate time to reflect on pipeline issues, identify root causes, and implement corrective measures to prevent future occurrences. Organizations can cultivate a robust and sustainable data engineering practice by embracing incremental improvements and learning from challenges.

By implementing these strategies and adopting a DataOps approach, organizations can optimize pipeline management, minimize operational overhead, and foster a culture of continuous improvement within their data engineering teams. This proactive approach enables teams to address challenges efficiently and evolve their practices to effectively meet evolving business demands.

Catch up on part 1 here

Want to try Matillions AI Co-Pilot feature? 

Niamh Sedgwick
Niamh Sedgwick

Product Marketing Coordinator

Niamh Sedgwick is a Product Marketing Coordinator at Matillion. Niamh is responsible for meticulously planning, executing and evaluating the effectiveness of content marketing campaigns, whilst also serving as a content strategist and analyst. She ensures the team’s organization in Asana to optimize workflow efficiency.