What do you do if your data engineering decisions are not aligned with organizational goals?

1 Understand the business context

The first step to align your data engineering decisions with the organizational goals is to understand the business context and the problems that the data is supposed to solve. You need to communicate with the business stakeholders, such as product managers, analysts, and executives, to learn about their needs, expectations, and challenges. You also need to understand the market, the customers, the competitors, and the industry trends that affect the business. By having a clear picture of the business context, you can identify the key data sources, metrics, and outcomes that matter for the business and prioritize your data engineering tasks accordingly.

2 Define the data strategy

The next step to align your data engineering decisions with the organizational goals is to define the data strategy that guides your data engineering efforts. A data strategy is a plan that outlines the vision, objectives, principles, and roadmap for how data will be collected, stored, processed, analyzed, and used to support the business goals. A data strategy should also include the data governance, quality, security, and ethics policies and standards that ensure the data is trustworthy, compliant, and ethical. By defining the data strategy, you can align your data engineering decisions with the data value proposition and the data culture of the organization.

3 Collaborate with the data team

The third step to align your data engineering decisions with the organizational goals is to collaborate with the data team and other data-related roles, such as data scientists, data analysts, data architects, and data stewards. You need to establish a common data language, a shared data vision, and a coordinated data workflow that enable effective communication, coordination, and integration among the data team members. You also need to leverage the expertise, feedback, and insights of the data team members to improve your data engineering solutions and ensure they meet the data quality, usability, and performance standards.

4 Validate and iterate your data solutions

The fourth step to align your data engineering decisions with the organizational goals is to validate and iterate your data solutions based on the data requirements, expectations, and feedback from the business stakeholders and the data team. You need to test your data solutions against the business criteria, such as accuracy, relevance, timeliness, and completeness, and measure their impact on the business outcomes, such as revenue, customer satisfaction, or efficiency. You also need to monitor your data solutions for any issues, errors, or changes that may affect their functionality, reliability, or scalability. By validating and iterating your data solutions, you can ensure they deliver value to the business and address the changing business needs.

5 Document and communicate your data decisions

The final step to align your data engineering decisions with the organizational goals is to document and communicate your data decisions to the relevant stakeholders and audiences. You need to document your data decisions, such as the data sources, methods, tools, and assumptions, and explain the rationale, benefits, and trade-offs behind them. You also need to communicate your data decisions, such as the data outputs, results, and recommendations, and demonstrate how they support the business goals and solve the business problems. By documenting and communicating your data decisions, you can increase the transparency, accountability, and trustworthiness of your data engineering work and showcase your value and contribution to the organization.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?