Data Engineering alone doesn't create value; it's realized through integration with business processes, platforms, and people. A holistic approach involves assessing overall value, then attributing ROI across Data Engineering, IT, and Business functions. This ensures value isn’t confined to specific environments but reflects the collective impact.

Identify pain points where data engineering can help. This includes: 1. Lack of common metric definition to measure company KPIs 2. Lack of reliable data for machine learning products 3. Lost revenue due to unreliable data Estimate impact of solving these with a mature data engineering team and processes. Make a clear and realistic proposal with timelines to iteratively deliver core datasets with quality checks, monitoring, and alerting .

To address stakeholder skepticism about ROI in data engineering. 1) Cost Savings: We should highlight how better data infrastructure reduces operational costs (e.g., optimizing cloud resources, automating ETL processes). 2) Increased Efficiency: Showcase faster data processing, reducing time to insights, decision-making and how robust pipelines minimize errors. 3) Scalability: Ability to handle growing data needs without reengineering. Sharing success stories and revenue growth which mainly focuses on metrics like cost savings, improved decision speed, or percentage growth in outcomes to solidify the argument.

Ensure your data engineering architecture delivers more value than it costs—not just in monetary terms, but by providing measurable benefits, such as reducing manual labor through automation, improving data accessibility, and enabling faster decision-making. Highlight specific use cases where the architecture has streamlined workflows or unlocked new business opportunities. Present clear metrics, such as time saved, cost reductions, or revenue growth driven by better data insights, to demonstrate ROI tangibly. Additionally, position data engineering as a foundation for scalability and innovation, aligning it with long-term business goals.

Data engineering ROI skepticism is closely related to the relative novelty of the field. In truth, data science and data engineering are supporting roles in a company. Their ROI are defined by the ROI of the projects they support. An organization should aim to have solid monitoring of their data engineering pipelines, such as: 1. Number of API calls, 2. Number of events triggered 3. Cost breakdown per platform This will allow an organization to map out their costs relative to each of the projects they support and, as such, the ROI they enable. TL; DR: Data platform monitoring usage and access allied with business outcomes!

Stakeholders doubting data engineering ROI? Time to flex. 1️⃣ Show them the magic: Turn messy data into sleek dashboards they can't resist. 2️⃣ Speak their language: It’s not 'data pipelines'—it’s saving time, cutting costs, and making decisions that don’t suck. 3️⃣ Win fast: Fix something they hate (like endless spreadsheets) and watch the lightbulb go on. 4️⃣ Drop the money line: 'What’s the cost of NOT doing this?' Cue awkward silence. Data engineering isn’t just plumbing—it’s the secret sauce for decision-making swagger. 🍔💼

To address skepticism about data engineering ROI, focus on tangible business outcomes like cost savings, efficiency, and faster decision-making. Share specific success stories and metrics that directly align with stakeholder goals. Use clear visuals to show the impact of reliable, scalable data systems on growth and innovation. Start with quick-win projects to demonstrate immediate value and build trust. Finally, maintain open communication, involving stakeholders early to align on priorities and showcase long-term benefits.

To convince stakeholders of the ROI in data engineering, focus on demonstrating tangible business value through specific use cases. Highlight how robust data pipelines enable faster, more reliable decision-making by ensuring data accuracy, accessibility, and scalability. Provide examples of cost savings through automation, such as reducing manual data preparation efforts or improving operational efficiency. Showcase success stories where data engineering has driven revenue growth, such as enabling advanced analytics or real-time insights for better customer targeting. Use metrics like reduced time-to-insight, increased productivity, or improved data-driven decision outcomes to quantify impact.

Focus on the big picture: explain how data engineering is the foundation for better decision-making, personalized customer experiences, and scaling operations. Share specific scenarios where accurate, timely data leads to direct business impact. Emphasize long-term value while showcasing short-term wins to build confidence. Please keep it simple and relevant to their goals.