Measuring Data Engineering ROI for Executives

Your Data Engineering Investment ROI Evaluation Process

The link between decisions made based on technology and their consequences is how businesses determine the ROI of an investment.

Why Conventional Measures of Data ROI Are Inadequate

Typically, when trying to assess the ROI of an investment in data, senior management uses some of the more conventional accounting metrics they are accustomed to. Three reasons why this occurs include:

• Data Engineering does not provide isolated results.
  In general, a new data pipeline entails multiple decision-makers working from many different sources. As such, it is impractical to assign revenue directly to any individual dataset.
• Benefits of data pipelines accrue over time.
  Because we trust that a pipeline will function as needed today, we are able to eliminate the potential for a future outage after a certain period of time (approximately 90 days). Learning from what we do with our data today provides education on how to use the technique more effectively in the future.
• Much of the value associated with data engineering and its associated pipelines is not directly related to revenue.
  The ability to reduce the possibility of experiencing a future outage, rework, or compliance risk contributes significantly to margin and confidence, though none of these factors creates new revenue.

As a consequence, the only effective way to attain a true representation of ROI from a data investment is by utilizing quantitative measures combined with a structured qualitative evaluation method.

What Elements Should Be Tracked by Leaders for Evaluating the ROI of Data Engineering?

Leadership should take into account multiple categories when calculating the ROI on data engineering, rather than just one number. These categories are aligned with organizational priorities and should include:

Delivery Speed (Velocity): How quickly are your teams able to deliver products and/or services to customers?

Relevant metrics for Delivery Speed (Velocity):

• Time
• Time to Maximum Value (TVD)
• Expansion of Existing Products and Services (Time)

The Value of Data Engineering

A well-designed data platform will create the greatest possible return on every dollar spent on data. Indicators associated with Data Engineering metrics for cost-efficiency and leverage include:

• Cost of Cloud Data by Analytic Workload
• Pipeline Dashboard Monitoring Redundancy
• Total Time Lost Working on Manual Fixes
• Total Cost to Rework Product Due to an Incorrect Data Source
• Cost Avoidance Creates Opportunities to Hire Fewer People or Delay Growth Related Investments

Minimizing Risk, Increasing Reliability

Most businesses don’t realize their entire data-related investments when determining ROI because they do not recognize data-related risks. Examples of data-related risk indicators:

• Type and Frequency of Reported Data Incidents and Consequently Their Severity
• Average Time to Identify and Resolve Data Related Incidents
• Compliance Audit Findings Related to Data
• Customer Experience Change Due to Inaccurate Data

Having reliable data minimizes an organization’s risk of incurring negative impacts from lack of confidence due to data failure.

The Future of Your Organisation is Being Built Around Data

Many organizations leverage data technologies for future growth:

• Using AI machine learning to create enable products
• Implementing self-service analytics
• Providing customers with a real-time customised experience
• Analyzing product line performance and tracking progress prior to recognizing revenue

These are among the best indicators of ROI.

How to Relate Data Engineering Efforts to Business Outcomes

A structured approach to applying business vernacular with data-related solutions:

• Align data projects programs with relevant business objectives. Examples:
• Improved data ingestion reliability faster product testing
• Utilizing the same metrics for executives reduces alignment time
• Quality control check prior to generating a report lower cost and less time correcting errors
• Develop a before-and-after timeline of the time taken to reach a decision.
• Track how often teams question the reliability of information received
• Track the number of incidents per quarter
• Focus on understanding trends, not just verifying accuracy

Illustrative Data Engineering Success Stories

Accelerated Timeframes:
Analytics reduced validation time for feature adoption from three weeks to less than three days. Teams can eliminate low-ROI features faster and focus on higher ROI.

Reduce Operational Incidents:
Quality Assurance ensures only quality metrics appear on dashboards, reducing churn and customer support costs.

Expand the Reach of the Analytics Team:
Standardized data models allow engineers to support twice as many stakeholders, deferring additional hires while increasing output.

Accelerated Timeframes of AI Initiatives:
Proper data preparation shortens model development cycles, improves speed-to-value, and reduces waste.

ROI measured via acceleration, avoidance, and time-to-value metrics, expected for every project.

Common Mistakes in ROI of Data Engineering

• Use of only one ROI
• Only measuring cost and not other factors
• Disregarding the level of adoption of the data asset
• Waiting for perfect attribution of the data asset
• Believing measuring ROI is a one-time process

Leaders should set clear expectations at the start to avoid these mistakes.

Key Times to Measure ROI

Critical points for ROI measurement include:

• Budgeting and Platforms Choosing effective cloud platform possibilities
• Creating and Growing Data Teams
• Justifying Investments in AI and Advanced Analytics
• Recovering from Negative Impacts of Past High-Profile Data Failures

A Model Framework for Executive ROI Measurement from Data

Steps for simple monitoring of revenue and opportunities:

• Define Business Outcomes (Speed, Timeliness, Quality, Cost, Risk)
• Identify Data Dependency of business outcomes
• Select Common Indicators, including leading metrics for forecasting financial outcomes
• Monitor Trends quarterly review direction and consistency adjust as needed

Maintain balance between basic principles of measuring ROI of data.

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