Understanding Return on Data: A Comprehensive Guide to Data Engineering & Analytics | Return on Data


This article was written by Sriram Sitaraman, Director of Technology at Material


In an era where data plays an integral part in business strategies, identifying ways to maximize the value of an organization’s data assets is paramount. Return on data (ROD) is the force behind an organization’s effort to turn raw data into actionable insights, ultimately driving business success.

The ROD reflects an organization’s ability to effectively capitalize on its data resources. By presenting the vast amount of data that they have in a meaningful way, the decision makers of an organization can gain valuable insights into revenue opportunities, cost-saving measures, areas of potential consolidation, etc. and make more informed decisions. Enterprise data management enhances the organization’s valuation and leads to higher returns when considering a sale or investment.

Measuring ROD on a regular basis can be quite complex as it involves a dynamic interplay of factors like data quality, evolving business goals and other diverse factors. It requires continuous adaptation and relevant metric alignment to measure ROD accurately.

In this article, we will explore a practical process for measuring return on data and discuss the strategies needed to unlock the full potential of organizational data.


Measuring ROD

  1. Setting clear objectives: Start with defining the data-related organizational goals—like boosting revenue, improving decision making, enhancing customer experience, etc.— as clearly as possible. This helps focus effectively on efforts like data collection and acquisition, processing and analysis, integration and utilization, governance and quality control, infrastructure and technology investment and workforce training.
  2. Calculate costs: Evaluate the organization’s expenses on data-related activities such as data collection, storage, processing, and analysis. This gives an idea of the company’s overall investment in data.
  3. Measure benefits: Quantify the positive outcomes resulting from these data efforts. Factors such as an increase in sales, cost reductions and improved customer satisfaction may help quantify the benefits of these data initiatives. For instance, a data-driven marketing strategy may have contributed to an increase in sales by a certain percentage. Data can also identify areas where companies can minimize operational expenses and quantify the same. An organization may also use customer feedback surveys, net promoter scores and other related metrics to quantify and measure improvements resulting from data-driven initiatives.
  4. Select key metrics: Choose specific performance metrics that align with organizational objectives. For example, if the objective is to increase revenue, relevant metrics would include profit margin and customer acquisition.
  5. Calculate ROI: Now measure the ROD using the basic ROI formula: ROD = (quantifiable benefits – costs of data-related initiatives) / costs of data-related initiatives.
  6. Assess data quality and analyze usage: Ensure that data is accurate, consistent and reliable. Poor data quality may negatively impact ROD. Additionally, analyze how the company uses data, whether employees are making informed decisions and whether data is underutilized. This is critical in optimizing data strategies.
  7. Regular Review: Data is dynamic. Regularly reviewing and refining the data strategy allows organizations to align with the changing business needs.

Strategies for maximizing ROD:

While the importance of assessing ROD is frequently underappreciated, harnessing it and implementing well-thought-out strategies—including visual data representation—can significantly enhance returns on data investment for businesses. Some of the strategies that organizations can adopt to maximize their ROD include the following:

Effective data governance

Effective data governance is critical in data management services within an organization, significantly enhancing ROD. The essential components of data governance are:

  • Data quality assurance – Minimizes errors by maintaining data accuracy, completeness and consistency through regular data cleaning and validation. This, in turn, optimizes ROD.
  • Data security – A robust data security and compliance policy ensures legal adherence, customer trust and reduced legal risks, ultimately contributing to improved ROD.
  • Comprehensive data catalogs – These are organized libraries of an organization’s data assets. These catalogs help discover data easily, quickly and accurately. This ease of access enables efficient data utilization and informed decision-making, which again maximizes your ROD.
  • Data stewardship – This involves assigning specific individuals or teams within an organization the responsibility of maintaining the quality and integrity of the data. This approach directly boosts the ROD by enhancing the data’s credibility and utility in strategic decision-making.


Leverage innovation to accelerate maximizing value from data

This strategy focuses on the importance of embracing advanced technologies and approaches to generate maximum value from an organization’s data assets. By integrating advanced analytics, artificial intelligence and data visualization, businesses can uncover deeper insights into data, identify new growth opportunities and improvise decision-making. This helps enhance ROD by optimizing data utilization and maximizing the overall impact of data in driving business success.

Initiate data culture and training

To create a data-first culture, organizations must create and foster an environment where employees are encouraged to understand data and gain the required skills and knowledge to use it optimally. By nurturing a data-centric culture and offering relevant training, companies empower their workforce to make data-informed decisions. These practices ensure that data is an asset that helps drive enhanced business outcomes.

Data integration

To create a holistic view of organizational data, you need to focus on streamlining different data sources within a company to build a unified and coherent data ecosystem, which enables better analysis and decision-making. This helps data become a more valuable and efficient resource. How? By breaking down data silos, minimizing data redundancy and increasing data accessibility—all leading to an improved ROD.

Next steps

Return on Data is not just a measure of the value of data. It’s a way forward for organizational growth. By prioritizing data quality, leveraging cutting-edge technology, nurturing a data-driven culture and promoting data integration, companies can transform raw data into actionable insights—ultimately leading to increased ROD.

Maximizing an organization’s return on data hinges on the quality and integrity of the data infrastructure. Discover how our data engineering and modernization services can elevate your ROD by ensuring your data systems are modern, scalable, and ready for growth by contacting us today.