Versatile Roles of Data

Data is at the core of today’s rapidly evolving business landscape, playing roles far beyond simple decision support. Traditionally viewed as a resource for operational guidance, the scope of data has significantly broadened, revealing a spectrum of strategic potentials. In a recent paper titled “Time to Reassess Data Value: The Many Faces of Data in Organizations” by Daisy Xu, Marta Indulska, Ida Asadi Someh, and Graeme Shanks, published in the Journal of Strategic Information Systems, the authors explore this expanded scope by identifying four key roles of data in organizations: data as a tool, data as a commodity, data as a practice, and data as algorithmic intelligence. Each of these roles highlights unique ways data can drive business outcomes, transforming it from a simple informational asset to a multi-dimensional driver of opportunity and competitive advantage. This article builds on their insights, examining the strategic implications of these roles and offering practical guidance for organizations looking to unlock data’s full potential.

1. Data as a Decision-Making Tool

Data as a tool positions itself as an indispensable asset for enhancing decision-making processes. By generating actionable insights, data empowers managers to make informed, evidence-based decisions that drive business strategy and operations. This role of data hinges on its accessibility, interpretability, and effective utilization, enabling better outcomes in areas like product development, resource allocation, and operational improvements.

2. Data as a Tradable Commodity

Viewing data as a commodity underscores its tradability and economic potential. In this role, data is treated as a marketable product that can be sold, traded, or shared in data marketplaces. This emerging model enables companies to generate direct revenue from their data assets, transforming data into a significant profit driver. Organizations can create value not just through internal analysis but by commodifying data—sharing insights or raw data with other businesses, thereby unlocking additional revenue streams.

3. Data as a Practice

The role of data extends further as a practice—where data becomes part of the daily workflows and operations of a business. In this role, data is embedded in repeatable routines and processes, supporting ongoing improvements in products, services, and customer experiences. This practice-driven approach to data use is essential in fostering innovation and learning within organizations, allowing for incremental, data-informed adjustments that drive long-term competitiveness.

An example of data as a practice is seen in digital platforms like Google Maps, which leverages continuous user interactions to refine its offerings and improve user satisfaction. The insights generated from this data network effect help organizations iteratively adapt and optimize their services, creating a responsive, customer-centered approach to value creation. In this role, data is not isolated; rather, it is deeply embedded within the organization’s infrastructure and routines, promoting resilience and adaptability.

4. Data as Algorithmic Intelligence

The role of data as algorithmic intelligence represents its power in driving automation and artificial intelligence. This transformation leverages data to fuel advanced algorithms that automate complex decisions, personalize customer experiences, and replicate intelligent behavior across operational areas. By enabling automation and augmenting human decision-making, data becomes a catalyst for efficiency and scale, especially in industries seeking to streamline labor-intensive processes such as customer support or logistics.

5. Understanding the Non-Rival Nature of Data

A distinctive aspect of data is its non-rival nature. Unlike physical resources, which are limited to serving a single purpose at a time, data can be used simultaneously by multiple stakeholders without losing its intrinsic value. This non-rival characteristic allows data to be repurposed across various contexts, creating compounded value without depletion. As companies leverage the same data both internally and in external transactions, they unlock opportunities for increasing returns and unique monetization avenues, setting data apart from other traditional business assets.

The Dark Side of Data Utilization

Not all data use adds value, and some practices fall into what could be termed the dark side of data utilization. In some cases, organizations engage in ritualistic data collection—gathering vast amounts of data primarily for appearances rather than actual value-driven purposes. This symbolic behavior often results in inefficient resource use, with data collected but underutilized, providing no meaningful insights or impact. Avoiding such pitfalls requires a strategic approach that emphasizes purposeful data collection aligned with actionable business goals.

Turning Data into Revenue Streams

One of the most promising trends in leveraging data is viewing it as a potential revenue stream. Data commoditization has become a strategic approach where organizations explore data marketplaces to sell valuable datasets or insights. This shift represents a new business model where data acts as both an operational and financial asset, offering companies like Uber the opportunity to augment their income through external data monetization, beyond improving internal processes.

The Imperative of Data Literacy and Accessible Platforms

To fully harness data as a strategic asset, companies must prioritize improving data literacy across their workforce. Data literacy should be a core capability, enabling employees at all levels to understand and derive meaningful insights from data. Investing in accessible data platforms and training ensures that data is democratized—available to everyone, not just specialists. This widespread data empowerment helps companies make consistent, informed decisions and fosters a culture of transparency and accountability.

Real-World Business Implications of Data Utilization

The strategic deployment of data can have substantial business implications. When data is treated as a tool, it empowers precise, data-driven decision-making that improves operational efficiency. This, in turn, helps reduce costs, allocate resources more effectively, and elevate product quality. Viewing data as a commodity allows companies to explore data markets and partnerships that generate new income streams. This approach opens up fresh business models, particularly for sectors that generate large volumes of valuable data.

Furthermore, incorporating data as a practice transforms the way organizations learn and innovate. It encourages the adoption of agile methodologies, where feedback loops are faster, and insights are continuously fed into business processes. Lastly, data as algorithmic intelligence creates an opportunity for businesses to automate repetitive tasks, improve accuracy in operations, and enable personalization at scale—ultimately enhancing customer satisfaction and driving down operational costs.

Real-World Application Examples

Microsoft and Uber serve as prime examples of how effectively data can enhance internal processes, facilitate continuous learning, and optimize services. By utilizing data as a practice, these companies have successfully leveraged data-driven insights to adapt their offerings to better meet market demands, resulting in sustained competitive advantages.

Netflix demonstrates the significant value data brings to personalization and user experience. By utilizing viewer data to provide personalized content recommendations, Netflix has not only improved user experience but also boosted retention rates. This data-driven strategy is an example of data being used as algorithmic intelligence—applying insights in a way that automates personalization and drives consistent user engagement.

Google Maps continually refines its services by analyzing data from user interactions, leveraging what’s known as the data network effect. The more users interact with the platform, the richer the data becomes, enhancing navigation accuracy and user recommendations. This example illustrates how organizations can use data as a practice, embedding it into a continuous feedback loop that drives service optimization.

Those examples serve as prime examples of how effectively data can enhance internal processes, faciltate contionous learning, and optimise services. By ulitising data, these companies have successfully leveraged data-driven insights to adapt their offerings to better meet market demands, resulting in sustained competitive advantages.

First Steps to Implementing a Data Strategy

To begin implementing an effective data strategy, organizations should start with the basics:

1. Define Data Objectives: Establish what the organization aims to achieve through data. Whether it’s enhancing decision-making, creating new revenue streams, or improving operational efficiencies, having clear objectives will help shape how data should be gathered, analyzed, and used.
2. Improve Data Literacy: Build data literacy programs to ensure that employees at all levels understand data’s potential. Investing in training and awareness helps in building a data-driven culture where every team can confidently use data to guide their decisions.
3. Invest in Data Platforms: Equip teams with accessible and robust data platforms. Modern, user-friendly tools help employees make sense of data without needing to be data scientists, making data-driven decision-making achievable across the organization.
4. Start Small, Scale Fast: Implement pilot projects in specific areas where data can add clear value. By starting small, organizations can iterate and refine their approach before scaling up data initiatives company-wide.

Food for Thought: The Future of Data in Business

The future of data in business is poised to evolve further as technologies such as artificial intelligence and machine learning continue to advance. Organizations should be asking themselves: How can we stay ahead in a data-driven world? How can we ensure the ethical use of data while maximizing its potential for business success? The answers to these questions will define not only the competitive landscape of industries but also how well companies can innovate and create meaningful value for their stakeholders.

By understanding and capitilasing on the many roles of data, organizations can position themselves not just participants in their industry, but as leaders in driving innovation and creating sustainable value.

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