Data as Asset
Data can create economic value when an organization uses it to improve decisions or operations. The author’s potential-asset framing asks what the data will be used for, who can analyze it and whether the resulting information will justify its cost.
THE IDEA
Data as Asset
An asset is something an organization owns or controls that is expected to provide economic benefits. Thinking of data as a potential asset makes the intended benefit explicit: what could the organization learn, and what would it do differently? Collection and storage alone do not answer those questions.
The source describes a historical imbalance in spending. Since the 1980s, it argues, organizations invested heavily in data infrastructure while devoting less to exploration. Its estimates put approximately 85%–90% of data-related spending into hardware and software, leaving about 10¢ of every $1 for extracting insights. These are historical estimates, not a current spending benchmark.
The author also questions how the remaining 10% was used. Standard reports and third-party analytics can help a business operate, but competitors may have access to much the same information. Such services therefore do not automatically provide a competitive advantage.
The practical question is how the organization will use the data. Answering it requires analytical skills, relevant context and a link to a decision. A useful plan explains both the infrastructure needed to maintain reliable data and the work needed to turn it into something people can act on.
When to use it
A startup deciding whether to invest in a data warehouse vs. hiring data scientists to generate actionable insights.
A manager evaluating the ROI of a new analytics platform, focusing on how it can enable unique decision-making rather than standard reporting.
An executive allocates data-strategy spending by identifying the decisions to improve, the analysis required and the infrastructure needed to support it.
A consultant advising a client on transforming their data from a cost center to a profit center by emphasizing analytical skills.
FROM THEORY TO EVERYDAY LIFE
In practice
Retail Personalization
A retailer collects customer purchase history and browsing data. Instead of simply storing this data, they apply advanced analytics to identify buying patterns and personalize product recommendations, leading to increased sales and customer loyalty.
Supply Chain Optimization
A manufacturing firm uses sensor data from equipment to predict maintenance needs and optimize production schedules. This transforms raw operational data into a strategic asset that reduces downtime and costs.
A MOMENT TO REFLECT
Put it to work.
How does your organization currently allocate resources between data storage/maintenance and data exploration/analysis?
What would it take to shift your data from being a generic reporting tool to a source of unique, competitive insights?
Identify one area where your organization uses third-party analytics. How could you differentiate by developing proprietary analytical approaches?
CONTINUE THE CONVERSATION
Source texts
- Evidence-Based Decision-Making How to Leverage Available Data and Avoid Cognitive Biases (Andrew D. Banasiewicz)