The value of water data has not been well documented, quantified, or communicated. We need to invest in our data infrastructure to make data more discoverable, accessible, and usable to inform real-time decisions. To do this, we must understand:
Water is undervalued,
water data even more so.
Why data are hard to value
These articles explore why data are difficult to value in economic, and other terms.
What are data, information, and knowledge?
Data, information, and knowledge are often used interchangeably. However, these terms represent different stages of value creation from data to decision-making.
What prevents data from getting to impact?
The value of data is best understood when data can be linked to a specific impact, but sometimes that doesn’t happen due to gaps.
Why are data hard to value? Data's unique attributes
What is the value of my data? What is the value of sharing my data with others? These seem like simple questions, but data are not simple assets. The unique attributes of data making them more difficult to value than traditional assets.
Why are data hard to value? Data as derived demand
Nobody wants data just for the sake of data. Data are valued for their end use (derived demand). When data producers and users are the same organization, assessing the value of data is straightforward (primary demand). But when data are shared and put to use by outside organizations (secondary demand), it is much harder to assess their value because producers and hubs often don’t know (1) how the data are used, (2) if the data lead to action, and (3) how demand changes over time.
Moving towards valuation by data purpose
Data are collected for different purposes, shaping their potential value. Here we explore common costs associated with data collection and the impact of different data purposes on its attributes and value.
Valuing public data
Public data are collected because they are essential to government function. Whether or not they are freely available to the general public varies between governments. Here we explore what public data are and the implications of charging for data in terms of cost recovery and impacts on data usage.
Funding public data
Rapid changes in technology require continual investments to update of data infrastructure and ensure data are preserved and available for future use. However, charging for data typically results in decreased usage and reduces the potential societal benefits of the data. We look at two examples of public agencies exploring the costs and opportunities of monetizing their data services.
Approaches to valuing data
These articles describe different methods to economically value data.
What methods are used to value data?
Brief descriptions are provided for methods that data producers, hubs, and users can use to assess the economic value of data. Links to full descriptions are provided for each method.
Number of methods described: 6
Modified Historical Cost method
The Historical Cost Method treats data as an asset whose value is at least equivalent to the cost of data collection. This method was modified by Moody and Walsh (1999) to account for the unique attributes of data that cause data to behave differently from traditional physical assets.
Market Methods can be used to value data by understanding the user’s willingness-to-pay. Market methods assess the value of data as revealed through markets and experiments or as stated on user surveys.
Business Model Maturity Index method
The Business Model Maturity Index method, proposed by Dell, assesses the value of data based on their relative contribution to a final outcome. This top-down approach relies on use cases and allows for estimating the value of data before (and/or after) an outcome is realized.
Decision-Based Valuation method
The Decision-Based Valuation method, proposed by J.B. Stander in 2015, seeks to estimate the relative contribution of data (similar to the Business Model Maturity Index method) while also accounting for data attributes (such as quality and frequency of collection) relative to the decision being made.
Data hubs are about sharing data and may not know how those data are being put to use. Instead hubs track the number of downloads and unique users for each dataset. A consumption-based approach to the Modified Historic Cost method can be used to assess the value of data hubs, with the underlying assumption that more data downloads is equated with more data usage and greater value.
Keep Research Data Safe method
Keeping Research Data Safe (KRDS) is a method data repositories use to track their costs and benefits. While designed for research-based data repositories, the method can be modified to describe the value data hubs create by integrating and sharing data.
Core principles for water data
These articles highlight principles the IoW adheres to.
Internet of Water Principles
The Internet of Water Principles were originally developed during the Aspen Institute Dialog Series on Water Data, and published in the 2017 report “The Internet of Water: Sharing and Integrating Water Data for Sustainability.” In 2021, the Principles were revised in consultation with the Internet of Water Advisory Board to reflect lessons learned over the first three years of project implementation.
Making public data FAIR
FAIR data principles have evolved from a collective effort of stakeholders seeking to make data Findable, Accessible, Interoperable, and Reusable. The Internet of Water supports the adoption of these principles.