By themselves, government data have no inherent value. Their value lies in their application—specifically, how the data can generate insights that will, in turn, inform a decision or action to improve outcomes in society. To extract value from their data, governments need to design a data strategy that includes the following three elements:
Identifying sources of value by understanding what kind of value is created from different forms of data, and whom it will benefit
Mapping the value creation process by describing the steps required to create that value
Determining data rights by agreeing about the parties that will be involved in creating value and the roles they will play
Governments should address these elements holistically, aligning them to the common goal of producing better outcomes for society.
Sources of Value. Government data can create value in a variety of ways, but the three main types of value are better public services, improved accountability, and higher economic growth. (See Exhibit 1.) These come about through improvements to systems and processes within an organization, improved interactions with citizens, and improved interactions between organizations. Better public services can be achieved by using data to find efficiencies and enhance collaboration. Improved accountability stems from using data to inform evidence-based decisions and enhance transparency. Higher economic growth can result when insights about industry are used to foster efficiency in the private sector as well as to promote equitable regulation.
Often, a single source of data creates value in multiple ways. For example, publishing surgical outcomes can help clinicians improve their own performance, help patients choose a hospital, and help citizens hold to account those responsible for the health care system.
To find the sources of value within a given portfolio, government agencies should first examine the data sets they already hold, and ask whether or not they are missing any opportunities to use them to create value. This is where open data can sometimes help, because often the quickest way to find new opportunities from existing data sets is simply to make them publicly available and then observe how people make use of the data.
More strategically, government agencies should consider how their organization creates value—in other words, their operating model—to identify opportunities where a smarter approach to data might allow them to create even more value in the future.
The Process of Value Creation. For government data to create value, they must inspire action. The process of creating value from data involves four steps, beginning with collection and ending with action. (See Exhibit 2.) Underpinning each step is a series of enablers such as IT infrastructure and organization structures.
The open-data movement is most interested in influencing the second and third steps in the chain: distribution (who can access data) and analysis (how they can use the data). But governments must concern themselves with activities across the whole chain so that they can be sure of making informed decisions on the basis of the best available data. This means that government agencies must make decisions at each step of the value chain, from choosing what information to collect to how to distribute it, how best to analyze it, and what actions to initiate as a result of that analysis. While governments need to oversee each step, they do not have to be directly responsible for delivering all the steps. Instead, they can draw on the skills of other parties (sometimes known as infomediaries) who may play a role across one or more steps—for example, by taking information from a number of sources and presenting it in an easy-to-read format or making it available through an app.
There is no shortage of data being collected and held by government agencies. But the challenge for governments when extracting value from data is to ensure that the data they collect in the first place will ultimately serve the purposes for which they were intended. To do this, governments need to consider what is required at each step of the data value chain. A promising example of how one government is doing just that is already under way in the U.K.
It is estimated that welfare fraud and error cost U.K. taxpayers £5.2 billion every year, or £165 every second. As well as being expensive, fraud undermines the public’s confidence in the welfare system. Better use of government data lies at the heart of a new strategy to reduce fraud and error by 25 percent by 2015.
The new strategy addresses each step on the data value chain.
Collection. To facilitate more accurate, timely detection of fraud and error, data across government agencies will be combined far more quickly and supplemented with data from outside sources (for example, credit reference agencies).
Distribution. Updates to the status or eligibility of individuals within the welfare system will be shared far more rapidly with the relevant agencies and public bodies. For example, local authorities will automatically be informed about changes to benefits or tax credits.
Analysis. It will be easier to crosscheck databases to highlight possible errors or fraudulent activity. For the first time, agencies will be able to perform these data matches in near real time when an individual files a claim.
Action. The new strategy should not only prevent a significant amount of new fraud and error but also highlight existing problems. A range of actions will ensue, such as using crosschecking to identify claimants suspected of having an undisclosed partner.
When it designed this strategy, the U.K. government carefully considered the implications and limitations of data sharing both between government organizations and with private organizations. A clear description of the data rights held by each party enables mutually beneficial data sharing and collaboration to take place while addressing questions of personal privacy.
Data Rights. Data rights describe who can do what with data. The allocation of data rights will determine the boundaries of value-creating functions and the competitive dynamics, if any, among players within the data value chain.
In the context of government data, government entities are usually the primary holders of rights, with the authority to trade or transfer those rights as assets. Along with those rights comes the responsibility of ensuring that governments account for the often competing interests of different parties. For example, if governments collect data on health outcomes and then license the right to use the data to a limited number of organizations subject to certain restrictions, it may contribute to a private market for health data.
Another hotly contested area is the status and ownership of personal data. The extent to which individuals have rights over data related to them is the subject of debate in many countries around the world. While techniques such as anonymization (the removal of personal identifiers) and aggregation (reporting data in summarized form) can be used to depersonalize data, they are also likely to reduce the usefulness of the data. Managing these tradeoffs, and allocating rights between governments and individuals, will be key to resolving the status and uses of personal data in the future.