As technology advances, our capacity to collect, share, and manipulate data is growing exponentially. In the private sector, many organizations are developing innovative ways to use data to transform their business models and unlock significant new sources of value. From conducting more sophisticated customer segmentation to overhauling recruitment, an organization’s ability to exploit data is becoming a critical source of competitive advantage.
In the public sector, governments control a large and ever-increasing amount of data about citizens, public services, and the world around us. From individual health records and school league tables to weather maps and economic statistics, the range of government data is diverse and the potential uses of those data are enormous. Like private organizations, governments face the challenge of getting the most out of their data—in this case, for the greater good.
Until recently, government data were for the most part jealously guarded, available only to those with privileged access. But with the advent of the “open data” movement, governments have been subject to mounting public pressure to freely release more data to citizens and corporations. (See “The Rise of Open Data,” below.) The movement is heralding a shift in governments’ relationship with data—from being a gatekeeper, ensuring that data are securely stored, to becoming a steward, safeguarding data for others. Proponents of open data argue that sharing raw public data is the key to unlocking their value. But while this push for transparency and accountability is commendable, it provides no guidance on other pressing issues, such as which data sets governments should collect in the first place or how they should manage sensitive or personal information.
In recent years, a variety of organizations and political movements have emerged with the mandate to improve government accountability by making data more publicly available. Many governments have responded by unleashing oceans of stored data.
In the U.S., the Obama administration has pioneered the development of Data.gov, a central Web portal for government data sets, and is promoting greater transparency throughout federal agencies. The U.K., France, Australia, and New Zealand have also set strong objectives to integrate open data into their governments’ technology strategies, and each has launched an open-data portal.
The European Commission recently launched an open-data strategy for Europe, which it expects will deliver an annual €40 billion boost to the EU’s economic growth. It is opening its vaults of information to the public for free through a new data portal, and it is backing the effort with €100 million to fund research into improved data-handling technologies.
Among NGOs, the World Bank publishes extensive data and visualization tools focused on health outcomes and systems in developing countries.
Indeed, the current debate about the public’s role in government data threatens to overshadow an equally important issue: how data can be better used by government to improve outcomes. We believe that, instead of focusing solely on whether or not to release data publicly, governments must also ask themselves a more fundamental question: How can they maximize the value of government data for society? In some cases, releasing vast data sets may serve this purpose. But in many instances, changes to how governments themselves use the data are likely to deliver the greatest impact.
To begin with, governments need to adopt a more strategic approach to the way they collect and use data.
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.
To derive value from the data they hold, governments face some unique challenges:
The practical difficulties of managing the huge volume of data they oversee, which makes it vital for governments to adopt a strategic mindset and prioritize what really matters—in some cases, by ceasing to collect data that do not create value
Differentiating between data that are required for compliance or regulatory purposes and data that are better suited to informing policy decisions
Balancing the rights of individuals to privacy with the benefits of using government data to deliver better outcomes for the broader population
Guarding against potentially perverse outcomes of making certain data publicly available; for example, while patients might use mortality rates to choose the surgeon they perceive to be the most competent, publicizing those rates could motivate clinicians to reject high-risk patients for fear of damaging their scores
Given that government agencies operate within a highly complex environment, the most effective approach to change is an adaptive one, in which steps are taken and outcomes observed before further changes are rolled out. Although there are challenges to overcome, some of them substantial, the potential value of government data is too great to overlook. Meanwhile, the volume and variety of the data being collected inevitably grow ever larger, with profound implications for individuals and society as a whole. If governments fail to take a strategic approach to data, now and in the future, they will end up neglecting their duties to their citizens.
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