Every community faces a similar challenge: there are many different kinds of health, human, and social services that are available to people in need, yet no one way that information about them is produced and shared. Instead, many organizations independently collect community resource directory data, each structuring it in different ways — yielding redundant, fragmented silos.
As a result, it’s hard to ‘see’ all the many different parts of our safety net. Many people never discover services that could help improve their lives. Service providers spend precious time verifying data rather than helping people. And without access to this information, decision-makers struggle to evaluate community health and program effectiveness. This yields underperforming systems that fail people and communities in tragic ways.
Broadly speaking, we are currently in the midst of a technological and cultural shift towards open platforms and open data. This shift makes it easier for more people to produce and use data in new ways; to develop and redeploy new technologies at lower cost; and generally to increase the value of data, and the strengths of the networks and communities that use it.
More specifically, a tremendous opportunity recently emerged for this particular field of community resource directory data: in 2013, Schema.org proposed a ‘civic services’ schema to the World Wide Web Consortium (the W3C). This schema represents an emerging standard that can enable community resource data to be read and delivered in new ways by search engines like Google, Yahoo, Yelp, etc.
Given this opportunity to make it easier for data on civic and social services to be discovered on the web, we have a mandate to bring governments, funders, civic institutions, and technologists together around a shared goal: let’s make it as easy as possible to publish, find, trust, and use this information. That entails the establishment of interoperability between the language of the web and the field of information-and-referral systems, and the discovery of new means to sustain the production of this data as an open resource.
‘Open data’ is a popular term right now. It’s also ambiguous. What does it mean?
Open means ‘free,’ as in ‘free speech.’ We are all entitled to it by fundamental right.
Open means accessible. We have “open access” to things like roads and libraries — these are public goods, and anyone should be able to use them. Likewise for our computer technology: open data can be accessed not just by users of one system, but by users of an open set of systems.
Open does NOT necessarily mean ‘anything goes.’ You’ve gotta return books to the library, and in good condition too. Even on open roads, there are speed limits, and eventually there are tolls, plus construction and cleanup crews, etc. You can’t yell ‘fire’ in a crowded theater. Etc.
Open does NOT necessarily mean ‘free’ as in ‘free beer.’ For something to exist in an open state, a lot of energy and resources must go into keeping it so. Those resources must come from somewhere (and we don’t assume they will automagically crowdsource themselves).
Open data can mean many things, but at its core, open data entails:
Availability: open data must be available as a whole, presumably downloadable over the internet. The data must also be available in a convenient and modifiable form. (There can be reasonable reproduction costs associated with certain kinds of access to open data.)
Reuse and Redistribution: open data must be provided under terms that permit reuse and redistribution, including the intermixing with other datasets. There should be no discrimination against fields of endeavor or against persons or groups. The data must be machine-readable. The data can be licensed to prevent changes, and/or to ensure clear documentation of changes.
Openness entails a state of possibility.
By making data available for anyone to re-use it in new ways, we can dramatically increase its potential value.
We assert this as a given: the production of open community resource data can, will, and should happen, one way or another.
We also assert that such production should occur in such a way that its costs are sustainably carried into the future; and furthermore that the means of production should be accessible to and shaped by local stakeholders. This is, after all, data about their community’s resources.
For the purposes of Open Referral, the concept of ‘open data’ is itself open to some degree of interpretation. Essentially, we are asking: how should this data be open? (If it is to be published in bulk for free, can premium real-time access via API require a fee? If the market won’t fully cover costs of its production in this way, should the government be expected to subsidize its production, in order to ensure access and quality? Etc.)
Broadly speaking, people seek ‘referrals’ to resources that can help them meet their needs. Community resource data connects people to health, human, and social services from which they can receive assistance.
Some services are provided by non-profit organizations, and other civic or cultural groups. Others are provided by local, state, even federal governments. All of these entities share information about their resources in different ways.
‘Information and referral’ refers to the field in which information about services is aggregated in community resource directories, and delivered (via referral) to people seeking help.
The Alliance of Information and Referral Systems (AIRS) is an accrediting agency that certifies ‘information and referral’ providers throughout North America. AIRS promotes official standards for ‘information and referral’ services, ranging from operational standards to data standards. AIRS has consulted with the Open Referral Initiative from its inception, and members of AIRS are active in our community and working process.
By standards, we refer to common ways of doing things. In the case of data standards, that means an agreed-upon set of terms and relationships that define and structure information, so that it can be readily transferred between systems.
With such common agreements, it can become ever easier to leverage existing resources and technology, and to develop and adapt new technologies at lower cost and broader possible use.
Standardizing data across places and institutions also makes it easier to analyze and evaluate data, which makes it easier to understand the health of communities and the effectiveness of programs.
Furthermore, the process of developing standards helps to bring stakeholders together. By building a community among users, producers, and service providers, we can accelerate the process of learning and innovation towards our shared vision of helping people and improving the health of communities.
A platform is a broad term that could mean a lot of different things — here we use ‘platform’ to refer to a system that makes its data available both to users and to external systems (which can be ‘built upon’ the platform).
For example, you can get a forecast from the National Weather Service by going to Weather.Gov. But the NWS also offers ‘a web service,’ otherwise known as an ‘Application Programming Interface’, or API. An API enables the data in one information system to be automatically accessible by other information systems. The NWS API enables developers to build applications that connect to the Weather.gov ‘platform’ in order to seamlessly provide public weather data to skiiers, photographers, rainbow chasers, etc.
Platforms enable their data to be accessed and used in all kinds of ways, many of which could not be provided by those who operate the platform themselves.
By ‘open platform,’ we specifically mean three things:
- An open platform enables its data to be both accessed directly by users and also published in open formats
- An open platform is powered by technology that is freely available through open licenses
- An open platform is a system in which interoperability and integration are the primary design objectives
With increasing adoption of open platforms sharing a standard format, we anticipate:
Decreased cost of data production (as data produced once can circulate through many systems)
Improved quality of data (as more use generates more user feedback)
Improved findability of data through web search and an ecosystem of tools and applications; Decreased cost and improved quality of new and redeployed tools (websites, applications, etc).
Improved quality of referral services (as patterns of resource allocation shift from maintaining data to delivering data)
Meaningful use of resource data for research purposes, such as community health assessment, and policy-making and resource allocation.
Healthier people and more resilient communities.
While various systems (including the Ohana API) do enable organizations to submit updates for their own information, there are a number of factors that limit the effectiveness of organizations as reliable sources of information about their own services.
Organizations might not designate the responsibility for managing all of this information to any single person. A single organization might offer many services through various programs at multiple locations. And these are often stressed environments with limited technical capacity and overburdened staff. It can be hard for organizations to keep track of all of their own services!
Organizations sometimes submit information about services that is vague or not entirely accurate. When updating their own records, organizations’ staff sometimes submit information that is composed to promote their organization in general, yet not precisely describe the information about services that is needed. This tends to yield information that is not useful to someone who is looking for a service.
Organizations are asked to update their information so many times in so many different community resource directories that they get confused or frustrated.
Keeping this information up to date just isn’t a high priority when organizations already have more clients coming through its doors than they can handle.
As a result, we assume that organizations’ self-reported updates should be considered one input among many in the effort to produce and maintain accurate data about services.
Government and funders do require various kinds of data about their grantees, but it’s generally non-standardized and not specifically about services themselves.
We anticipate that, as a standard format becomes adopted and demonstrates value, governments and funders may begin mandating this information as a condition of funding. (But it doesn’t seem feasible to attempt to make that happen before a format has been demonstrated as viable and gaining adoption!)