Good decisions need good data

Emerging learnings from Buenos Aires’ Data Collaboration

Open Data Charter
May 28 · 5 min read

by Marisa Miodosky and María Lago, BA City Statistics Bureau

Photo by Malena Gonzalez Serena

The pandemic has made evident that data is crucial for policy making. Circulation measures, economic subsidies for negatively affected sectors, and vaccines provision schemes (among other decisions) were designed based on data. At the same time, there has been an increasing awareness about the importance of having good data that is periodically collected, accurate and reliable. In Argentina, this has been the case for infection rate, deaths, occupation of intensive care beds and some other indicators related to the health system.

The production of care-related activities data by different sources, which makes up the core of our data collaboration project, has been less rigorous. In December 2020, the Open Data Charter and Buenos Aires City’s Statistics Bureau partnered to identify data sources, build information to show how care duties are organised in Buenos Aires City and set up an indicator system to inform policy making.

A good indicator system needs to include diverse sources of information, including modern techniques such as statistics being built from registry data. Buenos Aires is pretty much aligned with that process.

For this purpose, our first efforts included data stakeholder mapping to analyse public and non-government data sources that can account for care supply and demand. We ended up having 8 public entities that are data providers, together with 44 non-government institutions that might be data users (7 local civil society organizations, 7 institutions from the private sector, 15 from academia, 9 unions and 6 international organizations).

Despite there being a huge production of online surveys by non-government organizations, private firms and some studies conducted by universities, most of that data has serious limitations. Among its main constraints we can identify sample representativeness, collection methods, completeness and more importantly their frequency — these are one-time studies that will not be repeated or continued again.

Based on this scenario, we decided the initial stage of the Care Indicator System should be grounded in just government-generated data, from two main sources: (i) statistics collected and published by the Statistics Bureau and (ii) registry data produced by different ministries and public entities.

The first part was relatively easy, as statistical data is available and accessible for the Statistics Bureau as it comes for the periodical surveys carried out by the institution, with common structures and standards. The challenge came when working with registry data, since these needed to be curated through technical expertise as they were not collected for statistical purposes.

In order to have quality and usable datasets, data stewards need to have proper incentives and clear benefits to grant access to the data they guard. Without a doubt, technically-sound and well-processed data for policy making could be one of them. And here is where the BA City Statistics Bureau has a role to play.

The Bureau’s experience recognises that data sharing standards and common understanding of methodologies and data collection methods is crucial for a public indicators system’s trustworthiness and reliability. Trust in data quality is dependent, in part, on the credibility of its source. BA City Statistics Bureau has had a long track record of unquestionable reputation.

The scenario described above demonstrated the need for data sharing standards that allow for the construction of statistics from registry information. Inspiration for this came from data share agreements, the CABI Data Sharing Toolkit and the FAIR attribute for good registry data: Findable, Accessible, Interoperable and Reusable.

While we interacted and exchanged information with other public entities that steward the data we needed for the Care Indicators System, it became evident not all public entities would want to share their data nor saw what added value they would get from the process. To address this constraint, questions such as: ‘what’s in it for them?’;what would other agencies get by sharing their records with me?’; ‘is there a particular service or insight it could be shared as an exchange?” needed to be formulated. Clearly identifying why that other organisation or entity would share their data, helps to organize a strategy set on clear incentives and value propositions that helped in the process.

  • Value proposition #1: Make them own the project. It’s not them sharing raw data with the Statistics Bureau (as just data providers), but they are also leaders of the project — it must be a collaborative effort.
  • Value proposition #2: Offer them added value to their data. The Bureau has the skills and technical capacity to develop deeper analysis, process the data through statistical systems, build indicators, graphics or visualizations.

A clear data sharing and governance model that can ensure the Care Indicator System’s sustainability and efficiency to inform public policies, as well as provide useful information that showcase how the care duties are socio-economically distributed in Buenos Aires city, needs to be grounded on:

  1. A sound model that can identify clear incentives for data sharing;
  2. Responsible stewards for data collection and publication; and
  3. Data sharing and publication standards and clear methodologies for data collection

In the next few months, ODC and the Statistics Bureau will be engaging with key stakeholders identified as either care suppliers or demanders in a series of workshops and consultations, with the ultimate goal of ensuring that the care indicators are indeed useful for the issues that matter most to the public.

It is expected that the platworm will be launched soon, together with use cases and impact stories to account for the care duties’ scenario in Buenos Aires.

The conversations held in the process of building this platform have identified other potential data collaborations in other areas that will put us on the path towards a future where the true potential of administrative data for public good is released.

As we mentioned in our first blog, the pandemic has brought to light, in an unprecedented way, the importance of care for the sustainability of life and its unequal distribution among providers: households, public services, the market and community organisations. Ad-hoc surveys have also confirmed that women have suffered the most for caregiving burden. In order to properly address these issues with a gender perspective more data is needed. Not just any data, but quality data.

Our Caring Indicator System project with the Government of Buenos Aires City was selected to be a part of Open Data Institute and Microsoft’s #PeerLearningNetwork. The network aims to help address the data divide and help organisations realise the benefits of #data. Read more about the #PeerLearningNetwork here.

For further information on this project, please read our Peer Learning Network final report here. You may contact us at info@opendatacharter or mmiodosky@estadisticaciudad.gob.ar

opendatacharter

Towards a culture of open and responsible data use by governments and citizens.

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