Open Knowledge Foundation has been working on the Frictionless Data project to remove barriers to working with data. It is important for everybody involved in the publication of data to have access to tools that help automate and improve data quality. The Frictionless Data Field Guide details how anyone interested in publishing data can use the Frictionless Data software to improve the quality of their datasets. Designed for newcomers to data publishing, the Field Guide offers accessible and practical methodology alongside clear examples.

Opportunity

From 2017 and over a period of three years, the Open Data Institute is running several initiatives under their Research and Development programme to “build data infrastructure, improve data literacy, stimulate data innovation and build trust in the use of data”.  Frictionless Data was one of the first initiatives to receive funding in this programme because of the alignment of aims towards making the publishing of reliable and high-quality open data easier, faster and less costly. Frictionless Data provides a solution to all three of the programme’s core goals of improving quality, speed and automation, and cost of open data.  

This support provided a unique opportunity for our team to thoroughly document and update Frictionless Data software, and to produce the Field Guide to support our push for publishing high-quality data for open data use.

Normally, publishing high-quality data translates to longer publication processes and higher costs. The Field Guide addresses these complications by introducing easy-to-use tools and better workflows to address data quality issues.

Writing for a broad audience of data publishers, whose technical know-how is spread across the spectrum, introduced a challenge in terms of how to pitch the Field Guide; however, we want the bar to engagement kept low in order to encourage many people to try data publishing for themselves.

The Frictionless Data Field Guide provides step-by-step instructions for improving your data publishing workflow based around six key issues for data publishers.

How we helped

The Frictionless Data Field Guide details open data publication approaches with a focus on user-facing tools for anyone interested in publishing data.

Over 10+ years, we have  developed and iterated on tools for working with tabular data,  which are built with open data publication workflows in mind. The case studies are a good indication that there is a high degree of flexibility for extended use cases, handling different types of open data. The software featured in this field guide are all open source, maintained by Open Knowledge Foundation under the Frictionless Data umbrella and designed to be used independently of each other to meet specific needs, or alongside each other to achieve better data quality.

The Field Guide work started with a proposal submission to the Open Data Institute. Once accepted, we carried out an extensive audience mapping over several weeks and came up with a wishlist of all the material we were keen to produce in support of this work. During this time, we also looked at other organisations’ approaches to producing engaging content. All of this preparatory work informed our iterative design process. After we agreed on the scope, audience and approach, the rest of the time was spent developing and iterating content before launching in late March 2018. The final stage of the process - now underway - is dedicated to running workshops to create awareness of the Field Guide resource and the tools referenced, which we continue to maintain actively, and to grow and learn with our community of users.

Results

Our Frictionless Data: ODI section of our Measure dashboard offers real-time statistics on interactions and use of tools during the entire phase of development of the Field Guide. More about the Measure project and current statistics as at April 5, 2018 can be found in this document.

The Field Guide currently focuses on use of tools built and actively maintained by the Frictionless Data team. In the future,  potential work on additional guides could include other tools by the Frictionless Data community and the broader open data community.

The Field Guide can also be used as a great onboarding resource for communities in the data space. The examples given are also designed to serve as a gentle introduction to Frictionless Data,  ensuring users understand the premise of the larger concept, and the context within which to use the tools.

Key learnings from this work include:

• As the tools we build can be used by the broader open data community, the team presents narratives with multiple examples on how to use them. This enables us to make key fixes on user-facing tools, resulting in significant updates to functionality;
• Audience mapping is an informative process that can be widely applied. As we build tools to be solutions to known problems, we are also able to define different audiences and the distinct scenarios through which they encounter the same issues, thereby creating tools that cater to broader audiences;
• Frictionless Data and specifications can be abstract, so showing how it can help users in their day-to-day work quickly helps people to engage. When they see the value of the end result, they can better understand how it works and therefore be more successful in their efforts.