Handbook on Data Collection / Phase Eight: Share Data and Communicate Insights
After analysing and visualising your data to make it useful (phase seven), you can share it with others. By sharing data, you allow others to access and use it. Sharing data could lead to the following mutual benefits:
- Enable new data uses
- Lead to new collaborations between various stakeholders
- Improve transparency and accountability
- Empower communities to act based on the data
- Reduce the cost of new data collection projects
- Increase the impact and visibility of the work
- Provide resources for education
- Encourage inquiry and debate
But before you start, do you know who will be interested and which format and channel will work for them? Are any other steps required to ensure that the data is useful? In the design phase of your data collection project, phase two of the Handbook, many of these questions will already have been addressed and pave the way for effective data sharing. Making use of the prep work you’ve already done, invest time in preparing the data sharing process to ensure that your data is communicated in a productive way. For instance, your report should be easily discovered by others, not buried many levels deep in a website.
|Preparing the data sharing process|
To ensure your communication is effective and your right target group is reached, answer the five "W" questions.
|Why: What are your motives for sharing the data? What do you hope to achieve with your content? Translate these motives into concrete goals and write them down.|
|Who: Who is your audience? Your answer will determine what the most effective approach is to sharing your materials. Without it, the sharing effort may be fruitless. Are the end users included in your sharing effort? They are best placed to enrich and validate the findings and they are probably the audience that can benefit the most.|
|What: Deciding which data to share and how can be challenging. Do you share aggregated data and conclusions, or raw data? Are there risks you need to take into account regarding confidentiality or potential misuse which require anonymisation?|
|When: When did the data collection take place and when is the target group most likely to engage?|
|Where: When you know why, with whom, and when you are going to share the content, you can decide where the content should be shared.|
- 1 Target audience
- 2 Channels and formats
- 3 Measuring success
- 4 Feedback loop
- 5 Processed versus raw data
- 6 Open, shared, and closed data
- 7 Example of open data sharing
- 8 Two examples of open data standards
- 9 To share openly or not?
- 10 Data rights
- 11 Challenges of data sharing
- 12 Conclusion
- 13 Suggested reading
- 14 Acknowledgements
A good start can be to question who you’ll be sharing the data or main indicators with, and to identify these groups according to the type of organisation or community they belong to, their position within the organisation or community, their main purpose for using the data, and their preferred channels.
Where the target audience is located may be an important aspect in relation to which channels should be used, depending on which forms of communication are available.
Channels and formats
To reach an audience, you can use multiple channels and formats. Your choices determine the success rate of the sharing. Examples of channels are:
- In person
- Mobile phone
- Social media
- Broadcasting: radio and television
The Internet has a wide diversity of (social) media platforms, which includes websites and email. So, the question is, your target audience uses which of these channels? If you pick the wrong channel, sharing is unlikely to be successful.
There is a large diversity of formats, including:
- In-person: At conferences, networking events, roadshows, workshops, focus groups or webinars.
- Writing and online: Websites, newsletters, contact databases, articles, presentations, policy briefs, factsheets, brochures, posters, E-learning platforms, photos, or videos.
When looking at the specifics of the data sharing, there are four key types:
- Story: For data to be meaningful to a general audience, it is important to find meaning in the numbers. Without a storyline, the output may be limited to just a description of numbers.
- Tables: Using tables effectively helps minimise the number of data values in your text. It also eliminates the need to discuss less significant variables that are not essential to the storyline.
- Charts: A chart is a visual representation of statistical data in which the data are represented by symbols such as bars or lines. It is a very effective visual tool, as it displays data quickly and easily, facilitates comparison, and can reveal trends and relationships within the data.
- Maps: Maps are the most effective tools to visualise spatial patterns. When carefully designed and presented, they are more than just decorative features in a statistical presentation. They can help people identify and highlight distributions and patterns that might not be apparent from tables and charts.
Data for the development sector is very diverse in nature. For example, it can be infrastructure related, services related or policy related. Each type of data requires its own format in order for it to be optimally shared with the chosen audience using the preferred channel. Often, a combination of the above formats works best. For instance, to compare a baseline water point inventory with a second inventory of water points you may want to use a table. You can use text to explain the differences, and illustrate key elements, which are location specific, using maps. You can then summarise and conclude with text.
An important consideration is language. What is the literacy level of the target audience and which languages do they speak? Which solutions can you develop to ensure everyone understands? In Kenya, for example, does the target audience understand English, Kiswahili, or would a more common language be more useful?
In many cases of online sharing you can measure success. For example, you can use website analytics to record the number of visitors, visits, downloads, and shares. This information may be just an indication. For more depth, ask for feedback using evaluation forms to understand how people have made use of the data and whether there are any expected or unexpected results. A thorough evaluation can ensure success in future data sharing efforts.
Sharing data back to the very people and communities involved is often overlooked. There are various reasons to build this into the project plan, including the validation of the data collection and analysis and to involve them in decision making regarding potential changes to their environment and lives. There are simple ways to do this, for example by printing the data and results on a poster and feeding this into a discussion on location, as illustrated in this example from India. It is likely that new insights will be gained by examining the materials together with the people who know the context best.
Processed versus raw data
When you share with others, you would probably rather share processed data; the results of data analysis. The analysis may be quite straightforward or it may be complex, consisting of advanced statistical or geospatial formulas. Certain choices are made in the process of data analysis and the results (insights, interpretation, conclusions) may not fit the needs of the audience. Taking potentially sensitive data into account, it may be more useful to share the raw data. This allows the consumer of the data to either verify the analysis or perform a different type of analysis. Another option is that the shared data is used as a data source for other projects, which is typically the case in rural waterpoint mapping and monitoring.
Sharing data does not necessarily mean that you should put everything you have on the Internet. The key question is what purpose it serves. This video, Open / Shared / Closed: The world of data made by the Open Data Institute, differentiates between open, shared, and closed data.
Open data is data that anyone can access, use, and share. This usually refers to raw data. For data to be considered open, it must be accessible (published on the Internet), available in a machine-readable format, and licensed so that anyone can access, use, and share it commercially and otherwise.
Closed data is data that can only be accessed by its subject, owner or holder.
Shared data can be of three types:
- Named access: Data that is shared only with named people or organisations.
- Attribute-based access: Data is available to specific groups who meet certain criteria.
- Public access: Data is available to anyone under terms and conditions that are not open.
See the Open Data Institute article Closed, shared, open data: what’s in a name? for more information.
Example of open data sharing
SMARTerWASH in Ghana
SMARTerWASH is a project of the Community Water and Sanitation Agency (CWSA) in Ghana, which sets out to monitor water and sanitation in six regions of Ghana. The SMARTerWASH project aims to strengthen the information and communication technology (ICT) infrastructure and by ensuring interoperability between systems.
Following data collection using Akvo Flow in 131 (of a national total of 216) districts, data from 23,001 handpumps, 938 piped schemes, near 15,000 Water and Sanitation Management teams (WSMTs), and 131 service authorities were collected. This data was processed and made available in offline factsheets (regional and district level) and in an online water atlas, using an Akvo Site template. Everyone who is interested in the water, sanitation and hygiene (WASH) data in Ghana can access this information in different formats.
Two examples of open data standards
Water point data exchange (WPDx)
The global rural waterpoint data repository Water Point Data Exchange (WPDx) is an example of a combined raw, open, and shared data source. Basic details are shared such as location, date, and type of infrastructure. Those who want more data related to these records can request them directly from the relevant organisation, enabling a sharing-on-request system.
International Aid Transparency Initiative (IATI)
IATI is a worldwide standard for structuring and sharing project and programme data. At the centre is the IATI Standard, a format and framework for publishing data on development cooperation activities, intended to be used by all organisations in development, including government donors, private sector organisations, and national and international NGOs. The IATI registry acts as an online catalogue and index of links to all of the raw data published to the IATI Standard.
It may be complicated to judge whether data should or can be shared openly. You may also consider sharing data on request. A general rule you can apply is to “share, unless...”. This refers to the fact that there are more (potential) benefits of sharing data than in keeping it to yourself or your organisation. “Unless” refers to reasons for not sharing, which may relate to private, confidential, or high risk data. Private data must always remain closed unless explicit approval is given by the persons or communities involved. High risk data may be referring to a post-war situation. There are various ways to anonymise data in this quick overview or detailed guide. Some key terms in this realm are data ethics and responsible data, which we have defined in the Handbook glossary. The message here is that you should check whether sharing data or insights poses a risk and to take measures accordingly. A good practice is for an organisation to develop its own data policy. An example of this is the five-point responsible programme data policy by Oxfam.
When sharing data openly, you need to consider what the conditions and requirements are for the user of the data, such as the license which applies to the data. Some examples include Creative Commons licenses, Open Government licenses, and bespoke or custom-made licenses. To support you in making a choice which license to use for sharing data openly, you can consult Choose a License.
Challenges of data sharing
The growing availability of shared data ensures operational and collaborative opportunities. However, there are some risks and challenges around data sharing:
- Publishing certain data that may violate legislation. Sharing certain data may be prohibited by law or infringe upon someone’s rights or freedoms.
- Publishing data that can be interpreted in different ways. Users might intentionally or unintentionally misinterpret the data (to cause scandal, to get a competitive advantage, to cause harm to other subjects, etc). Some data could be misused or wrongly interpreted.
- Finding the right audience: There might not be an audience for your data because it’s not possible to locate the dataset or because nobody knows it’s available.
Thinking and developing your sharing strategy can ensure the other steps in the process bear fruit. At all times, it is important to be mindful of sharing the data and insights with the right target audience. Does the format and channel you choose fit their needs? Did you choose the right license for your data and consider data ethics? And is it possible to gather information about the usage of the shared data and insights?
- Data infrastructure: Closed, shared, open data: what’s in a name?
- Well that was a failure, ICTworks
- What does Open Access in RRI mean?, RRi (Responsible Research Innovation)
- Implementing Responsible Research and Innovation in research funding and research conducting organisations – what have we learned so far?, by Ellen-Marie Forsberg, Clare Shelley-Egan, Miltos Ladikas, and Richard Owen
Authors: Lars Heemskerk (Akvo.org), Marten Schoonman (Akvo.org)
Contributors: Arun Kumar Pratihast (Akvo.org), Annabelle Poelert (Akvo.org), Beatriz Medina (Water Environment and Business for Development, WE&B)
|The Africa-EU Innovation Alliance for Water and Climate (AfriAlliance), is a 5-year project funded by the European Union’s H2020 Research and Innovation Programme. It aims to improve African preparedness for climate change challenges by stimulating knowledge sharing and collaboration between African and European stakeholders. Rather than creating new networks, the 16 EU and African partners in this project will consolidate existing ones, consisting of scientists, decision makers, practitioners, citizens and other key stakeholders, into an effective, problem-focused knowledge sharing mechanism.|
|AfriAlliance is lead by the IHE Delft Institute for Water Education (Project Director: Dr. Uta Wehn) and runs from 2016 to 2021. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689162.|