This post is by Janna Rous at Humanitarian Data. The original was published here on April 29, 2018
Imagine this picture of data literacy at all levels of a programme:
You’ve got a “donor visit” to your programme. The country director and a project officer accompany the donor on a field trip, and they all visit a household within one of the project communities. All sat around a cup of tea, they started a discussion about data. In this discussion, the household members explained what data had been collected and why. The country director explained what had surprised him/her in the data. And the donor discussed how they made a decision to fund the programme based on the data. What if no one was surprised at the discussion, or how the data was used, because they’d ALL seen and understood the data process?
Data literacy can mean lots of different things depending on who you are. It could mean knowing how to:
- collect, analyze and use data;
- make sense of data and use it for management
- validate data, be critical of it,
- tell good from bad data and knowing how credible it is;
- ensure everyone is confident talking about data.
IS “IMPROVING DATA LITERACY FOR ALL LEVELS” A TOP PRIORITY FOR THE HUMANITARIAN SECTOR?
“YES” data literacy is a priority! Poor data literacy is still a huge stumbling block for many people in the sector and needs to be improved at ALL levels – from community households to field workers to senior management to donors. However, there are a few challenges in how this priority is worded.
IS “LITERACY” THE RIGHT WORD?
Suggesting someone is “illiterate” when it comes to data – that doesn’t sit well with most people. Many aid workers – from senior HQ staff right down to beneficiaries of a humanitarian programme – are well-educated and successful. Not only are they literate, but most speak 2 or more languages! So to insinuate “illiteracy” doesn’t feel right.
Illiteracy is insulting…
Many of these same people are not super-comfortable with “data”, but to ask them if they “struggle” with data, or to suggest they “don’t understand” by claiming they are “data illiterate” is insulting (even if you think it’s true!).
Leadership is enticing…
The language you use is extremely important here. Instead of “literacy”, should you be talking about “leadership”? What if you framed it as: Improving data leadership. Could you harness the desirability of that skill – leadership – so that workshop and training titles played into people’s egos, instead of attacking their egos?
WHAT CAN YOU DO TO IMPROVE DATA LITERACY (LEADERSHIP) WITHIN YOUR OWN ORGANIZATION?
You might be directly involved with helping to improve data literacy within your own organization. Here are a few ideas on how to improve general data literacy/leadership:
- Training and courses around data literacy.
While courses that focus on data analysis using computer programming languages such as [R] or Python exist, it might be better to focus on skills-development on more popular software (such as Excel) which is more sustainable. Due to the high turnover of staff within your sector, complex data analysis cannot normally be sustained once an advanced analyst leaves the field.
- Donor funding to promote data use and the use of technology.
While the sector should not only rely on donors for pushing the agenda of data literacy forward, money is powerful. If NGOs and agencies are required to show data literacy in order to receive funding, this will drive a paradigm shift in becoming more data-driven as a sector. There are still big questions on how to fund interoperable tech systems in the sector to maximize the value of that funding in collaboration between multiple agencies. However, donors who can provide structures and settings for collaboration will be able to promote data literacy across the sector.
- Capitalize on “trendy” knowledge – what do people want to know about because it makes them look intelligent?
In 2015/16, everyone wanted to know “how to collect digital data”. A couple years later, most people had shifted – they wanted to know “how to analyze data” and “make a dashboard”. Now in 2018, GDPR and “Responsible Data” and “Blockchain” are trending – people want to know about it so they can talk about it. While “trends” aren’t all we should be focusing on, they can often be the hook that gets people at all levels of our sector interested in taking their first steps forward in data literacy.
DATA LITERACY MEANS SOMETHING DIFFERENT FOR EACH PERSON
Data literacy means something completely different depending on who you are, your perspective within a programme, and what you use data for.
To the beneficiary of a programme…
data literacy might just mean understanding why data is being collected and what it is being used for. It means having the knowledge and power to give and withhold consent appropriately.
To a project manager…
data literacy might mean understanding indicator targets, progress, and the calculations behind those numbers, in addition to how different datasets relate to one another in a complex setting. Managers need to understand how data is coming together so that they can ask intelligent questions about their programme dashboards.
To an M&E officer…
data literacy might mean an understanding of statistical methods, random selection methodologies, how significant a result may be, and how to interpret results of indicator calculations. They may need to understand uncertainty within their data and be able to explain this easily to others.
To the Information Management team…
data literacy might mean understanding how to translate programme calculations into computer code. They may need to create data collection or data analysis or data visualization tools with an easy-to-understand user-interface. They may ultimately be relied upon to ensure the correctness of the final “number” or the final “product”.
To the data scientist…
data literacy might mean understanding some very complex statistical calculations, using computer languages and statistical packages to find trends, insights, and predictive capabilities within datasets.
To the management team…
data literacy might mean being able to use data results (graphs, charts, dashboards) to explain needs, results, and impact in order to convince and persuade. Using data in proposals to give a good basis for why a programme should exist or using data to explain progress to the board of directors, or even as a basis for why a new programme should start up….or close down.
To the donor…
data literacy might mean an understanding of a “good” needs assessment vs. a “poor one” in evaluating a project proposal, how to prioritize areas and amounts of funding, how to ask tough questions of an individual partner, how to be suspect of numbers that may be too good to be true, how to evaluate quality vs. quantity, or how to see areas of collaboration between multiple partners. They need to use data to communicate international priorities to their own wider government, board, or citizens.
Use more precise wording
Data literacy means something different to everyone. So this priority can be interpreted in many different ways depending on who you are. Within your organization, frame this priority with a more precise wording. Here are some examples:
- Improve everyone’s ability to raise important questions based on data.
- Let’s get better at discussing our data results.
- Improve our leadership in communicating the meaning behind data.
- Develop our skills in analyzing and using data to create an impact.
- Improve our use of data to inform our decisions.
This blog article was based on a recent session at MERL Tech UK 2018. Thanks to the many voices who contributed ideas. I’ve put my own spin on them to create this article – so if you disagree, the ideas are mine. And if you agree – kudos to the brilliant people at the conference!
Register now for MERL Tech Jozi, August 1-2 or MERL Tech DC, September 6-7, 2018 if you’d like to join the discussions in person!