On February 10, 2022, the Digital Power Datahub and the Partos Digital Lab together kicked off the Data Awareness series with the Intro to Data Awareness. This series of 6 training courses develops the Datahub especially for the members of Partos; non-profits in the development cooperation industry. The aim of the series is to make development cooperation specialists data-wise, so that they can make and measure more impact.
Watch the Intro to Data Awareness below or read the most important take-aways from Digital Power colleagues Rogier and Marieke, and Gigi Pasco Ong-Alok from partos below the video.
Data is basically everything, but it is never neutral
All data we create, collect and store is data, both at work and in our private lives. Data enables us to innovate and helps us make choices based on information and not just our gut feeling.
But contrary to what we often think, data is never neutral and it also introduces the necessary risks. We need to realise that we, humans, influence the way data is collected, stored and analysed. This means that we include our biases in this apparently objective process and there are risks involved.
One participant in the introduction summed it up aptly: "Data is data, the way you use it can create opportunities and risks."
Data-driven vs. data-informed
During the introduction, an important discussion surfaced about the difference between data-driven and data-informed working and their pros and cons. More and more organisations claim to be data-driven. In practice, a fully data-driven way of working means that every choice in your organisation is automated based on data. This works fine in 'straightforward' processes, such as switching on your central heating when the temperature in the house drops below 20°C, but in complex environments so many (human) factors play a role that automation is not always desirable.
Working data-informed, on the other hand, means that you strategically consider what data means to you and what you want to achieve with the help of data. It leaves room for an expert's perspective and therefore quickly leads to a more nuanced decision-making process. Choices such as introducing cuts (and which ones) generally call for a more balanced approach than an algorithm that says "if this, then that".
The data point of view
If your entire organisation is data-wise you only need a (few) data specialist(s) to do the data work.
A data-wise organisation is one that sees the opportunities and risks of data and is able to put them into actions. For that you need a team that approaches projects from 'the data point of view' and a few specialists (depending on the size of your team) who can put that perspective
into practice. For this it is crucial that there is constant communication between programme specialists and data specialists.
Data literacy in the PMEL cycle can help non-profits make and measure more impact.
Most non-profit organisations elaborate their impact strategy in a Theory of Change. Measuring real impact properly and thoroughly remains a major challenge. Although we all know that in an ideal world impact can be translated into SMART indicators, in practice impact is incredibly complex. How do we bridge this gap between measurable indicators and complexity?
Data literacy is (part) of the answer to that question. The good news is this: if you constantly carry out based on the PMEL cycle (project planning, monitoring, evaluation, and learning), and make choices based on data, you are already data-informed! The bad news is, that's easier said than done.
The road to a data-wise organiaation starts with a tight, data-wise planning. In the planning phase of projects, it is crucial to establish clear definitions and activities and to do a baseline measurement. From then on, the PMEL cycle should be constantly 'running', even in a small task like checking an assumption, you work through the four steps.
How do you become such a data-wise organisation?
That's what the rest of the Data Awareness series focuses on. On April 14, we continued with part 2: Data Strategies for scale. In June we discussed the successes and challenges related to data awareness in the organizations of all participants.
In October 2022 we will continue with a practical case arising from the Partos Innovation Festival.
Receive data insights, use cases and behind-the-scenes peeks once a month?
Sign up for our email list and stay 'up to data':
You might also like:
From ethical data to action
The introduction of the new privacy law (GDPR) in 2018 has ensured that many organisations put privacy high on the agenda. In this article you can read about the 5 ethical risks of working digitally and using data. We also share a concrete solution: the Responsible Data Framework.
Storytelling using data
Organisations collect as much data as possible to map out the Customer Journey. After analysis, this combination of quantitative and qualitative data provides insight into the what and why of customers or visitors. These insights should prompt action. It is important to communicate the insights well, so that the right actions are taken. You can do that with storytelling.
From data to action
Every day, we collect data. Think of customer data, website visitor behaviour, information about conversions through all your off- and online channels and the performance of different teams within your organisation. But how do you use that data effectively?
What is a data architecture?
Working in a data-driven way helps you make better decisions. The better your data quality, the more you can rely on it. A good data architecture is a basic ingredient for data-driven working. In this article, we explain what a data architecture is and what a Data Architect does.
What are cookies?
Cookies. This word comes up a lot in the world of marketing and online analytics. But what exactly are those cookies? And are there different types of cookies?
The impact of ITP on analytics and the user experience
The quality of web analytics implementations
How good is your web analytics implementation? How much confidence is there within the company regarding those figures? In this article we first explain why a web analytics tool will never have 100% accurate data and why that is not a bad thing. Then we look at the practice: how good are most implementations really?