Data, even open data, is not a panacea. It is not something magical that can automatically solve problems for us. This is why it is equally important to talk about what data cannot do and the common misconceptions about data that beginner (and even experienced) data practitioners make.
Remember that data is oftentimes an incomplete representation of the world. Because of this, the outcomes of any data analysis or visualization should always be verified and validated to ensure that what the data is saying and what's actually happening are the same. This is particulary true when it comes to data models that try to predict real-world phenomena. When the model and the real-world do not agree, don't be tempted to change the real-world to fit the model.
When answering questions and solving problems with data, it will not give you answers by itself but it will help you know where to look and what to focus on. You can think of data as witnesses to a mystery you are trying to solve. It is never wise to rely solely on one witness. You need multiple witnesses in order to corroborate and verify their accounts. At the same time, some witnesses may give you conflicting accounts. This is where you need to use your critical thinking and investigative skills in order to uncover the truth.
No matter how much data you gather about a community or about your data subjects, it cannot replace actual conversations with people. Building community and engaging with people builds trust. It also introduces you to possible biases that the community, you, and your data have.
By engaging with your data users, you understand what they need from the data and how they need the data presented to them for it to be meaningful and understood. By knowing the audience, it becomes easier to communicate the data to them.
Community building is an essential part of any data-driven project and one that can never be replaced by any amount of data.
One of the most common mistakes that people new to data make is that they have a tendency to focus solely on the data and how they can present it. They think that because they have the data, they should use it and create visualizations from it. This shouldn’t be the case.
The creation of data visualizations, data platforms, and dashboards should not be the end-all be-all of a data-driven project. The primary goal of a data-driven project should always be to properly communicate its findings to its intended end-users. Sometimes this means creating data visualizations but sometimes it doesn't. A visualization may help but just because you create one does not mean that you get your message across clearer.
In fact, most of the time, complex data visualizations and dashboards that are improperly designed and that did not go through consultations with the final data users actually waste the effort put into data collection and analysis.
There’s a saying that open data without users is just as bad as closed data. In the same manner, having a beautiful data visualization or dashboard without users is the same as having none at all.
A dashboard by any other name by Katya Abazajian provides some good points about the use of dashboards by local leaders and how we might improve them.
"Dashboards aren’t dead because they’re not useful tools, but because they’re a bandaid for lack of power and agency in local governing to solve big problems. They scratch at the surface of structural inequities repeatedly and without effect, like picking at a wound without ever allowing it to heal" (A dashboard by any other name (Katya Abazajian, Civic Source))
“Data dashboards might be useful if they gave more grounded, truthful answers in the present and asked more creative, speculative questions about the future.” (A dashboard by any other name (Katya Abazajian, Civic Source))