Part 2 of 3 – Being Data-informed and Data-inspired
This is the second in a series of three articles on the differences between “data-driven”, “data-informed”, and “data-inspired” approaches to working with data, and when to choose among these approaches.
As discussed in our previous blog post in this series, organizations are constantly trying to leverage vast amounts of data to improve processes, make better decisions, and realize better results. For years the thinking was that this improvement should come from being “data-driven”, however it’s clear that it is more nuanced than that. There is a continuum of approaches for leveraging data within your organization, and different questions or needs within your organization will likely warrant a different approach.
The Data-driven approach was presented in the first piece in this series. Now, we present the other two approaches along the continuum: Data-informed and Data-inspired.
When should you be Data-informed?
If you want to look at past performance in order to decide what your next steps might be, you should likely use a data-informed approach. Combining Key Performance Indicator (KPI) data with your experience can help drive your thinking and strategies. The level of human engagement and data is more balanced here, versus a purely data driven strategy.
One great example of being data informed is the work we are currently doing with the District of Columbia Department of Transportation (DDOT). The District’s Vision Zero goal is simple: “By the year 2024, Washington, DC will reach zero fatalities and serious injuries to travelers of our transportation system, through more effective use of data, education, enforcement, and engineering.” So, they need to be tracking their performance and understanding where the problem areas are. However, straightforward GIS queries and techniques weren’t cutting it. They want to answer complex questions about the safety of their roadway network in order to identify and implement safety improvements. AppGeo is working with DDOT to model their roadway characteristics and crash data in an ArangoDB graph-based database in order to perform complex transportation data safety analysis. The goal of this project is to get data into the hands of the people that need it – transportation planners and safety analysts – and provide easy access to useful information about accident-prone areas. Users can then combine this information about the safety and performance of the DDOT roadways with their own expertise to design and implement safety improvements. In this way, they are data-informed – they are using data about transportation performance (both good and bad) to help inform and guide future safety improvements.
Being Data-informed means you are combining data and information with your own experience and knowledge to inform your thinking and next steps.
We’ve also developed a data-informed approach to bring clarity to what can be a very emotional process — school redistricting. Data and scenarios help guide the conversation, but ultimately social emotional needs really impact the decision. There is fear that changing district boundaries will break up neighborhoods, destroy adolescent friendships, and create long bus rides. And these are all very real potential outcomes of any redistricting process. But a data-informed approach can help mitigate these fears. In these situations, data-informed visualization provides an effective communication tool enabling angry, fearful parents to see the challenges and contribute to an optimal scenario. It has changed the conversation from “this is destroying my neighborhood” to “here’s a better place to draw the line”.
When should you be Data-inspired?
And finally we reach Data-inspiration! At this end of the continuum, human intuition and inference are tapped to the greatest degree. It’s where data is used in an exploratory context to inspire creative solutions to problems. Two great examples of when to use this approach stem from efforts to galvanize urban renewal.
We’ve been working with New York City’s Office of Environmental Remediation to build and launch their Searchable Property Environmental E-Database (SPEED) Portal with the goal of promoting redevelopment and productive use of land. Through the web-based tool, potential developers can discover properties undergoing environmental remediation, making information that was once bound up in cryptic files available for exploration. The map gives all stakeholders, including neighborhood residents and activists, access to the information they need to assess scenarios and ultimately improve neighborhoods.
Pawtucket’s Qualified Opportunity Zones are available for exploration in the city’s MapGeo site.
Similarly in Pawtucket, RI, the city’s interactive map is aiding in the discovery of properties that fall within the “Qualified Opportunity Zone”. These zones were designed to spur economic development and job creation in distressed communities by allowing tax benefits to investors who invest in these communities. Using the city’s interactive map, the public, real estate companies, businesses and investors can conduct their own data explorations. An investor can investigate potential development locations — looking at proximity to public transportation or open space or other businesses — and evaluate the potential growth of their investment. In both of these examples, human action is inspired by the data but the process is not data-driven.
A layer displaying Distance to the train station Qualified Opportunity Zones are available for exploration in the city’s MapGeo site.
Being Data-inspired means you are using various data to help assess and explore potential outcomes or investigate interesting commonalities or patterns.
Next up (Part 3 of 3) – How do we Implement a Data Strategy? Now that we have shared and discussed the continuum, continue onto the final piece in the series which will provide some tips and recommendations for effectively implementing the right data strategy for your organization’s questions and challenges.
Previous post (Part 1 of 3) – Is being data driven always best? Click here to view the previous blog in the series, outlining differences between being “data-driven”, “data-informed”, and “data-inspired” approaches to working with data, and when to choose among these approaches.