Think back to the last time you scrolled through rows and rows of data neatly compiled in a spreadsheet. You could read the numbers, you could maybe even identify what a single number meant, but in reality, you couldn’t see any information in the data. Does a data dump help or hinder a decision-maker?
What is information blindness?
Information blindness, described by Charles Duhligg in his book , Smarter, Faster, Better, occurs when our mind stops absorbing data because there is too much to take in. In this current age of information overload, he laments, we are vulnerable to information blindness at times. To be clear, information blindness is not an idiom for ignorance, it’s meant to be interpreted literally — the information cannot be seen in the depths of data presented.
The idea of information blindness, or the inability to see information within a pile of data, is a concept that information providers and data consumers should be aware of and take into consideration when navigating the information sharing space.
We succumb to information blindness in many ways. For an example that we can all relate to, think of all of the information we have at our fingertips (or ear drums) thanks to Google Search. We may think that the ability to find the answer to almost anything within seconds makes us immune to information blindness; but you’d be wrong. There is a spectrum of usefulness between finding facts, piecing together information, and understanding what it means. And only with understanding can we turn on our decision-making power. When we are handed a mountain of data we cannot immediately see information and therefore cannot make decisions. Instead of quickly moving on to the meaningful task of making decisions we have to direct energy to discovery of information. Too much effort is spent on the search.Consider the classic analogy of searching for a needle in a haystack. If there is a piece of information we want to show people based on a plethora of data, we can’t hide it within all of the data.
The holistic harm of information blindness will grow as we as society continue grant access to more and more data. To be clear, having access to data is a GOOD THING. The goal in talking about the predicament of information blindness is not to stop providing data or looking for data to convert into information, but rather figuring out better ways that data providers can convey data as information to their target audiences.
How productivity experts suggest overcoming information overload
Productivity experts claim to have answers for how individuals can work to circumvent information blindness. These experts promise that data and information consumers who can implement these strategies will become better time managers and smarter decision-makers. We don’t believe that these “new” strategies revolutionize thinking. In fact, our teachers started instilling these techniques in all of us in elementary school. For example, we all practice scaffolding, or the practice of breaking up learning into smaller and smaller chunks. We’ve learned information sorting, summarizing, and outlining in school. These skills follow us into the real world and take on utmost importance when we are trying to sift through vast amounts of information in the expanding online universe of data. As another example, consider almost every website you have ever visited in the past decade. Banners and drop down lists ubiquitously appear along the tops of the pages and hamburger icons hide a suite of pages to visit.
The OurCommonCode.org website has a banner that helps visitors find the information they seek with more success.
All of these navigation tools scaffolded the information in the website so that you can explore more efficiently and effectively, with more of a likelihood of leaving
One more note on flooding leading to blindness information
People make better decisions when they get handed incremental pieces of information. They can see the data as information and therefore can make decisions based on what they see. A trickle, in other words, is better than a tsunami. When someone gets to an equilibrium state of the amount of information they can absorb or use for their decision-making, the rest becomes an overload.
How to effectively fill an information bucket
What is the best way to fill up a bucket with water?
If you crank open the fire hose and try to fill the bucket, the bucket will literally just fly away and you’ll be lucky if you get any water. If you try to fill the bucket using a tiny tablespoon, the process will take so long you’ll get bored and walk away without ever getting any significant amount of water. But, if you turn on a garden hose, point it into the bucket, fill up the bucket, and then take the hose out, you’ll have a bucket full of water. What you choose to do with that bucket, that’s a whole different article.
Suffice it to say, information blindness stymies our ability to make decisions, even if we are swimming in data. The goal, therefore, for any data provider should be to structure data sharing in a way that doesn’t overwhelm the recipient but rather guides them through the data towards smarter, faster decision-making options.
Does Information Blindness Exist in the Environmental Data Space?
Yes. Data producers in the environmental space, as a whole, historically, have put in place a lot of practices that default audiences to feel information overload. Let’s focus specifically on water quality data. To be fair, compared to most spheres of information that have a shocking overabundance of data, like which cinnamon bun recipe to select from the internet, water quality data suffers from a relative void of available data. In fact, asking your smart speaker “what’s the water quality near me” won’t yield any results (yet). This lack of searchable water quality is an ambitious topic for a different conversation.
Fact Number One: There is not a flood of water quality data available to search for.
For now let’s focus on the data monitoring programs have collected about the quality of their waterway and how providers share that information to stakeholders. Ultimately, we want to ask, do data providers deliver data in ways that help audiences absorb information or do we reinforce information blindness through poor data delivery options?
We recognize three common analysis sharing strategies employed by NGOs, state, and federal agencies to share water quality data with a variety of stakeholders. These strategies result from woefully small budgets for full cycle data management. While evidence shows that data visualization is an important element of successful data use, many data visualization tools require funding that small-scale projects may not have.
Fact Number Two: raw water quality data does not deliver any benefit to decision-makers, whether they are the general public, legislators, or researchers.
Operating on a shoestring budget often meant, for the longest time, that environmental movement did not have access to robust options to convey information. The cheapest options perpetuated information blindness because they presented data in non-compartmentable or staggered ways.
Fact Number Three: Presenting simple analytical visualizations can deliver critical information to decision-makers and overcome information blindness.
Common Water Quality Data Sharing Strategies
Spreadsheet Sharing
Any water quality monitoring program that delivers results of data collected at specific stations in the form of a spreadsheet is putting up a giant roadblock to anyone trying to understand that data.
Groups trying to provide data so that any decision-maker can analyze the data often overshoot the target by making too much data available without any context or pathway for decision-making.
To interpret that data, users benefit immensely if they know where the station is — spreadsheets or any data source without an easy means to geo-reference stations will hold back decision-makers capabilities and effectiveness.
Social Media Data Dumps
I’ve even seen these lists of monitoring stations displayed on social media. The data providers see themselves as providing a quick list of all information that stakeholders need in a handy snapshot.
Example of a social media monitoring update. The Choptank Riverkeeper has a multi-media approach to sharing weekly updates including inputting data into Swim Guide. On Facebook, information is delivered with simple pass-fail details but neglects to directly point audiences to this information.
As the example above shows, many watershed monitoring programs provide a weekly list of the stations where they monitor and a status update on water quality. They take this part of their job very seriously and commit to going out weekly to test and share results. This is incredibly useful information to share from both an environmental and public health perspective, but pretty irrelevant to anyone unable or unwilling to do the legwork of tying the location name to a geographic point and then making decisions based on all of the information.
Annual Report Charting
Another common way that groups share data is through annual reports complete with lots and lots of graphs analyzing the data. Often, these charts show trend-lines of parameters at each station, such as dissolved oxygen levels. Presented in a semi-scientifically structured format, complete with detailed analysis, these papers provide details beyond what an average audience can interpret but woefully inaccessible for more rigorous researcher-re-use or someone hoping to make an immediate decision about water quality — such as if it’s safe to go swimming.
Annual reports do often include static maps indicating station locations. This is critical information to provide to any audience, whether they claim expertise in water quality analysis or they simply want to know if they can safely let their kids swim at the sandy beach without exposing them to excessive fecal matter.
Non-Threshold Interactive Mapping
Have you ever come upon an interactive map on the internet that provides you with a lot of information? Often on these maps let a user click on a point and a wealth of information pops up. The user can then scroll through individual sampling events to review the readings collected. No analysis marks the information and the information cannot be consumed in any fashion that shows trends or helps bring the user to any conclusion about the data.
What do all of these data sharing strategies have in common?
The sector has begun to address the legacy issues that exacerbate information blindness and obstruct decision-making power, but these legacy tools still exist and are woefully overused.
Spreadsheets, charts, and single event mapping show users that data exists and demonstrate general information about water quality but give very little opportunity to turn that data into information that can be used for actionable decision-making. While this data is still valuable, and its existence is critical, we have to ask ourselves, what is it worth?
Making Environmental Data Visible for Decision-Making
At The Commons, all of our software as a service products include at least one data visualization feature to allow data owners to produce easily accessible and understandable analytical visualizations. In Water Reporter, data sources power maps and station cards to show stations, parameter specific trendlines, and threshold indicator information.
In FieldDoc, Restoration Professionals and Funders can create Atlases of their practices and programs that demonstrate the collective impact installation of best management practices have on reducing water pollutants.
Components to Effective Data-Driven Decision-Making Visualizations
Each of these visualizations have a few critical pieces to help drive decision-makers rather than derail them.
- Only build visualizations that deliver clear, concise messages.
- Recognize that different audiences want to use the data to answer different questions, so we provide pathways to more information.
- Don’t try to deliver all of the information at once.
- Make the data interactive.
Data Visualization Features Forestall Information Overload
In Water Reporter, our groups create responsive maps based on their data. The maps showcase the location of their monitoring stations, the current readings for a specific parameter, trends over time, and indicators for thresholds. This map delivers a simple message, such as if the water quality meets basic levels. Users can then click into a station to review the current reading for all parameters. If the monitoring program has thresholds for a parameter that indicate a status change, the information is clearly delivered within the station card. A user wanting to know more can click again into a parameter to look at a trendline over time — up to ten years — to see how often a parameter fell into a certain category. Ultimately, we designed a self-directed scaffolding that recognizes the limitations of most audiences in interpreting data without any guidance as well as the desire for audiences to be able to ‘handle’ the information to a certain degree.
For researchers looking to use the data that find this scaffolding to be limiting, the data source owner can choose to make their entire data set downloadable rather than try to anticipate the questions that the researchers might have and then pre-create a series of charts to support their work. In other words, for some, access to a machine readable dataset is a better solution for funneling information than trying to pre-create scaffolds for users.
Interactive Data Visualization applications offer an excellent strategy for water quality program managers to deliver critical information to a spectrum of audiences while avoiding the pitfalls of information overload, such as information blindness. Ultimately, we all want the data that we invested time, energy, and money into collecting to help inform decision-making that leads to healthy choices and cleaner water.
