You don’t need complicated software to learn how to work with data

Most data trainings are focused on computer-based tools. Excel tutorials, Tableau trainings, database intros – these all talk about working with data as a question of learning the right technology. I’m here to argue against that. Building your capacity to work with data can be done without becoming a “magician” in some software tool.

Data literacy is not the same as computer literacy. This is an important distinction, because there are lots of people that are intimidated by computer technologies; but many of them are otherwise ready and excited to work with data. In my workshops with non-profits I find that this technological focus excludes far too many people.  Defining data literacy in technological terms doesn’t welcome those people to learn.

To support this argument, let me start by describing what I mean by the skills needed to work with data. In my workshops we focuses on:

  • Asking good questions
  • Acquiring the right data to work with
  • Finding the data story you want to tell
  • Picking the right technique to tell that story
  • Trying it out to see if your audience understands your story

With Catherine D’Ignazio, I’ve been creating hands-on, participatory, arts-based activities to support each of these. Some involve simple web-based tools, but none are about mastering those tools as the skill to learn. They treat the technology as a one-button means to an end. The activity is designed to work the muscle.

Curious about how those work? If you want to learn how to start working with a set of data to ask good questions, use our WTFcsv activity. Struggling to learn about the types of stories you can find in data?  Try our data sculptures activity to quickly build some mental scaffolding you can use.

Those are two quick examples. Here’s a sketch of all the activities we are building out and how they fit into the process I just described:

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Some of these are old, and well documented on DataBasic.io; others are new and lightly sketched out on my Data Therapy Activities page; the rest are still nascent. We’re trying to build a road for many more people to learn to “speak” data, before they even touch tools like Excel or Tableau. These activities support this alternate entry point to data literacy; one that is fun and engaging to everyone!

Don’t get me wrong – there is certainly a place for learning how to use these amazing software tools. My point is that technology isn’t the only way to build data literacy.

You don’t need to be a computer whiz to work with data; you can exercise the muscles required with hands-on arts-based activities. We’re trying to build and document an evidence base demonstrating how the muscles you develop for working with data outside of computers easily transfer to computer based tools. Stay tuned for future blog posts that summarize that evidence…

Fight the Quick Chart Buttons

I despise the “quick chart” buttons. This post explains why, and tries to help you go from making charts to telling stories.

Here’s an example of the quick chart buttons in Excel:

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Excel’s list of chart buttons doesn’t help you pick the right chart to show your data.  Caveat: newer versions try to help with a “Recommended Charts” option.

Most of our chart-making tools don’t help us pick the best chart to tell our data story, and this is a big problem for chart makers. They just offer up a set of options to let you quickly make a chart. That doesn’t help you put together a data story! We just end up with lots of bar charts and line charts 😦

I love chart picker guides like the PolicyViz’s Graphic Continuum, Abela’s Chart Suggestions, and the FT’s Visual Vocabulary.  These guides reframe the question of picking a chart as a question of identifying your story. That is a crucial distinction.

The visual depiction of information in a chart is an editorial process, not some objective representation of the data. The visual mapping of the data onto shape, color, position, and size are all subjective choices you should be making make. These should be conscious decisions, not at the mercy of some tytranical default button. The result of all these decisions should be a chart that is closer to a story then simple raw data.

Look at the difference between these two charts for an example:

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Same data; different story.

The chart on the left might tell a story about Dragon Fruit underselling as compared to other fruits.  The chart on the right might tell a story about apples being a dominant player in the market that needs to be fought.  These are two very different stories; and all I did was change the color of one bar!

The key question is: what is your story? what chart can help you tell that story?

Anyway, back to the quick chart buttons. They don’t help you pick which chart to make! Bar charts are good for showing comparisons between a few categories within a dataset. What about when you want to show changes over time (line chart)? Or a distribution of two variables (scatter plot)?  Or the promotional share of one category compared to the total (pie chart)?

Different stories demand different charts.  So next time you’re putting a chart together, start by thinking about the type of data story you’re trying to tell. Then use a guide to find the right chart to show it. Don’t be seduced by the promised simplicity of the “quick chart” button!

Approaches to Teaching Data for Non-Profits

Recently The National Neighborhood Indicators Partnership and Microsoft Civic Technology Engagement Group launched a project to expand training on data and technology to improve communities.  I’m pleased they’ve included Data Therapy as one of the resources they highlight to help you think about building your data culture.  Check out their training guide and their catalog of resources!

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On a related note, if you are someone that does a lot of training and capacity building, or an organization that wants to be doing that, checkout the podcast and recording of a conversation about enabling learning with School of Data.

Making Tools More Learner-Friendly

I often advise learners to be careful with what tools they choose to spend time learning.  Some powerful ones have steep learning curves, full of jargon and technical hurdles.  Others are simple and self-explanatory, but can’t do more than one thing.  I’ve been trying to find better ways to connect with tool builders and talk to them about how they need to build learner-centered tools.

Catherine D’Ignazio and I put these thoughts together into a talk for OpenVisConf this year.  This is a super-dorky conference for data viz professionals… just the place to find more tool builders to talk to!  We put together an argument that data visualization tool as informal learning spaces.  Watch the video below:

New DataBasic Tool Lets You “Connect the Dots” in Data

Catherine and I have launched a new DataBasic tool and activity, Connect the Dots, aimed at helping students and educators see how their data is connected with a visual network diagram.

By showing the relationships between things, networks are useful for finding answers that aren’t readily apparent through spreadsheet data alone. To that end, we’ve built Connect the Dots to help teach how analyzing the connections between the “dots” in data is a fundamentally different approach to understanding it.

The new tool gives users a network diagram to reveal links as well as a high level report about what the network looks like. Using network analysis helped Google revolutionize search technology and was used by journalists who investigated the connections between people and banks during the Panama Papers Leak.

Connect the Dots is the fourth and most recent addition to DataBasic, a growing suite of easy-to-use web tools designed to make data analysis and storytelling more accessible to a general and non-technical audience launched last year.

As with the previous three tools released in the DataBasic suite, Connect the Dots was designed so that its lessons can be easily planned to help students learn how to use data to tell a story. Connect the Dots comes with a learning guide and introductory video made for classes and workshops for participants from middle school through higher education. The learning guide has a 45-minute activity that walks people through an exercise in naming their favorite local restaurants and seeking patterns in the networks that result. To get started using the tool, sample data sets such as Donald Trump’s inside connections and characters from the play Les Miserables have also been included to help introduce users to vocabulary terms and the algorithms at work behind the scenes. Like the other DataBasic tools, Connect the Dots is available in English, Portuguese, and Spanish.

Learn more about Connect the Dots and all the DataBasic tools here.

Have you used DataBasic tools in your classroom, organization, or personal projects? If so, we’d love to hear your story! Write to help@databasic.io and tell us about your experience.

Telling Your Story Well

I just hosted a workshop today at the Stanford Do Good Data / Data on Purpose “from Possibilities to Responsibilities” event.  My workshop, called “Telling Your Story Well”, focused on how to flesh out your audience and goals well so that you can pick a presentation technique that is effective.  We did some hands-on exercises to practice using those as criteria for telling your story well.

One key takeway is the reminder to know your audience and your goals before deciding how to tell your data-driven story.

Folks dove into the activity we did – remixing an infographic to target a specific audience and an achievable change.

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For example, here’s a sketch of one group’s idea of an interactive data sculpture that dumps stuff on you based on how much water your purchases at a grocery store took to generate!

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UN Data Forum: Data Journalism (live blog)

This is a liveblog written by Rahul Bhargava at the 2017 UN World Data Forum.  This serves as a summary of what the speakers spoke about, not an exact recording.  With that in mind, any errors or omissions are likely my fault, not the speakers. This was a virtual session, with all the speakers calling in via video.

Introductions 

John Bailer: New & Numbers is an old idea.  Cohn’s book targeted journalists to hep them communicate to a broader community. Alberto Cairo’s Truthful Art book is a more recent example of this.  John runs a Stats & Stories podcast to explore these questions as well.

Trevor Butterworth: Trevor is an Irish journalist with a background in the arts. He wrote for major publications as a freelancer about cultural issues, back when this was called “computer-assisted reporting”.

Rebecca Goldin: Trained as a mathematician, Rebecca worked as a professor of mathematics.  She reconnected to lok at how people talked about numbers and statistics.  Now she supports educational needs of journalists, and how people think and communicate about statistics.

Brian Tarran: A journalist by training, Brian received no training on numbers. He ended up working with the Royal Statistics Society and that’s how he ended up working on stats.

David Spiegelhaler: Coming from a mathematician and medical statisticians, he is now a Professor for the public understanding of risk.  His job is to do outreach to the press and public. David does statistical communication, focused on risk. Number are used to persuade people, so we need to do this better to inform people better to think slowly about a problem (instead of manipulating their emotions).

Idrees Kahloon: Idrees is a praticing data journalist at the Economist, having studied mathematics and statistics. At the Economist he works on building statistical models.

How to make sure what you’re doing will work with statistics?

Idrees: Runs into this quite a bit, sitting between academics and journalists. This means applying rigorous methods, but on a deadline.  Its hard to explain a logistical regression to the lay audience. You have to be statistically sound, but also explainable. The challenge is to straddle this boundary.

David: Influenced by the risk communication field, but there is no easy answer there.  So you decide what you want to do, and then test if it is working the way you want. Use basic visual best practices, and then the crucial thing is to test the materials. Evaluate it.

Brian: At Significance Magazine, a membership/outreach magazine, the goal is to bring people into statistics. There are guidelines to follow, around engagement and ease of reading. The goal is to encourage authors to draw analogies to things they understand.  One example is in an upcoming issue about paleo-climatology; focusing on climate proxies in recent history. The author explains this by comparing it to how Netflix creates recommendations to users. That kind of metaphor is the best way to get these things across.

Rebecca: As David hinted at, you have to know your audience. The first step is to understand who it is you are writing for, and what is their background. So perhaps instead of logistic regression, you might need to focus on explaining the outcome (ie. not the process). With journalists in a workshop, the main challenge for them is around understanding how to express uncertainty.  This is the greatest challenge that people face.  Pictures and stories are often the best techniques here, rather than technical language

Trevor: Our statistical understanding is very nascent. To build a better foundation, surveying journalists helps you understand what journalists do and do not know about science and statistics.  Journalists assume researchers know how to design a study and analyze results. You have to understand that isn’t necessarily the case. You have to ask basic questions about study design, data collection, and data analysis techniques.  One of the goals is to build a network of statisticians to help journalists do this.  So a parallel project is to help researchers understand these statistical concepts.

Examples of successful and/or unsuccessful communication? and why?

Trevor: Science USA created this network of statisticians at academic institutions around the US, and journalists are using this online widget to ask them questions.  That interaction is a great success to build on. Science that supports a policy is taken up by various constituencies, and filtered by values. When studies turn out to be poorly done, communicating that gets really hard. People who have adopted knowledge to promote it are not equipped to make judgements about what process of technique was wrong. So they try to shoot you down, from ad-homnym point of view. In the US talking about policy with evidence without becoming tribal has become too hard. So the question of “is this a good study” gets lost very quickly, replaced by a partisan/political interpretation of who you are, and your motives for critiquing a study.

Rebecca: When a journalist does have more than an hour to sort through a concept is when we have an opportunity for great success. For example, Rebecca worked with a journalist looking at false-postivies vs. false-negatives. The journalist created a graphic that ended up on 538.  The conversation helped her clarify what the mathematics would tell her.  Some failures involve when you’re speaking with a journalist that just can’t wrap their head around an idea.  When they can’t slow down enough to understand something like an inference. This is difference between writing about a certainty (which journalists want to do) and a quanitifed uncertainty. Other times the mathematics are just knowledge disconnects, like explaining a confidence interval without the listener understanding what an interval is. There are lots of requests coming in, which points to a shortage of people with these skills in the newsroom. So lots of people are recognizing this need.

Brian: The expertise didn’t exist in the newsroom 15 years ago.  In his first year, Brian wrote about councils surveying citizens about an issue. This ended up putting citizens and council at odds, because the journalists couldn’t explain what the survey told them, or better ways to do this. We just did a terrible job of explaining the fundamentals in a way that could generate bridges between people. For a success, in magazine for it is too hard to convey the details to help people do statistics themselves.  We need to show people how to think like a statistician.  This is about a process, and questions you ask.  There is an new column called “Ask a Statistician” which tries to get at this directly. Hopefully over time this will build to something great.

David: One success is keeping certain stories out of the news that don’t have good science behind them.   Another one is the translation of relative risk to absolute risk.  If there is a change in risk, you need to show the baseline risk. There was a story about eating a bacon sandwich, how risk of some disease increased it. The morning story was terrible, but in the evening after much promotion the story was told correctly, indicating this would only increase 1 out of 100 cases. Even thought the BBC training introduces this, the journalists cannot do it on their own. Another reported how a study said sex was decreasing in the UK, due to phones and technology. David made a joke about this being due to Game of Thrones, but a journlists didn’t get the joke and wrote up the headling “no sex by 2030 due to Game of Thrones”. This is the danger of clickbait, produced by secondary outlets republishing with a crazy headline.

Idrees: The polls in the last year is a great example of both how to do it well and poorly. There were many models in the US about the election outcome, where some set out what the uncertainty was (like 538 giving Trump a 30% chance of winning), but others did not (like the Princeton election commission). Some think it is ok to just report marginal error, and ignore if the sample is good.  Idrees shares a paper about 50,000 tweets about the death of Joe Cox.  To test this they gathered a population of tweets, sampled it, and measure how many were celebratory.  Their data shows this was an order of magnitude less.

Q&A

Responding to David and Rebecca’s comments, we’ve found that we need to separate percentages and chance. Has anyone come across guidelines about how to describe change? A lot believe you should do it in terms of “1 in 100” type language.

David: This is a disputed area. Using words like “probability” and “chance”, so people use an expected frequency – “of 100 people like you, 5 would have it”.  This is slightly better than “1 in 100” language. There is always metaphor and analogy involved. Using a phrase also depends on the imagery and appropriateness for the audience

Rebecca: When talking about 1 having something, and 99 not having something, you have to say “of people like you”.  This is a critical piece that stops people from arguing against these types of descriptions. You must express what the denominator is… precisely who we are talking about. Visual depictions can help this a lot.  Also comparing risks or frequencies can help. How does each option effect your risks and outcomes.  It is important to pair these.

For Trevor and Rebecca, who have been training journalists: what is the most important single skill for reporters to better work with data?

Trevor: To be pessimistic, most journalists can’t visualize the concepts in statistics.  Especially for probability, uncertainty, and distributions. You have to start with design of the data gathering effort. This leads to a certain approach of doing reporting. The best thing to do is to bring journalists and statisticians together.

Rebecca: In terms of basic numeracy, the most important thing is understanding absolute vs. relative risk.  They understand proportion and percentages, so they could understand this distinction ins a short amount of time.  So many studies do this now, and people know how to interpret it. The intuition is there. This is attainable.

Brian: Read the Tiger that Isn’t book. If everyone read it and appreciated the ways numbers could be misinterpreted, this would improve things a lot.

Idrees: The idea of being able to understand a distribution of outcomes. This is about getting across an expected value and a bell curve.  This is all tangled together though, so it is hard to understand one bit and not another.  Hard to see one silver bullet.

David: To agree with Rebecca, changing relative to absolute risk is vital.  Then doing it in whole numbers, and so on. Journalists are intelligent; they are used to critiquing and their intuition is good.  They often lack to confidence to go with their intuition when data comes in. They should go with their guts.

John: Look at some of the questions in the News & Numbers book mentioned earlier.

A key theme here has been about counting people who aren’t usually counted.  What alternative data sources do you use to capture and explain these populations.

David: Using mobile phone data is probably one piece of the discussion that is relevant.

John: The census in US tried to enumerate populations like homelessness with formal study design… like looking at a proxy of people receiving services related to their status.  Probably the audience is better informed than the panel.

A few years ago, we found that in 40% of journals data was incorrectly presented graphically.  We have to start really young to get people’s brains to start working differently. This goes beyond numeracy.

David: the Teaching Probability book is aimed at 10 to 15 year old.  It uses the metaphor of expected frequency as a basis.  If you do that it leads to probability.  Converting relative to absolute risk is included in this, based on the idea of what does this mean for 100 people.  In the UK probability has been taken out of the primary school curriculum. Recent psychological research says statistical literacy underlies general decision making skills; it is crucial.

Trevor: The kind of information literacy we teach children is quite poor. Cultural change is possible. The News & Numbers book, despite nailing the problems, had little effect on the culture of journalism. New outlets like Wonk blogUpshot, 528, Vox and others say cultural change around the importance of data is happening. There is a danger o naivete, suggesting the wrong idea that we don’t need statistics anymore because we have big data.

John: We need to be training the trainer, the help the teachers to be equipped to communicate these ideas.

Brian: At their local school they discuss improving the teaching of mathematics, but none of the teachers are confident enough to do this.  They need more confidence. People are too willing to accept the idea that you’re “bad at math”; we need to break that down.

Closing Remarks

Rebecca: The takeaway is to tell a story.  Veer a little from the technical truth to try and tell a story that frames the information in a way that is non-technical. Don’t be scared to say something a little bit incorrectly, to better convey what you want to say.  People will remember better what you say, and become more curious.

Idrees: Data journalism is kind of a new thing, so we will have wrinkles. If you write to an editor about something that is egregious, they actually listen.

Brian: We want to be telling a story, like a feature article not an academic paper. Tell a story the way you want to be told a story. Present your work in that way, with a story structure that feels good.

Trevor:Statistical should not be dry; try to have a real conversation.  Numbers don’t speak for themselves.  Also, recognize the limits of your own background. Think like a designer that communicates knowledge. The name of the game is collaboration.

David: Respect a journalistic approach. That means working with them, but at a minimum it means working out the crucial points, develop a story, and try it out with people.

John: This has been an outstanding conversation.

 

 

 

 

 

 

UN Data Forum: Integrating Geospatial Analysis (Live Blog)

This is a liveblog written by Rahul Bhargava at the 2017 UN World Data Forum.  This serves as a summary of what the speakers spoke about, not an exact recording.  With that in mind, any errors or omissions are likely my fault, not the speakers.

UN created the Committee on Geospatial Information Management (GGIM), which brought the topic to the fore within the UN. They’ve worked across countries on standards and solutions.  In addition, they wanted to make sure that this was married to statistics. This panel will talk about the challenges and benefits of this integration in their countries.

SDGs and Geospatial Perspectives – Tim Trainer

Tim Trainer is the Chief Geospatial Scientists for US Census Bureau. SDGs are geospatial, statistical, and require both international collaboration and multi-stakeholder partnerships. There is a IAEG-SDGs that is a working group on geospatial information to support the SDGs. For instance, they are looking at Tier III indicators that could move up to tier II if there were better geospatial information.

Digging into the SDG targets, take target 11.7 as an example, which is about “safe, inclusive, accessible, green and public spaces”.  Each of these doesn’t have a well-agreed upon information. To meet the target within the goal, we need a good definition of each term and we need to know and interrogate the data.  We have to decide if the data is “good enough”.  This pushed us to ask about the preferred state, what we’ve got, what can be helpful, and what is harmful.

Statistical data in the US can be broken down by county, census track, and census blocks (9 million of them).  In Europe they don’t need small area geography like that. On top of that you pull in the statistical measures. To do this type of integration, you need to assess, extract, link, create and develop; all mostly manual processes.

Relaving Unknowns in Statistical Information – Derek Clarke

Dr. Clarke is the National Mapping Organization in South Africa.  Tabular information is very elementary, and often human unfriendly.   Mapping increases that and allows for visual comparison. The level of details (region vs. sub-region) can indicate how useful some data is for development planning

Geospatial information is most commonly represented as a map. Dr. Clarke talks through an example map that show a sparse distrution of schools across a large area, with mountains and rivers between them. Integration reveals unknowns in the statistical information.

South Sudan has both the geospatial and statistical bureaus in the same department.

Distracting Peple with Truth – Greg Mills

Greg Mills works at Vizzuality, a socially conscious data design company. Even though we talk about data and integration, our starting point is with people. Greg shows videos of birds performing a mating dance; expressing its genetics through dance to find a mate.  Another way to think is that it is expressing truth.  At Vizzuality we try to create that dance, but with data. Further, we try to equip others to dance better.  Some people learned to dance not the truth, which has been happening a lot lately.

Greg wants to share a few techniques that they use to help:

  • “Design is to decide” – when you are integrating you make choices, and passive choices don’t turn out too well.  Another idea is pgressive disclosure.  The Global Forest Watch is their example of this.  They start with the pink to show where the forest is gone. Then you can dig into protected areas afterwards. So you hook people and then draw them in.
  • The “one-stop-shop” isn’t necessarily the best way to share your information.   Greg shares a map created with Carto to show where UK tourists spend money in Spain on holiday.  Most of these things are built to be embedded in other places.
  • Maps aren’t the only way to convey information.  The Soy Deforestation map is an example of this. They augment maps with other forms of information.  With a SanKey diagram they see the flow of trade and then people can filter by attributes.
  • A key challenge is to find data, bring it into a workflow, and create things with it.  With the NYC Mayor, they are creating a central place to determine what their priorities are – a data dashboard for NYC. The key was a simple way to connect data across departments. They call this “data highways”.

The Data Revolution – Sharthi Laldaparsad

Sharthi has worked at Statistics South Africa for over 20 years.  Sharthi argues that the data revolution is about connecting geospatial and statistical information. StatsSA has been doing this integration for years now. We’ve got standard geographic frames / building blocks, with reliable sampling frames. South Africa has a national development plan based on a well-functioning statistical system.

The Global Statistical Geopsatial Framework has 5 principles.  These range from standards to usability.  Unfortunately some datasets in South Africa don’t always includes the geographic indicator they have defined.

Policy analysis builds on this integration. Will this tell us what the priorities are? Population maps show how South Africans love the city. Another map, of buildings completed, can show how the pattern of construction has stayed the same – uneven. Looking at new VAT registrations you can see how and where businesses are being created. These are the types of maps you need to know how to grow the economy and create jobs (a policy goal).

Q & A

In terms of governance structure, who is responsible for the data?

There is an issue of cost, accessible, and accuracy. The free satellite data for sub-Saharan Africa is out-of-date, for example.

Is there a plan or project to represent the SDGs spatially?

What about leveraging the private sector data, and citizen-generated data?

Sharthi: The National Spatial Data Infrastructure (NSDI) is under the department of Rural Development in South Africa.

Dr. Clarke: Yes, they wanted it to be more centrally placed.

Tim: In the US our statistical responsibility is distributed.  The Census Bureau is the largest, but for instance the Transportation department has its own. The same is true for Geospatial data.  The census bureau manages the boundary lines, an address list, and the road network.  The last might be surprising, but they need to code every respondent to an address, which is based on the street network. Regarding the GGIM, the expert group on SDGs formed a working group and just met for the first time in August. Then in Dec they met to dig into which Tier III SDG indicators could benefit from geospatial information.  For example, If you need to know the rural population that lives within 2 kms of a road, you have to have some geospatial information like housing units and roads.

Dr. Clarke: In response to mapping sub-Saharan Africa, agreement that Africa is poorly mapped. Often there is better data out-of-country than in-country. The national mapping organizations are poorly funded.  This doesn’t help collect and maintain the geospatial information. For satellite imagery, there are efforts to collect it and provide it to the country. We hope the situation will improve. At the same time, in Equatorial Africa, all you’re going to see clouds for most of the year.  Imagery like a sense in an aircraft will give you better answers there.

Tim: This is an example where partnerships could be a win. The census scanned the web looking for localities that had contracted their own local imagery. After that test they contracted with another organization that had a well-maintained database of this, which is much better.  Engaging with the private sector can benefit you.

Greg: Regarding accessibility of data, just today we’ve been talking about huge numbers of publicly available datasets.  So why are they not used more?  Partially this is because our human structures don’t match data structures. We have to understand those in order to improve this.  There is a gap that needs to be filled.