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My thoughts on running a small data and communication business are vinified products rendered from insights from Seth Godin. The pomace has been removed from the maceration, filtering and aging has transpired and to bring the enology analogy full circle--the wine is ready for sipping. Here are 3 that I access daily--maybe even hourly 1. You aren't making your products or offering your services for everyone. Stay true to yourself and in the face of rejection--whisper to yourself--"I didn't make this for you" 2. Don't bid on pricing. This has been useful in my work freelancing or working as an entrepreneur. Seth's thoughts (paraphrased by me) are it is a useless race to the bottom. I agree. I don't sell dollars. Clients need to value the tasks or questions they are trying to solve--and pay accordingly. 3. Now that you have your ducks in a row--what are you going to do with the duck--simply brilliant. I have even titled data talks with this exact phase. As a thank you I gifted Seth a bronze duck when I met him at the On Being Gathering. He actually teared up and gave me a hug.
The theme of data gathering and sourcing was used by Cole in a podcast replay, Dataklubben, that I caught on my way to the gym. Think about our roles as data analysts. We gather/source data--lots of it. But what are we looking for? The single pearl of truth perhaps but in the process we shouldn't leave huge piles of oyster shells in our wake. If certainty is truly the goal you will likely be disappointed. Think how many oysters you would have to shuck or eat to potentially find a pearl. Maybe what we really need is targeted well thought out questions--and well calculated probabilities.
I thought it was just me. I bristle at all of the pay walls, sponsored content, and monetization of conversations that matter. Or should matter. How can we have meaningful dialogue if access to the conversation is stratified by who is willing or able to pay to listen?
When I was a newbie medical writer my contract contained a requirement that I be provided with all requested references. A few earlier projects had been completed with me footing the bill or having to travel to a nearby medical library. For the first go-around, they would send the freely available articles but try and substitute the pay wall articles with just the abstracts. Nope. Not good enough. I teach writers that the devil is in the details. And by details I mean methodology and results sections. What do you think the clinical landscape would look like if we only wrote about articles we could freely read allowing paywalls to retain part (arguably, the majority) of research papers? I tell you this story simply to own up to a source of the triggering solicited when being poked to pay up or look the other way. Don't get me wrong, I have access to many journals through my press credentials, access to a fine local academic library, and even the National Press Club library. I also pay a few Patreon accounts, news subscriptions (NY Times), and a few others like Paris Review and Harper's Weekly. What I don't have is a bottomless wallet to pay for vanity podcast subscriptions (I don't mind listening to your ad roll for free access), digital news aggregators, and for profit content morphing into sponsored content. I remember bumping into a sparkly new journalist at one of the big online news sites. We had gathered for a medical research discussion and he said he was so excited to have been hired. Admitting he knew nothing about medicine or healthcare he shared that the hiring team had told him--"that's okay, we can teach you what you need to know."--gasp. We build too many walls and not enough bridges--Isaac Newton
A recent podcast by Manoush Zomorodi and Jen Poyant shared the not so common plight of a creative voluntarily shutting down her business. Spoiler alert--she refused to lower quality, commoditize her contributions, or become another monetized blog selling out for profit. Amen. They close up shop in August but go take a look at what our new "money, money, money" mentality is costing us.
Her website is Design*Sponge and it will be missed. Here is an article that integrates nicely with where the article is headed, The Cost of Being Disabled written by Imani Barbarin a contribution from blogger Crutches & Spice--discussions from the intersection of liability, race, gender, and media..
We all know why the temptation exists. The money can be eye popping. I was able to avoid turning to stone by leaving pharma and starting my own data and writing consultancy. I do okay financially but not the same depth of okay as when I wrote what I was asked. period.
I couldn't return to an era where I didn't know the harms being perpetuated in lock-step with the good. The data large and small companies did not want to include. The cursory distortion of data insights toward marketing and away from actual science or unbiased discovery was hard to miss. I read the article by Christopher Booth, MD, a medical oncologist and recognized the duplicity. You might be sacrificing your reputation at the moment you decide to change your industry ties from "none" to "some". ...Since that time he has had no relationships with industry. Moreover, he now “sees” industry influence in almost all facets of patient care, medical education, clinical research, and even certification exams (in which the correct answers are based on pharmaceutical funded guidelines).--From the $80 hamburger to managing conflicts of interest with the pharmaceutical industry
Not sure why there are few teeth in the discussion of clinical trial data and how to teach all of us scalable literacy to inform or observe what should or shouldn't be happening at the point of care. What are some of the distortions we find when we become aware of industry influence in clinical trials?
In summary, we have found that modern RCTs in breast cancer, NSCLC, and CRC are substantially larger and more international in scope than those of earlier decades. Although methodology and quality of reporting seems to be improving over time, serious deficiencies persist, particularly in the identification of the primary end point and by not including all randomly assigned patients in ITT analyses. There has been a substantial shift toward industry sponsorship of oncology RCTs. Over the past 30 years, authors’ endorsement of novel therapies has increased while relative effect size has remained stable.
Before I teach data literacy workshops on how to read clinical literature--I begin with the history. You need to understand how effect sizes and p-values can be influenced by the sheer increase in sample sizes, over powered studies can make spurious associations seem larger, and the rise of the surrogate endpoint. Dig a little deeper and you can appreciate the evolution from little or no industry sponsorship of clinical trials (1990s) to upwards of 90% now funded by industry.
I haven't been able to see exactly what is being taught by panel discussions on writing about clinical trials or societies asking members for money to access articles--but what I have been able to see is not worth your time or effort.
Have questions? Reach out over on twitter @datamongerbonny. The blog will always be free. Thank you to those of you that have supported this work for so long.
About a week ago a reluctant client gave me a call. He mentioned being a follower of this blog for a few years but felt like surveys they create in-house were perfectly functional--until during a live webinar I randomly used a survey template that his company had created--and was a wee bit critical. Now let me be honest. I don't pull the smaller companies to task but focus on the bigger companies with deeper pockets and analytics departments. It is frustrating to see low quality multiple choice questions written in the absence of any concern toward research methodology or rigorous question design. 
The good news is I have been invited onsite to a few locations to deliver scalable solutions. It is much easier to drive change in an organization when the process is implemented across a team or a company.
I am not particularly clever about how to market these opportunities but several forward thinking clients decided what they needed. We scheduled a few live customized webinars to support the in-person training and now we are off to the races. Let me share a few of the insights that seemed the most helpful and easily integrated into processes that either already exist--or should. I have nothing against Survey Monkey. If you are a skilled analytics professional or a subject matter expert with learning platform expertise and can distinguish satisficing question responses and how to avoid them--go get that banana.
I find Qualtrics to be just the right temperature--not too simplistic and accompanied by adequate support and direction to help the novice at least become aware of the possibilities beyond low value question design.
We won't delve into the statistical layer that makes it all possible--not yet--but conjoint analysis is the right tool to answer complex decisions like those made at the point of care. Health care providers and patients consider multiple attributes (characteristics) and features of an intervention or recommendation in complicated ways that it is impossible to measure or address if you are only asking in a Likert or multiple choice format. I can recognize when teams aren't up to the challenge. In these cases, just use ranking questions. At least you can safely talk about probabilities if you know how options are prioritized.
You might find a little context helpful. If you want to prioritize a behavior, examine what influences decisions, predict how cost impacts therapy selection, or uncover competitive advantages from another product--conjoint analysis is the right way to go.
If I had a nickel for every client that mentions question fatigue as a reason to not develop an impactful survey instrument I would be writing this from Bora Bora with a drink floating a little paper umbrella. My standard argument? Participants/respondents are willing if there is a high-value trade-off.
We are trying to uncover, "Are health care providers/patients/payers willing to sacrifice______ in order to have ______?
First step is to define the attributes of whatever intervention/behavior/commodity we are attempting to measure value decisions. Within these attributes are levels. The statistical modeling will draw combinations of attribute levels and present choice sets to the respondents.
When presented with choices--respondents select the higher level of utility.
Below is a framework that you can follow to design a DCE of your own. The first step is to consider the attributes and assign levels. This works surprisingly well in healthcare and there are many resources to help you get started.
There isn't likely to be anything ready to go out of the gate but if you step through some of the methods carefully you can build your foundation and then perhaps reach out for the analytic platform. Using discrete choice experiments to inform the design of complex interventions
The critical component is to realize the limitations of low-value data gathering when it does not capture the considerations at the point of care. Health economics considers cost-benefit and trade-offs. Building a better survey strategy can serve as a first step in evaluating characteristics leading to improved behaviors and patient outcomes.
You can send an email to schedule a free 15 minute discussion or schedule directly below...
People don't want what you make Seth explains that "marketing is the generous act of helping someone solve a problem. Their problem". It may sound strange but think about the one thing you are passionate about creating. For me, it really is about the underlying truths that obfuscate better decision making in healthcare. And the strategy--at least for me--is to liberate the data. Not just numbers because they are meaningless without curation and a narrative. If I was going to hand over a "product" in the way Seth describes, it would be data literacy. Why? Because it is scalable and attainable. Currently it seems to be claimed by those with power and access to proprietary data but what if I could show you where the non-proprietary data lives, teach you how to access it, and empower you individually or enterprise-wide how to curate the data for both empathy, information, and insights? Seth Godin also differentiates between tactics and strategy. "Tactics are easy to understand because we can list them. You use a tactic or you don't. Strategy is more amorphous. It's the umbrella over your tactics, the work the tactics seek to support." It is okay to share your strategy. The tactics are specifically how you will execute your strategy--those you need to protect. I'll go first. There is a problem in healthcare. The problem that everyone wants to solve is never clearly defined. Everyone has the solution. How is that possible? If everyone wanted to see all of the data--even the data that might oppose a strongly held belief or tension--we would at least be walking in the same direction. I see a better alternative, come with me--Seth Godin. “The sense of having walked from far inside yourself / out into the revelation, to have risked yourself / for something that seemed to stand both inside you / and far beyond you, that called you back”--David Whyte 
In my mind, I always imagined a resource like this one, EDITED, for healthcare analytics. You don't have to squint too hard to imagine a stack of patient files instead of a rack of clothing in the image above. The biggest data challenge might be that healthcare isn't just one thing right? We have to rely on latent factor models to better represent the complexity of what we mean when measuring any metrics related to healthcare.
A blouse is a blouse, and I suppose retail represents a commodity industry while healthcare is quite something else. Let's see if there aren't any insights we can model or at least pay attention and appreciate. Think of a data point included in a visualization not unlike the "tragic sweater" described by Meryl Streep in the Devil Wears Prada. There are downstream decisions made based on data purity and quality although perhaps not in the same way as a cerulean blue pigment but you get my drift... We need to do better with accessing the right data. I have been there. You locate a great data source that seems to have the variables you need to say something meaningful about a specific topic or insight. But there is one more step needed before the self congratulations and fist bumping. Are you confirming a thought or a belief? I was directed to some work by Byron Katie about cognitive restructuring and naturally, I thought of data insights. It goes something like this...are these insights true? Can you absolutely know they are true? Is there data out there that might challenge this reaction? A thought is harmless until we believe it. It's not our thoughts, but our attachment to our thoughts, that causes suffering.--Byron Katie 
The lovely Mona Chalabi hand draws all of her charts and graphics to remind us of the human element.
Our biases and frailties are all evident in the data questions we formulate, the sources we access, and even the insights we generate. We need to look at the broader context of the low-hanging fruit and see what lurks beyond. Imagine if there was a "data" repository similar to the broad market snapshots of retail clothing. Maybe monthly trend reports are needed to move the conversation in healthcare toward a more robust understanding of the open source data. I am definitely thinking along those lines as I reach a broader audience interested in wrapping a story around their data. Maybe the devil actually wears...data.
I will try and share some of the free discussions as I travel around the country talking about data literacy in healthcare specifically.
You can register for a free webinar on March 29th at 1:00 PM EST here...The 5 second rule and your data: how dirty is it? In the event you are not aware of your latent appreciation of the ATU Fable Index, I give you The ATU Fable Index: Like the Dewey Decimal System, But with More Ogres by Cara Giaimo for Atlas Obscura. Aarne--Thompson--Uther type 333? You are now joining the catalogue of Little Red Riding Hood variants. The index began recently in 2004--but the initial investigations began way before the aughts. It is quite a robust collection and reminds me a bit of the archetypes from Greek Mythology. Easily distilled into broad categories by syntax like The Heroes Journey for example...  One of my literary indulgences is reading Harper's Magazine. As of late, it serves as procrastination from the final weeks of my executive education program in Applied Data Science by Columbia School of Engineering. As well as several data projects, countless presentations, an ultra race in March and a writing workshop in April. Data literacy and telling stories from data began after reading my first book by Stephen Few and his famous quote--Numbers have an important story to tell. They rely on you to give them a voice. Harper's Magazine included an essay in their March edition, The Story of Storytelling. Since Charles Darwin published On the Origin of Species in 1859, scientists have repeatedly proposed that the laws of biological evolution apply not just to bird and beast but also to creatures of the mind. During the years when I was primarily working as a medical writer I was often asked to answer the questions, "So what?" Clients wanted to create a curiosity or an empathy in their stories--a plea to communicate why we should care about a specific therapeutic area, device, or statistic. Unlike their predecessors in the nineteenth and early twentieth centuries, modern biologists no longer depend primarily on morphology and anatomy—on appearances—to establish evolutionary relationships between organisms. They can also compare their DNA, which maintains a record of familial mergers and divisions over great spans of time. I would argue that a great story begins with a question. Often, teams focus on the data and the bigness of their ideas instead of the fundamental question. First, we need the framework of our curiosity. And only then can we begin looking for the right data to flesh out deep moral insights and possibilities... If we remove their layers of symbolism and subtext—which have been interpreted and reinterpreted for millennia—and focus on their narrative skeletons, we find that they are studded with practical and moral insights: people are not always what they seem; the mind is as much a weapon as the body; sometimes humility is the best path to victory.  I learned and promptly incorporated this creative method of looking at graphs and deciphering their meaning for a section of the data literacy courses I teach. The graphs are released on Tuesdays, facilitated on Wednesdays and discussed with statistical follow-up on Fridays--all with support of the American Statistical Association. Here is the beauty of the method:
 An important habit to integrate into any presentation or workshop--or even anything you write, is the following. Bring experts up on the stage with you. Clearly it isn't feasible to grab the actual scientist/physician, or expert but when you familiarize yourself with their body of work you can integrate their insights into your discussions. Plenary Sessions by Vinay Prasad MD, MPH is an essential part of my podcast habit especially since I work with a lot of oncology data. You can easily apply the principles of the NY Times, What's Going on in This Graph lessons to your understanding of clinical publications. The recent JAMA Oncology journal article, Profiling Preexisting Antibodies in Patients Treated With Anti–PD-1 Therapy for Advanced Non–Small Cell Lung Cancer introduced a familiar bias as identified by Vinay Prasad and others, the guaranteed-time bias or how I recall learning about it--immortal-time bias. In a nutshell, this means "immortal time is a span of cohort follow-up during which, because of exposure definition, the outcome under study could not occur". This can be by trial design, death, or the fact that the study outcome can not occur--because perhaps the study participant did not live long enough or dropped out of the study. So what do you notice? The objective of the JAMA Oncology article states the following, "To assess the safety and efficacy of anti–PD-1 treatment in patients with subclinical disease with advanced NSCLC and with or without preexisting autoimmune markers, including rheumatoid factor, antinuclear antibody, antithyroglobulin, and antithyroid peroxidase; and to assess potential clinical biomarkers that may be meaningfully and conveniently associated with clinical benefit or with irAEs following anti–PD-1 treatment." I notice that you have to search until figure 3 to notice that there is no statistical difference in overall survival between patients with or without pre-existing autoimmune markers (rheumatoid factor, antinuclear antibody, antithyroglobulin, and antithyroid peroxidase). I also noticed that Main Outcomes and Measures section ignore overall survival and only state the measures such as progression free survival (PFS) and immune-related adverse events (irAEs), which by the way, did report p-values < 0.05. The antibody markers are either present at baseline or not. All good here. A hazard ratio of 0.72 with a p value of 0.19 are arguably nothing to write home about. Figure 3  What do you wonder? Now you might have missed this without being guided by the discussion in the Plenary Sessions podcast but the paper does away with the overall survival argument until we view graphs comparing irAEs. Why does this matter? Unlike the presence of antibodies at or before time "0", adverse events occur after time "0". You have to receive the treatment before you can experience an adverse reaction. This is where the immortal-time bias becomes relevant. What do you need in order to experience adverse events? You need to be alive. These irAEs have been associated with response to nivolumab. Perhaps the group with irAEs have longer overall survival because they had to survive to have the immune related adverse event. See the problem here? The potential for guarantee-time bias (GTB), also known as immortal time bias, exists whenever an analysis that is timed from enrollment or random assignment, such as disease-free or overall survival, is compared across groups defined by a classifying event occurring sometime during follow-up. Figure 1 cleverly moved to the front of the article regardless of its dubious immortal time-bias and surrogate end-point.  Figure 2 below is looking at progression-free survival only with or without presence of any antibody  There are ways (likely known to the authors) to avoid this type of bias. For starters, use landmark survival analysis to plot all patients alive at say week 10. Did the patients with immune-related adverse events have better outcomes? Read this article if you are interested in details of avoiding time bias in clinical trials, Challenges of guarantee-time bias. Here is a visually helpful description from the research paper. Patients who undergo transplantation seem to have longer survival times. However, patients in the transplantation group must survive at least until donor is available (blue), and their survival reflects total time before and after transplantation (blue and gold).  Have data questions? Send them along...
If a picture is worth a thousand words, a graphic is at least worth a million numbers. 
If you work in health policy, medicine, or health economics I recommend you listen to The Week in Health Law podcast. I remove pods from my playlist all the time. Occasionally from a perceived terse exchange on twitter about a subscription paywall but more often influenced by verbal tics such as vocal fry and upspeak, or limited perspectives on complex topics.
You don't get those distractions when listening to discussions of health policy law on the TWIHL podcast. North Carolina is finally sweating off the snow from a record snowfall--that I dodged during travels to DC and NYC. I laced up my sneakers and headed out the door for a misty run along the creek. The podcast queue always keeps me company. This whole brain thread was launched by a simple question I was asked on social media. How do I use cost information (data) for gap analysis in medical education. There are too many data points to answer the question simply. Listening to a technical podcast by experts in health law creates opportunities to dig a little deeper into questions. And to question long-held "answers" roaming around the status quo. To appreciate the impact of legal precedent, policy, and a new digital economy still regulated or "guiding" by outdated regulations enacted before we had our current technologic advances--we need context. When should "guidance" yield to regulation? When do we reach the point of "too late", the toothpaste is already out of the tube? I suggest that data questions need to be informed by the realities of choice at the point of care. Who is really making the decisions that influence patient care? 
A common theme throughout is "bigness." How big should companies, industries, governments be allowed to get before the ill and often unintended consequences are all but palpable? Not only what is the role of regulation but is there historical precedence? Is there something we can learn?
The Curse of Bigness by Christopher Ketcham
Today's episode featured discussion of a roundtable discussion on "The Law and Policy of AI, Robotics, and Telemedicine in Health Care." I use SIRI on my phone to create notes so that when I return to my office, I can recall the vast number of rabbit holes I will enjoy rappelling down as time permits. What follows is a summary of a few insights with links so you can also design an exploration based on your interests but first, a little back story.
I don't know about you, but I have not heard the tech industry discuss publicly the concerns of bias and discrimination integrated into AI or other devices. The solution to our healthcare woes are just a click away as algorithms replace human cognition and we seek hacks to fast track our journey to better health.
For starters, we lack a robust definition of device that differs in an appreciable way from that of obscenity, "I know it when I see it." -- 1964 United States Supreme Court Justice Potter Stewart. The Lochnerized first amendment and the FDA: toward a more democratic political economy
Lochnerizing refers to a ruling in a 1905 Supreme Court case where corporations were granted constitutional rights by invalidating democratically enacted laws. For example, the case struck down a New York State law limiting the number of hours a baker could work in one day citing the law would violate the constitutional liberty of a contract, between two "persons."
The Columbia Law Review published this informative article as a response to the Columbia Law Review's 2018 Symposium. The article discusses in a non-trivial manner the weaponizing of the first amendment and the impact it has had, and will have on the Food and Drug Administration (FDA). “[T]he majority has chosen the winners by turning the First Amendment into a sword, and using it against workaday economic and regulatory policy. Today is not the first time the Court has wielded the First Amendment in such an aggressive way. And it threatens not to be the last. Speech is everywhere—a part of every human activity (employment, health care, securities trading, you name it). For that reason, almost all economic and regulatory policy affects or touches speech. So the majority’s road runs long. And at every stop are black-robed rulers overriding citizens’ choices. The First Amendment was meant for better things.”--Janus v. AFSCME, Justice Kagan dissenting
It is an important discussion on a current trend of the U.S. courts to "pivot away from the model of private market regulation upon which the FDA is approach is built."
In this era of Too Big To Fail we could do worse than revisit the writings of Louis D. Brandeis, Supreme Court judge from 1916 to 1939 and prolific author. Here is a taste of his views on concentration of power within banking industry, What Publicity Can Do. THE NEW UTILITIES: PRIVATE POWER, SOCIAL INFRASTRUCTURE, AND THE REVIVAL OF THE PUBLIC UTILITY CONCEPT
This final article is an important one as it reminds us of the earlier landscape, reviews the current challenges, and puts forward potential solutions. There is a tendency to silo much of the digital debates around privacy and the rights of consumers without rolling the healthcare wagon into the debate.
From the control of banks over financial stability as well as access to finance and credit, to the control of internet service providers (ISPs) and telecom companies over broadband infrastructure, the problem is the same: private actors possess the means to undermine the public value of essential goods and services upon which many businesses, communities, and individuals depend.--THE NEW UTILITIES: PRIVATE POWER, SOCIAL INFRASTRUCTURE, AND THE REVIVAL OF THE PUBLIC UTILITY CONCEPT--K. Sabeel Rahman
There is a powerful delineation between power brokers in healthcare and consumers rebranded as patients, wielding market power now empowered as patient-centeredness--heralded as a way to equalize the debate.
Definition of social infrastructure--” Where private actors accumulate outsized control over those goods and services that form the vital foundation or backbone of our political economy—social infrastructure—this control poses dangers. By defining social infrastructure as a concept, this article provides a way to diagnose essential goods and services and therefore potentially problematic accumulations of private power."--THE NEW UTILITIES: PRIVATE POWER, SOCIAL INFRASTRUCTURE, AND THE REVIVAL OF THE PUBLIC UTILITY CONCEPT--K. Sabeel Rahman
Collectively we praise industries such as pharmaceuticals and healthcare technology when they "do good" through public endowments, investments, and charitable giving but we rarely if ever challenge them to do less harm through persistent self-serving policies that limit access to medications and prolong financial benefits. The threat is always around innovation.
Drawing from these disparate debates over net neutrality and TBTF financial firms, I extract four key elements of a twenty-first century framework for public utility regulation: firewalling core necessities away from behaviors and practices that might contaminate the basic provision of these goods and services—including through structural limits on the corporate organization and form of firms that provide infrastructural goods; imposing public obligations on infrastructural firms, whether negative obligations to prevent discrimination or unfair disparities in prices or positive obligations to proactively provide equal, affordable, and accessible services to under-served constituencies; and creating public options, state-chartered, cheaper, basic versions of these services that would offer an alternative to exploitative private control in markets otherwise immune to competitive pressures.--THE NEW UTILITIES: PRIVATE POWER, SOCIAL INFRASTRUCTURE, AND THE REVIVAL OF THE PUBLIC UTILITY CONCEPT--K. Sabeel Rahman
The Aspen Institute also discussed the liabilities of bigness and what we can and can't accomplish.
These final quotes from the article speak to the rise of social determinants and constructs into the debate surrounding reform. I mean true reform. Not the current model of working within a broken system as we tiptoe around real change.
Progressive reformers thus understood public utilities not just in terms of economies of scale and laws of nature, but in moral and social terms. Industries triggered public utility regulation when there was a combination of economies of scale limiting ordinary accountability through market competition and a moral or social importance that made the industries too vital to be left to the whims of the market or the control of a handful of private actors.
These are thoughtful discussions that deserve consideration as we struggle with enormous inequality and unsurmountable financial burdens in society as a whole.
...firms and sectors that might warrant greater regulatory oversight by examining three overlapping conditions: the economics of production; the downstream uses of the good or service; and the degree to which the good or service is a necessity that makes its users particularly vulnerable to exploitation. The presence of all three features indicates a firm or sector that is “infrastructural,” where the concentration of private power over these services poses a unique potential threat to public welfare. THE NEW UTILITIES: PRIVATE POWER, SOCIAL INFRASTRUCTURE, AND THE REVIVAL OF THE PUBLIC UTILITY CONCEPT--K. Sabeel Rahman 
My favorite recent article about journalists was sent to me by a colleague. An article over on Vox.com titled, My advice for aspiring explainer journalists. There is a lot of good information nestled within and more than a few whiffs of irony.
Explaining that most journalists (employed) are white, elite, and went to an exclusive college while sprinkling words like "epistemic crisis" and inserting logarithmic curves to explain distribution of knowledge amused me. But David Roberts also candidly reveals the obvious secret we all know but don't talk about--you don't need journalism school--gasp. Our new digital landscape has created fenceless terrain where we can hopefully step outside of google analytics and distill granular elements of stories, contextualized to answer the "so what" metric.
I never fancied myself an "explainer" journalist but here we are. I like to think a bit wider about an issue and hopefully start meaningful conversations. When I go to conferences I never know where the flare will be found. Many colleagues report the facts and nothing but the facts but how do you know they are indeed factual?
Ahhhh...have we stumbled upon an epistemic crisis?
I recently spoke to a large group of Tableau users in Raleigh. I called the talk "We Can Only Connect the Data we Collect" to remind those of us in the data and analytics space to be mindful of data sourcing.
Are we using data because it is easy to find or because it answers the carefully formulated question we designed?
It doesn't stop there. When you work in a field where you are often one of only a handful of women invited into conversations you see things. I seek out colleagues with design aesthetics, indomitable spirits, and perseverance toward an outcome or goal. When you can call them friend--all the better.
Amy Herman taught me a new way to think about data. I was intrigued by her work teaching visual intelligence at the Frick and connected with her book Visual Intelligence: Sharpen Your Perception, Change Your Life.
I have always had a passion for art. Traveling for work I prioritized visits to local museums over cocktail socials with clients or late evenings in hotel restaurants. The lessons I have learned help me engage audiences with exercises in perception and bias before we even look at a chart or a graphic. My collaborators enjoy the sketches and design thinking pulled into creative briefs to better visualize scope and design of data projects big and small. You have Amy to thank. Watch the freshly minted TED talk below. Distill it down to the 4 "A"s as described in the TED talk, ASSESS, ANALYZE, ARTICULATE, and ACT--and wait for the donut. There is always a donut. You pretty much can’t get away from bacon or whiskey in the south. Put a doughnut in it and you’d be good to go—Hillary Scott, American country music singer-songwriter |
Browse the archive...
 
Thank you for making a donution!
In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.
Remember the quote by Upton Sinclair... “It is difficult to get a man to understand something, when his salary depends upon his not understanding it!” Sign up for our newsletter!
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