Come for the donuts, stay for the data!

Be an outlier--the view is better

12/12/2018

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.

Because they were supply constrained, newspapers and newspaper journalists focused on what was new, what just happened, the incremental development.

But lots of times, readers had no way of making sense of those developments or contextualizing them. They were getting the leaves, but they’d never gotten the trunk.--David Roberts

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?

The new year is full of interesting developments.

I will be finishing up the Applied Data Science certificate from Columbia Fu Foundation school of Engineering and Applied Science.
Leading more workshops in Data Literacy--you can request more info here
Publishing a book, Autobiographicacy: a year visualizing data
Releasing the new updated newsletter. We went on hiatus when we reached our new subscription level. Happy to bring immediately useable information and relevance to our heterogenous subscribers
Creating a bigger presence on Tableau Public to share interactive visualizations

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data visualizations to use in proposals or grants (just ask)
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Pre-release copy of book, Autobiographicacy: a year visualizing data

extracting essential data elements--one drop at a time.

12/6/2018

I irregularly visit NYC on a somewhat regular basis. Business meetings, visits with family, museums--and wonderful friends where we pick up where we left off.

Kathyann and I hunted for a place to grab some lunch and talk about what old friends talk about. We settled on Distilled NY and the menu graphic captured my attention.

I realized I am basically a data distillery.

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.

The aforementioned Kathyann designs jewelry that matters. There is a Power Collection that reminds me of the boom and pow of old batman cartoons.

Near and dear to my heart is the I AM COLLECTION. I never had the right answer when confronted with the question, "what are you?" growing up biracial during the 70s.

Kathyann taught me the answer--anything I want to be.

How does she describe her designs? "Uncomplicated, imbued with meaning and intention"

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

Your data is a mess...

12/2/2018

I primarily use data sources that are publicly available. I travel around teaching data literacy and want to share data anyone can use to answer their data questions or hone business insights into actionable tactics. My biggest pet peeve is being sent a tool to simplify data curation--and then a bill several weeks later. I like a challenge so I encourage colleagues to learn a few skills to eliminate the middle man.

Look at this ugly little packed bubble chart. They are horrible little charts. Our eyes are not able to differentiate quantities in spherical formats. Okay maybe some do look bigger but by how much? I picked this monstrosity because while searching through clinicaltrials.gov for recruiting trials sorted geographically I noticed how a lack of standardization during data collection can leave chaos.

Another obvious data flaw is the wide variety of labels for studies within the database. What influences the classification scheme by colorectal cancer, colon cancer, colorectal carcinoma or even colorectal adenocarcinoma?

Tableau Prep is useful for sorting through the mess with a wide variety of tools to help clean up a dataset. I will admit I can be a little loosey goosey when analyzing in discovery mode. This particular task will be shared with an audience hoping to improve their skills at utilizing clinicaltrials.gov database. Look at the lack of clarity in how the conditions are captured...

How can you be certain you are capturing data about clinical trials of patients with colorectal cancer when there are so many different descriptions of similar but perhaps not identical characterizations?

Following a bit of data wrangling I was able to filter down to 3 broad categories shown to the right of the original snapshot. I keep them grouped so I can still drill down if I need more granularity or want to verify the findings. This visualization below is not with the clean version of grouping conditions--I was actually looking for specific types of Phase II data. This included editing classifications captured as Phase 1/2, Phase 2, Phase I/II, and Phase II...yikes.

What I am actually doing is comparing demographics between clinical trial participants and Phase II historical proprietary research data I have permission to access. How have the populations included or excluded from clinical trials in colorectal cancer changed, evolved, or stayed the same?

Can you see why this might be relevant in this current healthcare ecosystem where the prices of oncology drugs continue to escalate but the clinical efficacy is ill-defined?

Progression-free survival is a recurrent primary endpoint used to rate and compare randomized clinical trial findings but what does this mean for point of care decisions between community oncologists and their patients with colorectal cancer?

"We can only connect the dots we collect"...Amanda Palmer

Selling pain repackaged as freedom...

11/19/2018

Healthcare is a system many of us seek to change. But what might actually be happening? We are slowly being changed by the system. What if we succeeded in improving health across our nation? Hospitals would close, economies would shift, and profits would diminish.

What happens if we reimagine patent law and large profitable pharmaceutical companies are challenged to introduce generic and more affordable drugs onto the market much sooner?

Introducing a little transparency into the actual costs borne by industry would allow better solutions to be developed and imagined. We shouldn't ignore the research & development in academic settings and how that is funded by tax-payer dollars. No. Not in every case but we aren't allowed to fill silences with perturbations of a system where so few profit at the expense of all of us.

What if we stop believing that innovation is fueled by economic excess instead of economies of sustainability, technology, and viability?

Have you ever heard an industry client question the profits they are making on their blockbuster drugs or only lamenting that they deserve the windfall because of other pipelines that were silently halted?

And I basically became very interested in the silences, what we were not allowed to talk about or what we, just by custom, didn’t talk about when we came together to talk about making the world better.

So the Aspen consensus was: You can tell the rich and powerful in our age to do more good, but you can never tell them to do less harm. You can tell them to give more, but you can’t tell them to take less. You can tell them to share the spoils of extreme capitalism, but you can’t tell them to renovate capitalism.--Anand Giridharadas

Read the latest headlines about Michael Bloomberg's ginormous investment in the Johns Hopkins? Anand also asks why not question a system where billionaires have so much excess to donate?

Working at the level of systems means that problem components are in interdependent and constantly changing relationships. Linear approaches that address one component or factor at a time are inadequate in addressing dynamic conditions.

Stakeholders show a variety of motives and behaviors for engaging with systems. Designers must reconcile competing goals and performance expectations through participatory methods that value stakeholder input and ongoing feedback from the system.

And issues of sustainability, technological feasibility, and economic viability are crucial to making design solutions work over time.--Trend I: Complex Problems--Meredith Davis AIGA

I am working on a data literacy project where I am investigating the opioid crisis integrating data many have not considered. The question posed by Anand again is chilling and speaks to the larger driver of a crisis looking for linear solutions--that won't work.

Why write words into conversations if you are not willing to dig deeper and illuminate contradictions?

I think what the investigation has left me with is thinking about, even as a writer, how do I make sure that I am using my power, however limited it is, to interrogate systems and ask the questions you’re not supposed to be asking, instead of doing the kind of journalism or thinking or speaking that merely props up power.

But it also shows up for me every day in thinking about, am I using my voice to say inconvenient things that might cause me to lose friends or social capital, to put it in those market terms, but that are part of maybe pushing us, in some small way, to where we need to get as a society?--On Being Krista Tippett in conversation with Anand Giridharadas

Working as a consultant I was once jokingly accused of heckling the very client that hired me to provide expertise in immuno-oncology. I didn't see it that way but I was never brought in "live" again. The company, in my opinion, had made a bold choice in pre-clinical formulation regarding the FC region that triggers cell-mediated cytotoxic effector functions--in particular antibody dependent cellular cytotoxicity (ADCC).

I requested the pre-clinical data so I could review and see if there was perhaps a unique aspect of this particular molecule we could highlight to help differentiate a "not first to market" investigational drug. When I received the document, the section I requested was not included. I found this interesting but was dismissed and never saw the information. After a few conversations that included "mansplaining" from an apparent team Google search I could see the consensus irrevocably leaning toward the status quo--I dropped it but still recall how little we are willing to probe when trying to secure the next round of business.

All around, us the winners in our highly inequitable status quo declare themselves partisan of change. They know the problem, and they want to be part of the solution. Actually, they want to lead the search for solutions. They believe that their solutions deserve to be at the forefront of social change. They may join or support movements initiated by ordinary people looking to fix aspects of their society. More often, though, these elites start initiatives of their own, taking on social change as though it were just another stock in their portfolio or corporation to restructure. Because they are in charge of these attempts at social change, the attempts naturally reflect their biases.

The initiatives mostly aren't democratic, nor do they reflect collective problem-solving or universal solutions. Rather, They favor the use of the private sector, and its charitable spoils, the market way of looking at things, and the bypassing of government. They reflect a highly influential view that the winners of an unjust status quo--and the tools and mentalities and values that helped them win--are the secret to redressing the injustices. Those at greater risk of being resented in an age of inequality are thereby recast as our saviors from an age of inequality.--Winners Take All: the elite charade of changing the world--Anand Giridharadas

Try and find the book. I think it is an important discussion that needs to happen.

I agree that most of us are decent people trying to prop up an indecent system.

But whom are we serving when economics of billion dollar revenues are valued instead of improved patient outcomes, when investments are made in direct to consumer advertising, lobbyists, and the latest M&A are broadcast as successes?

There are harms associated with doing good. Why aren't we discussing them on a broader platform? Why are so many of us in pain? Is the solution a "pill" or upstream investments to reduce social vulnerabilities?

What are we actually being sold?

Your data team: "ontogeny recapitulates phylogeny"

11/12/2018

The dubious history of Ernst Haeckel and debunking of his biogenetic law aside, I still love the catchphrase he generated--"ontogeny recapitulates phylogeny." The embryo does in certain instances, reflect evolutionary history--pharyngeal gill slits anyone? But it isn't as reminiscent of Lamarckian ideology as our modern day understanding of the complexities of genetics reveal.

I recently heard the phrase attributed to nascent data teams. Think of the pluripotent stem cell for example. Many a data project begins with a small nimble team with an undifferentiated skill set. Angela Bassa, Director of Data Science at iRobot, shares that one of the earliest paths to "value" is by answering easy data queries with an architecture that takes care of itself.

Over time, a data team evolves and grows the ability to mature into anything. This leads to the specialization and differentiation of mature data teams. This sounds tremendously like how data projects evolve in the wild. When the journey is new, you may find yourself dabbling in a bit of the data engineering and data architecture bits regardless of focused expertise in formulating data questions.

The truth of the matter is, everyone has data. Data scientists worth their salt have the skill of formulating a data question and selecting the right statistical and visualization tools to formulate a solution. We strive for replicability, interpretability, and fit for purpose.

Full disclosure. When I am launching new data strategies with clients I focus on team building. One of the best pieces advice, I "borrowed" from Amazon CEO Jeff Bezos.

Perhaps you have heard of the 2 pizza rule.

"It's simple. The more people you pack into the meeting, the less productive the meeting will likely be. The solution?

Never have a meeting where two pizzas couldn't feed the entire group."--Business Insider

If you scour adds for data scientists, what you may notice is a focus on tools rather than asking if you have the imagination to envision what an answer to a data question might look like. In my opinion, the method should be second to the actual problem a team seeks to answer. It reminds me of a data professional that works within a large hospital system in my neighborhood. There are so many legacy systems in his department, they can barely communicate. The seasoned IT team has preferred ways of computing while the newer team members are more likely to be comfortable with the latest and greatest.

How many pizzas do you think it would take to feed those folks?

Listen to a recent post about managing data science teams--this post is a bit of a summary--the episode is worth your ears...

Secret ingredients of a robust medical education strategy

11/3/2018

Headlines within Continuing Medical Education broadcast record attendance at events, "Innovative" learning strategies, and tactics that lead us to believe we are making significant inroads in closing the gaps between evidence-based medicine and actual physician behavior at the point of care.

All those successes include big data integration and improved data literacy, or do they?

First, a little context. The CME industry includes close to $3 billion dollars in educational investments from a wide variety of financial categories including registration fees, commercial support, advertising and exhibits income, private donations, and government grants.

The ACCME's Accreditation Criteria require providers to produce educational activities that are designed to change competence, performance, or patient outcomes. Providers are then required to analyze the changes that were achieved as a result of the activities.
Figure 4 illustrates the percentage of CME provided in 2017 that was designed and/or analyzed for changes in competence, performance, and/or patient outcomes.--2017 ACCME Data Report

Here is where the rigor starts to unravel. The measurement of competence and performance is inherently subjective or observational and simultaneously not evaluated utilizing sophisticated analyses. And the teeny tiny attention to actual patient outcomes is alarming.

Likert metrics, poorly constructed surveys utilizing outdated research methodology, and narrow focus to measure the activity instead of the actual physician level engagement or impact on patient outcomes appears to go unnoticed.

The Accreditation Council for Continuing Medical Education (ACCME) President and CEO writes in the annual report:

CME has responded to the changing health environment. Healthcare is practiced in teams--and clinicians need to learn how to work effectively as teams for the benefit of their patients. While CME traditionally has been focused on physicians, it has never excluded other professions. This data report shows that the number of interactions with learners other than physicians, such as nurses, pharmacists, and physician assistants, has increased nearly 90% over the past 10 years. Overall, learner interactions (physicians and other learners) grew 57% during that time.

Why does this growth matter? Degree and training programs are the foundation of clinician education; for the rest of their careers, clinicians rely on accredited continuing education to help them provide high-quality, safe, compassionate, and effective care to the patients they serve. Those activities help clinicians absorb and process rapidly changing medical information and practice an ever-diversifying set of complex skills.--Graham McMahon, MD, MMSc Accreditation Council for Continuing Medical Education (ACCME)

Not to be all sturm and drang but I disagree regarding the claim that CME has responded to the changing health environment. I spend a lot of time in Washington DC and rarely see CME colleagues participating in discussions around value-based reform at the FDA, Brookings Institute, PCORI, Kaiser Family Foundation, or National Press Club.

Following a presentation at Alliance I was asked to write an article for their open access publication, Almanac. I decided to pull the draft when I was challenged to provide citations from publications to support the findings and assertions regarding the importance and relevance of System's Thinking in medical education. How can innovation occur if we continue to simply rinse and repeat what we have been doing for decades? Why are so few willing to be first--seed a hypothesis and proceed from there?

A recent small survey of ~100 writers of CME needs assessments should stimulate a larger query of hypothesis test pathways to improve how we engage and evaluate learning in continuing medical education. What it revealed--at least to me--is a potential skill desert unable to provide relevance and insight in our healthcare big data ecosystem.

I have offered over the last few years to help improve the research methodology but my offer never received a response. I also reach out to colleagues with insights revealed from global travel and experience with comparative health systems--also crickets.

Respondents of the annual survey were primarily staff employees 56% (n=60) followed by freelancers 33% (n=35). I hope next year they will ask about roles within the company and link it to educational levels or areas of study. My concern is low data literacy among members of CME teams responsible for writing NAs or proposals seeking funds.

It would be a more rigorous study if unique identifiers of respondents allowed a stratification of data yielding a "tidy" data analyses--"Tidy data sets are arranged such that each variable is a column and each observation (or case) is a row"--wikipedia

It appears writer's of needs assessments defer to the client or employer to designate what to include in a "first-rate" assessment. Lacking a definition of "first-rate" is also problematic. We might be comparing apples to tennis balls here.

The top 3 highest ranked choices for what should be routinely included in needs assessments as measured in survey:

medical literature review 65% (n=70)--all publications are not created equal, how is data quality measured?
learning outcomes data 45% (n=48)--need more specifics here to be able to aggregate with confidence
clinical practice guidelines 42% (n=45)--funding source of guideline? Date of guideline?

This data should have been ranked--as it tells nothing about actual behavior when aggregated. If it was ranked, we could then assign probabilities.

Writers are also asked about tables, charts, or graphics--what to include in a "typical" needs assessment. Top 3 responses: (these also should have been ranked not "select all that apply" for valuable insights).

Alignment of practice gaps with learning objectives and desired outcomes 65% (n=68)
Results of clinician survey related to practice gaps 43% (n=45)
pre test/post test data from previous activities (with to without p values) 41% (n=43)

Coming in last (behind "I don't include tables, charts, or graphics", 17% (n=18) we find "Statistics showing effect size(s) from previous learning activities", 12% (n=12).

The problem here is that charts (my collective term for visualizations) are actually little arguments. They need to be read and interpreted and if necessary, challenged--not just slapped into a document.

I think I noticed a Likert scale in the bunch but without ranking we know very little. For example, the response of 5 for one question is not comparable to a response of 5 in a different question.

I do applaud the authors of the survey (both work in CME) for efforts to measure the practice environment but I believe we left a lot of data on the table. You can read more here for thoughts on improving our approaches to CME.

When you know better, you do better (in theory)

I have worked in CME for at least 15 years or so. I consider myself a recovering CME professional. I had to step out of the bubble to notice the limits of our specialized and industry specific practices. Work in health economics, health policy, and biostatistics highlights the limited scope of "analyses" integrated in design, development, implementation, and evaluation in CME.

If anything, the role of CME is even more critical as medical education frameworks evolve to address the shifting healthcare landscape. Providers are expected to negotiate health policy, health economics, and the rigor of clinical research in decisions made at the point of care. The patient is now a stakeholder and must be informed regarding risk/benefits of treatment options and impact of care on quality of life. Community resources are also engaged in many consultations to aid in improving patient outcomes challenged by social determinants of health. That's a lot of heavy lifting for a poorly constructed multiple choice question.

As fee-for-service is replaced by value-based healthcare the traditional educational structures must evolve along with the architecture of outcomes and assessment. Most importantly, we need to define “value” based on each stakeholder.

There are trade-offs especially in the evolving age of patient-centered care. Insights gleaned from population health are informative but critical thinking at the point of care requires the ability to synthesize multiple scenarios and outcomes both clinical and economic, risk benefits and cognitive biases and heuristics. Our challenge is to embrace the integrative thinking that allows us to consider
assumptions, current reality, and critically assessed practices all together.

Too often we focus on one element or we defend assumptions as truth in stark either–or terms. We often set up false dichotomies when the most meaningful solutions lay between polar positions. I am talking to you writer of multiple choice questions to evaluate provider competencies and practice behavior.

The increasing complexity of practice necessitates the development, refinement, and application of additional and better assessment methods. These opportunities must include an emphasis on competencies beyond medical knowledge and basic clinical skills, such as systems thinking, quality improvement, interprofessional teamwork, and patient safety, while concomitantly attending to identity formation, wellness, and resilience.

In the modern age of big data we have access to control groups generated from American Community Survey Census Data, real gaps in care reported within the National Practitioner Data Bank, and limitless sources of data pointing toward patient outcomes such as MEDdra and FDA databases. Why are we still recycling outdated insights or industry funded guidelines as primary sources of information to guide care and improve outcomes at the point of care?

One way to measure treatment benefits and patient preferences for treatment is through the use of a stated preference discrete choice experiment (DCE) survey.These surveys are designed to understand the strength of preference for the different constituents or attributes of a treatment intervention or care service.

The choice experiment methodology is a powerful means of capturing patient, caregiver or physician strength of preference for the attributes of a treatment and makes it possible to understand the relative importance of attributes with respect to the degree to which people are willing to trade between them.

If you have questions I can be reached here...or on twitter @datamongerbonny

Inspiration is cheap, rigor is expensive...

10/24/2018

You might not need statistics. Hold on. Before you start doing cartwheels down the hall--this only applies if there is no uncertainty in your data.

Perhaps you have data collected from a continuing medical education activity. You are only interested in how the attendees navigated through your course and what your data demonstrates about this group and this group only.

But what if you want to be able to extrapolate these findings to the broader population? Now we have introduced uncertainty. It is impossible to reach every single member of your target audience roaming around the planet. Now we are dealing with uncertainty and the mathematics needed to talk in probabilities--statistics--"inferring proportions in a whole from those in a representative sample"

How would primary care physicians, for example, that did not attend your educational activity answer the questions you asked throughout the engagement? Better yet, what attributes of your activity are members of the educational audience accessing at the point of care? Was it your activity that nudges a participant toward evidence-based behavior? Are you certain it wasn't another program or resource, colleague conversation, alternative type of media, or other fractional or incremental activity?

I primarily focus on ideas. First, looking at wide and shallow datasets and mining for hypotheses. These are only for inspiration. The rigor is downstream of the initial queries. Collaboratively a question is identified and the team finds the best tools for the job.

Survey design is often needed to gather data.

Does your team include someone with the skillset to carefully perform mathematical approximations for continuous data, categorical data, sliding scale data? How do you determine number of questions, correct for the number of questions, construct survey to minimize response bias, and satisficing?

Who evaluates the original purpose of survey? Is a survey the best tool? What is worth measuring?

In my case, the toolbox includes R language, Python, SAS, Qualtrics, and Tableau.

The biggest problem in collaboration usually rests around a Type III or IV error.

Type I error (incorrectly rejecting the null hypothesis)
Type II error (not rejecting the null when you should)
Type III error (you correctly reject the null hypothesis, but for the wrong reason)--Statistics How To

Type IV error is a specific type of Type III error (correctly reject the null hypothesis, but make a mistake interpreting the results)

A few examples:

Aggregation bias (the wrong assumption “what is true for the group is true for the individual”--think Simpson's paradox or ecological fallacies
Running the wrong test for your data

Data clients typically invest or inherit large datasets. Perhaps they purchased an extract of claims data, CMS data, or hospital system data (intentionally vague to not reveal client identities).

Here is the caution.

Many business teams are guilty of mining their data for inspiration. If it stopped there--we would be okay. The problem exists when the attempt is made to introduce statistical "rigor" into the same data set.

Cassie Kozyrkov, Chief Decision Scientist at Google Cloud does a great job clarifying why this is problematic. Think of sitting down in the morning with your cup of tea looking forward to a nice piece of sourdough toast. The toaster pops up and holy cow, your toast appears to have the image of Elvis carefully outlined on the lightly browned surface.

If you want to investigate the miracle--or better yet--the exploits of your toaster, would you put in a fresh slice of bread or put the Elvis toast back in the toaster?

Inspiration is cheap, rigor is expensive--Cassie Kozyrkov

(data)modeling--how much crap can you take?

10/15/2018

A lot of modeling is how much crap you can take--Lauren Hutton

My first gig working in pharma was a lesson in acronyms. I had a 12 month contract at GSK and I learned an exponential amount. Everyone used verbal shorthand and I jotted the scattered letters down to look up later.

I think it softened my skin. I have no problem asking the questions. The biggest surprise is how few of us challenge the answers.

Although I have experience as a bench scientist, academic writer at a large academic medical center, as well as a lot of agency work--the added layer of pharmaceutical experience was the real hat trick.

Starting my data journey, I often heard statisticians discussing data models as if they were a thing I could pull from a shelf. They sound vetted and official don't they?

A data model is quite simply an "accumulated set of discovered relationships" within a dataset. Make no mistake the models are tested, but if sufficient "target variables" or "class labels" are not identified and considered, you can imagine the limit of insights to be gleaned from analyses.

Approached without care, data mining can reproduce existing patterns of discrimination, inherit the prejudice of prior decision makers, or simply reflect the widespread biases that persist in society.

It can even have the perverse result of exacerbating existing inequalities by suggesting that historically disadvantaged groups actually deserve less favorable treatment.--Big Data's Disparate Impact Barocas & Selbst

Working in health policy and accessing databases filtered by race, economics, and a wide variety of demographic target variables--it is critical to consider all of the "possible values of the target variable into mutually exclusive categories".

A 360 perspective is imperative while data mining. The ability to target variables specified and parsed to avoid systematically disadvantaging protected classes leads to actionable insights. It is a necessarily subjective process but can be improved with data literacy and applied to non-binary classifications.

Here is an important graphic from The Senate: Affirmative Action for White People published in the NY Times--written by David Leonhardt. Civics lesson rewind: residents of Washington, D.C., and Puerto Rico do not have voting representation in the Senate.

Right now, about four million American citizens have almost no congressional voting power, not even the diluted power of Californians or Texans. Of these four million people — these citizens denied representative democracy — more than 90 percent are black or Hispanic.

They are, of course, the residents of Washington, D.C., and Puerto Rico. Almost half of Washington’s residents are black, and nearly all of Puerto Rico’s are Hispanic.

“Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” – Dan Ariely

Walking down New Hampshire Avenue on my way back to my hotel, I stumbled upon the Democracy Tree. It is nestled between the roots of this majestic tree. Here is what it says:

This Tree is dedicated to the more than half million veterans, taxpayers, and citizens of the District of Columbia who, despite fighting in foreign wars, paying their full measure of taxes and continue to have no voting representation in the Congress of the United States of America.

"Taxation without Representation is Tyranny."
Foundry Democracy Project, Foundry United Methodist Church

The results are pretty outrageous. The Senate gives the average black American only 75 percent as much representation as the average white American. The average Asian-American has 72 percent as much representation as a white person. And the average Hispanic American? Only 55 percent as much. That’s right — the structure of the United States Senate treats a Hispanic citizen as only about half as important as a white citizen.

My point in all of this is surprisingly not political. The illustration here is to show that technology and advancements in our Big Data culture are forgetting the nuance of machine learning, AI, and algorithms--the insights are only as good as the data we feed into them.

"Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers."

The full quote below is an important example of how stewards of data (and aren't we all) have a unique responsibility to be thorough and informed as we explore and mine data. Garbage in, garbage out but worse yet--discrimination in, discrimination out...

Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with.
Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers. In other cases, data may simply reflect the widespread biases that persist in society at large. In still others, data mining can discover surprisingly useful regularities that are really just preexisting patterns of exclusion and inequality. Unthinking reliance on data mining can deny historically disadvantaged and vulnerable groups full participation in society.
Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm’s use rather than a conscious choice by its programmers, it can be unusually hard to identify the source of the problem or to explain it to a court.--Big Data's Disparate Impact Barocas & Selbst

Being the one that knocks...

10/12/2018

I know, I know, you didn't ask but I have some advice for you. Before you attend the conference or join the organization--think about who is being served by your attendance or membership. Are you "picking yourself?" as Seth Godin would say or are you part of someone else's narrative? I have joined organizations like the American Medical Writers Association but got very little in return. You become the product as they sell your data for a profit--I know--who doesn't anymore.

My pivot toward value and independence came when I found the right organizations. Most of them are focused in journalism so the art of storytelling and data literacy began to emerge.

I learned this lesson a single pain point at a time. When I didn't pick me, I ended up writing client biased content orchestrated by agency x, pharma company y, or medical education company z.

Earning press credentials provides access to medical and industry conferences--for free. When I was forced to tell sponsors what I was going to write about--I informed them I had no idea. Until I enter the space and have conversations, listen to the lectures or keynotes, or participate in the Q&A how can I possibly know what to write about? But that is how most journalism platforms work. You pitch or get pitched and then get to work. I reject this bias. I pick me. Or better yet, I pick you.

I reflected on this recently at a "meet and greet" for new members of the National Press Club in DC. After recent years of finally picking myself, I was invited to become a member. I think of the familiar creed among journalists,

Afflict the comfortable and comfort the afflicted

I now write about healthcare and all the different moving parts. Think of the piles of information and sort them by health policy, health economics, health law, clinical research, or whatever web that wipes across your mind.

We are taught to follow algorithms. Write the manuscript, book, article, or report as instructed. For a while, we comply, until we don't. We finally realize that you can't win by being more average than your competition. Better at following the blueprint. Think about your education or perhaps you have children navigating the educational system.

Watch them dutifully follow rubrics, guidelines, outlines, and doing what they are told. Our educational system is obsolete. We don't need these skills in modern society. We need creators, inventors, innovators--we need insights. If you have an idea of something you want to see in this world--listen to the short podcast by Seth Godin. Tag. You ARE it...

Well-meaning colleagues at the Press Club were excited to introduce me to members, experts in their fields, communicators, all to network--because I mentioned that I work for me. But it was weird. They were guiding me toward mentors. I reminded them that not all who wander are lost. I don't want a job. I am the job.

Who are you talking to right now? Who is it you think you see? Do you know how much I make a year? I mean, even if I told you, you wouldn't believe it. Do you know what would happen if I suddenly decided to stop going into work?

A business big enough that it could be listed on the NASDAQ goes belly up. Disappears! It ceases to exist without me. No, you clearly don't know who you're talking to, so let me clue you in. I am not in danger. I am the danger. A guy opens his door and gets shot and you think that of me? No. I am the one who knocks!--Walter White, Breaking Bad

Where I am headed next...and hope to see you!

Or reach out @datamongerbonny for scalable talks on data literacy, data sourcing, governance, or building your own data pipeline...

Knowledge is power but information is liberating

10/6/2018

The title of today's post is attributed to a statement made by Kofi Annan about education. On the surface, what could be more anodyne than knowledge right? But what if a large percentage of the collective, narrowly defines information based on a single perspective?

The remaining part of the quote, "Education is the premise of progress, in every society, in every family" might be the key element. I want to encourage you to pursue education daily. Many peers, mid-career or beyond, tuck into comfortable cozy careers either in a subject niche, algorithmic informed writing habit, or simply decide to focus on regulatory and compliance template driven writing. I am here to tell you--those days are waning. Big data is here and has invited CRISP-DM along for the ride. Briefly, the 6-phases of cross-industry standard process for data mining include understanding your data, preparation, modeling, evaluation, deployment, and business understanding.

You might be thinking--hey wait a minute--I am a writer not a data person. Okay. But here comes the hard no. If you aren't data informed or aware, what are you actually writing? Opinions?

The Government are very keen on amassing statistics - they collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams.--Josiah Stamp

Last week was a return to travel, workshops, and a smattering of data literacy work from the setting of a local conference on interdisciplinary statistics and combinatorics. I had grounded myself for a few months. Opting to step out of the frenzied and chaotic world of conference attendance and ask myself an important question--who is being served by me being in the audience?

Am I able to bring information immediately applicable at the point of care or am I listening to "experts" proclaim mind-numbing self-promotion and shameless marketing? Media credentials can get you front row access to most conferences but I would also argue--beware the chattering masses--they have heterogeneous motivations and goals. It is quite all right to return to square one--learn what you didn't know, you didn't know...

Thursday afternoon I traveled to North Carolina State University to attend a 2-hour workshop R for Document Creation (R Markdown). Because I am handling large datasets and would like the data to travel with the visualizations I am learning how to make this happen. I am learning R (with Python refreshers to follow) to expedite working with non-proprietary healthcare data. The datasets are large and often in need of cleaning and preparation for analysis. Check out your local universities. Free workshops are a great way to add a few new "shiny" tools to your skillset.

Here is an example of how Edward Tufte uses R markdown to prepare margin figures in his books and writings...The beauty of R--or at least for me--is the simplicity of the scripts. You can name them and save them for quick updates or application to additional data sets.

Using or learning R has been impactful and instrumental in allowing me to approach data visualization with a question informed by multiple datasets but not limited by interoperability or access. When you "knit" the code--voila the image will be published with the code either displayed or neatly hidden away--your choice.

This weekend, I wandered over to a local conference being held at an alma mater, UNC-Greensboro. Invited as media I scanned the conference brochure looking for sessions of interest (and was not disappointed).

I will be writing more in depth about the sessions but for now the familiarity of Simpson's paradox and refinement of my ability to interpret gene signature data were highlights.

You can amass data. That is the easy part. Big data lakes host a wide variety of potential information but you will need buckets--S3 or Hadoop clusters to organize. You will need pipelines to your data in order to begin the process. Upstream insights to confirm the right data is being generated (garbage in, garbage out), all 6 steps of CRISP-DM, and a governance strategy to protect your data and control access.

Follow along for information and guidance--or reach out for a hand. You never know what you might find swimming beneath the surface...

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In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.

Question the quality of the evidence. The motivation for disseminating the evidence.

Who stands to benefit the most from its uptake?

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!”

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When you know better, you do better (in theory)

Where I am headed next...and hope to see you!

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