data talks, people mumble

Cautionary tale to "see and define" not "define and see"

10/4/2019

“For the most part we do not first see, and then define, we define first and then see. In the great blooming, buzzing confusion of the outer world we pick out what our culture has already defined for us, and we tend to perceive that which we have picked out in the form stereotyped for us by our culture.”--Walter Lippmann (American Journalist--Sept. 23, 1889—Dec. 14, 1974)

The basis for teaching data fluency stems from our very own heuristics. Mine too. None of us are above debate regarding our intentions or assumptions.

Allow me to share a specifically non-instagramy moment. Due to a confluence of events--apparently rodent friendly weather, living on a park with a creek, and a lively chicken coop--we became infested. Mainly the yard and coop but who knows? If truth is indeed "poisoned at its source" according to Walter Lippmann, we attacked with gusto.

After all, we defined the problem therefore the solution appeared to be quite straightforward.

Well let's think this through carefully. If the goal was a systemic ridding of the rodent population from the yard--we were advised by professionals to follow the protocol.

But often, in pursuit of an outcome we forget about the stench. In this case I am referring to fetid carcasses decaying just about everywhere you can imagine. Out of sight but definitely not out of olfactory range.

The unpleasant imagery may cloud your ability to see the data adjacent similarities but stay with me here. A good data question often requires the holding of tensions. The pursuit of a complete data set to indeed be empowered to curate empathy or insights. Culturally defined norms often make it unavoidably tempting to believe low hanging fruit and reinforce pre-existing assumptions.

We might ignore relevant information out of fear, repulsion, or conscious bias.

But eventually the stench of bad data will win. And we will need to clean up the mess and start over.

Where to connect--click on images for links to more info!

Value is in the eye of the beholder...or maybe hidden in the LI algorithm

10/1/2019

The emerging age of "value" based medicine has outpaced important discussions on metrics and tools to examine value outside of profit motives and limited variables. The buzz-worthiness of terms like big data and value might leave marketing professionals frothing at the bit--the rest of us are or should be moving forward with business as usual.

There is no such thing as big data. Large unstructured datasets didn't simply arrive at the beckon of marketers. And don't get me started on value. Value for whom? If we are indeed in the age of "personalized" anything--why don't we start with refining these general terms to engender meaning and the appropriate granularity.

I originally wrote the bulk of this article on LinkedIn. I have no idea what drives their algorithm but I will share that where other platforms (including this one) yield hundreds of eyeballs in a single day--their metrics show that from thousands of followers on LI only 7 bothered to read it. Maybe it is a dog I don't know. What I do know is that it is packed with resources you might find interesting and useful.

If I am wrong...woof, woof.

I probably receive anywhere from 20 to 40 messages every week or so from individuals with questions about data literacy, specific data packages, or data visualization with Tableau. There are a lot of questions that I hear that I never thought to ask. I never asked for permission. Many people ask should I learn Python or R?

What is the best book to read to learn Tableau? These are personal and specific to individual interests and deserving of exploration. Mainly because like anything, your mileage may vary.
And then, there is the tired question about what is the difference between a data analyst, data scientist, data engineer, statistician, blah, blah, blah...I will leave you to explore this well worn debate elsewhere. There also is a bit of a territorial debate where member of these distinct groups poke fun at the other less worthy participants.

Here is what I learned. The brightest and best want to bring you along. They encourage, share resources, and are welcoming.

A recent podcast featuring Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review stopped me in my tracks. A year ago I was reading through his work and wrote a few blogs about his ideas. Curious about a pending part two to the discussion, Statistical Paradises and Paradoxes in Big Data(I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election, I sent him an email.

He promptly responded. Apologized about the delay in the Part II, sent me a copy of the article above and several others he thought might be of interest (along with access instructions). Meet him in the podcast below and learn about his role as Editor-In-Chief of The Harvard Data Science Review.

The Mission and Scope of the HDSR is captured by this quote below:

As an open access platform of the Harvard Data Science Initiative, Harvard Data Science Review (HDSR) features foundational thinking, research milestones, educational innovations, and major applications.

It aims to publish contents that help to define and shape data science as a scientifically rigorous and globally impactful multidisciplinary field based on the principled and purposed production, processing, parsing and analysis of data.

Read the tag-line and I dare you not to be excited if you are even slightly data adjacent.

A microscopic, telescopic, and kaleidoscopic view of data science

Here is one of the articles referenced in the journal section brilliantly labeled as CORNUCOPIA: impact, innovation, and knowledge transfer.

(A)Data in the Life: Authorship Attribution in Lennon-McCartney Songs
Apologies for the interactive experience that will likely derail your tasks at hand as you explore the features below. If the link brings you to 3 vizzes, pick number 3 to activate the visualization below.

I have been taught that items should be bundled in threes so here is one more. I recently read a great article in Medium, Anaconda is bloated--Set up a lean, robust data science environment with Miniconda and Conda-forge by Ted Petrou. Here is how this was a lifesaver. I work with large datasets in healthcare and medicine. The last thing I needed was a clunky giant installation of valuable but likely not needed programs on my MacBook. Thanks to Ted and his willingness to walk me through a few hiccups I am up and running. If you are a Python person and value just-in-time instruction I suggest you check out his workshops (a few free ones are there for a test drive) over on DUNDER DATA.

Although I completed an executive online course from Columbia School of Engineering in Applied Analytics--once I returned to the real world I wanted expertise at a granular level in the packages and libraries that I actually use daily. Ted is so clear and patient and provides the detailed explanations of why things work a certain way and how to customize your experience specific for your data needs.

Want to connect? Follow along on twitter or catch me live at Women in Tech Summit next month or RStudio conference in San Francisco in January!

Later this month I will be presenting at the Tableau Fringe Festival on October 25th! Announcements about the line-up will be out soon...

Survey says: learn simple code to liberate insights

8/19/2019

The National Health Interview Survey (NHIS) has monitored the health of the nation since 1957. NHIS data on a broad range of health topics are collected through personal household interviews.

For over 50 years, the U.S. Census Bureau has been the data collection agent for the National Health Interview Survey. Survey results have been instrumental in providing data to track health status, health care access, and progress toward achieving national health objectives.

I posted a simple glimpse of National Health Interview Survey( NHIS) data on LinkedIn describing a few tools for working with large public datasets. The questions were coming fast and furious into my messages.

NHIS is an important data source for many reasons and you should know how to access the findings, especially if you are working with population data or interested in social correlates of health.

Not sure why folks aren't comfortable asking questions in comments but nevertheless I decided to use the ease of the platform here to finish the thread.

Here is the original graphic I posted demonstrating how easy it is to get a quick read of your data. The data dictionary will help you understand the variables but when you work with this data a lot--it is in your veins.

I demonstrate below how you would change column headings and give you a hint regarding the next step. I only changed a few as an example.

Because many of the NHIS responses are coded in numeric responses these all need to be changed to categorical.
It becomes obvious when you look at the profiler data below.

If you know the codes for say F10DVCT DURING THE PAST 12 MONTHS, did {you/any member of the family} receive care from doctors or other health care professionals 10 or more times? Do not include telephone calls.
1 = Yes
2 = No
7 = Refused
8 = Not ascertained
9 = Don't know

You can look at the data and see how many households answered "yes" for example. Scroll document below:

surveydata.html
File Size:	51227 kb
File Type:	html

Download File

Would you like tailored instruction on how to work with your data?

For example, unlike public data, when we are the ones designing survey instruments we have latitude in producing high value platforms that don't require analytic contortions to evaluate the responses.

My quick insights are the following:

Likert? No
Multiple choice? No
Ranking? YES

We are adding online workshops to the calendar as I will be traveling extensively throughout September and not available for onsite programs or workshops until mid-October (limited availability).

Select one listed or suggest something specific!

If the data is unclear--what exactly are you writing about?

Ready to integrate Python with Tableau?

8/2/2019

As soon as I completed the Columbia School of Engineering Executive Education program in Applied Data Science I became responsible for translating successful weekly assignments and assessments into an effective and scalable work flow. It is one thing to complete a high-level course at a prestigious university--it is quite another to know how to gain actionable insights outside of a classroom.

My interest in large non-proprietary healthcare databases exceeded anything Excel could hope for and I was amazed at the automation attainable in understanding a few open source languages--R and Python. I believe this is what I needed under the hood of the dashboards and insights clients were requesting. You may or may not know about TabPy--API Tableau Python Server--allowing remote execution of Python Code within a Tableau workbook. Also in Tableau Prep but let's not get ahead of ourselves. The limiting reagent for me was getting everything updated, dowloaded, and connected.

A lot of what is discoverable at the moment is somewhat out of date and might cause you to sip a scotch a little earlier in the day than what is socially acceptable--allegedly.

So my point here is to update Toward Data Science or GitHub (although the Github link I am providing is the updated version shared with me by Nathan Mannheimer a product manager at Tableau).

Download the Github repository and follow instructions in the README.md file. I use juptyer lab so had specific steps to get everything up and running.

I activated my Anaconda environment and downloaded the required files. It is pretty easy. Hit the create button and name your environment--I opted for TabPy. The files I installed into this new env included Numpy, Matplotlib, Pandas, Conda, scikit-learn, ipython, and Spyder (so I could have a testing environment for calculations in Tableau). I updated Python, TabPy, and Anaconda--I had a bit of a circling the drain day when I didn't realize that links were using older versions...live it and learn it!

Once you have your environment created you can click and open your preferred coding window...

I am a loyal MacOS person so your mileage may vary--navigate to Terminal enter "Conda create --name TabPy python=3.7"

Making sure you are in the right directory "cd TabPy-0.7" in my case and now run the "source startup.sh"

Once you have everything properly installed head over to Tableau. Under the Help menu scroll down to Settings and Performance >> Manage External Service Connection.

These are the settings you want to select. Click "OK", close the window and open again to Test Connection.

If you have everything installed correctly--you will receive the blessed notification below. Once I sorted out the old links, outdated software, and steps for MacOs--it all came together but I am hoping to prevent you from the aggravation.

Any questions find me on twitter @datamongerbonny or at the next Healthcare TUG on August 14th...

As true as "turnips is"...

8/1/2019

“It was as true," said Mr. Barkis, " as turnips is. It was as true," said Mr. Barkis, nodding his nightcap, which was his only means of emphasis, " as taxes is.”
― Charles Dickens

You may or may not be familiar with the term PSYOP. It has been defined by Lt. Col. Brad Carr, as "the ability to operate in an approved area to influence the behaviors and attitudes of foreign target audiences, in line with national objectives."

When you speak of non-warfare elements of defense you are likely referring to messaging and tactics supportive of interests of the United States. I will stop right here because this isn't intended to be political --only informative.

Data is power. You can manipulate people and their behaviors based on what triggers them or to use industry language--their psychometric properties. You may have stumbled across these assessments at the hands of Cambridge Analytica's nefarious but amusing little quizzes or innocently through a hiring process.

Risk be damned, I now know what kind of vegetable I am.

These types of questions are designed to explore cognitive abilities, personality traits, and behavioral tendencies. In the wrong hands, they can instill targeted and scripted fear mongering and hostility unrelenting until you are driven to a particular candidate.

The documentary below mentions a disturbing statistic--by identifying and targeting potential voters identified as "persuadables" about 70,000 voters from 3 states determined our last presidential election.

I don't care if your candidate won or not--this is not how a democracy works. Mind meld a populace through profound misinformation and baiting? And we allow it to happen. We have polarized around measures of "fear" and "anger" designed to motivate hate and separateness.

Watch the trailer and if you have Netflix--watch the movie.

But here is where measuring objective reasoning and cognition are simultaneously manipulative and also an integral component of understanding why we do what we do.

As an analyst, I constantly review data collection strategies, collected data, and the output from survey instruments. Most industries, with the exception of continuing medical education (CME), are interested in influences of behavior. Shared decision making has guided patients to perhaps ask questions but we have done nothing to evaluate the quality of the responses they may or may not be provided.

Not just measuring how someone answers a multiple choice question and deciding you have influenced their practice of medicine but what drives their decision making?

How likely are they to change behavior based on results from a new randomized controlled trial? How do they evaluate objective findings? Are they concerned about industry funding? Are they influenced by their peers? How do they seek answers to a clinical questions? What publications do they follow? How data literate are they? Do they consider costs at the point of care? Do they use probabilities to evaluate risk/benefits?

The list goes on and on. If you aren't using some form of decision analysis or stepping outside of summary statistics--what do you know for certain?

Are we okay thinking that just because you present an audience with information that it will be distilled and operationalized?

Don't be a turnip...

Keep making noodles...

6/18/2019

"Po: Maybe I should just quit and go back to making noodles.

Oogway: Quit, don't quit? Noodles, don't noodles? You are too concerned about what was and what will be."

Often, when working on data projects, I come across useful quick snippets of code that help with the Herculean task of sourcing data. I have quite an elaborate scheme of organizing a library of resources in my Dropbox folders. Here is one I recently discovered and have put to immediate use.

In the last two posts I relied on df.info() to explore a few datasets. A lot of times you just want to hop in and hop out but there are occasions where you need a bigger lens to look at big data. Here is where Pandas Profiling is superb. Read the article below for more details but I am pulling the most relevant steps into the blog for your review and direct application to healthcare datasets.

10 Simple hacks to speed up your Data Analysis in Python

I had to revise some of the code to make it work for me. I tend to use Jupyter notebooks and running commands from the shell ! work better for some reason. This is the code I ran instead of the installation code from the article.

Now we need to import the necessary packages--pandas allows you to convert your tabular into data frames. The link in red is a file I imported from my dropbox files of data sources. To grab the link from your dropbox file simply copy the link provided when you click "share"--replace the "0" with the "1" you see in the file URL below.

I do a lot of work evaluating population health data. This sample is from Community Health Status Indicators (CHSI)--a bit dated but a good practice dataset to experiment with and learn a few new data skills. I have an extensive checklist of curated data sources shared with workshop attendees or clients but I am happy to share an edited sample with anyone -- reach out to me either on twitter or LinkedIn.

When you are evaluating datasets, the ability to have an overview of number of variables, missing data, and the types of variables is quite handy. You can also review a list of variables highly correlated with each other and therefore eliminated from comparison. All of the visuals are loaded with the simple code above. I broke it into segments for the purpose of our conversation but the html link will take you to the full output.

Certain data questions won't require all of the variables in a particular table. Each variable is summarized here and provides useful information on how the variables were coded (categorical for example), distinct counts and missing data are also provided as well as a graphic where warranted. My best practice is usually to review in Python (or R) and use Tableau Prep to remove unwanted columns. More on that in future post.

You can also view sample data here as well although I tend not to focus too much here since we are able to generate a lot of summary data elsewhere.

One more option to view your data dynamically instead of simply in a static readout is using the code below. The # is commented out information and will not interfere with your code.

The x-axis below is the total number of data points while the Y axis is the value of every feature for that particular data point. Hit play to see interactivity.

Obviously a curated dataset is much less crowded but you can select variables for comparison using tools in upper right menu.

data collaborations...

You can reach out for specific and scalable conversations about formulating data questions, sourcing nonproprietary data, tidying and cleaning data, analysis, visualization, and communicating your findings.

We now do on-demand webinars in addition to onsite workshops tailored to your data conversations.

Here is a link to the html version of the notebook if you want to scroll around.

Exploring large datasets part deux...

6/17/2019

Many clients and collaborative partners are excited to visualize their data. The previous post, The voyage of data discovery, and this one are attempts to create awareness around not only the tools but the complexities of formulating a question. This is all happening upstream from our final visualizations.

I have done a lot of work data sourcing. Finding open source data containing databases for query and exploration. The challenge is the scale of big data. Navigating to clinicaltrials.gov is a great example. Here is how I tackle looking around the tables in Python.

I gave a teaser about simple code I use for R last time. Direct any questions to the comment box or reach out on twitter @datamongerbonny.
Again, I am using jupyter notebook as I do work in data literacy and teach workshops--all requiring detailed descriptions. And the biggest reason? This is how I was taught Python in an executive education program at Columbia School of Engineering.

Alternatively you can run code right into your text editor. I use this if for my eyeballs only but it's a little messy if you are trying to create a narrative. This image demonstrates a snapshot only of how many individual files are in the database. I like to explore them individually or combine them in meaningful ways depending on the question to be formulated or answered.

Explaining the details of accessing your files or creating them is a bit beyond this post but we do have workshops forming all the time. I aim to keep it general for those that might just need a nudge or can reach out with a question. If you have downloaded a csv file you can import into Anaconda where you have your Python saved. This way, when you call the file, it is pulled into the console.

For example I imported the pandas module (or library) and assigned the common alias pd. The reason for the alias is to simplify the writing of code. The basic format is alias (pd), and function (read_csv) with the parentheses containing the arguments.

These tables are quite large so often during exploration I will call head( ) for the top 5 rows in each column. You can get an idea of what data type is in each column. On tables unfamiliar to me I will often use the info( ) function. It's a more granular overview--how many rows, columns, datatypes, and issues like memory usage. This is helpful if you are limited in memory and will need to edit your data request for specificity.

Having a quick list like the one above is handy when you are writing code and need to recall the column names and tables where they are located. I will continue to work through data sourcing and tidying before we begin steps to visualization. Remember it all begins with a well articulated question before we can identify and curate the best available data for generating insights and answers.

The art and science of asking questions is the source of all knowledge--Thomas Berger, American novelist

The voyage of data discovery...

6/15/2019

The real voyage of [data] discovery consists not in seeking new landscapes, but in having new eyes--Marcel Proust

The ability to grab nonproprietary data for question generation and exploration might be the most neglected resource. For example, continuing medical education companies create programs at an immense profit often based on hunches, heuristics, or whatever the funder or sponsor deems worthy. What might the landscape or outcomes consider if there were better survey analytics or relevant metrics analyzed?

I have tried. Most often I am told that the preferred mechanism or platform is to keep the status quo. Useless pre- and post-evaluation measuring nothing of value or multiple choice formats that do not mirror how physicians take care of patients. The ever present Likert style questions confusing unimodal and multimodal scales. I will say two things here--again. Values on a Likert scale are not comparable.

A score of 5 on one question is just that--a five on one question. Second, if you start using ranking scales you will improve your data quality and analyses exponentially. Why?

How are we communicating about point of care interventions in the absence of decision choice experiments, determinations of value for each stakeholder, and collaborations with patients?

Think about probabilities. In medicine we measure probabilities. What is your risk for a disease, what are the chances you will respond to treatment--think about large scale risk/benefit questions. Today we aren't tackling surveys though--you can read Write Better Surveys. Period. for the price of a cup of fancy coffee or stay tuned to future posts.

I wrote a quick post over on LinkedIn that generated a lot of interest and not a small amount of private messages and queries. As much as I would prefer to engage colleagues publicly to foster broad discussion I get it--you are a shy bunch. I shared my method for jumping into a large dataset and looking around. Obviously, there is more work to be done but once you can see what questions might be able to be answered with data--you can ask better questions and begin questioning data.

A big dataset with a lot of options for data curiosity is the ClinicalTrials.gov database of the US National Library of Medicine. A friend was curious about the reason clinical trials are terminated and he had some static spreadsheets with aggregated answers. I thought it might be interesting to dig a little deeper so gave him the interactive visualization above, studies reporting "low accrual" or studies reporting reasons other than "low accrual" available at Tableau Public and this one as well, actively recruiting clinical trials for colorectal cancer.

I typically start over on the website for downloading content for analysis--Clinical Trials Transformative Initiative (CTTI)'s Database for Aggregate Analysis of ClinicalTrials.gov (AACT). I suggest reading the page to better understand your options for data but if you are a beginner, scroll down to the link for the Clinical Trials project. This simplifies the download of aggregated data from the publicly available relational database.

Once you click on download, it will take you to the following options. If you want to run a local copy of the AACT database (static) you can install PostgreSQL server and viola, there you go. I no longer use this option as from time to time the code will break and not accept my password. It creates this loop of resetting password and waiting and waiting.

I started using pipe-delimited files and working in R. I will show you a brief line of code or two so you can see how easily this option might work for you. The advantage of pipe-delimited files? They look like this column 1|column 2|column 3|column 4 so instead of being separated by a delimiter that might also be a value, such as a comma (in csv for example)--you have the pipe character.

Due to the size of a large database, it isn't recommended that you open it up in Excel for example. I like to work in RStudio. Because I teach a lot of courses in R, I prefer R Studio Cloud (the data and environment stay with each instance of a project) but for now let's just look at RStudio.

If you want to learn more we can set up a remote session where you can log into my cloud environment and interact live while we explore a database of interest (see below)...

I pulled a single table from the downloaded file and here is an example of how you can quickly examine data with a few lines of code. I quickly sampled 200,000 samples for quick information on column headings and can make a quick estimate regarding what I might find. For example, look at the column "reason".

We have over 50 other tables to explore for granularity by condition, clinical trial phase, year of start or completion, results availability, or clinical trial design to name a few.

Here is the list of other tables in the dataset. Make sure you review the data dictionary to help you select tables of interest.

Closer examination of the tables in the database are informative and I am happy to explore the data with you to help guide you through how you might use either R, Python, or Tableau Prep to clean up data for visualization. Stay tuned...

The data that didn't bark...

5/29/2019

Are you a Sherlock Holmes fan by any chance? One of the more famous tomes involves the disappearance of a valuable racehorse.

The "curious incident of the dog in the night-time" is easily explained: the dog made no noise, because no stranger was there. As Holmes explains: "I had grasped the significance of the silence of the dog, for one true inference invariably suggests others.... Obviously the midnight visitor was someone whom the dog knew well. It was Straker who removed Silver Blaze from his stall and led him out on to the moor"--The Adventure of Silver Blaze (Wikipedia)

Obviously there are other reasons the dog may not have barked but the most dramatic of course, was described by Holmes. This reminded me of silent data. Hear my out--I am going some where with this.

We are accustomed to looking at the same things the same way. I attended the Committee on Informing the Selection of Leading Health Indicators for Healthy People 2030. I remain unimpressed by the Healthy People 2020 metrics for many reasons. Sure it's a start but the problem remains--if data is old, aggregated to too high of a level, and chock full of low hanging fruit--what are we striving for?

Amy O'Hara from the Massive Data Institute at McCourt School of Public Policy, Georgetown University discussed data sources for objectives and the Leading Health Indicators. I could relate with her tasks the most. It is all about what you measure. The data that is now being gathered to expand definitions of poverty was not gathered to be shared. These were all internal databases not designed for harmonization--hence we have this massive problem with data quality and intersectionality.

Should we do what we have done before because it is easy and the data is ready?

The non-barking dog--or should we evolve our questions and take the practical steps to build a system that we can improve. Incentivize people to understand what is being measured. Head over to HealthyPeople.gov and you will see what I mean.

If reducing diabetes-related deaths (age adjusted, per 100,000 population) is the target, how are we going to focus on the disparities in counties, zip codes, and communities? We are not measuring the right things. I would argue that within National Target Met (green states) areas are many communities suffering from disease. We need granularity. We need new measures--we need a barking dog.

Akimbo is one of those podcasts that delivers time and time again. Seth's clarity and insight can help redirect your focus if you start monitoring the wrong things.

Seth Godin argues we need to place the right metrics on the table. What story do we tell ourselves, our co-workers, and customers? It matters...

Money has one advantage. It is easy to measure but that isn't the point. We want change we can be proud of. Lots of organizations measure money but they are leaving a trail of obesity or disease, discomfort or unhappiness. This creates systems that can't sustain for the long haul.--Seth Godin

What are you measuring?

Finding true merit in a deep river...

5/20/2019

I have been thinking about the complexity of the questions we ask and the ease with which we accept simple answers. Often, a question isn't even articulated. We have data. Lots of it and many of us want answers. But how do we navigate the noise? A solitary clear bell among a cacophony of distraction.

Professionally, I work a lot with publicly available data sources such as IPUMS and US Census Data. I know that race is not a useful variable. I question what you are measuring when I see poverty rates reported, or gender captured in a survey. Here is the problem. First, race is a social construct. If you want to measure social determinants--measure them. Don't use race as a proxy. Same in medical cohorts. If you stratify by race you need to identify what you are looking for--before the analyses.

How should we look at poverty? I look at the interests of the public good--what constitutes social welfare. Shortcuts inclusive of solely income stratification will never illuminate problems or solutions. How about aligning edges of variables that measure social inequality, power, relationally, social context, complexity, and social justice as described by Collins and Bilge below.

Positing that contemporary configurations of global capital that fuel and sustain growing social inequalities are about class exploitation, racism, sexism, and other systems of power fosters a rethinking of the categories use to understand economic inequality. Intersectional frameworks reveal how race, gender, sexuality, age, ability, and citizenship relate in complex and intersecting ways to produce economic inequality--Intersectionality, Patricia Hill Collins and Sirma Bilge

Unemployment, poverty, racial and gender discrimination, homelessness, illiteracy, poor health, and similar social problems within a nation-state constituted threats to the public good that were just as prominent as external enemies beyond its national borders.--Intersectionality, Patricia Hill Collins and Sirma Bilge

A recent article in The New Yorker, The Philosopher Redefining Equality reminded me of the intoxicating siren song of Baader-Meinhof phenomenon--a type of cognitive bias.

Elizabeth Anderson is the chair of the philosophy department at University of Michigan. Her belief that "equality and freedom are mutually dependent, enmeshed in changing conditions through time" captured my attention and improved my awarenesses of the fluctuations of what we mean when we use terms like "value".

"Her first book, “Value in Ethics and Economics,” appeared that year, announcing one of her major projects: reconciling value (an amorphous ascription of worth that is a keystone of ethics and economics) with pluralism (the fact that people seem to value things in different ways)."

"... Instead of positing value as a basic, abstract quality across society (the way “utility” functioned for economists), she saw value as something determined by the details of an individual’s history."

Andersonism holds that we don’t have to give up on market society if we can recognize and correct for its limitations—it may even be our best hope, because it’s friendlier to pluralism than most alternatives are. And we shouldn’t commit ourselves to an ideal system of any sort, whether socialist or libertarian, because a model set in motion like a Swiss watch will become a trap as soon as circumstances change. Instead, we must be flexible. We must remain alert. We must solve problems collaboratively, in the moment, using society’s ears and eyes and the best tools that we can find.

I spend a lot of time explaining the granularity of variables routinely pulled into data queries. You might not be measuring what you think you are measuring.

We certainly don't need any more noise drowning out important conversations--do we?

True merit, like a river, the deeper it is, the less noise it makes--Edward Wood

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