Thursday, February 25, 2016

Philosophy of Analytics, Lesson One: Begin with the End

Introduction

I have a Masters in Philosophy.  More specifically, I have a Masters in Analytic Philosophy, which is the method of philosophy most popular in the United States and the United Kingdom (as opposed to Continental/Existential/Phenomenological Philosophy).  When I began this blog, I titled it "Philosophical Analytics" to emphasize my interest in both philosophy and analytics (i.e., data related analyses and visualization) and my desire to continue to engage in both disciplines.

Recently, after reading the title of my blog, someone asked me what my philosophy of analytics was.  I had to think a while on this as I had not really formulated my impressions on doing analytics into well formed thoughts, principles, or assertions.  But this is a good question, a perfect question, for me.  This is the first of a series of blog posts that are an attempt to rectify that and to put forth my analytics philosophy thus far.

A Philosophy of Analytics

This blog is not about  how to build a predictive model in R, chart in Excel, or pull a query in SQL.  It's aim is more philosophical and methodological.  We could even call it "meta-analytics".  It will address the why or the why not behind the specific how.  Why should we create this dashboard? Why is this predictive model being used?  We are reflecting on the purpose of using various analytical tools and products.

But we can take a step back even further.  Why do analytics at all?  What is the purpose of analytics? How do we do analytics well?  The answers may be obvious to some, but others may have never asked these questions before.  We know that some people like shiny, flashy, and brightly-colored charts, graphs, slides, and dashboards.  They are impressed by regression lines and cool statistics.  But apart from the job security and budgetary victories that may be scored by throwing analytics left and right, what is the real point of analytics?

This blog post (and others like it) will focus on the nature of analytics: who uses it or does it, what it is, when is it appropriate to use, where should it be used, why is it used, and how best to use it.

Lesson One: Begin with the End

Aristotle specified four "causes" to account for or describe any object:
  • the material cause: what the object is materially made of.
  • the formal cause: how the object's material is arranged or shaped.
  • the efficient cause: what brings about, creates, or changes the object.
  • the final cause: the object's purpose, it's end.
Our object is analytics.  It is materially made of Excel sheets, Tableau dashboards, SQL queries, big data stores, R regressions, and Azure predictive models.  It's form consists of colors, lines, plots, and tables placed on billboards, websites, and desktops.  It is efficiently caused by developers from backend to frontend and by users that consume and provide feedback. But it's purpose?  What is it all about? In short:
The end, the purpose, the telos of analytics is this: data-driven decision making.
Why do we make charts or build models?  Why do we collect terabytes of data?  Surely not just because it's fun.  We do these things in order to gain insight into our domain of interest, to understand what is going on. Just because?  No!  We do this because we have to make decisions in our domain of interest, and we want to make good decisions. 

A business needs to understand what it's customers want through sales trends.  It needs to know how to allocate resources, where budgets need to be cut or expanded.  It does so by looking at its sales and financial data appropriately summarized and charted.  A politician needs to know where differing demographics stand on multiple issues so that he or she can most favorably present himself or herself to that demographic.  This is done through collections of voting records that have been grouped according to these demographics and along the lines of key issues.

Perhaps you are simply interested in learning more about a subject (as I often am), and perhaps your explorations have no immediate practical application.  Ok, fine.  Then we can describe the purpose of analytics in a more sophisticated way:
The purpose of analytics is to (1) justify or change beliefs with the use of evidence in the form of data  (2) in order to generate true (or truer) beliefs that can then be used to make decisions related to one's goals, and that, (3) because they are more in accord with reality, are more effective in bringing about one's goals.
 Let's break this down.  First, we are gathering data and presenting it to ourselves or to others to change or strengthen our beliefs about a given domain.  We are trying to understand what really is going on in the world, changing our beliefs about it if necessary.  If the data and subsequent analysis is good, then it will accurately represent reality in a useful way.  For example, we collect weather data and build models that predict what the weather will be like tomorrow so that we can have a true belief about the weather tomorrow.

Now why might that be important?  This leads to the second part.  We take our justified beliefs, supported by the evidence, to make decisions with regards to our goals. If I have the goal of having a good time while hiking outside, what I decide to wear outside will have a direct impact on whether I do have a good time if, contrary to my belief that it will be sunny, it in fact rains and I have decided to hike in a t-shirt and shorts.  If my belief were changed by the evidence that it would in fact rain, then I could make the decision to wear a jacket and have a more enjoyable time.

And this leads to the third part, that data supported decisions are more effective in bringing about our goals. We use data to build a model of reality in a specific domain, and then using that model to represent reality, we make a decision that is lived out in reality.  In the case above, I would look at the data related to weather patterns and historical trends along with the predicted outcomes for what the weather will be like for my hike.  Because the data and models suggest that it will rain, I make the decision to wear and rain jacket.  Consequently, when it does rain, I am not soaked and I still have a good time hiking (which I would not have had if I had gotten soaked).

The goal of my hike was to have a good time.  And by using the analytics related to weather to inform my choice of clothing, I was able to have a good time.  The data and models enabled my hike to be successful, that is, effective, in bringing about my goal.  I was empowered to make a decision more in accord with reality precisely because I had evidence to support my belief that it was going to rain.

Putting It Into Practice: What is Your End?

Ok, so the purpose of analytics is data-driven decision making, that is, to justify/change beliefs that lead to decisions that accomplish one's goals.  So what?  Well, thinking about this purpose of analytics can radically change how you approach your analytics projects.  Most importantly, if the point of analytics is to help one to accomplish one's goals, then before any analytics project can be undertaken, this question must be answered: what are the goals?

Have any of you been told to look at the data, build a dashboard, create a model, and then report back with what you find?  That's like being put into a closet filled with junk and told to find something useful.  What is useful?  It all depends on what one's goals or aims are.  Any number of items may be useful, but they will be useful with respect to certain goals (and not others).

In a business, we can always tie it back to money: increasing profit/revenue/sales or decreasing expenses.  But let's be more specific.  In fact, be as specific as possible.  For your organization, how does it make money specifically?  What role does it play in the larger context of the company?  What are the specific goals of your organization?  What decisions need to be made that could use better data to drive those decisions?

Perhaps your organization saves money by improving code efficiency.  Perhaps your team increases sales by reducing transaction times.  Maybe you improve revenue by increasing the conversion rate of sales through the company website.  Whatever it is, think about the specific goals of your team/organization/company, and then plan your analytics solution to capture, measure, and display the progress in meeting those goals.

Suppose that your organization's goal is to improve click-through rates on the website. Once you know that, you know that you need to gather data related to clicks and visits by users, locations, dates.  And you know that you need to calculate the click-through rate and display this over time (perhaps a line graph) to determine if the organization is improving in meeting its goal. You gather the data with sufficient granularity so that locations that are lagging in click-through rates can receive targeted investment.   Other important decisions can be informed by the data that you have gathered.

Without having this specific goal in mind, you would have no idea what data to gather, what measures to calculate, or what visualizations to create.  And your project wouldn't go anywhere or provide any business value to anyone.

Conclusion

You wouldn't begin a journey without a destination, right?  So don't begin an analytics project without a destination, a specific goal, in mind.  Otherwise, you will wander about aimlessly for a time in the sea of data before your project is cancelled.  Instead, figure out what the end, the purpose, of your analytics will be, and then figure out how to concretely get there.

Before you start, begin with the end.


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