Predicting Music Popularity and Marketing Analytics

What do Taylor Swift's videos have to tell us about marketing?

There's a machine learning competition running here in Salt Lake City that deals with predicting the popularity of Taylor Swift videos from snippets of music. And by snippets I mean 1/10th of a second of a song. The goal is to classify whether a song is a 'success' or 'not' (if you can take 30m views as not being successful and 300m as success.)

Source

In marketing we try to understand what makes a consumer tick, but in this case an audio track has been converted to a series of numbers that look something like the following. Now imagine 3,300 columns of numbers for each sample...

  Success     V1        V2      V3        V4       V5      V6      V7     V8        V9
       0       -5787    9566    -511    -2274 18589   1170    2232   5073   -2578
       1        1067   -  521    1524     -209    -957    -777  -2666     716   -3273
       1          397     -314   -1701   -2568    -463   1123    1041     916      789
       1          784   -1017    2038     4134   1453    3731   3644    2759  -2033
       0     -19632 -13344 -14428 -13195  -3940  -1306  -1206   -1303  -1256

This is where you have to trust the machine and the team. There will be no conference room debate as to what impacts success - the math just works. And that raises an important question: how do we now improve the consumer experience and deliver on the brand promise?

Keeping with the music theme, a musicologist can deconstruct the signals into themes and styles much in the same way that Pandora was created from a music genome. As a result the experts won't be just the ones who can apply a Restricted-Boltzmann-Machine to a classification problem, but those who can translate the results. Interpreters will be in as much demand as the coders.

Keeping my Rosetta Stone polished.

Rainfall is a Path to Purchase

How does rainfall relate to sales?

The weather service now measure things with such precision that we can determine flat (rain) vs. elongated (ice) droplets, etc. A new project on data science competition site Kaggle focuses on predicting how much rain actually hits the ground from a series observations on the way down. The objective of the contest is to estimate the likely outcomes from an hour's worth of data. Such models have implications for agriculture, highway safety, and other resource allocation projects.

Weather now measured in two-dimensions

This sounds like marketing mix and the path to purchase.

Marketing is often faced with a similar problem: how to align out of store activity, digital or otherwise, with in-store sales. Like rain, sales vary with time and space so the analogy is apt.

Recently Yicheng Song and colleagues at Boston University have been working on identifying common path-to-purchase analyses that combine multiple touch points and discrete sales outcomes. Their approach can be summarized as follows:

1. There are patterns to the path to purchase (clusters of consumers emerge along common lines)
2. The path and outcome varies by initial stimulus (catalog, email > online, offline)
3. Paths traverse on and offline steps  (variance is a matter of degree, not one or the other)

What I find interesting is that the approach works with segments and plugs missing data (all too common in path to purchase work) to get a handle on what is likely to happen. Like most marketing analyses these days we don't know anything with certainty but can only talk in directional terms. This means we might be able nudge the pendulum one way or another with a change in the marketing mix but stuff still happens on the way to the store.

Maybe there's something more to be learned from the rain.

Image Recognition - a first look

Does auto-classification of images work?

There is a trend toward "Analtyics-as-a-Service" where companies can leverage specialized functions on demand. There are predictive modeling options like IBM's Watson and MonkeyLearn for text modeling. There are even image recognition options emerging that offer auto-classification.

And as part of a personal interest on figuring out how content works I'm experimenting with various ways of tagging content - images, descriptions, and reviews - to see what might emerge.

So as a test I submitted the following picture to a service I found: imagga.

Red Duvet

So, here's what came back as suggested tags.

• Shopping cart
• 3d
• People
• Man
• ...
• Jukebox

Not exactly what I would have picked...so I did it again.

This seemed to produce more useful tags (and had higher confidence scores).

• Sofa
• Furniture
• Bed
• Interior
• ...
• Relaxation

The tags come with confidence scores - not sure what they mean yet - but the scores for the second picture were higher. Definitely an interesting learning exercise. More to follow...

More Implications of Agility

How will agile development affect your calendar?

In the second and third installments of "what Agile means to the rest of us" posted originally on LinkedIn Tom and I discuss new behaviors you should expect to notice when working with an Agile team.

A new set of questions will emerge and they focus on the business value that a project delivers. The discussion is all about outcomes, not how things are accomplished. This is done to set the right priorities. Gone are the days where available resources defined priorities -- "we can't do that because we don't have {fill in here}, but we can do this because we have {fill in here}."

The only way in business to hit a target is to course adjust - frequently. Agile projects are dynamic and the team adapts as we figure things out. So, clear your calendar and expect to meet frequently.

The new normal

Search Trends, Football and Campaign Measurement

What does football tell us about how to measure campaigns?

A recent installment of Think with Google had a topical piece on football.  In it is the following chart showing the trends in "American Football" broken out by the terms that the search contained: What, Who, When, Where, and Why.

The annual spike is the playoff season.

American Football Searches: What, Who, When, Where and Why

Besides the obvious point that people look more for facts than explanations there appears to be something else.

Episodic events create step changes and the pattern is cumulative.

The latter is somewhat related to the network effect, but with Internet penetration probably close to saturation or at least hitting the second flat slope of growth one might not expect the same growth for the next five years.

What I do find interesting is that the level doesn't go back to status quo ante kickoff, there appear to be slight residual effects.

The marketing implication is two-fold. First, if you want 'superbowl' like results you need to create momentum thru repeated episodes. Second, the window of time in which you measure the success of a campaign should be broken into at least three parts: Pre-Campaign, In-Campaign and Post-Campaign.

Agile for the Rest of Us

What does it mean to be agile?

A friend of mine who has a lot of experience delivering software products based on Agile Development recently asked me about the training course he's developed. I thought it was a perfect setting to explain what the implications of Agile are to the rest of the organization.  If IT is doing things totally different, then we probably should be changing our expectations as well.

The first of several posts appeared on LinkedIN earlier.  The meat of the matter are five behaviors and their impact.

1. Business Value Focus - we need to focus on the what, not the how
2. Dynamic and Adaptive - we won't have a Gantt Chart on the wall, we'll be moving things around
3. Collaboration and Feedback - we can't just attend the kickoff meeting; we're embedded resources
4. Transparency - that wall in #2 is public for all to see
5. Continuous Improvement - we're not shooting for the moon in one step, but taking small steps to learn from

Each or subsequent posts will expand on the five changes above.
If you're interested, Tom Abshire can be found over on LinkedIn or on the AgileFluent site.

Step 1 in Generating Insights: Grab a Marshmallow

How should we approach developing insights?

The typical path to insights is lined with roadblocks of making sure the anticipated results are scaleable, generalizable and solid enough to base a business on. We typically don't go looking until we're sure that what we find is usable and workable. To be honest, we're handcuffing ourselves; maybe there is another path.

In The Marshmallow Challenge kindergartners routinely trounce MBA's at building the tallest structure possible out of spaghetti, tape and string because they do rather than plan.

From: source

Because they aren't riddled with fear of failure, they just try lots of approaches. In 18 minutes they cycle through ideas, learning along the way. In the meantime MBA students, and executives, are planning, allocating resources, and probably doing a bit of posturing to be seen as successful. By the time 'the experts' finally get to the objective, they've run out of time and have to bet the ranch on one tact. The results are predictable.

The rapid iteration of 'test and learn' needs to be applied more often in insights development..this can be in terms of thought exercises "How might it be different?" or allocating budget to learn rather than sell.

Insights are things you don't know you need to know, so how can they be judged a priori?

Touch Points and the Illusionary Path-to-Purchase

How do consumers end up where they do?

We try to understand the path-to-purchase by stitching together data from various touch points and appending them to an artificial identifier meant to represent a consumer.

I'm not sure if it is a love relationship or a hate relationship with technology, but marketers use a variety of data-related tools to connect with consumers and figure out how they came to buy. According to recent Winterberry research on "Marketing Data Technology" (registration required) firms use 12+ different tools on average in the course of marketing. And with that many tools attempting to measure the influence of marketing on behavior something is likely to be amiss.

At a recent conference on data and analytics John Pestana of ObservePoint illustrated the complexity of all this by listing the tags or the scripts on a single web sites that log consumer behavior.

This major media/news site had:

• 18 applications
• 29 tags
• 124 variables

His question: What are the odds that something is broken? Like finding correlations in Big Data, it is pretty much guaranteed. Breaks or discontinuities in data capture mean we have to guess about what happens in between what we can observe. It is worse if different definitions are used for the same event. We've all been in meetings where the sole purpose was to agree on who had the right numbers, when we should be talking about how to help consumers choose our products and services.

The biggest issue is not the quality of the data we do collect, but rather the fact that we're missing all manner of influences and behaviors. Integrating our touch points does not come close to providing a 360-degree view of the consumer.

Maybe we should ask a different question: What if we captured the path first?

Since we're likely to sleep with smartphones no reason we can't imagine passively capturing our path and then overlay marketing on that. Sure there are challenges - permission, revolving MAC address, proximity, etc. But nothing a panel couldn't solve.

I'd rather have gaps in marketing than blind spots on the path.

A Great Combination of Brand, Positioning and Packaging

What happens when it all comes together?

Some times you just smile.

http://www.boxedwaterisbetter.com/

The Millennial Skills Gap

Why don't the best and brightest work here?

This story is based on conversations around a panel discussion at the Utah State of Data, an event discussing the impact of data on education, finance, employment, government, and technology. While localized to Salt Lake City there are some general ideas about positioning and differentiation of a specific market.

Salt Lake City's biggest differentiator is the outdoors. "Life Elevated" is often aligned to what one can do within 20 minutes of downtown. We also have infrastructure - the NSA data center is here so the pipes got to be big, a hospital system that services 10 states and thus unique service delivery needs, and a work force with a decent ethos of helping rather than competing.

But it appears we cannot attract and retain the best and brightest young technologists. The logic woven from several conversations goes like this:

• A career is nothing more than a series of interesting projects and changing jobs to do something new different is common in strong tech markets (a year at Facebook, two at Twitter, a startup and then a new product launch and sabbatical at Google). Interns brought here don't convert because there aren't enough big cool things going on like the Bay, Seattle, Boston, Austin, etc.
• Job hopping creates a rapid rise in salaries for high-demand skills, and not only in technology, as companies compete for scarce resources with great credentials. Within a market it is business-as-normal, but across cities it makes "market-competitive compensation" surveys from your HR team irrelevant. If I'm willing to move, then the market is the country not your locale.
• SLC is not a "Friends" city where millennials conduct life 24*7 in coffee shops and other urban venues. We'll be lucky if we get to 18*6.  So, even if we get the best there isn't a lifestyle typically associated with high energy folks.
• We are good for families so we can attract and retain those wanting to settle down a bit, i.e., those in their 30's or 40's. They have experience.
• At that stage of our lives job hopping isn't viewed as a means of defining oneself. And without movement, salaries don't rise based on intense competition.
• If salaries aren't competitive we can't attract the talent we desire.  

And the circle begins again.

It would seem we're caught between our aspirations "Silicon Slopes is next Silicon Valley" - but based on deal flow funding so can Saint Louis, Munich, Seoul and Cincinnati (Beijing tops the list in terms of number of deals.)

So what kinds of companies should we create when the workforce is a) experienced and b) content? Rather than core game changing technologies, are there other classes of things we should focus on rather than the "next Google" (and good luck with that anywhere.) 

• Can we rent company founders and focus on later stages of the evolution requiring experienced management?
• Can we blend technology, the willingness to help and experience to solve human needs?
• Can we take what is unique and different and position ourselves better?

5 Problems with Marketing Analysis

What do we often forget to mention?

I missed an important birthday last month; the banner ad turned 40.  We're certainly not going to see the 40% CTR that this ATT ad achieved back then.  In fact we're not likely to see much above a single digit or even decimal place.

At the time I don't think anyone had an idea what the click thru rate would be, but in today's data-driven world we should have some clues.  We have all kinds of systems and analytic tools that predict what a CTR might be. But each of those come with one or more constraints that we often forget to convey.

1. Time, data and resources will narrow the options we can use to analyze a problem; need an answer today? or next week?
2. Explanations always rest on assumptions as we reduce the problem to one that is manageable; did we remember to tell anyone?
3. Our mental models are biased to begin with, and we may not realize it.
4. Data is left behind due to data collection procedures or application code; design choices made before we started the analysis limit what we can and can not do/say.
5. Our use of data may be different than how it was intended; this is a common question when creating mashups particularly with public data.

So, say out loud "this is what we don't know"....

The Need to Link Data to Marketing

How do we come to understand things?

In the era of "big data" this and "big data" that, we have a problem. Our minds are wired for language not data. Rarely do we have direct intuition about data, what it means, and what we ought to do with it.  We need to invent the Rosetta Stone that bridges the hieroglyphics of machine learning with the ancient language of marketing.

Consider the following examples:

• What is the cost function of loyalty?
• What does unsupervised learning tell us about branding?
• Where does gradient descent take us in terms of understanding engagement?

On the left side are data terms; on the right are marketing terms.   Each takes a common, yet ill-defined marketing idea (loyalty, branding and engagement) and maps it to an aspect/method of the big data world.

• Loyalty has a cost (or a profit) that comes at a price. What is the allowable marketing cost of achieving it?
• Branding is often described as being in the mind of the beholder; thus we should not label things as A or B but rather be surprised by how consumers cluster things.
• Engagement is a cumulative set of interactions that may (or may not) move us toward conversion; we need to understand the marginal contribution of each step.

Only if both audiences, data folk and marketers alike, can ask and answer these and a myriad of similar questions will we be able to translate from one world to another.

Digital Differentiation and The Chore of Choice

How does differentiation apply in a digital world?

This post was inspired after reading "Digital Brand Differentiation - 3 Frameworks to Follow and asking the question above.

Differentiation is required when we are faced with the chore of choosing.  It works because it provides a focused benefit that we can internalize and compartmentalize as "that does this".  So when we have "this" need we lean toward buying "that" product.  It is a step toward branding.

The qualifier 'digital' raises some questions to consider - some apply to digital products others apply to digital tactics for analog products.

1. What new dimensions are available upon which to differentiate 'digitally'?  (Which old ones don't matter any more?)  
2. How does digital information access and transparency change the relative importance of 'being different' in the decision process?
3. Are there categories where it is more/less important to be different because of digital?  Why so?
4. With the digital consideration set now being defined by the amount of time we invest, is it more important to be the first to be perceived as satisfying the basic need rather than doing it differently?

Jack Trout's "Differentiate or Die" is a good place to start on the first question since it highlights 20+ dimensions upon which create that distinction in people's mind.  Written in 2001 it probably doesn't cover the full implications of digital so could use an update from a digital perspective.  For example, I don't recall it using communal content (social networks or reviews) as the basis of differentiation.

A counter argument to the differentiation argument was offered by Killian Branding in a post entitled ""Differentiate or Die" is dead. RIP, USP".  That post concludes with the statement "Visibility is now more important than differentiation. Mission One: Get into the selection set."   To paraphrase the logic, if you're not at the top of the list (Yelp, TripAdvisor, Google, etc.) it doesn't matter how you are different.

What makes digital different is that the consideration set emerges/contracts/grows over the decision process - it is no longer an all or nothing proposition of being in front of the toothpaste shelf or looking at Thai restaurants in the yellow pages.  This would suggest that the emphasis on differentiation may rise/fall across the journey based on inferred intent.

The 5 Persona of Big Data

How do we decide WHICH Big Data is right?

The stories about Big Data talk about it as if it is one thing; it isn't. Big Data actually encompasses several different universes based to some extent on the fact that there are different underlying technology that can be deployed. And since technology is versatile but not omnipotent it makes some sense to first try to articulate what one is trying to achieve rather than just saying "let's do Big Data".

And any time there is hype like we're seeing today a gap emerges between what the business thinks it wants and what the technologists believes it can deliver.

To help bridge that gap it might help to describe the various approaches as people with skills and characteristics. To provide context, I'll start with the old guard and then introduce the new characters.

• Rachel - is the oldest with a very structured and ordered view of the world, her motto is "everything has a place, and everything is in its place" and put there immediately. She doesn't handle ambiguity very well and her rigid out look makes it difficult to change things as the world around her changes. She struggles when either too much complexity exists or the scale of the problem is too big. We all know Rachel as "relational databases" introduced in the '80s/'90s when computation, storage and memory were all expensive. She is showing her age. Think business intelligence and MySQL or Oracle.

• Chloe - views the world thru a different lens. Rather than looking at everything as well-structured events like Rachel, she is more interested in how groups of those things overlap on key attributes. She is less interested in "how many" than in "what are the commonalities/differences" in a set of things. In addition she clearly recognizes that every thing isn't known about everything so is quite comfortable with uncertainty. These holes in the data create a sparse matrix that is often best handled by "column-oriented databases". Think highly variable data elements and GoogleBigTable or Hbase.

• Kayla - looks at the onslaught of data and says "I can manage this if I spread it around across as many buckets as possible." Her goal is to preserve things as quickly as possible and to make sure they are retrieved upon request. She defers understanding and definition to others. In the purest sense "key-value pairs" make the data opaque by design, trading insight and thus latency for scale and availability. While excellent at writing information out her challenge is trying to organize and integrate data from several processes that all use the same term to mean different things. Think extreme write and access of user-supplied data and Apache Cassandra or Amazon Dynamo.

• Danielle - has probably the most unstructured view of the world "everything is a chunk of stuff and tags". Success then is dependent on the completeness and accuracy of the descriptors assigned to a chunk since searching its entire contents can be costly. The unstructured view applies to connectedness as well as content. In the "document store" world there are few relationships between the chunks beyond the tags. Consider posts tagged with #BigData - the hashtag is the only commonality. There are no implicit relationships beyond that fact. Think content management with mongoDB or CouchDB.

• Grace - is the most social and believes relationships are everything. She thrives in scenarios where the existence or nature of a connection matters more than what is connected. This focus makes it very easy to extend the universe in new ways as new things happen or new actors emerge. Grace exists as "graph databases" that have been around for a while, but most noticeably in social networking and social sciences. Think recommendations, communities of interest and Horton or Neo4J.

Which persona we adopt depends a lot on what we want to accomplish. And it is quite possible we'll end deploying more than one solution in our quest to "do Big Data."

{re}thinking with Data

How do we go about finding insights?

Let's start with what we mean by 'insights' -- here's my working definition in the context of marketing:

the identification of a previously unknown connection between marketing activities and consumer behavior that changes how we align our solutions we human needs.

Or, simply the finding of something "I didn't know I needed to know."

The typical story line for doing analysis consists of the following chapters:

1. State your objective
2. Define your strategy and tactics for achieving them
3. Create a measurement framework of KPI's that defines success
4. Articulate the dimensions of the business/behavior you need to understand
5. Set targets or goals that you need to compare to in order to track profess

And along the path you're defining a technical implementation and data capture plan to get from #1 to #5 and then refine.

If we're not careful, there are several risks in that plan.

First, we don't do all the steps...there is a tendency to jump in and build something; we forget to ask "why are we doing this in the first place?" enough times.    Think about the difference between Cliff Notes and the real thing.  A good essay requires reflection.

Second, we prematurely narrow the list of potentially valuable options.  This "focusing illusion" creates a bias because we tend to look no further than the first idea.  This is akin to judging a book by its cover. 

Third, we often view the problem in terms of outcomes related to our current business model not what might have caused them. While it is imperative to have metrics to track, they are simply links between behavior and performance.    Conversion rate is not a behavior.

The remedy to these risks is to spend more time thinking and that requires deep domain knowledge as well as the ability and willingness to explore.

For a good read try "Thinking with Data" by Max Shron

Using Your Own Customers to Crowd-Source Analysis

How can we leverage the fact that we're creatures of habit?

We often talk and read about benchmarks by tactic.  For instance 'email open rate' is tracked because it is the gate keeper to engagement and involvement.   As an example Silverpop reported the median open rate in APAC in 2012 as 27.2%.

I chose a two-year old number from 5,000 miles away in order to focus on the fact that these metrics are generated thru the lens of the campaign, not the consumer.

The health of a continuity email program relies on involvement over time and leads to the important question: How many more emails will you open?

The nice thing about 27.2% is that it is derived from nothing but 0s and 1s - consumers did or did not open the email.   If we look at the campaign or program level we now have a series of events from our intended audience that help answer that bigger question.  It turns out that there are two truths about consumer marketing metrics:

1. Even if your numbers are flat, it is highly unlikely that it is the same consumers each week.
2. The number of times consumers do something very often follows a predictable pattern.

In the case of email, consumers act just the way they do with buying products - that is there is a decay in the number people who do sequentially more things. 

The number of additional emails opened looks like this for one program.

Yes, the campaign had a problem with relevancy - something originally hidden by the rate of acquiring the email list.  Message: fix the communication strategy.

This is a very simple example of crowd-sourced analytics, there are lots of behaviors that can be treated in a similar fashion.  In fact there is a whole class of work being done in anomaly detection that takes advantage of habits.

How to Make Sharing Work

Why do people share? And with whom?

A recent post on LinkedIn about the phenomenon of sharing made the point:  we all ask for a share, but virtually no one offers a reason as to why we should bother.   Making it clear what we're offering and what action we want should be basic marketing.

But what should our expectations be about sharing?

The following is some of what Google learned in the development of circles.

First, circles exist indicating that people categorize others according to some meta-association.  There are likely some standard classes of association - think function like work or school and strength of the relationship like fraternity or building association.  It is easy enough to prove this, just compare one's FB friends and LinkedIn connections - some overlap, some exclusivity for defined reasons.

This means sharing must be selective - if we have circles then we don't intend to send everything to everyone.  Therefore we need to consider the why and who questions of context and audience.

Why do people share? the reason or context of sharing can be for one of three reasons.  It may be...

• personal - stories or opinions about oneself
• conversation - contribute to a discussion
• evangelism - spreading the good word (or funny video)

Who is the recipient? the audience is often based on a choice as to whether the content/context combination is...

• private and appropriate for only a select few, 
• relevant to a community of interest, or
• interesting to the masses.

So, before throwing the share button on every thing possible give some thought to how context and audience relate to your offer and call to action.

There is the obvious questions about what is shared...but I'll leave the content discussion for another time.


Source: extracted from David Huffaker's discussion of extracting meaning from data in "Doing Data Science".

3 Implications of Implementing Analytics

Are there {un}intended consequences of being data-driven?

As analytics moves closer to what Bill Franks of Teradata recently described in a post on operational analtyics there are organizational changes looming on the horizon.   A couple of things come to mind:

• Predicting the Future Creates the Future: If analytic output is implemented by the business then the creators need to share responsibility for success or failure.  This changes the "analysis as a service" model quite a bit.
• Opportunities Will Be In-Market Before the Business Case is Written:  The emerging trend in all of science is to analyze the data to uncover new theories, whereas in the past we started with a hypothesis and then collected the data to test it. Analytic-driven discovery inverts the venerable command and control approval process.  Which leads to...
• Results Trump Responsibilities:  This central tenet about rewarding value creation over resource control comes from accenture's blog discussing the twelve self-evident truths of being digital.  Proving an impact has more weight than claiming one.

This suggests that future line managers will come from the ranks of analysts.  

5 Questions To Ask After Each A/B Test

Why do we test?

Very often the discussion about testing is framed in the measurement side of things.  "To improve ROI" is a fairly typical the answer to the question above.  Yet, from an analytic or data science perspective that actually misses the point.

The reason we test is to gain knowledge.

To be sure testing green buttons vs. red buttons should be framed in terms of conversion metrics.  But more importantly we need to be asking ourselves the following questions:

1. Why did it work?
2. What are the segment characteristics of the winning option?
3. Among which segment did it NOT work?
4. What do the results tell us about the decision making process?
5. Where else in the pipeline or funnel can we apply this learning?

Understanding the answers to these questions will likely have a bigger impact on the business then testing purple in the next round.

Big Data vs. Data Science

What is the difference?

A lot of conversations I'm in having these days ask about these two phrases:  Have I done it? Can I lead a team doing it?   To answer I've had to put some stakes in the ground and define them from my point of view.

• Big Data:  a state in which current systems and capacities are simply overwhelmed. One cannot use traditional thinking or tools because the data doesn't fit in memory on a single machine.
• Data Science: the process of interrogating data in hopes of improving the human condition.

While Big Data is a state of being it is by no means static.  Like the rapids on the Inga river it can be a massive torrent of moving droplets.  The bigger the wave, the more a Data Scientist {team} needs computer science skills to navigate from point to point.  And unlike its predecessors "Data Science" as a discipline starts from a different place: given data, what questions could be answered?   Empirical, theoretical and computational sciences start with a question and don't actually have much data - they tackle different problems through observation, logic/proof and Big Hardware.

Because we're looking at the world passing by as a torrential stream of bits we need to have a goal, an objective or a problem to solve. One simply doesn't just jump in, there needs to be a plan and a lot of preparation (did I mention a LOT of preparation) grounded in experience, math and statistics.

Big is in the eye of the beholder.

Having worked with US and Canadian clients there is a line in the sand where things seem big.   For example a reasonably sized loyalty program for a national US retailer is considered big by Canadian standards since it is larger than the total population.  Frame of reference matters.

Science is a pursuit, a line of reasoning not an algorithm.

Along the path we need to visualize, explain and communicate what we've learned to date. Sometimes it is enough to know that a tactical change improves conversion because of correlation; other times we need to explain why and address causality.

Big Data is not Data Science and Data Science is not Big Data although it is quite clear the two overlap and the most frequently mentioned stories come out of that intersection.

Congo: The Grand Inga Project

The story of Steve Fisher and friends running those rapids was released in a documentary in 2012.