My A.I. Quoted Socrates: Data Science’s Make or Break Moment

We’re moving very quickly into data sizes and result set complexities that exceed our ability as people to evaluate. That’s led to the rise of “big data” and data science.  Our solution is an increasing reliance on machine learning methodologies to parse through what we can’t and reduce the complexity to something manageable.  How we program that parsing is leading to a massive problem for data science.  How we handle this and other challenges will determine the future of the profession.

The Problem of Bias

What’s signal and what’s noise or, asked another way, what’s important and what’s not? People develop heuristics to make that determination.  Study decision making and it’s obvious that those heuristics are deeply biased.  As a result we can make leaps of intuition and we can also fall for the simple tricks casinos play on us.  Our biases blind us to certain information while causing us to rely more heavily on other types.  The bottom line is that what people think is important is heavily influenced by our biases.

Machine learning in data science is represented as exactly the opposite. That’s the cornerstone claim, right?  If machine learning’s capabilities are no different than our own heuristics then what’s the point?  “Big Data” is supposed to be providing a different perspective; one rooted in data, able to see beyond our own biases and limitations.

Please don’t think I’m entering into the machine vs. human debate. I’m making the point that “Big Data” is supposed to be different than what we do on our own.  Without that difference, it falls short of its potential.  Is it still useful for simplifying large datasets? Yes but isn’t that just efficiency allowing us to speed up the process?  That’s great but no major advance.  We’ve been using technology to speed up processes for a while now.  Will data science achieve its promise if it relies on the same heuristics we do? No more often than we would on our own which means, no.

But Data Scientists Are Trained To Detect Bias, Right?

We are. We look for bias in data sources and results.  Any data source which excludes a portion of our target population is biased.  When results mirror our assumptions too closely (100% of emails which contain the word ‘Viagra’ we reported as spam) they are looked at for bias.  When correlations lead to a flawed hypothesis (As the numbers of pirates have declined average global temperatures have risen) they are tossed out as irrelevant using further experimentation to refute the hypothesis.

The experiment is really our last line of defense against bias. Experiments have moved us beyond faulty assumptions about a flat earth and taken black holes out of Sci-Fi.  In data science, a disciplined scientific method is moving businesses past bad assumptions and proving out new business models.

Anyone who’s done a data experiment knows most are time consuming, labor intensive efforts. So to achieve the promise of “Big Data” which is the removal of our own biases to reveal genuine insights, we sacrifice the speed business craves.

Here’s the Problem

We are left with two options. Introduce our own biases and realize the same results we get on our own but faster.  Remove our biases and make new discoveries slowly.  Neither scenario is ideal.  Again, I’m not disputing that progress has been made but what I’m saying is we’ve only achieved incremental progress while we’re promising a revolution.

Automation is the solution to this that I hear most often. We’re already automating the heuristic approach which dramatically illustrates the problems bias presents.  Automating the experimental approach leads to a completely different issue.  If you automate the experimental approach then the question becomes which hypothesis do we test?  Again we need to remove our own biases so let’s automate the hypothesis discovery process.  That leads to a lot more hypothesis discovered which leads to a lot more experiments and slows things down even more.  Let’s automate a process to prioritize the most important hypothesis first.

Here’s where our AI starts quoting Socrates. Is the pattern important because the programmer thinks it is or does the programmer think a pattern is important because it is important?  The first solution, the pattern is important because the programmer thinks it is, is obviously biased which we’re trying to avoid.  The second solution means the programmer cannot be trusted as the source for the heuristic to determine what patterns are important.  The machine must therefore create its own by experimenting with every pattern it finds to determine an unbiased heuristic.

What defines experimental success? Is a business model successful if it leads to short term profits at the expense of longer term success?  Is a business model that pays tomorrow at the cost of today’s success better?  Is a business model only a success if it works both in the short and the long term?  Is success defined by revenue, margin, business value or some combination?

That’s the rabbit hole. Automation and every solution I’ve heard presented, breaks down to some level of bias which skews the results towards an unacceptably high level of failure.

Why Am I Tilting At Windmills?

If we don’t make some progress towards answering the big questions, this becomes just another IT fad. As data scientists, we have an opportunity to take what we’ve started and build a discipline with legs.  We’re linked through our education and approach to academia and our value links us to business.  That’s a rare pipeline.  Showing the business value in decreasing the bias in reinforcement learning and unsupervised learning to improve the accuracy of prescriptive and predictive analytics is a big part of that.  I think it’s the first big question we face and a make or break moment for our profession.  We can take the hard road and work the solution or we can lower expectations.

I’m advocating for the hard road while I’m seeing a lot of colleagues working to lower expectations. I’m all for being realistic but a lot the initial projections for data science are realistic.  Data driven business model generation, real time marketing personalization, real time pricing, demand forecasting, decision modeling, etc. are all attainable goals.  I don’t see how backing away from what’s possible because we’ve encountered problems is part of the scientific or engineering approach.  We run towards problems not away from them, right?

If you look at what Google’s done with data, their approach and success drive my sentiments. They’ve been faced with the choice of lowering expectations or working on complex problems throughout their time in business.  They choose to work the hard problems around data collection, analysis and presentation.  Typically they’ve flown against those who don’t understand why they’re tilting at windmills like self-driving cars, drones, augmented reality and many others.  The results have built one of the most successful companies of our generation.  If you look at their competitors who have taken to lowering expectations like Bing or Yahoo, the results have been significantly less successful.

In the current business climate, the problems we walk away from are the opportunities others seize. Choosing to work the problem is deciding to take our opportunity.  So here’s data science’s moment; rise to the challenges or leave them for someone else.

But that’s just my bias. Yours is the one that counts.


How to Run a Data Science Experiment & Why It’s Critical To Big Data Success

The biggest jump in data science’s ROI comes when a business matures from correlation to causality based initiatives. I worked with a global retailer last year to improve their in store average sale by increasing average number of items.  We started by surveying sales associates who led their stores in these categories.  “What do you do to get the customer to buy more from you?”  As you can imagine, we got a wide variety of responses.

I knew we had a lot of noise and a little signal in the responses. If we had used correlation techniques we would have done something like select the most common responses and present, “86% of high performing sales associates use suggestive selling to increase their average sale.”  Data science is able to do a lot more than state the obvious.  Deep insights come from causal relationships.

So we experimented with the responses. We trained a variety of techniques and measured the results on individuals’ average sale and average number of items.  We found more noise.  Regional differences, differences between sales people, and training techniques all caused variations which blurred experimental results.  Hypothesis became increasingly granular and experiments became more controlled and precise.

That’s when we started discovering gold. Initiatives with names like, “Plan to Increase Lowest Performing 15% of Sales Associates in the US Southern Region’s Average Sale By 45%” came out of our findings.  Just over 90% of these initiatives have achieved or exceeded their goals.  The retailer has the skills in place now to assess what went wrong with the other roughly 10% of initiatives and further refine their understanding through additional experiments.

There’s value in this approach but for most of my clients, it’s the first time they’ve undertaken anything like this. With repetition, I’ve come to learn the patterns that lead to the best practices in data experiments.  It’ll come as no surprise that these patterns are what hard scientists have been preaching to their students for a very long time now.

Every Experiment Needs a Review Process

The experimental process needs oversight. There are too many business, ethical, privacy, bias, and domain concerns to not have multiple eyes on any experiment that a company undertakes.  There are so many ways for personal bias to creep into an experiment or for someone who’s well-meaning to do something unethical.  This has been my biggest takeaway from data science experiments.  Something will go wrong if experimentation is contained in a silo.

Streamline Everything

The faster your business can go from hypothesis generation to proving or refuting it, the faster your business will act on the insights and move on to the next one. The first few experiments will take a long time but don’t feel like that’s the norm.  Speed is key in business and data science experiments should get faster as the business gets more experience running them.  Data science alone is a competitive advantage today because only a few businesses have those capabilities.  As data science becomes more pervasive, the advantage will shift to speed and sophistication.

Use a 3 Phased Discovery Process

The first phase is detection. This is what statistical data scientists are really good at.  They find correlation between multiple elements hidden in massive data lakes.

The second phase is experimentation. Experimental data scientists use the discovery of correlation to generate a hypothesis and design an experiment that will prove or refute that hypothesis.  Then they run the experiment and analyze the results.

The third phase is application. An applied data scientist can take the experimental result and visualize it in a way that’s easily understood, meaningful and actionable.  They’re the connection between experimental results and ROI.

Typically an individual will have the skills to do a single phase with more senior data scientists able to do two phases.

Transparency Is Hard But Necessary

Make sure everyone knows what’s going on. Specifics are proprietary so those shouldn’t be disclosed.  The fact that data is being gathered and experiments run needs to be disclosed to all involved.  If anyone has an issue with that, there needs to be a process in place to omit them from the data gathering and experimental process.

Even Proven Theories Get Overturned About 10% to 20% of the Time

It happens in science and it will happen in business. It should be no higher than 20% of the time or something is wrong with the experimental process.  If no thesis is overturned or subsequently refined, that’s a problem too.

Experimentation – A Sign of Growing Data Science Maturity

Companies start with data science running strictly correlation techniques.  These are the ones best supported by current software offerings and data science skills.  As these capabilities mature the correlations move from obvious to very obscure.  However, the value of correlation is limited and the business needs typically outgrow correlation within a couple of years.  That’s because correlation is descriptive and the business needs prescriptive and predictive.

Experimentation is the next step and the insights follow a similar trajectory; starting out by yielding obvious insights and quickly migrating to obscure insights. The value of these obscure insights isn’t as limited.  It leads to a more granular understanding of customer preferences, competitors’ actions, employee productivity, and investor sentiment among many others.

These types of granular insights lead to models that allow a business to understand the most likely impact of their actions as well as understand the full spectrum of available choices. When a company is able to see beyond the obvious choices their people become more innovative and creative.  When a company is able to see beyond the obvious impacts of their decisions their people become more strategic.  The hypothesis of data science is this shift towards creativity and strategy will yield better business outcomes.  So far, the data looks promising to prove this hypothesis.

Big Data’s Big Pitfalls

There are a lot businesses getting into big data this year and even more in the planning stages for next year. Yours may be one of them.  I’ve seen firsthand the positive impact big data initiatives make on businesses.  The cost savings, revenue streams and competitive advantages are well evangelized.  The pitfalls are not.  Here are a few ways I’ve seen data initiatives go wrong.  Please add to this post by sharing your stories in the comments.

Small Data Packaged As Big Data

This is a common pitfall of any change; taking the old way of thinking and applying new tools. Big data is a new product rather than an incremental improvement on small data.  Where small datasets were able to tell a business that 80% of all customers… or 40% of all employees…, big data has the ability to be much more specific and granular.  It reveals insights like customer Ryan A. has a 52% likelihood of making a second purchase in the next 3 months and a 91% likelihood if we send him a special offer of free shipping.  To realize the potential of big data, the business needs to raise its expectations.

As the last example reveals, big data is also prescriptive. It shows a clear course of action in many cases where as small data typically requires significant interpretation to determine a plan of action.  Small data packaged as big data often leads to paralysis by analysis and conflicting conclusions.  Incomplete analysis shouldn’t be tolerated.

Big data insights reach conclusions about causality while small data focuses on correlation. When the two get confused in a presentation it leads to poor decision making.  Google used a corollary model for flu predictions.  It worked in the short term but failed publicly and catastrophically in the long term.  Fortunately no one was taking any actions based on the model but businesses often use corollary models to inform business strategy decisions with erratic results.

When I see data point correlation I use this example to show why they are logic traps. Over the last 200 years as the numbers of pirates have decreased, global temperatures have increased.


Based on these two data points shouldn’t we be spending more time fighting global warming by increasing the number of pirates worldwide? On its face, that’s ridiculous because we have prior knowledge telling us this conclusion should be dismissed.  What about if the two data points were number of products on a web page and average sale amount?  Those two sound plausibly linked when shown increasing on a graph together.  In reality it presents no more solid proof than pirates and global temperatures.

What correlation shows is cause for a hypothesis and justification for an experiment. Experimentation is a key tactic of big data strategy.  It allows us to establish a causal relationship between multiple variables.  That’s why we say big data reveals deep insights.  It reveals why something is happening rather than telling us something is happening and leaving the rest to our interpretation.  Again, the business needs to raise their expectations to realize the potential of big data.

The lesson from these stories is that initiatives need to go all in. A small data initiative needs to stay that way even with access to larger datasets and big data analytical tools.  A big data initiative needs to think in terms of large datasets and big data tools.  A mixture leads to failures.  They also show that the business needs to expect more from big data.  Big data tools and datasets should lead to better quality analytics.

Making the Jump from Algorithmic To Heuristic

Algorithms are theories / equations that help us make predictions under certainty. That means we know all the variables, options, probabilities and outcomes.  It’s the low hanging fruit of big data and so it’s what gets done first.

As the business becomes more accustomed to data enabling decisions, the questions being asked of data become more complex. That leads to a greater number of increasingly complex algorithms.  These take significant skill to create and implement as well as greater horsepower to run.  They also make visualization increasingly difficult.

As a result, job descriptions for data scientists become increasingly hard to fill because they require in depth knowledge of complex scientific and statistical principals coupled with high end programming skills. Costs rise as hardware needs increase and the company starts to produce customized solutions to their specific business needs.  This is the big data maturity chasm and it’s a result of the law of diminishing returns.

An algorithmic approach has significant limitations and needs to be replaced early on in the adoption of big data with a heuristic approach. Heuristics, simply put, are what allow us as people to recognize patterns.  Heuristics allow machines to recognize obscure patterns in very large sets of data.  These deeper patterns are the big insights of big data.  Without heuristics businesses tend to abandon big data without really getting what they paid for.

Complexity, Uncertainty & the Irrational

If no one gets it, no one will use it. That’s true of a lot of technology.  With big data, complexity is inherent and that scares people away.  Big data is pigeon holed as a marketing only tool or not ready for prime time because the complexity escapes from the data science group.  As soon as a business user sees a differential equation their perception of the tool changes and that’s a difficult thing to undo.  It slows adoption of big data in a lot of companies.

Uncertainty has much the same effect on business users. Not knowing what big data can do and what the overall strategy for big data is within the company makes it hard to get a handle on how big data will impact them specifically.  It’s hard to ask the right questions and propose initiatives that would benefit the organization.  Goals, a big data strategy and people explaining big data in business terms are all critical pieces to removing uncertainty.

Even groups that don’t benefit from big data need to be included. They don’t need a voice at the table but they do need a clear understanding of what’s happening.  I won’t bore you with the war stories but I’ve seen some very irrational reactions to being left in the dark about the business’s big data strategy and goals.  Those reactions are well worth the few hours of education required to avoid them.

Data Governance

Many big data pitfalls revolve around data governance. Data governance covers a range of topics:

  • Data Collection
  • Data Integrity or Data Quality
  • Privacy
  • Security
  • Ethics and Compliance

Ignoring these issues creates hurdles the business will have to face later. Facebook has recently generated some backlash for their data experiments.  Target and other retailers are dealing with the costs of customer data breaches.  Google frequently deals with concerns stemming from their wide ranging collection and use of personal data.

In the best case scenario, poor data governance still increases the cost of big data. In the case of data quality issues it can cause a business to stop trusting the data and all the reports, insights and analytics generated from that data.  Privacy, security and ethical issues can cause customers to lose faith in the brand and business.

A business needs policies and processes to manage its big data. Collection and usage policies need to be well communicated to customers and consistent with other customer brand experiences.  Just like any other product, data needs quality testing regimes to insure it meets the expectations of those using it.  These aren’t complicated steps in and of themselves but the combination of all the issues surrounding data governance usually lead to something being left out.  An oversight team or program manager can prevent that pitfall.

Awareness Is Most of the Solution

Big data is no longer a wild, wild west type of technology. It’s matured and stabilized quickly.  Trial and error are no longer necessary realities of being an early adopter.  There are great products and a lot of expertise available to help businesses realize the promise of big data in a well-managed way.

However, as with any other technology rollout, it is not problem free. Knowing what the pitfalls are allows for better planning and a smoother implementation.  That’s key for successful initiatives and companywide adoption.

What Can Big Data Do For Your Pricing Strategy?

I call people involved with creating pricing strategies margin magicians. It sounds so much better than pricing strategist and it’s a lot closer to the truth.  The magic is a balancing act between margins and volume, supply and demand, competition and competitive advantage.  Data already plays a big role in determining price and has for a very long time.  When I talk to teams about data enabled pricing they come to the conversation saying, “We already do that.”

Where Small Data Can Take Pricing Strategy

Your business probably does too. The business has talked to customers and found that there is a spread of prices they’re willing to pay as well as a spread of prices being charged.  Senior leaders have asked the question, why is that?  Why are some customers willing to spend more and some willing to spend less?  You probably have data on that too.  Brand, quality, features and other common themes rise to the top.  The experiments you’ve run have revealed behavioral trends too.  Things like categorical thinking come up and influence how people perceive price.

Let’s ask the deeper question about why customers pay different prices. It’s a question about how we make buying decisions.  Let’s take a customer at random.  They’re looking to buy a product in a competitive market so they have options.  Many pricing strategies hold that, all things being equal about the products, the lowest price wins.  If products are differentiated from each other, then the one which is the best fit for the customer’s need at the lowest price wins.

That’s because most pricing strategies assume customers to be rational decision makers which could not be farther from the truth. Rational decision making only happens when the customer knows all possible options (decision outcome pairs in the decision space) and has enough information to be certain about how much value (probability and loss or utility) they’ll get from each option.  Does that describe many/any of your customers?

Customers make decisions under uncertainty. As a result two customers with identical product needs can have two completely different prices that they’re willing to pay.  There’s a solution for that called price discrimination.  We offer the same good at different prices to different customer groups.  Since customers talk to each other we’ve also had to come up with clever ways to justify price discrimination.  A plane ticket usually costs more closer to the day of the flight than it does two weeks in advance.  Clothes go on sale at the end of the season or during holidays.  Buy smaller quantities and you’ll pay more than someone buying in bulk.  Better negotiators get a better price.

Big Data Starts To Add Value with 4 Basic Insights

If you look at enough datasets and experiments about customer buying behaviors in relationship to price you’ll discover just how deep the irrational decision making runs. To make a long presentation short, customers pay whatever a company can convince them to up to a budgetary maximum.  That’s a big data insight about pricing and intuitively you’ve always known that.  The data demonstrates that pricing doesn’t operate alone in customer decision making.

To get the most out of your data you have to ask the right questions. With respect to pricing strategy the right question is how do I use price to maximize the value I get from each customer?  Without big data many pricing strategies look at this question from the perspective of a single sale.

The better metric is Customer Lifetime Value (CLV) or the total value of each customer over their entire relationship with the business. Before you think I’ve brought you a ridiculously difficult problem to solve check out this free CLV calculator from the folks at Harvard.  All you need is some basic info about your customer buying habits and retention rates.

Thinking in terms of CLV is leading to some very innovative and lucrative pricing strategies. If you look at how Google and Amazon price, you’re looking at some of the most sophisticated pricing strategies out there.  They are driven by large datasets and are aimed at increasing CLV.  That leads to the second big data insight about pricing.  Companies can use pricing in tandem with product, brand and marketing strategies to increase CLV.  Again, intuitively you probably already knew that.

The key next step is gaining a deeper understanding of the customer. Using analytics to learn then predict how likely the customer is to be loyal, how many products they’re likely to buy and how much a business can do to drive both behaviors are all critical parts of a data driven pricing strategy.  Companies like Sephora drive 80% of all sales through their customer loyalty system and have amassed a significant dataset on customer buying behaviors.  Casinos do much the same thing with some casinos logging 90% of all play through their loyalty system.  Grocery stores have a high percentage of spend through their rewards cards.  CRM is also big in the B2B space, providing the same data for analysis.  The result is a picture of CLV that allows businesses to tailor pricing to drive loyalty/repeat spending and maximize margins on infrequent or one time customers.

The third big data insight comes from a fairly sophisticated layering of loyalty, pricing and marketing data from these datasets. Brand engagement is a significantly higher driver of customer loyalty than price.  Loyalty systems that build a connection to customers through personalized engagement and experiences, like Sephora’s, have much higher CLVs and retention rates than those using discounts.  To make a long data presentation short (if you want the long version email me), discounting frequently leads to the opposite of the desired behavior.

The only thing companies do by indiscriminately lowering prices is train customers to game the system for lower prices. All a competitor has to do to lure those customers away is offer a lower price and the lost margins meant to drive loyalty have resulting in exactly the opposite behavior.  Retail has learned this lesson the hard way and is now working its way back to a more profitable business model.

I worked with a manufacturer who had started running discounts at the end of each quarter to drive additional volume. It was successful so the company continued the practice for two years before they realized a problem.  Customers became trained to hold their orders until the end of quarter.  Margins dropped significantly so the discounts were stopped.  Competitors continued to offer their discount programs and customers were lost.  I was told by a Macy’s store manager in the early 2000’s that they’d trained their customers to wait for the sale and they didn’t know how to reverse that trend without losing customers.  It’s a problem that spans across markets.

This goes back to the first big data insight.  Customers pay whatever a company can convince them to up to a budgetary maximum.  With discounting a business is convincing a customer to pay a lower price.  Tell a customer that a $39.99 product is on sale for $24.99 enough times and they believe the product is only worth $24.99.  That leads to the fourth big data insight.  Only discount when it adds to the customer’s engagement with the brand.

Starbucks is a good example of this. Through their loyalty program customers buy a certain number of drinks and then get one free.  Rather than sending the message of this drink is worth $0, it says, “Thanks for your business. Here’s how much we appreciated it.”  Apple is another good example with the iPhone.  When a new model comes out, the old model is discounted.  That opens the older model to a new market, driving volume but it tells customers with the old phone something too.  Your phone is not as valuable as the new one.  That perceived loss of prestige drives upgrades in a couple of their customer segments.

We’ve come full circle, returning to the deep dive into customer decision making. If we were rational decision makers, utility would reign supreme over our decision making process.  That would put price at the forefront of the process.  However we’re not rational and our perceptions, beliefs and biases play heavily into our decisions.  A strong brand connection plays more into the equation than pricing.

Big Data Can Do a Lot More For Pricing

Big data is most effective when insights are layered and they begin to reveal patterns that were previously unknown to the business. Once it’s been revealed, pricing strategy’s role in maximizing CLV and enabling brand engagement is better understood.  The pitfalls of discounting are easier to avoid.  The focus can shift from fairly obvious insights to discovering new patterns in customer segments.  This is where an algorithmic approach breaks down and heuristics become a lot more successful.

Heuristics allow new patterns to be recognized by the machine. The first four insights sound like common sense because intuitively we’re able to come to the conclusions ourselves through experience and anecdotes.  Those types of insights are what algorithms are able to reveal.  Sift through enough datasets and some basic patterns become obvious.  There are other patterns in the data that aren’t so obvious.  Detecting those require heuristic methods that are able to detect subtle patterns in very large datasets.

The goal moves from categorizing customers after a few interactions to categorizing the customer during their first; from broad categories to increasingly granular ones. These categorizations when combined with our four basic insights about pricing allow for real time pricing strategies that are effective across multiple channels.  Point strategies maximize margins while also enabling loyalty and repeat purchases.  Heuristics allow these pricing strategies to be personalized by customer category with increasing granularity.

Longer term strategies can also be created. Customers change over time.  Businesses grow or shrink and people become more/less affluent or sophisticated.  From a CLV perspective it’s inefficient to allow customers to leave a brand because of these changes.  Tiered pricing and product lines are one method big data reveals is effective in preventing these types of departures.  Toyota doesn’t want to lose high end customers because they don’t have high end cars so they created the Lexus line.  Nissan has Infinity and Honda has Acura with the same goal.  Mercedes wants to expand its reach to younger buyers and has introduced new car lines to accomplish this goal.  Those buyers now begin the loyalty cycle earlier and CLVs grow.  The combination of brand loyalty and tiered product/pricing strategies also becomes more granular, again allowing for greater personalization.

This increase in personalization goes a long way towards creating that connection between customer and brand that I discussed earlier. The goal of big data enabled pricing is increasing levels of personalization that drive increasing levels of brand loyalty and higher CLVs.  As the business’s proficiency with big data and advanced analytics grows, the categorizations become faster, more accurate and more granular.

The Bottom Line: Why Use Big Data For Pricing Strategy?

There are two drivers for big data and heuristic enabled pricing: customer preferences and competitive pressures. As customer loyalty systems become more prevalent and increasingly sophisticated, customers are beginning to expect higher levels of personalization.  They’re expecting pricing to match their levels of loyalty.  Progressive recognized this trend a few years ago.  Their loyalty system gives pricing and perks to new customers based on their loyalty to their last insurance company.  The message is clear, “If your last company didn’t appreciate your loyalty come to us and we will from day 1.”

This is the second driver for data enabled pricing. Competitors are luring customers away with data enabled pricing strategies.  This will drive even the staunchest holdouts to adopt the methodology.  Those businesses that don’t will find it difficult to compete in the next two to three years.  The bottom line is big data enabled pricing is a matter of business differentiation in the short term and business survival in the long run.