With Big Data - Less Is More




Making Sense of your Data and applying Smart Analytics

It’s a familiar experience - scrolling endlessly through thumbnails representing Netflix’s hundreds of hours of recommended viewings tailored “for you” on its homepage, only to bypass each one with relative disinterest. Even with behind-the-scenes algorithms hard at work trying to predict your preferences, the list of recommendations presented to you is often paralyzing. How can it be that despite Netflix spending millions of dollars each year on understanding and predicting what you would like to watch next, so many options on your home screen fail to hone in on the one standout answer to a seemingly simple question — “What should I watch right now?”


The Netflix problem is just one of many common examples of so-called smart algorithms offering choice overload. As it turns out, when faced with numerous choices of equivalent value, your brain experiences a miniature logjam as it tries to sort through the relative value of each choice in order to make a deterministic decision. This choice overload phenomenon appears in your life more and more these days in the ever-increasing “Information Overload” era we live in. It’s the reason you have a hard time deciding on a restaurant when driving down Main Street looking for a bite to eat or why nothing in the refrigerator seems appealing after a recent grocery run.


Beneath the surface of your business, a similar phenomenon takes place thousands of times a day, impeding decision-making. Oceans of raw data flow through your daily operations waiting to be parsed, analyzed, polished, and ultimately put to use to enhance your bottom line results. Big Data is the backbone of businesses looking to better understand consumer behavior, successful marketing strategies, and how to improve inefficient processes — so how can it be regarded as a hindrance?



For Starters, We Ask the Wrong Questions


Amassing an impressive amount of data takes work. In fact, it takes so much work that it feels like, after the massive processing power and time necessary to accumulate data, the rest of the road should be an easy one.


This is among Big Data’s biggest misconceptions. Simply having more data does not mean all that data is decipherable and certainly doesn’t mean that it is easier for us to synthesize and understand. It may not even provide better decision-making, recommendations, or strategies. Data does your business no good if you spend time and effort collecting it only to ask it the wrong questions, or if it consistently makes your understanding of the underpinning of your business much more complicated instead of much simpler.


Did you notice a trend in the examples of choice overload above? They all began with a bad question: What should I watch? What should I eat? What groceries should I buy? These questions don’t serve to narrow down the data set from the overwhelming list of options available. That’s where smart and simple data that drives an explicit and immediate decision comes to the rescue. Smart data asks the right questions: Which new release did I miss in theaters that I should watch right now? Where can I find the best steak on this side of town? Should I cook something tonight or finish yesterday’s leftovers?


Filtering available data down to 1 to 3 highly deterministic options allows you to process it, use it, and even discover additional questions you may have failed to ask of it in the first place. So why do we work so hard on presenting hundreds of choices? If you want more accurate results from your data collection efforts, you must consider how to properly process, filter, and present the output so that only top-level answers and high-probability recommendations remain. In short — know where to draw the line between substance and noise and keep it (very) simple.

Big Data’s Usefulness Relies Entirely On How Well You Manage It


If your business is fortunate enough to employ the proper means for Big Data collection, processing, and analytics, you’ll find that many decisions (particularly in your marketing efforts) still risk losing a customer or transaction if not addressed by an action in real-time. Allowing Big Data to inform those decisions can save the transaction if applied properly.


However, vast amounts of data must rely on quality sources to be effective and relies wholly on the proper management and application of the information available to resolve specific issues or concerns. Asking the wrong questions of the data, cutting corners, or waiting too long for a response from Big Data as it filters out irrelevant information may create a disconnect between your business and its customers.


Used correctly, Big Data can generate a profound understanding of your customer, but also has its limitations as a solution to a short-term marketing trend or sudden problem with customer service. Smart data can dive in and address more specific questions without going through dozens of hands. In essence, smart data and Big Data work together to inform a company how to be consumer-focused and how best to earn the loyalty of customers who feel comfortable trusting that your business understands their needs.

Big Data Can Retrieve Insights From Unexpected Places


Earlier this year, in Indianapolis, 330 college football players tried to impress a crowd of NFL scouts and team officials as part of the critical pre-draft process. The future success (or catastrophic failure) of NFL franchises, each worth billions, hinges on these scouts conducting accurate evaluations.


The best way to get an accurate sense of each player’s ability is for each scout to sit down and view every second of college tape available on every player, diligently noting every outcome of every play and scenario. In practice, such a process the month before the draft would require more man-hours than humanly possible — thus, the NFL combine. Drills, measurements, and 40-meter dash times don’t replace a player’s tape entirely, but the combine’s small battery of tests serves to cut through a sea of unimportant variables to at least collect a sample of a player’s ability.


But what if all those hours of tape would have revealed something they missed? That’s where Big Data steps in. Big Data can go collect the information an untold number of man-hours could not and, when processed correctly, provide important details from deep within the weed of available data previously not possible to access.


Similarly, in business, extensive data collection and analysis can and should inform decision-making. Though some situations benefit from emphasizing decisiveness with a sample of data over wading through unimportant information, Big Data can go beyond the sample and provide exact, precise insight into scenarios sample data can never provide.


Cultivating a system in which Big Data addresses consumers’ wants with precision will always stand out more to customers than any data which, while robust, lacks substance.

Too Wide a Scope Means Learning Nothing at All


For years, companies tried a multitude of methods to gauge brand loyalty and customer satisfaction. Finally, after conducting extensive research through numerous questionnaires, thousands of customers and half a dozen separate fields, data uncovered one survey question directly correlated with the purchase and referral behavior of customers. Instead of asking about a given customer’s satisfaction with a purchase or how easy the checkout process was, the survey asked specifically about brand loyalty — customers answered on a scale of 0 to 10 how likely they would be to recommend a company to a friend or colleague, and the basis of today’s Net Promoter Score was born.


The lesson learned? Too much information at once can distract from the big picture. Getting lost in minute details of the customer experience once appeared a worthwhile exercise — after all, understanding customers was the ultimate goal. But NPS represented something all Big Data usage strives for, which is determining the correct level of detail for a given problem.


Tailoring the right amount of data based on situational factors is the challenge facing all those who seek to use it, and the benefit to those who know-how. At its center, this is the balancing act each organization must perform to leverage data efficiently, determining which questions to ask of it, how to manage the information gained in response, and how to precisely address a challenge with the appropriate amount of detail to keep Big Data on your side instead of getting lost in its vast sea of noise. Perfecting this balance will prove transformative to your business and change Big Data from a hindrance to a powerful ally — all the more reason for business leaders everywhere to learn its uses, test its abilities, and ultimately put Big Data to work.


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