Analytics White Paper

Little Data

You are not feeling well, so you visit your friendly family doctor. He puts you in a new electronic scanner and generates 28 trillion measurements of your temperature all over the surface of your body.

He then saves all of these big-data measurements and, using advanced statistical algorithms and supercomputers, announces that your temperature is 98.6 degrees Fahrenheit. What a relief! Big data to the rescue.

The Bandwagon

As the “big data bandwagon” picks up momentum, consultants, professors, conference organizers, authors, magazines, blogs, software firms, pundits, crooks, private equity firms, and computer hardware manufacturers clamor to get aboard. Rarely has a bandwagon attracted so much attention or so many passengers.

The basic premises of big data appear to be that:

• More data are always better than less data.
• Volume, variety, and velocity of data create new sources of potential knowledge and prescience.
• With big data, all questions can be answered; the “why” will finally be revealed to the human race, and the future can be accurately predicted.

Is big data an accurate picture of the future, or is it simply a mirage shimmering in the distant desert heat? Is it the pathway to ultimate truth, or is it only a bandwagon of exaggerated promises and illusory dreams?

The truth is that the solution to marketing and business problems—and the identification of strategic opportunities—often lies in the realm of little data, not big data. You don’t have to boil the ocean to determine its salt content. You don’t have to eat the whole steer to know it’s tough. Most times a doctor only needs to take your temperature with a $20 thermometer, not a $10 million scanning machine. The great opportunity is not more data faster, but better data—and better analytics.

The Limits of Data

The preponderance of business data—indeed, all data—in the world is historical data, or “tracking” data, such as financial data, sales data, customer behavioral data, weather data, and inventory data. Virtually all data tend to be backward-looking, analogous to looking in the rearview mirror to steer a car forward.

No matter how current or instantaneous data are (i.e., the velocity) or the sheer amount of data, the backward-looking bias is an omnipresent limitation. We might see trends in that data that give us an inkling of the near-term future, and we might be able to find out what has driven a firm’s success in the past, but most historical data are of limited value in predicting the future. Another limitation of most business data is noise. All data consist of two main components:

• Noise—Random variations, static, aberrations, errors, missing values, and distortions.
• Signal—Relevant, valid information in a recognizable, reliable pattern.

The human race has spent the better part of the last two million years trying to separate the signal from the noise, and for the vast majority of that time, our ancestors conjured up myths and magic to explain their experiences and observations. It is a pattern that the perceptive can still see at work in corporate America and Washington, D.C. The scientific thinking and scientific systems that have evolved over the past 5,000 years represent a revolution, a great leap forward, in our ability to separate the signal from the noise.

Inflationary distortion is another major limitation of business data. Inflationary and deflationary forces vary by product category, by industries, and by time, and rarely does anyone have the measures or means to correct for these distortions. We can think of the changing value of the dollar and variations in currency exchange rates as other types of pervasive noise.

Data You Can Trust

Often, without thinking, we tend to see all data as equal, but rarely is this true. The corporate world is awash in data. It streams in from all directions 24 hours a day, and the data deluge continues to worsen. Most large companies today have at least 100 to 200 times as much data in their collective databases as they had 30 years ago. Has all this data helped them make better decisions? Would (or could) anyone argue that today’s corporations are making better decisions than they did 30 years ago?

I would argue that most major corporations are making poorer decisions now, compared to 30 years ago, despite the rising tide of data. In fact, the growing flood of data is part of the problem. More data often means more confusion. Which data are correct? What data can be trusted?

Here’s a point of view on the trustworthiness of various types of data, ranked from most trustworthy to least trustworthy:

Objectivity

Social and political pressures within a large organization tend to undermine the objectivity required for determining the trustworthiness of business data and accurately determining its meaning and import. The trend toward low-cost, do-ityourself surveys by the inexperienced in corporate America is creating a growing number of bad decisions and big mistakes, based upon survey data of questionable objectivity, accuracy, and validity.

The normal controls and safeguards practiced by independent research companies are rarely followed by do-ityourself research functions in larger companies (and these internal groups often use copyrighted questionnaires “borrowed” from research companies—which expose large companies to unknown legal risks). The objectivity of professional, independent, experienced third parties can greatly improve the quality of data and its interpretation. (Confession: The author might benefit financially if corporate America acted on the assertions in this paragraph).

Little Data

Corporate decision-makers often would be better served if they relied on tried-and-true tools and systems from the world of little data, rather than illusions from big data. Sampling theory teaches that if the sample is random, one can measure the behavior or mood of the whole by talking to very few people.

A sample of 1,500 is sufficient to predict who will win a presidential election. A sample of 200 to 300 respondents is generally sufficient to predict how much the whole population will like a new product or service. A sample of 200 users can test a new peanut butter in-home for a week, and from this it, can be precisely determined if the product is optimal and what its market share will be once introduced. These are examples of little data.

Survey research is relatively inexpensive, yet very accurate, because professional researchers know the source, stimulus, context, and history—and have tried-and-true measuring instruments, normative data, quality assurance, and controls. Marketing research can be designed to be forward-looking and predictive, rather than backward-looking. Experienced researchers can create alternative futures and measure the relative appeal of the differing visions of the future. These professional researchers can predict the sales volume of new products within narrow tolerances, based on survey research.

They can optimize the formulation of a new product via product testing. They can accurately predict the effectiveness of new commercials long before they air. They can measure the size and composition of an industry or category with amazing precision, based solely on scientific sampling and surveys. They can use qualitative research methods (ethnography, depth interviews, focus groups, and online forums) to discover unmet needs and hidden dreams that can become templates for new product development (long before a trend in anyone’s tracking data suggests an opportunity). These researchers can optimize products, services, ads, and store designs to maximize sales, all based on choice-modeling experiments within surveys.

All of this research is based on little data. The data are derived from random sampling, carefully controlled experiments, and/or scientific surveys. The sample and sampling error are known; the stimulus is known; the questions are known; the context is understood; and the meaning of the answers is known. Despite the marketing hoopla and gurus touting big data, little data often provides a more accurate basis for sound corporate decision-making.