Truth-Seeking in The 21st Century, Or What 4 Monks Can Teach You About Big Data

About six months ago, we re-launched our blog under the a new masthead: Truth in Data. But, Truth in Data is destined to become more than a blog; it is an important conversation that is just getting started, it’s a concept that affects all of us, and it’s something you’ll be hearing more about in the coming weeks as we launch a new newsletter and announce a thought-provoking event around the concept of finding the Truth in Data. 

With that said, I’d like to explain what we mean when we say, “Truth in Data.”

We all know the FTC regulates the Truth in Advertising laws that protect consumers from false advertising that would lead them to purchase products based on untrue claims such as Taco Bell’s “seasoned beef” that contained no actual seasoning or other, more potentially-damaging claims. There is also the Truth in Lending Act that protects consumers from inaccurate and unfair credit billing and credit card practices. And while Truth in Data legislation may be on its way to help our children’s children manage the overwhelming tidal waves of data that affect our decisions, our business and our government every day, we believe right now is an important time in history to bolster awareness and education about the risks and the rewards of relying on data for so many things.

So what’s the big deal? Isn’t data inherently truthful on its own accord? 

While it is tempting to assume that data delivers an objective truth to which we can all agree, I learned in the 4th grade (while sneaking a little post-bedtime TV) from the wise Benny Hill not to assume anything.

“Objective” data can be as misleading as any outrageous advertisement claiming to enlarge (or reduce) some body part. Grappling with the “truth” in “data” reminds me of an ancient parable about four blind monks who came upon an elephant in the jungle. The first monk touched the trunk and stated, “Elephants are like serpents.” The second monk touched the tail and disagreed, “No, elephants are like a brush.” The third says, “No, no, elephants are like a pillar,” as he touched the leg, while the fourth felt the elephant’s side and argued elephants are like walls. Their argument escalated until they decide to listen to one another and collaborate to find out the full truth about what an elephant is. They were calm until a sighted man walked by and blew their minds, telling them they were all blind. 

Now, replace that big elephant with big data – your customer data to be more specific – and replace those blind monks with your IT, marketing, product and sales teams. Everyone sees a different part of the customer, and they are correct to some degree about what they see. But they may be missing the big picture … or worse, they might not even know that they are blind.

In our “infographic of the day,” dashboard-monitoring world where we gobble data through all of our available senses at an alarming rate, it’s important to realize that when data is available, there might be more to the story than what appears. It is up to you – and me – to seek out the truth.

As a truth in data seeker, you need to be aware of obvious, yet potentially misleading things such as:

  1. Source(s) of the Data: Are the individuals you are collecting data from representative of the customers you are seeking to build better relationships with? And if they are, is your methodology affecting the outcome? As Chris Goward from points out in this cartoon, “The act of observing a thing changes that thing. When people know they’re being observed, they may be more motivated to complete the action.” This phenomenon is known as The Hawthorne Effect.
  2. Sample Size: If your sample sizes are small, certain qualitative data could still be valuable. Keep in mind however, that some insights – such as percentages – may be meaningless.
  3. Visual Presentation: These graphs display identical data. If you fully-understand the range represented on the y-axis, this may not be a problem. However, if you are comparing various graphs that don’t share the same range, this can cause a lot of confusion.
  4. Logical Meaning: “Statistics are a funny thing — it’s entirely possible for them to be technically true and yet utterly unhelpful. Be sure to find the data that’s sensible.” says Daniel Dannenberg on the Vertical Measures blog. Misleading data can be problematic no doubt, but it isn’t always bad. Sometimes it just means that the greater truth is hiding beneath the topmost layer. That’s why it is so important to have access to all of your data in one unified place and have powerful tools that allow you to sort, segment, explore possibilities and discover truths on your own. All this is nothing new; Darrell Huff wrote How to Lie with Statistics in 1954, well before big data and infographics were popular Pinterest board titles. But today, it is more relevant than ever. We have so much data at our disposal, and the quality of our businesses and lives are at stake. It is up to us to collect and use it wisely.

Why care so much? 

We care because we think truth is important. And we think data is important. Luckily, they go together really well. Here’s a quick breakdown:


“People need the truth about the world in order to thrive. Truth is important. Believing what is not true is apt to spoil a person’s plans and may even cost him his life. Telling what is not true may result in legal and social penalties. Conversely, a dedicated pursuit of truth characterizes the good scientist, the good historian, and the good detective,” Encyclopedia Britannica articulates nicely.  


Data is one of the greatest natural resources in history. It has the power to define our future and determine our success. The vast range of potential value just waiting to be realized is one of the greatest and most exciting technological, personal and business challenges of our time.

Truth in Data

If we recognize data for what it is and remain vigilant in our pursuit of truth, we will discover meaningful insights, enable new ideas and improve lives. In contrast, resorting to blind faith in data would be a mistake. 

At Umbel, we strive to foster a healthy dialogue to help us all become responsible, data-aware citizens who approach data with respect as both a science and an art. As Nietzsche alludes to in the opening quote, it is less important that we “know” the “true” answer behind big data – if such a thing exists – but that we develop a system and an interpretation that works for us. 

Find the truth in your data, and it will set you free. Umbel can help