Data > Information > Knowledge: The Sequence of Value

December 7, 2021

Adam Audette, SVP, SEO Business Leader

In SEO and data science, and any venture leveraging or relying on data, the progressive sequence of value from data increases exponentially as knowledge is gleaned from raw amounts of information.

My dad John Audette, one of the original Internet pioneers in digital marketing, is famous for coming up with great quotes. One of my favorites is his theory (well before any of this was found via a random Google search), that data is only really useful in how it expands out into actionable or insightful knowledge. This continuum looks like a linear sequence as follows:


While it’s not really neat and linear, there is a sequence to the process of gleaning knowledge from data. We’ll break down each of these specific headings in detail in the following piece. But before that, I think it’s important to qualify a few things.

1. The idea that we’ll ever get to “Wisdom” is probably ridiculous, at least in digital marketing and SEO; perhaps there’s a field out there somewhere that relies on data to achieve wisdom, but I haven’t seen it.

2. Everyone, all the time, uses data. Most everyone all the time gets information from these data. But almost no one, ever, gets to the level of knowledge from an initial data set.

3. This continuum isn’t necessarily static or linear; one doesn’t go from a single stage of Data to Information only and in that order. It’s a framework to understand, not a prescriptive approach. That means data, information, and knowledge are continually shared parts of the whole process of learning. Leveraging data to build ideas, glean information and arrive at knowledgeable conclusions is the art and science of SEO (and pretty much every other field, especially in digital).

From a personal level, I believe so much in this idea of Data > Information > Knowledge that I even tapped into it to create my title at a previous company back in 2013. To say it was borderline ridiculous to have the title, Chief Knowledge Officer is an understatement, and by now, you hopefully know my position on roles being more important than titles. Putting the self-conceit implied by a title like that aside, it was a great ice breaker and talking point, but otherwise had no actual purpose (and didn’t at all reflect my functional role). But it was fun. For once, I actually secured a title that reflected the Data > Information > Knowledge continuum.

Breaking Down the Inputs

Let’s go through each of these individually and see if we can define them in the context of this value continuum.

1. Data: nobody needs me to define this word. In the context of the value continuum, data is where we start. We need data in order to do anything. But data isn’t enough. It’s only a collection of stuff, disorganized, perhaps dense and maybe massive. It’s messy. It’s like a huge pile of paper reaching up to the sky. How are we going to get information out of this?

2. Information: the ability to arrive at useful information from the analysis of raw data pretty much encapsulates 90% of the work being done today. It took a long time to get here, too: in the ‘80s and ‘90s massively sized servers housed relatively minuscule sets of data, with barely any useful information extracted. The ‘00s have certainly demonstrated a profound evolutionary change in this regard, and information is always gleaned from data, otherwise, what’s the point? However, we haven’t consistently gotten beyond that threshold.

3. Knowledge: this is where we want to be. To be honest, I doubt even 10% of the work done with data helps people arrive at the acquisition of knowledge. Usually, we get information. But for those that are able to step across the chasm that exists between data and storytelling, there is tremendous value to be gathered from the exercise. The future is all about storytelling using massive data sets. And that’s essentially the art and science of extracting insights (knowledge) from data.

4. Wisdom? Nah, it’s borderline preposterous to think we can arrive at wisdom through the D>I>K>W continuum. But maybe someday. For now, I think knowledge is enough!

Why This Matters

When you think about it, becoming more knowledgeable from robust data is really the primary aim of SEO, and more broadly, digital marketing. Where does one start when they want to explore the opportunity of a given industry, vertical or competitive set? The data. Where does one go when they want to chart the opportunity of a site? Data. Where does one go when they want to calculate the ROI of a given strategy or tactic? … Data … every time.

But the data isn’t enough. There are mountains of data available to us today. The problem is no longer access to data, although it can be argued access to high-quality data is a different story. The problem is leveraging these data to glean insights. It’s data that provides the knowledge to achieve all of the examples I listed above. And yes, most of the time it’s probably gathering data to provide more information around a given problem. But sometimes, and if you’re working in this area you’re truly at the cutting edge of the value chain, it’s using data to provide actual knowledge about a given problem. In a sense, that’s what great data science, business intelligence and SEO is: using data and technology to gather knowledge.

Almost no one is doing this!

Quick Aside: Data Fidelity and SEO

It must be acknowledged that the data available to the SEO industry is pretty shoddy. Sure, we have analytics. But the sessions are masked by their query. Sure, we get the landing page, but often analytics suites confuse or roll up direct, referral, social and organic search into the same black box.

So we turn to Google Search Console (or Bing), and we have much better data. It’s primary data, not a third-party scraper, but it’s often rounded and averaged. We get query-level data, but the date periods lag. And the list of problems go on.

So we turn to third-party data aggregators (read: scrapers) such as Ahrefs, or SEMRush, or Brightedge, Conductor, Screaming Frog, Botify, OnCrawl, DeepCrawl, Moz, etc. There are too many tools SEOs need to use, with too low data quality. We are blessed with tools, but cursed with too many, and overwhelmed by their lack of quality and precision.


There has never been a better time to be a data scientist. The world (and brands) are moving further right on the Data > Information > Knowledge continuum, and they need the best people and companies to help them on the journey. For those companies and people that exhibit true skill in the art and science of data storytelling, there is truly more demand than could ever be satisfied. As an industry, SEO is just growing up. We’ve gone from the affiliate marketer “smart person with a computer” SEO Wild West, to a fully industrialized and enterprise channel. Digital marketing now accepts SEO as not only a viable strategy, but even a critical one. The ability to glean knowledge from raw data represents a competitive advantage. Companies that can do this will go farther, faster, and see quicker time to results than the rest of the pack.

Data > Information > Knowledge: where are you on the value continuum?

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