Insight – It’s the Content Marketer’s Dark Horse

by | Nov 8, 2016 | Competitive Analysis, Content Marketing, Marketing Technology

There, I said it. Insight is the dark horse of your content marketing strategy—and all your efforts.

Why? A dark horse, as defined by Google, is “a candidate or competitor about whom little is known but who unexpectedly wins or succeeds.” And this, my friend, is the reality staring back at you from your retina display: little is known about what insights truly are, yet they are the key to your content marketing success.

How and why are they important? Insights, by definition, are the deep understandings, discovering and realizations that transform the way we serve our customers, how we do business, and ultimately—how we innovate.

I’ll say it again, insights are the key to innovation, and innovation drives transformation.

What are the Keys to Insights? Big Data, Tools, and People.

It’s time to stop calling big data a buzzword; it is firmly implanted in the reality of our daily lives. Collecting mountains of data on everything imaginable from every possible source (and, ideally, using that information to make smarter business decisions) is common not just in tech but across all industries.

For all the talk of big data’s promise, from eliminating credit card fraud to treating cancer (more on this later), the challenge is execution. As marketers, we know that data by itself is pretty useless. As you probably know, it’s what we do with the data that counts. The insights we get from big data – the ‘aha’ moments when we use data to break the mold, discover new ways to do business or innovatively deliver the content our customers actually want and need – are what’s really important.

So how do we get from A to B, turning piles of overwhelming data into meaningful insights for better marketing? Increasingly, the answer is artificial intelligence or AI. The newest AI tools, like IBM’s much-talked-about Watson, can provide insights we’ve never before been able to access. They can analyze, cross-reference and interpret data at a speed humans just can’t while also providing personalized, human-like recommendations. They also get smarter the more we use them. 

Big Data 90 Percent of Data Created Last 2 Years

We’ll dive deeper into how AI is working to solve big data problems – and why all of this is exciting for digital and content managers everywhere—but first, let’s define big data and talk about some of the latest trends and strategies in big data analytics. 

What is Big Data?

Here’s an amazing stat: 90 percent of the data in the world today has been created in the last two years alone, according to IBM.

This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data.”

You’ll find all sorts of formal definitions for big data, but the most common involve the three Vs: volume, velocity, and variety. Technology research firm Gartner describes big data as “high-volume, high-velocity and/or high variety of information assets.” In other words, big data means a lot of data, data that is arriving quickly and/or data that is really varied in nature. 

Harvard Business Review also uses the three Vs to describe big data, and to separate the concept from the related science of analytics: “The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into a business advantage.”

HBR goes a little deeper into what each of the three Vs means with some stats and real-world examples:

  • Volume – More data cross the internet every second than was stored in the entire internet just 20 years ago. For instance, it is estimated that Walmart collects more than 2.5 petabytes of data every hour from its customer transactions. A petabyte is the equivalent of about 20 million filing cabinets’ worth of text. 
  • Velocity – Real-time or nearly real-time information makes it possible for a company to be much more agile than its competitors. A group at the MIT Media Lab used location data from mobile phones to infer how many people were in Macy’s parking lots on Black Friday, making it possible to estimate the retailer’s sales that day even before Macy’s itself. 
  • Variety – Big data takes the form of messages, updates, and images posted to social networks; readings from sensors; GPS signals from cell phones, and more. Mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations. 


So…why is Big Data Important?

As Weatherhead University Professor Gary King told Harvard Magazine, it’s not the quantity of data that is revolutionary.

“The big data revolution is that now we can do something with the data.” 

The revolution lies in improvements to statistical and computational methods such as algorithms. Kings goes on to tell the magazine, “One colleague, faced with a mountain of data, figured out that he would need a $2-million computer to analyze it. Instead, King and his graduate students came up with an algorithm within two hours that would do the same thing in 20 minutes.” This is true digital transformation.

The article, “Why Big Data is a Big Deal,” pointed out that new ways of linking data sets have played a large role in generating new business insights. The tools being developed now have applications across all industries, from medicine to astronomy. The technology is bridging the gap between big data and using that data to find real-world solutions to problems. 

The process of harnessing big data and putting it to work is often called big data analytics. Big data analytics help organizations leverage the right data to accurately establish trends and predict future outcomes, according to Robin Purohit, an executive with BCM Software, Inc. This is where insights begin. 

Unfortunately, finding, identifying and analyzing the right data isn’t exactly easy. In reality, because of the massive amount of data that is available, the process can be wildly complex – and wildly expensive. As talented as our staff members are, analyzing every piece of that data is humanly impossible. 


Because of this, the market for big data technology is exploding. Companies are hungry for tools that glean meaningful insights from loads of unstructured data, from social media posts to photos. IDC predicts that a massive $50 billion will be spent on big data solutions and services by 2019. 

Interestingly, Forrester Research describes big data as a ‘major disruption’ in the business intelligence and data management landscape. Why a disruption? Because traditional data companies held the keys to the research and data you want and need. The only thing standing in the way right now is the (until recent) absence of tools to help you use it.

Companies are scrambling to find affordable technologies that will help them store, process, and query all of their data. Innovative solutions will enable companies to extract maximum value from big data and create differentiated, more personal customer experiences.” Not only is this disrupting to traditional data companies, but it can lead truly innovative companies down the path away from change—to legitimate disruption and digital innovation.

As digital marketers, what we’re all really looking for is a way to use all this data we have to improve business—to create better customer experiences, provide more relevant content, increase sales and so on. While some organizations have succeeded in this, many more are on the cusp because of new and improving big data technologies. 

Big Data and Analytics Trends

Big data is used across all industries – high-tech, banking, healthcare, retail, manufacturing – and by companies large and small. Even outside the business world, new and unexpected uses are popping up all the time. 

For example, big data has become a huge part of campaign strategy, helping forecast and mold election outcomes, Forbes recently noted. Since Obama’s 2008 campaign, data scientists have been mining data to identify, market to, and turn out voters. Complex software this election cycle has been used to predict exactly how many districts Hillary Clinton is likely to carry. Donald Trump’s campaign has paid millions of dollars to an overseas big data firm. 

Universities are using big data analytics to boost graduation rates by predicting when students might be in trouble and sending that information to their advisors, NPR reported

All the way in Saudi Arabia, the government is gearing up to use big data technologies to help diversify the economy away from its huge dependence on oil revenues, a recent Computer Weekly story said. 


Some trends that are dominating the big data world now include:

The right data – As I alluded to above, the amount of data is not always as important as the quality. Experts like HBR are saying it’s not so much about ‘big or small’ but the right kind of data. 

Open-source processing – Tools like Apache Spark are increasingly being used for fast, large-scale data processing. For example, online betting site Sky Bet is using Spark to tailor its promotional offers by combining in-depth information about individual customers with real-time information about their activity on the site. 

NoSQL technologies – These databases for unstructured data are dominating the market right now. NoSQLs can quickly process unpredictable and messy data in a way that traditional databases cannot. 

These are just a few of many trends in this fast-growing world of big data. Check out this Datamation slideshow to learn more. 

How Artificial Intelligence can Help Content Marketers Make Sense of Data

Artificial intelligence appears to be the next big wave in making sense of big data. AI has been around for decades in some form, of course, but modern iterations are far more advanced because they can think and reason in ways that are scarily similar to humans. The latest technologies are making short work of turning big data into real, useful insights that solve some of our biggest problems. 

artificial intelligence makes sense of big data

Much of the buzz lately has been around Watson, IBM’s artificial intelligence platform. Watson, as the company describes it, is a cognitive technology that can think like a human. We can use it to analyze and interpret all kinds of data, including unstructured texts, images, audio and video, at a speed and volume that wouldn’t be possible by humans. 

If Watson sounds familiar, you might remember it as the ‘supercomputer’ that managed to outsmart Jeopardy’s greatest all-time champions, Ken Jennings and Brad Rutter, in 2011. Those were the early days of Watson, but the technology has morphed far beyond game shows. 

So why should content marketers care? The promise of technologies like Watson lies in the depth and scope of information processing and content analysis that’s possible. A million content marketers—as skilled as they are—would be hard-pressed to match it. More on this in a moment, but first let’s take a look at some of the cool ways Watson is being used to solve various big data problems: 

  • IBM itself is using the technology to recommend cancer treatment in rural areas where oncologists are few and far between. Watson can act like a doctor by reading patient medical records, analyzing cancer images and recommending the best treatment plan for a person. Similarly, Anthem is using Watson to make treatment recommendations to its providers. 
  • Canadian neuroscience experts have announced that they’ll tap IBM Watson to speed time to discovering new drugs for Parkinson’s disease. The average drug takes 10 years to get to market, as data researchers are overwhelmed by the sheer volume and pace of emerging data. Researchers are planning to use Watson’s data-crunching to shrink that lag time.  
  • Media company Conde Nast is using Watson’s Personality Insights service to help build and strategize social influencer campaigns for brands. Watson can pull unstructured data from social media feeds to provide advertisers in publications like Vogue, Vanity Fair, and The New Yorker insights into their campaigns.

These are just a few of the things Watson is doing as a relatively young product. It’s mind-boggling to think about what it – and other AI products – might accomplish in 10 years.

AI for content marketers

As a leading content and digital marketing agency, we’re always faced with the problem of too much data and information to analyze and not enough people to do it. It’s a problem that’s only going to grow, and most of us have agreed by now that technology – in some form – is the answer.

Here are some ways AI technology can make your content marketing smarter, faster and more efficient:

  • Analyzing what we’re doing well in terms of content marketing, where the holes are, and suggesting where we could add more valuable content (plus, every time you write, the tools gets smarter and offers better suggestions)
  • Evaluating thousands of pieces of competitor content to discover what they are and are not doing well, and where opportunities for better engagement lie
  • Analyzing how your target audience is consuming content
  • Helping you test different versions of copy before launching the best one

Specifically, tools like, an AI-driven virtual research and analysis tool, can help you gain unique insight. To be successful in content marketing, you need to understand what content you’re owning in your space, what your competitors’ content strategies are, what the market values for content, and your opportunities for thought leadership and content scale. A truly successful content marketing strategy is built on competitor analysis and insight.


While we’re currently the first—and only—digital agency leveraging the in beta, soon, every digital marketing agency of the future will have some form of AI-driven research tool at their disposal.

Interested in joining us for the limited AI beta for content marketers? Contact us today.

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