More and more companies are realizing the potential of big data analytics and Data-Driven Marketing. For many business leaders, though, the core concepts surrounding a big data strategy are still relatively new –and unfortunately, they’re largely misunderstood. In fact, whenever I talk to a roomful of executives I’m almost always struck by how much confusion there is about fundamental terms, let alone higher-level computational nomenclature.
So, I’d like to do whatever I can to help set the record straight. I’ve decided that every now and then, I’m going to devote a blog post to clarifying the definitions of a few terms we’re all hearing pop up with increasing frequency whenever we have a conversation about driving business value. I firmly believe that in order to effectively communicate with one another –whether that’s between companies or within companies –we need to come to a shared understanding about essential big data vocabulary.
Let’s start with the basics.
What is Data-Driven Marketing?
I find that some business leaders think Data-Driven Marketing means “all that Facebook stuff.” But that definition is much, much too simplistic! Even if you rolled together all the interactions on all the social media platforms, you’d still only begin to scratch the surface of what Data-Driven Marketing truly entails.
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At Teradata, we view Data-Driven Marketing as the combination of collecting and connecting large amounts of data, rapidly analyzing it and gaining insights, and then bringing those insights to market via marketing interactions tailored to what’s relevant for each customer.
To us, Data-Driven Marketing is the blending of data to inform businesses, strengthen customer relationships and make sounder decisions that aren’t based on gut, but rather based on valid, demonstrable insights gleaned from both digital and traditional campaigns.
Data-Driven Marketing is fueled by analytics. So, now let me take a step back and answer the next obvious question:
What is analytics?
At its core, analytics is the discovery of meaningful patterns in data. The term has become somewhat of a “catch-all” to describe a variety of different of functions and applications that relate to a wide range of business solutions. As Gartner explains it:
“Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user.
Today, the word “analytics” is commonly used to express how businesses describe, predict and improve business performance –although, technically, the word “analysis” may be a better choice in that context. Analysis is defined as the process of breaking a complex topic (or substance) into smaller parts to gain a better understanding of it. Therefore, “analysis” is the application of analytics and data to solve business –or any type of problem –that is informed by analytics and data.
Given that between now and 2020, the sheer volume of digital information is predicted to increase to 35 trillion gigabytes – much of it coming from new sources including blogs, social media, internet search and sensor networks –it’s clear that companies are going to need to increasingly rely on analytics to make sense of it all.
Remember: The data by itself does not provide value. It’s the analytics and the analysis that provide value.
Going forward, you’ll need analytics to generate actionable insights, and then you’ll need a Data-Driven Marketing strategy to turn those actionable insights into increased sales and revenue growth.
Is there a term or concept that’s causing confusion in your organization? Let me know in the comments below, and I’ll add it to my list to tackle in a future blog post. Working together, we can add clarity to all of our conversations about big data and data-driven marketing.