Data-Driven Marketing in 2016: Bigger, Faster, Better

Category: Analytics, Data, Database
Last Updated: 16 Jun 2020
Essay type: Process
Pages: 5 Views: 264
Table of contents

As technology-savvy, data-driven marketers, our job is to get out the right message, in front of the right audience, at the right time, to drive the right (desired ) onsumer behavior.


And we have to do that more efficiently and more effectively than we've seen traditional approaches do

Order custom essay Data-Driven Marketing in 2016: Bigger, Faster, Better with free plagiarism report

feat icon 450+ experts on 30 subjects feat icon Starting from 3 hours delivery
Get Essay Help

Our job, then, isn't easy -- and it’s not for the faint of heart. Indeed, data-driven marketing requires a new mindset, an organizational commitment and new tools and techniques. Hence, the need for data. Big data.

Big data, a broad term for the accumulation, storage and manipulation of data sets so large and complex that traditional methods are inadequate, has accelerated its maturation. 

As the amount of this data has exploded, sophisticated companies have developed new ways to capture, synthesize and act on it. In just a few short years, big data has come to dominate the lexicon in marketing --- and the business landscape overall. Along with the cloud, big data's new tools and technologies have forever changed digital marketing.

Those tools are helping us to move beyond big data, in fact, to aggregate and process data at a faster pace, in real time, across a broader set of touchpoints. In 2016, "fast" will be the new "big," driven by the pervasiveness of personal devices generating information about behaviors that, to date, have been invisible. 

Imagine correlating heart-rate data with TV-watching data.  Devices exist that capture those data sets over time. But what to do with them? The challenge, as always, is to figure out how: 1) to efficiently and effectively tap into this newfound richness; 2) harness the power in these new data sets; 3) take action on them in a way that moves the sales needle.

Already, many prognosticators have peered into their crystal balls to divine where the market is headed. And, many of them will be wrong. Where do I see this going myself?  Where is my company placing our bets? While the following predictions may be off by several degrees, I believe that, directionally, this is the evolution for big data in 2016 and beyond: 

1. Converged platforms will disrupt current data architectures.

When it comes to solving data-related problems, the tech landscape is still separated into transactional and analytic roles. Technologies and techniques for "converging," or unifying, these types of environments are becoming much more commonplace. We are inching closer to the realization of the efficient, unified database, where raw transactional data, combined with predictive insights, is readily available in a user-friendly manner.

In the big data space, Hadoop had been the golden standard, but today, companies need faster, newer and more robust ways to extract ROI from massive and constantly growing data pools. Efforts like  and the are driving this new movement.

2. Stream (real-time) analytics will become a focus.

Once we have the converged platform, data access will be easier. But what about timeliness? A platform that unifies all available data but is not current (due to processing lags) is necessary but not sufficient. This is where stream analytics come in.

Stream-analytics technology provides the building blocks for the ingestion and processing of data that streams from devices, applications and systems in real time. It represents a core upgrade in combining traditional analytic techniques with a high-performance technical architecture, and will allow marketers to correlate behaviors across the broader ecosystem of devices.


3. User-friendly machine-learning implementations will become readily available.

Machine learning has been applied to marketing analytics problems with the goal of finding predictive patterns in data. The challenge with operationalizing machine learning, however, isn’t with the results, but with the process. Access to both the algorithms and results remains out of reach for most marketing professionals.

This year, we will see momentum building around user-friendly machine-learning and data-exploration platforms. From , to Microsoft Azure’s Machine Learning, to Google’s Tensorflow, the industry is heeding the call to expose the power of machine learning to marketing departments.  

My company, ’s, own patent-pending ScaaS (scoring as a service) engine is an example of a solution that builds on these systems, to create a stronger product for data-driven marketers. But the introduction of these products does not mean that data scientists will become less important.

Rather, the transition of marketing professionals, from mad men to mad scientists, will only accelerate as these new tools allow marketing teams to focus on even more complex problems.

4. Metadata and master data-management processes will re-emerge.

This year, 2016, is poised to pick up the data “pace” and provide access to broader user populations. But with that acceleration comes risk. That risk lies in the more personal information we will have access to. And to mitigate that risk will require process and structure.

We don’t see things getting so burdened with process as to squelch them. But we do see a return to organizational aspects of data management, with the inclusion of metadata and master data ("data about your data"), occurring. Higher-level organizational data oversight, at the CIO and data-steward levels, will ensure that as we accelerate our data-driven marketing trains, we won’t careen off the tracks. 

5. The Internet of Things (IoT) will become more ubiquitous.

The most interesting item on the horizon poised to change marketing for the next decade is the emergence of the Internet of Things (IoT). The IoT is the network of real-world objects or "things" embedded with technology, which enables those objects to collect, manage, process, and exchange data.

Think about the marketing progression, from direct-marketing databases, to offline customer databases, to online customer databases, to online customer behavioral databases. And then apply all that to the layering of mobile, social, and display actions, etc.  

Each technological development (phone, web browser, smartphone, smarthome, smartwatch, etc) has provided the potential for a new layering of data about customers and their behavior. The full realization of the IoT will accelerate data value incredibly quickly. 

Marry those improved techniques for processing with utilizing data in real-time, and you'll have an ecosystem that can help us grow and thrive for the foreseeable future.

In sum, we are on the cusp of something truly amazing: new devices, new interactions and new data, being captured at record speed, then processed in real time by the next generation of unified architectures and tools. 

This year will build on the momentum of 2015 to create something bigger, faster and better. And that will bring us one step closer to realizing the full potential of data-driven marketing.


Cite this Page

Data-Driven Marketing in 2016: Bigger, Faster, Better. (2018, Feb 20). Retrieved from

Don't let plagiarism ruin your grade

Run a free check or have your essay done for you

plagiarism ruin image

We use cookies to give you the best experience possible. By continuing we’ll assume you’re on board with our cookie policy

Save time and let our verified experts help you.

Hire writer