Straight Talk on How MarTech is Faring Amidst the Big Data Deluge

The MarTech industry is in a constant state of flux, and always in search for the next “Big-Thing” in technology. In the last five years, most companies have realized the power of data and felt that’s the holy grail until CDPs came into the picture and broke the glass ceiling. Not data, but the insights from data make sense (and dollars) today.

Big Data is nothing new for marketing and sales. However, the hard facts and its complexity are beginning to cast its impact only now. In any Marketing Technology company or in the companies that leverage Marketing Technologies, Big Data plays a very important role in helping the Analytics and Insights teams to deliver decision-making framework and consultation services. In 2018, the Interactive Advertising Bureau (IAB) and Winterberry Group found out that US marketers spent close to $5 billion on data management and integration products, pushing the boundaries in the maturing era of Big Data and Business Intelligence.

In our candid conversations with leading MarTech and AdTech professionals, we hear about their challenges and failures in dealing with complex business paradigms that arise from the client-side. Most of these challenges originate and are lost at the basic level of audience identification, segmentation and targeting.

Today’s MarTech vendors could be leveraging data in some way or the other to segment audiences based on their various behaviors across online and offline platforms, such as website, mobile, live video, social media, events and conferences, brick-and-mortar stores, and connected devices. The list of such devices is only growing; the rise of Big Data is eventually putting intense pressure on how MarTech companies deal with it.

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A very basic Big Data-MarTech integration would entail Insights and Analytics teams to perform certain tasks, such as:

  • Building multi-channel customer profiles across millions of first-party and third-party data and linking to CRMs or DMPs.
  • Highlighting and filtering Marketing Attribution insights to demonstrate the effects of marketing and media strategies on various levels of the funnel or flywheel.
  • Turning unstructured data into structured, non-repeatable pipelines.
  • Finding the best use case solution when choosing between Hadoop On-Premise and Cloud.

You may have heard about the 4Vs of Big Data. But, in MarTech, we will focus on the 4S of Big Data applications:

  1. Speed
  2. Scale
  3. Sustainability
  4. Security

Marketing Technology customers rely on data management and analytics platforms to find real-time solutions to their queries. With faster computing and ease of Big Data analytics, speed is achieved. Scale and Sustainability are inter-related, and dependent on the size and revenue model — in addition to in-house expertise in dealing with and managing Big Data applications. Powered by AI and Machine Learning, the scale and sustainability have been largely automated.

The most important part of MarTech- Big Data framework is security. That’s where the biggest challenge and opportunities are holed up.

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As the complexity of Big Data in MarTech conversations increase, we risk slipping into the ‘Black Hole’ of Big Data applications within Marketing Technology and AdTech. Some security experts also call the next stage of Big Data in MarTech as ‘Black Box’ solutions — you only search for it when the operations fail/ crash land.

That’s where Big Data maneuvering is so critical for any MarTech professional. From identifying the present and future revenue opportunities to managing risks and disaster recovery, there are tons of information available in those Black Box solutions.

As Big Data applications in Marketing Technology stacks come of age, we will find better machinations of processes between the Cloud service providers, data integration, data warehouse, and Business Intelligence/analysts. The aim of any MarTech-Big Data integration would be automation of data preparation, governance, analysis, and visualization that provides countless avenues to unlock real business insight across the entire organization and customer value chain.

If applied properly, Big Data teams in Marketing Technology companies could answer these questions at various levels of complexity.

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The common inquiries that are answered are:

  • What is Big Data for CMOs — and why is it critical to modern marketing?
  • How does Big Data for MarTech differ from other, data-centered technologies, such as IT, Finance, Advertising, and so on?
  • What are the primary use cases for Big Data-driven MarTech today? How are they likely to mature in 2020-2025?
  • How has the Big Data market evolved over time?
  • Which key players are investing and acquiring resources to bring Big Data and MarTech together?
  • What key considerations should brands take into account before engaging Big Data in MarTech stacks?
  • How to unlock the value of Customer Data within a cost-effective model?
  • How to make analytics and intelligence available to everyone in the organization, for any use case?

If  you think, you can answer and clear the common myths about Big Data in MarTech, and overarching technologies, drop us a line at