Why You Need a Cloud-Scale Data + AI Solution

by Michael Finn

In today’s digital ecosystem, big data is essential to success — and data management is an unavoidable challenge. Today, many companies have been focused on building data lakes and/or implementing an enterprise data platform (EDP) to help them integrate data from multiple sources (e.g., order management systems, CRMs, billing systems, etc.) and make it readily available to business users.

As leading companies look to leverage behavioral data (also known as clickstream data) for their marketing and operations efforts, EDPs will inevitably need some serious help. Enterprise data platforms just aren’t equipped to handle the sheer volume and complexity of behavioral data generated by your company’s website, mobile apps, devices, advertising campaigns, emails, call centers, support systems, and more.

For this, you need a cloud-scale solution.

The original distributed processing and storage technologies like Hadoop and MapReduce were created by tech heavyweights to help enable them to make use of all the data generated by their customers and prospects. Today, these technologies have evolved and migrated to the major cloud platforms like Amazon Web Services, Google Cloud Platform, and Azure and are available for use by all.

Such solutions can be tailored to handle behavioral data sets, which are an order of magnitude — 10x or more — larger than the typical enterprise dataset. The enterprise data for an online shoe retailer, for example, might contain a record of every pair of shoes a customer has ever ordered. But behavioral data will contain a record of every pair of shoes the customer ever viewed online, every product video they have ever watched, every display ad they’ve clicked, every promotional email they’ve opened, and every other interaction they had with the company’s website and/or apps.

An enterprise data solution can’t store that volume of data.

Likewise, an enterprise data science management platform can’t provide you with the tools you need to apply AI and machine learning to cloud-scale data and incorporate it into your campaigns moving forward. 

And incorporate it you should. According to McKinsey, companies that leverage behavioral data and AI outperform similar companies by 85% in sales growth and more than 25% in gross margin. Behavioral data provides the signal you need to transform customer acquisition, retention, and personalization efforts for your business. Consider Netflix, where AI-driven personalized recommendations drive over 75% of their user activity. The company says this personalization vastly reduces customer churn, to the tune of an estimated $1 billion in savings each year. Similarly, Amazon’s personalized recommendation engine — powered by behavioral data and AI — generates 35% of the company’s total sales.

Successful cloud-scale data + AI solutions have three fundamental characteristics:

  • Distributed: Data can be distributed to multiple servers and processed independently. This helps with performance and scalability, since you can process on many servers simultaneously and recombine afterwards.
  • Elastic: With a cloud-scale solution, a sudden spike in usage won’t pose a problem, and you only pay for the storage that you need at the moment.
  • On-demand: With cloud solutions, resources like server time and network storage are available whenever you need them.

Ultimately, applying AI and machine learning to behavioral data is a cloud-scale challenge, and an enterprise-scale data platform and data science management platform won’t be able to solve it. For this, you’ll need a cloud-scale solution: a Marketing AI platform.

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Michael Finn

VP of Product Marketing at Syntasa

Mike is VP of Product Marketing at Syntasa (a Marketing AI Platform loved by Marketers, Data Scientists, and Data Engineers for its ability to unlock real value from their enterprise and clickstream data).

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