Marketing Technology Ecosystems, Part 4 of 6: Google Cloud

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analytics, Marketing, Marketing Technology

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In this series, we’re examining the underpinnings, the infrastructure that gives rise to marketing technology. Today, we’ll examine one of the biggest powerhouse players in infrastructure provision: Google Cloud.

Google Cloud

Google Cloud has fewer offerings than Amazon Web Services, but Google’s cloud service portfolio tends to be more advanced. Let’s take a look at the pieces that are relevant to us as marketers and marketing technologists.

Data Platforms

Great data platforms help us maintain clean, compatible data and permit us to choose just the right data from enormous raw pools.

Inside Google Cloud, we care most about these data offerings:

Structured data storage with cloud databases like Cloud SQL
Unstructured data storage with Cloud Datastore and Cloud BigTable
File and binary data with Cloud Storage

Google Cloud’s data storage options rival those of Amazon’s, though they’re slightly more expensive. However, their near line storage has fewer penalties for retrieving files than Amazon’s Glacier service; if you’ve got big data files you need to archive (like video shoots), Cloud Near Line Storage may be able to help lighten the load.

Cloud SQL is also 100% compatible with most MySQL applications you’re used to, so porting your existing marketing databases (including the MySQL database that powers WordPress, Mautic, and other systems) can speed up your marketing operations considerably.

Analysis Platforms

Great analysis platforms take in many different kinds of data, clean it up, normalize it, and spit out answers in multiple formats, from reports to charts to interactive visualizations.

Inside Google Cloud, we care most about these analysis offerings:

BigQuery, for large scale data warehousing and analytics
Cloud Dataflow, for streaming data such as social media firehoses
Cloud Dataproc, for high speed computation built on Spark and Hadoop, two of the biggest analytics platforms available today
Cloud Datalab, for interactive large-scale data visualization
Google Compute Engine, for virtualized computer processing and big, big number crunching

Marketers have dealt with or interacted with many of these services behind the scenes; services like BigQuery power massive database backends of popular apps like Snapchat and Angry Birds.

Of all these services, the one marketers should get to know most is BigQuery. We face enormous datasets today in marketing, datasets that vastly exceed the computational power of a laptop or even a workplace server. For example, we may want to query the entirety of the ACA healthcare database, a multi-terabyte file, or the entirety of the US Census Bureau database. BigQuery lets us do so in an efficient way.

Insight Platforms

Insight platforms combine our analysis of what happened and blend it with analysis of unstructured data to help us explain why something happened.

Inside Google Cloud, we care most about these analysis offerings:

Cloud Machine Learning Platform helps us to spin up rapid machine learning software
Vision API performs image recognition, helping us to interpret images for context (such as Instagram photos or Pinterest pins)
Speech API performs voice recognition, helping us to transcribe and interpret audio from any source, including video
Predictive API connects with our data and generates predictions and forecasts based on our data

Google Cloud’s insight platform is robust and powerful. Marketers will find the greatest benefit in the Predictive API. When properly implemented, the Predictive API generates insights in near real-time, helping us know which directions our data and analysis are headed.

Strategy Platforms

Strategy platforms help us to understand what to do next. This is the pinnacle of modern computing, artificial intelligence.

Google’s premier, publicly available artificial intelligence is TensorFlow. TensorFlow provides self-learning capabilities to any mathematically computable data, analysis, or insight. While it can be installed on a computer as small as a regular laptop, it needs heavy computing power to truly shine.

TensorFlow is a true artificial intelligence. Any self-learning, fully automated marketing solution which is computationally-based (such as lead scoring, for example) can be imported into a TensorFlow instance and the software can begin teaching itself. Imagine an AI application which manages your ad spending for you, or automatically sponsors social media posts with positive sentiment and a certain amount of engagement – then learns from its previous work to constantly improve.

For a sense of what TensorFlow can do, check out this massive repository of projects.

Conclusion: Google Cloud Works for Marketers

When we examine the various offerings in Google Cloud, we see robust offerings across the entire spectrum of data, analysis, insights, and strategy. The primary limitation isn’t the technology, but the marketer’s experience in working with the technology.

Google Cloud is somewhat notorious for its clunky interface and cryptic management tools. Once you become proficient with it, however, you can unlock the true power of its services for your marketing technology.

For its most advanced services at the insights and strategy layers, I would expect that marketers without technology skills will need to wait a year or two for pre-packaged offerings to go to market. These offerings will likely be powerful and extremely expensive, so consider hiring developers and engineers to save cost in the long term.

In the next post in this series, a strong entrant from a familiar name: IBM.

Marketing Technology Ecosystems, Part 4 of 6: Google Cloud