Google Cloud: Customer Data Platforms (CDP’s) Done Right

At Crystalloids we are highly specialized in designing and building Customer Data Platforms on Google Cloud. Using business cases before we start, we have performed dozens of implementations and architecture designs for CDP’s of any type. Not only do we design the architecture and develop the solution, we also use our managed services to assure quality and security.

To-date, each of our clients (past and present) remain on Google’s Data Cloud technology. This speaks to the internal and bottom-line value added by Google’s and Crystalloids’ solutions for all businesses.

Additionally, with Google Cloud technology you pay for only what you use, making our clients total cost low.

In this article, I will share why Google’s Customer Data Platform is a good fit for most businesses.

1. Flexibility for future requirements

One thing is clear — we can’t predict the future. Needs will always vary, legislation and consumer behavior will continue to change. The remedy is simple: a CDP that handles change. On Google Cloud you compose a CDP to your requirements and can apply changes quickly. This level of customizations allows you to mix and match GCP and third party solutions. For example, on Google Cloud you can centralize your data and analytics so you don’t have to migrate in order to test a new component like an ESP or ecommerce platform. Customization can feel intensive but that is not the case with our reusable code and templates.

2. Break data silos at speed with automated data transfers

Break data silos imposed by point solutions that lack proper interoperability with other systems and lock you in from both financial and technical perspectives. With Google’s transfer solutions you can automatically move data from hundreds of SaaS applications like Google Marketing Platform, Google Ads, YouTube, Salesforce CRM, Adobe Analytics, and Facebook Ads at no additional cost with BigQuery Data Transfer Service and third party transfers.

3. Faster predictive engagements with integrated AI/ML

We all know that Google excels in AI with models and model operations (ML Ops). With their variety of out-of-the-box options such as Recommendations AI to modelling with SQL with BigQueryML to bespoke modelling with Tensorflow, you can reduce time to value. Google also provides smart analytics reference patterns for common analytics use cases like ecommerce recommendation systems, forecasting customers’ lifetime value, and designing propensity-to-purchase solutions with sample code and technical reference guides.

4. Augment consumer data with Google & public datasets

You can enhance your analytics and AI initiatives with pre-built data solutions and valuable public datasets such as weather and socio-demographics including Google datasets like Google trends. These solutions are available via Analytics Data Hub and the Marketplace.

5. Advance with Google’s secure-by-design infrastructure

Take advantage of the reliable secure-by-design infrastructure and global low-latency network Google uses to protect your consumer PII. The rich set of controls and capabilities Google offers is always expanding and includes features like data encryption by default, identity- and role-based access controls, and Data Loss Prevention to discover and classify PII at scale.

6. Democratize data insights and take action

With Looker (Google Cloud’s enterprise platform for BI and embedded analytics) you can enable your marketing teams with self-service analytics. Once they’ve uncovered an insight, they can send audience segment lists to marketing platforms such as Google Ads, Facebook, LinkedIn and HubSpot. Thanks to Google Cloud’s open approach you can also easily integrate ISV solutions like Lytics and Flywheel Software for data activation on low-code or no-code for marketers. Also, you can connect any type of dashboarding solution such as Tableau, PowerBI.

Many organizations store data in multiple public clouds. Often, this data ends up being siloed, because it’s hard to get insights across all the data. You want to be able to analyze the data with a multi-cloud data tool that is inexpensive, fast, and does not create additional overhead of decentralized data governance. Using BigQuery Omni, we reduce these frictions with a unified interface to query datasets that live on Amazon Simple Storage Service (Amazon S3) or Azure Blob Storage.

7. Reduce lock-in & support of operating systems & languages

Open source has become a pervasive component in modern software development, and Google is no exception. Google uses thousands of open-source projects across internal infrastructure and products. Google Cloud is a challenger in the public cloud world because it remains the most open platform, with products such as container orchestrations service Kubernetes and machine learning framework Tensorflow. Also, Cloud Data Fusion’ is based on open source project CDAP and ‘Cloud Dataflow’ is based on open source project ‘Apache Beam.’

One of the reasons that Google Cloud Platform is one of the most open clouds is because it supports a wide range of operating systems, including Linux, Windows, and many others. Additionally, Google Cloud Platform offers support for a wide range of programming languages, including Java, Python, and many others. This makes it easy for developers to build applications using the tools and languages they are familiar with.

8. Unify with ease on the Cloud

All components in the console fit together natively and are tightly integrated. Suites such as Salesforce and Adobe claim proper integrations, but this is not always the case. At times these suites are established when companies merge or take over, and under the hood of the suite the integration can remain incompatible or with loose integration efforts. I have written about this in the article ‘Should I bring my data and analytics to Salesforce? 7 considerations’.

9. Scale on demand

Google has a well-known reputation for building highly scalable products that can handle a large volume of data. Now you get the assurance of a CDP that adapts to your growing data needs.

10. Same service, lower cost

Knowing you won’t overpay always catches the eye of our clients when learning about Google Cloud benefits. Compared with competitors (such as Amazon Web Services, Digital Ocean, Microsoft Azure) GCP’s advantage is the low cost. Unlike its competitors, with Google Cloud, you only pay for the services you use. Google applies per-second billing which is a great alternative to consumers having to pay by the hour, even if they use a service for a few minutes.

Summary

If you are looking to develop or redesign a CDP for your business, Google’s Data Cloud is a serious option to consider. With a powerful platform that offers a wide range of tools and services to support your efforts, and minimum cost and vendor lock-in, Google Data Cloud works for all your data and business needs.

If you’d like to learn more about where to start related to CDP’s on Google Cloud, Crystalloids has end-to-end services to guide you through your CDP journey. A good place to start would be with ‘From use case to CDP in 9 steps’.

To better understand the variety of ways you can architect and run a CDP on Google Cloud, read ‘Best practices to design and operate a CDP’.

Finally, you can always schedule a virtual meeting with me to discuss your goals and situation.