Extending AI and Innovation with Google Data Cloud
Meet the Authors
⇨ Google Data Cloud comes with prepackaged accelerated capabilities so essential tables are translated, harmonized, and normalized for ease of use.
⇨ With Cortex Framework, companies can leverage reference architectures and packaged content for common business scenarios.
⇨ Companies must overcome the barriers between data and AI before they can automate and enhance their data.
As the calendar turns over to 2024, businesses find themselves at a critical juncture. AI innovation has reached a point where the technology is mature enough to deliver real value. However, many businesses find themselves unable to take advantage of these opportunities. Some are unable to handle the volume of data required. Others have their data siloed in a way that makes it difficult to gain insights.
To alleviate these issues, Google Cloud developed its Google Data Cloud solution. It allows organizations to manage, secure, and observe their data, all with the added power of embedded machine learning and AI to solve problems and improve business outcomes.
Google Data Cloud
Google Data Cloud bills itself as a “unified, open, and intelligent data ecosystem” that caters to different use cases and personas within an organization.
“What we talk about when it comes to Data Cloud is all of the prepackaged accelerated capabilities that we’ve built. This means all SAP tables, Salesforce tables, and Oracle tables are all translated, harmonized, and normalized and provided in plain business speak for customers to consume it,” said Brian Jacobs Global Lead at Google Cloud. “That also includes BigQuery and Vertex AI: everything that gets you to real AI and ML models that can drive business outcomes like forecasting. Am I settling my currency in the right country or in the right bank account? Am I using advanced fraud detection capabilities based on pattern matching or anomalies against what we’ve seen for fraud?”.
The Google Data Cloud suite includes a serverless, multicloud data warehouse through BigQuery, a relational database through Cloud Scanner, an embedded platform for analytics and business intelligence through Looker, and the Vertex AI platform.
This all builds on the foundation laid out by Google Cloud Cortex Framework, which offers companies the tools needed to design, build, and deploy cloud solutions to meet specific business needs. With Cortex Framework, companies can leverage reference architectures and packaged content for common business scenarios. Once companies establish the best framework for their needs, Cortex Framework provides a launchpad for further innovation.
Many businesses that are considering cloud solutions are doing so to become data-driven organizations. That term is used often, but what does it really mean? It can be difficult to define, so Jacobs offered several characteristics of truly data-driven organizations.
Trust – Every member of an organization should have absolute trust in the accuracy and quality of the data they work with. But after an extended period of time, data is often altered, moved, removed from its context, and riddled with inconsistencies. Companies should ensure that their data is heterogeneous so they can keep pace with the rate of change in the world today. Cortex Framework addresses these issues from the source system, ensuring that business users can trust the data they rely on.
Agility – To be a truly data-driven organization, companies must be able to access the data they need the moment they need it. Organizations that labor over moving or reformatting their data risk falling behind. “We cannot have three-to-five-year transformations. We could 10 years ago, but the world moves too fast. You have to be able to do things at the edge, deliver new capabilities. You need to be able to do things in weeks or months, not years,” said Jacobs.
User-friendly – Data-driven organizations have data that is easy to consume. UX and enterprise search are essential to ensure that all members of an organization are able to leverage the data they need at the speed of thought or in real-time.
Google Cloud is uniquely positioned within the SAP landscape to deliver value to SAP users. The solution is differentiated from its competitors with several key capabilities that allow SAP users to reduce risk and enhance business value:
- Pre-built integrations – The pre-built integration that is delivered between the two stacks provides users with lower risk, lower cost, and faster capabilities. Google Cloud believes in
open standards and multi-cloud, meaning that users get to choose the best tool or the best platform for their purposes. Google Cloud BigQuery allows people the flexibility to work outside of Google’s stack if needed.
- Pre-built outcomes – The pre-built outcomes on Google Cloud are complimentary and designed as extensions. For instance, sales and marketing teams may prefer to use Google’s Looker, but financial planning may be easier for finance teams in SAP Analytics Cloud. On Google Cloud, users can select the solution that works best for them, depending on the use case.
- Enterprise pathway to generative AI – Google Cloud gives users a simple, easy-to-understand pathway to get to enterprise-level generative AI. Google users can access generative AI built in-house. Large language models (LLMs) already live in Vertex AI, which is part of the Google Cloud.
Barriers between Data and AI
As companies ramp into the AI space, many are under the impression that they can simply apply AI to their existing data to supercharge its value. Sadly, this is not the case. Companies must overcome the barriers between data and AI before they can automate and enhance their data. Google Cloud is working to break down barriers between data and AI to unleash the value that businesses need to stay ahead of the competition.
One of the most significant barriers is a lack of structure. AI has to have source structured system data in it. Many SAP users have to hire data scientists to execute table joins. Google Cloud Cortex Framework has Salesforce, Oracle, and SAP data. It can take the tables out of the application, join and harmonize them, and make the information digestible.
“Once you have that in a format which could be rendered in a dashboard or could be pulled up via conversational AI, you have a data set that can start to have advanced AI applied to it – not only for better demand forecasting, but now you can very easily integrate curated data sets like Nielsen data if I’m doing demand forecasting as a CPG or alpha fold data, if I’m doing drug discovery as a life science company or correlating new marketing campaign effectiveness. If I’m an industrial manufacturer, I’m trying to get my product out there, maybe I’m getting into the aftermarket business. All of those barriers are taken away. You have the live API streams, you know it’s all been systematized for this single platform. Google Cloud is what’s bringing it all together,” said Jacobs.
Use Case – Ulta Beauty
Google Cloud recently demonstrated its value when it helped Ulta Beauty, a leading cosmetics brand, transform its digital strategy. Ulta Beauty tuned to Google Cloud’s AI solutions to provide its guests with an enhanced and data-driven experience.
Through BigQuery and Cloud Storage, Google Cloud gives Ulta Beauty the ability to rapidly analyze data to give guests a personalized experience. Ulta’s AI-powered shopping tool, Virtual Beauty Advisor, offers interactivity and uses data to tailor recommendations to the user. This resulted in growing demand and boosted sales.
Ulta also now offers GLAMLab, an augmented reality service that allows users to test out products to see how they will look before buying them, enhancing consumer experience and confidence.
Ulta Beauty can now access, consumer, and process secure data. The company now has the agility to meet the evolving needs and tastes of guests. With Google Cloud, this experience can scale as demand grows.
Backbone to Data Strategy
While AI and other emerging technologies are exciting, companies must ensure that they have a solid foundation that they can build upon. ETL tools and building blocks like Google Cloud Cortex Framework are absolutely essential.
“You have to get foundations and pipelines in place. There are no shortcuts. You have to get whatever your corpus is going to be. You need to be able to put fast, agile, non-Frankenstein pipelines and foundations for your business,” said Jacobs.
In addition to the foundations, organizations should ensure that whatever solutions they utilize are scalable. Platforms should be vertically integrated and purpose built from the ground up. Otherwise, businesses take on costs, risks, and technical debt. They should take advantage of their provider and enrich their SAP environments so they can take on new functionalities more easily in the future.
What this means for SAPinsiders
Emphasize scalability. As data requirements and capabilities grow, SAP organizations must have a solution that can scale along with the expanding needs of their business. Data warehouses like BigQuery offer the capability, agility, and flexibility SAP users need to innovate.
Ensure trust. A company’s data is only as useful as it is trustworthy. All members of an organization should be able to access the data they need when they need it. A simple UX design can make a world of difference for users, allowing everyone to easily access essential data and work more efficiently.
Build a solid foundation. Before embarking on any data migrations or AI enhancements, SAP users should ensure that their pipelines are solid. A steady, tested technological landscape is the cornerstone of a data-driven enterprise.