On May 26th, the Google Data Cloud Summit kicks off, the first in a series of digital events dubbed the Google Cloud Summit series, taking place over the coming months. Google Data Cloud Summit is a half-day event focused on using Google Cloud technologies to advance data-driven transformation. If you have conflicts next Wednesday, don't worry because the content will be available for on-demand viewing immediately following the live broadcast of each event. I’ve been pre-briefed on the news which is under embargo but unfortunately can’t talk about it yet. You’ll want to tune in, though, if you’re into data-driven transformation.
The keynote
The event starts with a keynote presentation at 11 am CST entitled "Empower Transformation with a Unified Data Cloud Strategy" hosted by Gerrit Kazmaier (GM and VP, Database & Analytics Google Cloud) with Zebra Technologies and Deutsche Bank.
Zebra Technologies offer solutions at the edge across many industries, including retail, healthcare, manufacturing, transportation and logistics, and banking. The Google and Zebra partnership goes back many years with Zebra as an early adopter of Android. With Zebra’s focus on edge computing, I expect to hear about new AI and ML use cases, especially across the retail value chain.
Deutsche Bank and Google announced a cloud and innovation partnership back in December 2020. At the time, it was the first partnership of its kind in the industry designed to accelerate the bank’s transition to the cloud. I am particularly interested in how highly regulated industries such as banks are progressing to the public cloud. I will be listening to how the bank has utilized mobile self-service options and artificial intelligence-based recommendations to launch new products and services.
What’s in store
Following the keynote, the summit splits into four tracks running concurrently. Each track has a general session followed by a Q&A forum and several breakout sessions.
Trusted data foundation
In this track, you will hear about the latest innovations across the various Google Cloud Databases, including Cloud SQL for MySQL, PostgreSQL, and SQL Server, Cloud Spanner, a relational database built for scale,
Cloud Bigtable, a NoSQL database service for large analytical and operational workloads, and Firestore, a database for mobile, web, and server development from Firebase and Google Cloud.
I expect some discussion on the relatively new IT topic of cloud-native observability. For IT operations, observability means pulling together data from logs, metrics, traces, and events to enable IT operations to quickly identify root causes of issues and resolve them. In the cloud, these capabilities extend to Kubernetes and across all multi-cloud hybrid IT. In short, cloud-native observability involves new ways of leveraging technology to manage increasingly complex IT infrastructure.
Open and Flexible Insights
Google has been in the data business for a very long time, such that big data analytics and management are part of Google’s DNA. The message will create additional value using data to transform business, going far beyond reducing costs by moving from on-prem to cloud.
I am interested in how customers are adopting a multi-cloud strategy with Google Cloud, which involves seamlessly developing applications and analyzing data residing across multiple clouds. That includes understanding how Google assets such as BigQuery Omni, Looker, and Apigee contribute to that story.
Off-the-shelf AI-powered data solutions can help organizations, particularly smaller organizations that typically lack the required resources, create business value faster. Look for how Google is advancing the AI Platform to deliver purpose-built prescriptive on AI solutions.
Intelligent Applications
If you identify as a data scientist or a machine learning developer, this is the track for you. This track explains how to use Google's data app and AI services. Google provides a full suite of tools and integrated AI workflows.
One session caught my eye – “Foster a Culture of Innovation by Accelerating AI Experimentation."
One of the most critical skills in being an AI data scientist is building robust and trusted AI models. Many AI research methods use different tools for data collection, data preparation, and data modeling.
Having the skills to move AI models into production is a significant challenge for many, so anything that can speed up the process piques my interest.
Google AI notebooks offer an integrated and secure JupyterLab environment for data scientists and machine learning developers to experiment, develop, and deploy models into production. JupyterLab is a web-based interactive development environment for notebooks, code, and data. Users can create instances running JupyterLab that come pre-installed with the latest data science and machine learning frameworks in a single click.
Sessions will also explore Reference Patterns and Looker Blocks for pre-built data models so you can rapidly begin analyzing and intelligently rationalizing new data sets.
AI capabilities are crucial for customers to consider adopting Google Cloud, so expect Google to continue innovating in this area to attract new business.
Partner Insights
Google Cloud partners deliver 19 15-minute sessions that are already available on-demand once you have registered.
Wrapping up
Google Data Cloud summit is an event not to be missed by anyone using or considering using Google Cloud technologies and is into data-transformation. I am expecting some exciting technology announcements for AI, machine learning, data management, and analytics. I’ve been pre-briefed on it and you’ll want to tune in if you’re into “data”.
The schedule suggests a wealth of learning opportunities across Google Cloud Databases, prescriptive AI solutions, and data app and AI services.
You can find out more about the show and register to attend here. See you there virtually!
Note: Moor Insights & Strategy writers and editors may have contributed to this article.
Patrick founded the firm based on his real-world world technology experiences with the understanding of what he wasn’t getting from analysts and consultants. Ten years later, Patrick is ranked #1 among technology industry analysts in terms of “power” (ARInsights) in “press citations” (Apollo Research). Moorhead is a contributor at Forbes and frequently appears on CNBC. He is a broad-based analyst covering a wide variety of topics including the cloud, enterprise SaaS, collaboration, client computing, and semiconductors. He has 30 years of experience including 15 years of executive experience at high tech companies (NCR, AT&T, Compaq, now HP, and AMD) leading strategy, product management, product marketing, and corporate marketing, including three industry board appointments.
