In fast-moving organizations, it’s not uncommon for cloud resources, including entire projects, to occasionally be forgotten about. Not only such unattended resources can be difficult to identify, but they also tend to create a lot of headaches for product teams down the road, including unnecessary waste and security risks. 

To help you prune your idle cloud resources, we’re excited to introduce Unattended Project Recommender. It’s a new feature of Active Assist that provides you with a one-stop shop for discovering, reclaiming, and shutting down unattended projects. With actionable and automatic recommendations, you no longer have to worry about wasting money or mitigating security risks presented by your idle resources. Unattended Project Recommender uses machine learning to identify, with a high degree of confidence, projects that are likely abandoned based on API and networking activity, billing, usage of cloud services, and other signals. This feature is available via the Recommender API today, making it easy for you to integrate with your company’s existing workflow management and communication tools, or export results to a BigQuery table for custom analysis.

Thousands of projects can be unattended in large organizations, presenting major security risks

Your cloud projects can go abandoned or unattended for a number of reasons — ranging from a test environment that’s no longer needed, to project cancellation, to project owner switching jobs, and more. Not only can such projects contribute to your cloud bill (waste) but they may contain security issues such as open firewalls or privileged service account keys that attackers can exploit to get a hold of your cloud resources for cryptocurrency mining or, worse, compromise your company’s sensitive data. These security risks tend to grow over time because the latest best practices and patches are usually not applied to unattended projects. 

We experience this issue here at Google, too… In fact, it has been on Google’s internal security team’s radar for some time now, so we joined forces and looked into this problem together, starting with our very own “google.com” organization cloud projects. We quickly found some projects that were unattended, but remediating this issue was easier said than done due to challenges in several areas:

• Detection: With lots of signals available to you via sources like Cloud Monitoring, what are the right ones you should look at (e.g. API, networking, user activity)? How can you tell the difference between an unattended project and a project that has a low level of activity by design (e.g. a “shell” project that holds an auth token)?
• Remediation: Once you have identified a project that seems abandoned, how do you go about ensuring that it’s indeed an unattended project? How do you reduce the risk of deleting something that might be essential to a production workload, causing irreversible data loss? How do you solve this at the scale of your entire organization, beyond a one-time cleanup? 

Over the course of 2021 we built and tested a Google-internal prototype first, cleaning up many of our internal unattended projects, and then worked with a number of Google Cloud customers to build and tune this feature based on real-life data (thank you to all of our early adopters for working with us and your generous feedback that helped us shape this feature!) It was not uncommon for us to come across organizations with thousands of unattended projects, and we’re very excited to bring Unattended Project Recommender to all customers, in public preview.

Discovering and acting on unattended project recommendations

Unattended Project Recommender analyzes usage activity across all projects under your organization, including the following data:

• API activity (e.g. service accounts with authentication activity, API calls consumed)
• Networking activity (ingress and egress)
• Billing activity (e.g. services with billable usage)
• User activity (e.g. active project owners)
• Cloud services usage (e.g. number of active VMs, BigQuery jobs, storage requests)

Based on these signals, it can generate recommendations to clean up projects that have low usage activity (where “low usage” is defined using a machine learning model that ranks projects in your organization by level of usage), or recommendations to reclaim projects that have high usage activity but no active project owners. Here’s what an example post-processed summary list of recommendations can look like for the “foobar” organization that has 3 projects:

  Project ID: demo-project-307815
Recommendation: CLEANUP_PROJECT

Project ID: new-project
Recommendation: N/A

Project ID: bobs-playground-project
Recommendation: RECLAIM_PROJECT

In addition to the recommendations, you can also examine the underlying project activity insights that the recommendations are based upon. The insights provide additional information that can be useful for integration with your organization’s existing workflows and automation (e.g. send an auto-generated email or chat message to project owners based on the list provided by the owners field). Here’s an example insight payload:

  content:
  activeAppengineInstanceDailyCount: 0
  activeCloudsqlInstanceDailyCount: 0
  activeGceInstanceDailyCount: 3
  activeServiceAccountDailyCount: 1
  apiClientDailyCount: 18922           // Daily average API calls produced
  bigqueryInflightJobDailyCount: 0
  bigqueryInflightQueryDailyCount: 0
  bigqueryStorageDailyBytes: 0
  bigqueryTableDailyCount: 0
  consumedApiDailyCount: 0             // Daily average API calls consumed
  datastoreApiDailyCount: 0
  gcsObjectDailyCount: 11
  gcsRequestDailyCount: 0
  gcsStorageDailyBytes: 2663548
  hasActiveOauthTokens: false          // OAuth tokens used in the last 180 days
  hasBillingAccount: true
  numActiveUserOwners: 1
  owners:                              // List of project owners
  - activeOnProject: false
    member: user:user1@example.com
  - activeOnProject: true
    member: user:user2@example.com
  serviceWithBillableUsage:
  – Cloud Storage
  - Compute Engine
  vpcEgressDailyBytes: 264456938       // Daily average VPC egress bytes
  vpcIngressDailyBytes: 392435047      // Daily average VPC ingress bytes
  usagePercentile: 20                  // Level of usage relative to other projects

GCP projects are used in many different ways and for many different purposes. In case you get a recommendation to delete a project that’s being used in a way that’s out of the scope for this feature, you can dismiss the recommendation and it will stop showing up for the given project. 

Restoring deleted projects

When you choose to shut down a project using the projects.delete() method, it gets marked for deletion. After a project is marked for deletion, it becomes unusable, all resources within that project are shut down, and a 30-day wait period for the project and all of its data to get fully deleted begins.

In case a useful project is accidentally shut down, you have the option to restore the project within that 30-day wait period. Since restoring allows you to recover most but not necessarily all of your project data and resources, we recommend carefully examining the utilization insights associated with a project and considering any additional utilization signals that may not be captured by the Unattended Project Recommender before taking the cleanup action.

Early customer success stories

A number of enterprise customers are already using Unattended Project Recommender to keep their organizations clean of unattended projects and resources.

Decathlon, a French sporting goods retailer, is excited for the insight Unattended Project Recommender will bring to their environment, and are already deploying it as a part of their latest cloud security initiatives.

“After a thorough test of this feature and the validation of our CISO, we ended up deleting our first 775 projects, and no one complained! A great help to improve our security. The next step for us will be to operationalize it at scale, and implement a company wide policy for unattended resource management.” —Adeline Villette, Cloud Security Officer

For Veolia, one of the world’s largest water, waste and energy management companies, not only does this feature reduce security risks and waste, but also helps drive cultural shift and alignment with its ecological transformation strategy.

“This feature allows us to reduce our costs and security debt on assets that are no longer in use, and is also fully in line with Veolia’s philosophy of limiting its carbon footprint. After having tested Unattended Project Recommender on more than 3,000 projects throughout our organization, we are looking to bring it as proactive alerts to our project owners at scale.”—Thomas Meriadec, Product Manager

Box, a secure cloud content management provider, views it as a foundation for building a repeatable process to remediate unused resources.

“Unattended Project Recommender is a great fit for us. It gives us a unified view of project usage across our entire organization and enables us to address security risks of legacy projects in a systematic and organized manner, ensuring an even safer environment.” —Matt Bowes, Staff Security Engineer

Getting started with the Unattended Project Recommender

To help you get started, we’ve prepared a Cloud Shell tutorial (source code) that you can use to find unattended project recommendations within your own Projects/Folders/Organization. Click this button to clone the tutorial from GitHub and run in your Cloud Shell environment:

As you can see, listing recommendations for your projects only takes a few clicks with the tutorial (special thanks to Lanre Ogunmola, Security & Compliance Specialist, for making this look so easy)! For additional detail on using the gcloud CLI or API to discover unattended project recommendations, please refer to the documentation page.

You can also automatically export all recommendations from your Organization to BigQuery and then investigate the recommendations with DataStudio or Looker, or use Connected Sheets that let you use Google Workspace Sheets to interact with the data stored in BigQuery without having to write SQL queries.

As with any other Recommender, you can choose to opt out of data processing at any time by disabling the appropriate data groups in the Transparency & control tab under Privacy & Security settings.

We hope that you can leverage Unattended Project Recommender to improve your cloud security posture and reduce cost, and can’t wait to hear your feedback and thoughts about this feature! Please feel free to reach us at active-assist-feedback@google.com and we also invite you to sign up for our Active Assist Trusted Tester Group if you would like to get early access to the newest features as they are developed.

For far too long, cloud infrastructure has focused on raw speeds and feeds of building blocks such as VMs, containers, networks, and storage. Today, Moore’s law is slowing, and the burden of picking the right combination of infrastructure components increasingly falls on IT.

At Google Cloud we are committed to removing that burden. We’ve engineered golden paths from silicon to the console, with a recipe of purpose-built infrastructure, prescriptive architectures, and an ecosystem to deliver workload-optimized infrastructure. And at this year’s Google Cloud Next, we made some exciting new announcements across key workloads.

In this post, we will put the new Google Cloud releases and capabilities in the context of popular workloads: From AI/ML to high performance computing and data analytics, to SAP, VMware, and mainframes.

Powering AI/ML workloads

No single technology has the potential to drive more transformation than AI and ML. At Next, we announced several new AI-based services: Translation Hub and DocAI services, and the OpenXLA Project, an open-source ecosystem of ML compiler technologies co-developed by a consortium of industry leaders.

To build these innovative services, you also need a powerful infrastructure platform. Today, we announced the following additions to our compute offerings:

• Cloud TPU v4 Pods – Now in General Availability (GA), Google’s advanced machine learning infrastructure is based on the world’s largest publicly available ML hub in Oklahoma and offers up to 9 exaflops of peak aggregate compute. Developers and researchers can use TPU v4 to train increasingly sophisticated models to power workloads such as large-scale natural language processing (NLP), recommendation systems, and computer vision algorithms in a cost effective and sustainable way. In June 2022, Cloud TPU v4 recorded the fastest training times on five MLPerf 2.0 benchmarks, with up to 80% better performance and up to 50% lower cost of training than the next best available alternative.

• A2 Ultra GPU VM instances – Now GA, you can use A2 Ultra GPU VM instances with Compute Engine, Google Kubernetes Engine and Deep Learning VMs. A2 Ultra GPUs have the largest and fastest GPU memory of the Google Cloud portfolio and are optimized for large AI/ML and HPC workloads with use cases such as AI assistants, recommendation systems, and autonomous driving. Powered by NVIDIA A100 Tensorcore GPU with 80 GBs of GPU memory, A2 Ultra delivers 25% higher throughput on inference and 2x higher performance on HPC simulations than original A2 machine shapes.

Learn more about A2 Ultra and hear how you can accelerate your ML development and learn from our customers Uber and Cohere in breakout session MOD300.

Boost HPC and data-intensive workloads

Customers rely on Google Cloud to help them run data-intensive workloads such as HPC and Hadoop. The new C3 machine series (currently in Preview) is the first in the public cloud to include the new 4th Gen Intel Xeon processor and Google’s custom Intel Infrastructure Processing Unit that enables 200Gbps, low-latency networking. Learn more about top HPC best practices in breakout session MOD106.

You can pair the C3 with Hyperdisk (currently in Private Preview), our new generation block storage, which offers 80% higher IOPS per vCPU for high-end database management system (DBMS) workloads when compared to other hyperscalers. Data workloads such as Hadoop and databases may no longer need oversized compute instances to benefit from high IOPS. Learn more about Hyperdisk in breakout session MOD206.

Enable new streaming experiences

Media and entertainment customers want streaming optimized workloads that build on our innovation in edge and our global network. Here are some new developments to support streaming use cases:

• Cloud CDN, our original content delivery networking offering, now offers Dynamic compression to reduce the size of responses transferred from the edge to a client, significantly helping to accelerate page load times and reduce egress traffic.
• Media CDN, introduced earlier this year, now supports the Live Stream API to ingest and package source content into HTTP-Live Streaming and DASH formats for optimized live streaming. We are also enabling two new developer-friendly integrations in Preview for Media CDN: Dynamic Ad Insertion with Google Ad Manager, which provides customized video ad placements, and third-party Ad Insertion using our Video Stitcher API for personalized ad placement. With these options, content producers can introduce additional monetization and personalization opportunities to their streaming services.
• For advanced customization, we are introducing the Preview of Network Actions for Media CDN, a fully managed serverless solution based on open-source web assembly that enables programmability for customers to deploy their own code directly in the request/response path at the edge.

With Media CDN, customers like Paramount+ are able to deliver a high-quality experience on the same Google infrastructure we’ve tested and tuned to serve YouTube’s 2 billion users globally.

“Streaming is one of the key growth areas for Paramount Global. When we migrated traffic onto Media CDN, we observed consistently superior performance and offload metrics. Partnering with Google Cloud enables us to provide our subscribers with the highest quality viewing experience.” — Chris Xiques, SVP of Video Technology Group, Paramount Global

Bringing Google Cloud to your workloads

For customers in regulated markets and in countries with strict sovereignty laws, Google Cloud has a spectrum of offerings to help them achieve varying degrees of sovereignty. Sovereign Controls by T-Systems is now GA, and Local Controls by S3NS, the Thales-Google partnership, is now available in Preview. You can expect more region and market announcements in the coming months. And for the most stringent sovereignty needs, we offer Google Distributed Cloud Hosted for a disconnected Google Cloud footprint deployed at the customer’s chosen site.

We are also expanding functionality for existing offerings to give you even more options to run your cloud where you want, how you want.

• Anthos, our cloud-centric container platform to run modern apps anywhere consistently at scale, now has a more robust user interface and an upgraded fleet management experience. Create, update, and reconfigure your Anthos clusters the same way from one dashboard or command-line interface, wherever your clusters run. New fleet management capabilities let you manage growing fleets of container clusters across clouds, on-premises, and at the edge and for different use cases (isolate dev from prod, apply fleet-specific security controls, enforce configurations fleet-wide). And we are pleased to announce the GA of virtual machine support on Anthos clusters for retail edge environments. Learn more about how Anthos can help you run modern applications anywhere in breakout session MOD208.

• Google Distributed Cloud Edge GPU-Optimized Config, is now GA in server-rack form factors powered by 12 Nvidia T4 GPUs. GDC Edge is designed to enable low-latency and high-performance hybrid workloads as a complement to your primary Google Cloud environment or as an independent edge deployment. We’re seeing early adoption by customers and partners for workloads such as augmented reality and retail self-checkout. In addition, software partners such as 66degrees are taking advantage of GDC Edge GPU optimization to provide real-time insights on in-store product availability, while Ipsotek is using machine intelligence at the edge to perform crowd and foot-traffic analysis in large locations such as malls, airports and railway stations. Learn more about GDC Edge and new partner validated solutions here, or watch breakout session MOD207 to hear how you can modernize your data center and accelerate your edge.

Infrastructure building blocks for cloud-first workloads

For most new projects, developers prefer them to be cloud-first optimized workloads, and nearly half of all developers use containers today. Google Kubernetes Engine (GKE) is the most automated and scalable container management service on the market today, and when you use GKE Autopilot, developers can get started faster than other leading cloud providers. At Next ‘22, we’re excited to unveil:

• A new workshop to help you discover how to unlock efficiency and innovation with a GKE Autopilot — sign up to get started.
• A new security posture management dashboard in GKE that provides opinionated guidance on Kubernetes security along with bundled tools to expertly help improve the security posture of your Kubernetes clusters and workloads.

When building scalable web applications or running background jobs that require lightweight batch processing, developers are increasingly turning to serverless technologies. We’re helping developers choose serverless with numerous updates to Cloud Run, our managed serverless compute service.

• With new Cloud Run integrations, Cloud Run and Google Cloud services work together smoothly. For example, configuring domains with a Load Balancer or connecting to a Redis Cache is now as easy as a single click, with more scenarios on the way.
• Cloud Run customized health checks for services is now available in Preview, providing user-defined HTTP and TCP Startup probes at the container level. This capability allows Cloud Run users to define criteria as to when their application has started and is ready to start serving traffic, and is particularly useful for applications that might require additional startup time on their first initialization.
• To make continuous deployment easier for our customers, we added an integration between Cloud Deploy, our fully managed continuous deployment service, and Cloud Run. With this integration in place, you can do continuous deployment through Cloud Deploy directly to Cloud Run, with one-click approvals and rollbacks, enterprise security and audit, and built-in delivery metrics. Learn more on Cloud Deploy web page.

Learn more about how to build next-level web applications with Cloud Run in breakout session BLD203.

You also need confidence in the building blocks that make up your workloads from the outset. To help get you started, we introduced Software Delivery Shield, which provides a comprehensive suite of tools offering a fully managed, end-to-end solution that helps protect your software supply chain. The Software Delivery Shield launch also included the following announcements:

• Cloud Workstations, currently in Preview, provides fully-managed development environments built to meet the needs of security-sensitive enterprises. With Cloud Workstations, developers can access secure, fast, and customizable development environments via a browser anytime and anywhere, with consistent configurations and customizable tooling. To learn more about Cloud Workstations, please visit the web page and check out breakout session BLD100.
• A new partnership with JetBrains provides fully managed Jetbrains IDEs as part of Cloud Workstations. This integration can give developers access to several popular IDEs with minimal management overhead for their admin teams.

Read more about Software Delivery Shield.

Open source and open standards are an important part of building applications. With that, we are excited to announce that Google has joined the Eclipse Adoptium Working Group, a consortium of leaders in the Java community working to establish a higher quality, developer-centric standard for Java distributions. Also, we are making Eclipse Temurin available across Google Cloud products and services. Eclipse Temurin provides Java developers a higher quality developer experience and more opportunities to create integrated, enterprise-focused solutions, with the openness they deserve.

Pioneering new technology with Web3 optimized infrastructure

It’s incredible to see the energy in Web3 right now and the focus on developing the broader benefits, use cases, and core capabilities of blockchain. We are helping customers with scalability, reliability, security, and data, so they can spend the bulk of their time on innovation in the Web3 space — unlocking deep user value and building the next app to attract a billion users to the ecosystem.

Companies like Near, Nansen, Solana, Blockdaemon, Dapper Labs & Sky Mavis use Google Cloud’s infrastructure. Yesterday, we announced a strategic partnership with Coinbase to better serve the growing Web3 ecosystem.

“We could not ask for a better partner to execute our vision of building a trusted bridge into the Web3 economy and accelerating broader growth and adoption of blockchain technology. I started Coinbase with a desire to create a more accessible financial system for everyone, and Google’s history of supporting open source and decentralized ecosystems made this a natural fit. Our partnership marks a major inflection point and, together, we are removing barriers to blockchain adoption and accelerating innovation.” — Brian Armstrong, CEO, Coinbase

Lift and transform traditional workloads

Not all workloads were born in the cloud — or have completed their journey to it. Google Cloud offers a variety of programs and capabilities to help optimize traditional workloads for a new cloud era, regardless of whether you’re looking to migrate it as-is, do light optimizations, or fully modernize and transform:

• Migration Center – Now in Preview. For organizations looking to migrate to the cloud and transform their businesses, Migration Center can reduce the complexity, time, and cost by providing an integrated migration and modernization experience. It brings together our assessment, planning, migration, and modernization tooling in one centralized location with a shared data platform so you can proceed faster, more intelligently, and more easily through your journey. Learn more at the Migration Center webpage.

• Google Cloud VMware Engine – Google Cloud is the first VMware partner to market with VMware Cloud Universal support, simplifying the migration of on-premises VMware VMs to VMware Engine in the cloud. With a cloud market-leading 99.99% availability SLA in a single site, VMware Engine is helping large organizations like Carrefour move to Google Cloud.

• Dual Run for Google Cloud – Now in Preview, this first-of-its-kind solution helps eliminate many of the common risks associated with mainframe modernizations by letting customers simultaneously run workloads on their existing mainframes and their modernized version on Google Cloud. This means customers can perform real-time testing and quickly gather data on performance and stability with no disruption to their business. Once satisfied that the performance of the two systems is functionally equivalent, the customer can make the Google Cloud environment the system of record, and operate the existing mainframe system as needed, typically as a backup. Learn more about Dual Run in this press release or on our Mainframe Modernization webpage.

• Google Cloud Workload Manager – Now in Preview for SAP workloads, and available in the Google Cloud console, Workload Manager provides automated analysis of your enterprise systems on Google Cloud to help continuously improve system quality, reliability, and performance. Google Cloud Workload Manager evaluates your SAP workloads by detecting deviations from documented standards and best practices to help proactively prevent issues, continuously analyze workloads, and simplify system troubleshooting.

Learn more about how organizations like Global Payments and Loblaw Technology have migrated with ease and speed in breakout session MOD104.

Protect all your workloads

The foundation of any workload is storage, and this year we expanded the number of supported regions with our Cloud Storage dual-region buckets (GA), so you can ensure that your workloads are protected. In the event of an outage, your application can easily access the data in the alternate region. You can add Turbo replication (GA) with your dual-region buckets. Turbo replication is backed by a 15-minute Recovery Point Objective (RPO) SLA.

Also, we recently announced Google Cloud’s Backup and DR Service (GA). This service is a fully integrated data-protection solution for critical applications and databases that lets you centrally manage data protection and disaster recovery policies directly within the Google Cloud console, and fully protect databases and applications with a few mouse clicks.

Learn more about Storage best practices in breakout session MOD206.

Migrate, observe, and secure network traffic

We also announced many new capabilities and enhancements to the Cloud Networking portfolio that help customers migrate, modernize, secure, and observe workloads traveling to and in Google Cloud:

• Private Service Connect helps simplify migrations by giving more control to users and integrations with 5 new partners.
• Network Intelligence Center can monitor the network for you with enhanced capabilities like the Performance Dashboard that will give you latency visibility between Google Cloud and the Internet.
• We expanded our Cloud Firewall product line and introduced two new tiers: Cloud Firewall Essentials and Cloud Firewall Standard.

For more information on what’s new with networking, read this blog and check out breakout session MOD205.

Optimize costs

Our customers’ workloads also require technical and commercial options that can deliver the best return on their investments. Here are some new enhancements that can help:

• Flex CUDs – GA coming soon, Flexible Committed Use Discounts help you save up to 46% off on-demand Compute Engine VM pricing, in exchange for a one- or three-year commitment. Like standard CUDs, you can apply Flex CUDs across projects within the same billing account, to VMs of different sizes, and across operating systems. Learn more.
• Batch – Now GA, Batch is a fully managed service that helps you run batch jobs easily, reliably, and at scale. Without having to install any additional software, Batch dynamically and efficiently manages resource provisioning, scheduling, queuing, and execution, freeing you up to focus on analyzing results. There’s no charge for using Batch, and you only pay for the resources used to complete the tasks.

Learn more about how you can optimize for cost savings with Google Cloud in MOD103.

Optimize for sustainability

Any new workloads you develop should have the smallest possible carbon footprint. Google Cloud Carbon Footprint helps you measure, report, and reduce your cloud carbon emissions, and is now GA. Since introducing Carbon Footprint last year, we added features that cover Scope 1, 2 and 3 emissions, and provided role-based access to other users such as sustainability teams. Active Assist’s carbon emissions estimates related to removing unattended projects are also now GA. Learn how to build a more sustainable cloud with lower carbon emissions in MOD103, and start using Carbon Footprint today.

“At Box, we’re focused on reducing our carbon footprint and we’re excited for the visibility and transparency the Carbon Footprint tool will provide as we continue our work to operate sustainably.” — Corrie Conrad, VP Communities and Impact and Executive Director of Box.org at Box

“At SAP we’re working to achieve net-zero by 2030, making it crucial to measure carbon emissions all along our value chain. Collaborating with Google Cloud on Carbon Footprint ensures that accurate emissions data is available in a timely manner, helping our many Solution Areas make more sustainable decisions.” — Thomas Lee, SVP and Head of Multicloud Products and Services at SAP

Designed around your workloads

These are only a few examples of the many golden paths we are enabling for your workloads. We strive to be the ‘open infrastructure cloud’ that is the most intuitive to consume because everything is designed around your workloads, providing tremendous TCO benefits.

To get even more information on all of this and more check out the Modernize breakout session track and these “What’s New” sessions available on demand at Next ’22:

• MOD105 to learn about new infrastructure solutions for enterprise architects and developers.
• BLD106 to learn what’s new to help developers build, deploy, and run applications.
• OPE100 to learn about the biggest announcements for DevOps teams, sysadmins, and operators.

If you’ve ever used only your voice to authenticate a payment, place an order, or check an account over the phone, there is a good chance that Pindrop’s technology made it possible.

Founded in Atlanta in 2011, Pindrop provides software and technology that uses machine learning, voice recognition, and behavioral analytics to help detect and prevent fraud in voice channels like AI-powered call centers and solve the long standing identity problem commonly encountered in customer care and contact center experiences. Demand for Pindrop’s technology has grown considerably over the past decade, as more organizations and people adopt voice as an interface for customer service, transactions, entertainment and more. As that demand has grown, safeguarding these voice interactions from fraud or authentication loops continues to be a unique challenge.

Now, with frictionless voice-driven customer experiences at an all-time high, cybersecurity is more critical than ever. In fact, research shows that fraudsters are able to accurately answer knowledge-based authentication questions 92% of the time, as opposed to only 46% for actual customers. To address this, Pindrop is bringing its products and technology to Google Cloud in order to scale and develop new capabilities that will help enable the next wave of voice-first interfaces and call centers.

The new partnership will enable Pindrop’s teams to accelerate their development of new capabilities to address an ever-shifting landscape – like detecting deep fakes and robocalls, helping banks authenticate transactions, providing retailers with AI-powered call center support, and helping IoT makers optimize for security, speed and simplicity.

More specifically, the partnership will help Pindrop scale its own businesses, and meet ever-growing customer needs in several ways.

First, Pindrop is bringing its SaaS platform to Google Cloud, enabling customers to deploy it at global scale, on secure, sustainable, and highly performant cloud infrastructure. Not only does this give Pindrop the ability to deliver secure voice interactions with low-latency, it also aligns with its mission to enable customer choice when selecting and implementing security technology. And for customers already operating on Google Cloud, getting started with Pindrop can be done directly within their cloud environment, now that the company is making its solutions available on Google Cloud Marketplace.

Second, Pindrop plans to offer its voice secure technology with Google Cloud’s Contact Center AI (CCAI) capabilities to develop new solutions that will enhance call center experiences. CCAI capabilities in understanding, augmenting, and ML-processing conversations allows companies to deliver interactions that are helpful and keep costs low. These capabilities alongside Pindrop’s authentication and anti-fraud solutions will enable the company with the tools and technologies needed to create the next evolution of frictionless experiences in a secure way.

Finally, building on top of Google Cloud will equip Pindrop with multicloud capabilities that will offer new channels to increase revenues through. Using technologies like Google Kubernetes Engine, frequently chosen by startups and tech companies, to quickly launch and scale new ML applications up or down will also support Pindrop’s growth as it adds to its suite of technologies.

Supporting innovative startups and digital native businesses like Pindrop is in Google’s DNA, and we’re delighted to support Pindrops’ growth, and the success of their customers, with Google Cloud infrastructure, Contact Center AI, and go to market expertise.

You can learn more about Pindrop on Google Cloud here, and Google Cloud’s Contact Center AI solution here.

“Hello, Marks & Spencer. How may we help you?”

As one of the biggest and best-loved retail brands in the UK, Marks & Spencer (M&S) is known for the personalized service it provides to its 30 million loyal customers. For 135 years and in 57 countries around the world, M&S has worked to meet and exceed customer expectations for quality and service. Since the advent of the telephone, this has meant cheerfully servicing customers who call in to M&S branches or into their contact centers, no matter what their request might be.

“Retail is in a state of flux and M&S is transforming to better serve our customers so that we can compete and win. Automating calls into our stores with Google voice recognition gives us every opportunity to get things right for our customers and keep them coming back again and again.”

—Akash Parmar, Enterprise Architect (Digital Customer Engagement), Marks & Spencer

The company now has a goal of bringing one third of its business online by 2022. In order to engage more customers online, it has opened a new voice channel hosted on Google Cloud.

The company previously had switchboards in 13 different stores across the UK and Ireland (UKI), handling up to nine million calls a year. But as its retail offering evolved across multiple channels, it was becoming increasingly difficult to quickly and effectively answer customer service requests using an outdated switchboard model.

Customers might call in to order an outfit they had seen in a store, to inquire about returning a dress they bought online, or to recover a lost umbrella in a food hall. Each of these different requests required a different routing response from staff, and if the company didn’t act soon, it knew that the cost of managing the increase in call volume would lead to a significant cost impact. M&S decided it was time to make a technological leap forward to meet customers’ expectations in the new, omnichannel retail environment.

“Retail is in a state of flux and M&S is transforming to better serve our customers so that we can compete and win,” says Akash Parmar, Enterprise Architect for Digital Customer Engagement at M&S. “Automating calls into our stores with Google voice recognition gives us every opportunity to get things right for our customers and keep them coming back again and again.”

Boosting opportunities for customer engagement with voice recognition

M&S customers were used to dealing with their local store for anything they needed. But as stores were completely separate from the online business, customers weren’t able to purchase something they’d seen online by calling stores because the store staff didn’t have access to platforms needed to place an online order securely. For a company that places a very high importance on customer experience, this was unacceptable.

Akash Parmar, Enterprise Architect for Customer Engagement, was set a the goal by Chris McGrath, M&S Programme Manager, to ensure that the right channel and the right level of assistance was available to customers at any point before, during, or after purchase. Akash set himself the challenge of building a platform that could adapt to all of these channels and scale very quickly.

“We didn’t have the resources to build a speech recognition platform. DVELP removed that obstacle. It understood what we wanted to achieve and how Google Voice APIs and Twilio could get us there. Whatever we want, DVELP builds it for us. DVELP always presents options, never problems.”

—Akash Parmar

In 2018, Akash reached out to Google Cloud partner DVELP, one of the UK’s leading experts on the Twilio programmable contact center platform and Google speech recognition technology. DVELP recommended a Google Cloud-based natural language speech recognition platform that leverages the audio stream intent detection functionality in the Contact Center AI solution, Dialogflow, as the heart of an inbound-call-handling strategy. This strategy was designed to improve routing accuracy, give customers more self-service options, and increase analyst visibility into customer journeys.

“We didn’t have the resources to build a speech recognition platform. DVELP removed that obstacle,” says Akash. “They understood what we wanted to achieve and how Google Cloud Voice APIs and Twilio could get us there. Whatever we want, DVELP builds it for us. DVELP always presents options, never problems.”

Using Google speech recognition to improve customer experience

M&S wanted to use natural language to enable customers to speak and state what help they required rather than choose from a list of options. This would help them answer the millions of calls coming in and figure out what customers needed quickly.

In order to do that, DVELP needed to consider how best to address tying customer intent to actions, while maintaining flexibility. DVELP recommended the unconventional choice of not referencing intent in the application layer, but mapping the available actions to the information required to perform them. These actions were then used to build a “declarative dictionary” for the customer service team.

By focusing on actions rather than intents, the solution enables the customer services team to configure actions to intents in virtually any combination of key-value pairs. Leveraging the fact that Dialogflow can detect and respond to customer intents in real time, M&S has already reached 92% accuracy in translating customer declarations to actionable intents.

“With Google Cloud speech recognition and Contact Center AI solutions such as Dialogflow, there’s no information that we can’t make sense of. No matter where you call from, who you are, your age, your gender: you speak, and we understand.”

—Akash Parmar

Akash recalls that once customers became comfortable with the prompt, “in a few words, how may we help you?” they started providing simple, concise responses, and the customer learning curve quickly leveled out. At that point, Google Cloud speech recognition and Dialogflow took over. “The technology worked perfectly and the result was like magic,” says Akash.

“With Google Cloud speech recognition and Contact Center AI solutions such as Dialogflow, there’s no information that we can’t make sense of,” he says. “No matter where you call from, who you are, your age, your gender: you speak, and we understand.”

Enabling self-service contact center improvements with Dialogflow

It was important to M&S that contact center employees be self-sufficient in updating the platform to reflect changes in demand. They needed to be able to easily react to a spike in inquiries about a special offer, for example, without relying on the engineering team. At the same time, neither Akash nor the DVELP team wanted the staff to have to learn error-prone JSON inside contexts, or write responses in order to get necessary information from customers.

DVELP’s creative solution was to fill out the “Action and parameters” section of every intent. This is usually reserved for collecting information from customer declarations, but was also easily adapted to implementing custom key-value pairs. This is particularly helpful in making sure that the contact center is ready to handle new promotions as they arise. As sales and special events are communicated to the contact center from the head office, staff can program specific vocabulary directly into Dialogflow, thanks to Contact Center AI, ensuring that the M&S system is immediately ready to handle related customer calls.

Rolling out the platform to the UK and Ireland

In just a few months after going live, calls are being efficiently routed to the contact center and its existing customer service platform. At the contact center, staff can quickly and easily respond to customer requests, place orders, and process returns. Thanks to the natural language capabilities of Google Cloud, a simple customer request like “order the red children’s dress in the Bath high street window in size six” not only gets correctly routed, but provides data points for future personalized interactions.

Being able to accurately recognize customer intent 92% of the time after less than four months since deployment is an important milestone for M&S. With a concurrent 89% voice-to-text accuracy rate for Dialogflow transcriptions, M&S has rolled out the successful speech recognition platform to all of its stores in UK and Ireland and customer service contact centers.

Akash is so pleased with the performance of the new Google Cloud platform that he’s focusing on what new functionality he can add next to improve customer experience even more.

“We’re working on collecting product codes from customers using natural language so we can give them stock availability details,” he shares. “We also want to use Google Cloud to enable a more conversational experience when customers are searching for help or FAQs on our website. At the same time, we’re looking at Contact Center AI and Dialogflow to provide a virtual assistant experience for our webchat journey. Thanks to our new voice solution, we can clearly understand the key issues that our customers face on a day-to-day basis; the aim now is to start solving these issues through self-service and automation.”

Companies today are inundated with vast amounts of data from various sources. This overwhelming amount of data is meant to benefit the company, but often leaves data teams feeling overwhelmed, which can create data bottlenecks and result in a slow time to value. In fact, only twenty seven percent of companies agree that data and analytics projects produce insights and recommendations that are highly actionable (Accenture). This means that nearly 3 in 4 companies are not unlocking value in their data, which poses a huge challenge for organizations trying to move the needle and drive real business results. We at Google Cloud, however, saw opportunity in this challenge, which is why we created Data Analytics Design Patterns: cross-product technical solutions designed to accelerate a customer’s path to value realization with their data. These industry solutions bring together product capabilities alongside design methodology, open source deployable code, data models, and reference architectures to accelerate your business outcomes.

With Data Analytics Design Patterns, you get access to more than 30 ready-to-deploy data analytics solutions. Design patterns leverage the best of Google and our rich partner ecosystem, including Technology Partners & System Integrators. In this blog, we will cover 3 examples on how a design pattern can be applied to unlock the value of data:

1. Improve mobile app experience with Unified App Analytics
2. Maximize digital shop’s revenue with Price Optimization 
3. Protect internal systems from security and malware threat with Anomaly Detection 

Unified App Analytics

If mobile apps are part of your go-to-market strategy, you have several data sources that can provide invaluable customer insights. In addition to tools such as CRM (e.g. Salesforce) and customer care (e.g. Zendesk), you likely use Google Analytics to log app events and Firebase Crashlytics to gather data about app errors. But can you easily combine back-end server data with app front-end data to unlock customer insights? 

The Unified App Analytics design pattern makes it easy to plug all the disparate data sources into a single warehouse (BigQuery) and start analyzing it with a Business Intelligence tool (Looker). Once you have a complete and real time view of your customer experience with your app, you can take action. For example, if you notice an increase in app errors, you can quickly combine your Crashlytics data with your CRM data to narrow down the crashes with the highest revenue impact and prioritize their resolution. Further, you can automate your issue resolution workflow by creating a rule for any future crash that impacts a subset of VIP customers.

With the Unified App Analytics design pattern, you’ll gain access to valuable insights about your user experience with your app so you can inform your future app strategy. For example, NPR, an American media company, increased user engagement by showing content that better mapped to listener interests and behaviors.

Price Optimization

In a competitive and hectic global marketplace, strategic pricing matters more than ever, but often projects are consumed by the tedium of standardizing, cleaning, and preparing data—from transactions, inventory, demand, among other sources.

Price Optimization solution allows retailers to build a data driven pricing model. The solution consists of three main components:

• Dataprep by Trifacta: integrates different data sources into a single Common Data Model (CDM). Dataprep is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis, reporting, and machine learning.
• BigQuery: allows you to create and store pricing models in a consistent and scalable way as a serverless Cloud Data Warehouse service
• Looker dashboards: surface insights and enable business teams to take action with enterprise ready BI platform

With the Price Optimization design pattern from Google Cloud and our partner Trifacta, you’ll be able to rapidly unify multiple data sources and create a real-time and ML-powered analysis, leveraging predictive models to estimate future sales. For example, PDPAOLA, an online jewelry company, doubled sales with dynamic pricing adjustments enabled by a single data view.

Anomaly Detection

Organizations need to anticipate and act on risks and opportunities to stay competitive in a digitally transforming society. Anomaly detection helps organizations identify and respond to data points and data trends in high velocity, high volume data sets that deviate from historical standards and expected behaviors, allowing them to take action on changing user needs, mitigate malicious actors and behaviors, and prevent unnecessary costs and monetary losses.

The Anomaly Detection design pattern uses Google Pub/Sub, BigQuery, Dataflow, and Looker to:

• Stream events in real time 
• Process the events, extract useful data points, train the detection algorithm of choice
• Apply the detection algorithm in near-real time to the events to detect anomalies
• Update dashboards and/or send alerts

The challenge of finding the important insights and anomalies in vast amounts of data applies to organizations across all industries and lines of business, but is especially important to protecting the security of an organization. For example, TELUS, a national communications company, modernized their security analytics platform leveraging this pattern, allowing them to detect anomalies in near real time to detect and mitigate suspicious activity.

Get started

Turn your data into business outcomes with Google Cloud and our broad partner ecosystem by deploying Data Analytics Design Patterns at your organization. There are more than 30 Data Analytics Design Patterns ready for you to use. We have more than 200+ more ideas in the pipeline, so be sure to check in regularly as new patterns will be added soon. 

To dive deeper and find out more about how Data Analytics Design Patterns can help your organization accelerate use cases and create faster time to value, check out this video.