With SAP Sapphire kicking off today in Orlando, we’re looking forward to seeing our customers and discussing how they can make core processes more efficient and improve how they serve their customers.

One thing is certain to be top of mind – the global supply chain challenges facing the world today. It’s affecting every business across every industry, from common household items that once filled store shelves and are now on backorder, to essential goods and services like food and medical treatments, which are at risk. Even cloud-native companies are making changes to ensure they have the insights, equipment, and other assets they need to continue serving customers.

We are proud to work with SAP on many initiatives that are driving results for our customers and helping them run more intelligent and sustainable companies. I’d like to highlight three of these important initiatives and how they are helping address global supply chain challenges.

Enabling more efficient migrations of critical workloads

We know a key barrier to entry in the cloud is the ability to easily migrate from on-premises environments. Our cloud provides a safe path to help companies including Johnson Controls, PayPal, and Kaeser Compressor to digitize and solve large, complex business problems, reduce costs, scale without cycles of investment, and gain access to key services and capabilities that can unlock value and enable growth.

Singapore-based shipping company Ocean Network Express (ONE) has become more agile by running their mission-critical SAP workloads on Google Cloud and using our data analytics to improve operational efficiency and make faster decisions. They have gone from an on-premises data warehouse solution that would take a full day loading data from SAP S/4HANA, to using our BigQuery solution that delivers business insights in minutes.

Since The Home Depot moved its critical SAP workloads to Google Cloud, the company has been able to shorten the time it takes to prepare a supply chain use case from 8 hours to 5 minutes by using BigQuery to analyze large volumes of internal and external data. This helps improve forecast accuracy and more effectively replenish inventory by being able to create a new plan when circumstances change unexpectedly with demand or a supplier.

Accelerating cloud benefits through RISE and LiveMigration

At Google Cloud, we have dedicated programs to help migrate SAP and other mission-critical workloads to our cloud with our Cloud Acceleration Program for SAP.

For SAP customers moving to Google Cloud, we provide LiveMigration to provide superior uptime and business continuity. LiveMigration eliminates downtime required for planned infrastructure maintenance. This means that your SAP system continues running even when Google Cloud is performing planned infrastructure maintenance upgrades thus ensuring superior business continuity for your mission critical workloads.

We are also proud to be a strategic partner with the RISE with SAP program, which helps accelerate cloud migration for SAP’s global customer base while minimizing risks along the migration journey. This program provides solutions and expertise from SAP and technology ecosystem partners to help companies transform through process consulting, workload migration services, cloud infrastructure, and ongoing training and support. To secure your mission critical workloads, SAP and Google Cloud can provide a 99.9% uptime SLA as part of the RISE with SAP program.

Many large manufacturers have taken advantage of RISE with SAP to forge a secure, proven path to our cloud, including Energizer Holdings Inc., a leading manufacturer and distributor of primary batteries, portable lights, and auto care products. Energizer has turned to RISE with SAP on Google Cloud to power its move to SAP S/4HANA. The company wants to automate essential business processes, improve customer service, and boost innovation. It had been using a private cloud solution but needed to gain flexibility while better containing costs.

“SAP S/4HANA for central finance will help us automate essential business processes, improve customer service, and fuel innovation that grows our company’s leadership position globally. We selected RISE with SAP to begin our journey to SAP S/4HANA and maintain the freedom and flexibility to move at our own pace,” said Energizer Chief Information Officer Dan McCarthy.

Another example is global automotive distributor Inchcape, which moved its mission-critical sales, marketing, finance, and operations systems and data to Google Cloud. With its diverse data sets now in a single, secure cloud platform, Inchcape is applying Google Cloud AI and ML capabilities to manage and analyze its data, automate operations, and ultimately transform the car ownership experience for millions.

“Google Cloud’s close relationship with SAP and its strong technical expertise in this space were a big pull for us,” said Mark Dearnley, Chief Digital Officer at Inchcape. “Ultimately, we wanted a headache-free RISE with SAP implementation and to unlock value for auto makers and consumers in all our regions, while continuing to have the choice and flexibility to modernize our 150-year old business in a way that works for us.”

A new intelligence layer for all SAP Google Cloud customers

When moving mission-critical workloads to the cloud, companies not only need to migrate safely, they also need to quickly realize value, which we enable with Google Cloud Cortex Framework — a layer of intelligence that integrates with SAP Business Technology Platform (SAP BTP). Google Cloud Cortex Framework provides reference architectures, deployment accelerators, and integration services for analytics scenarios.

Like many large e-commerce companies, Mercado Libre experienced skyrocketing transactions that more than doubled in 2020 as people sheltered at home during the pandemic, and they are anticipating more growth. The Google Cloud Cortex Framework is enabling Mercado Libre to respond, run more efficiently, and make faster, data-driven decisions.

Continued partnership to support organizations around the world

Our longstanding partnership with SAP continues to yield exciting innovations for our customers, and we’re honored to work with them to help customers address the ongoing impact of global supply chain challenges. We’re looking forward to sharing new insights and innovations at SAP Sapphire this week, and to listening and learning from you about your plans and challenges, and how we can best support your transformation to the cloud.

The ability to quickly introduce and evolve payment options for products or services is essential for businesses, as nearly 50% of consumers who can’t use a preferred payment method abandon their purchase. At the same time, gift cards, branded credit cards and rewards programs are critical tools that companies rely on to build more loyal and lasting customer relationships. With Highnote, companies have an all-in-one embedded platform to quickly create payment cards and wallets, offer innovative rewards programs and credit, and provide sustainable wage access. It is the first platform that allows enterprises to make card issuance an embedded capability of their product without creating an entirely new (and costly) organization.

Creating an exciting fintech future with Google Cloud

When thinking about building the industry’s first end-to-end embedded finance platform, we quickly realized Highnote would only be successful if it enabled companies to truly innovate and quickly roll out new programs. To do so, the platform would have to be built on scalable infrastructure capable of securely delivering services with speed and reliability while offering easy access to actionable Big Data analytics.

Working closely with the team at the Google for Startups Cloud Program, we successfully implemented Google Cloud as a versatile, future-proof foundation of our platform—and built Highnote from the ground up in just one year. Highnote’s GraphQL-based API platform reinvents the card issuance process. Utilizing the developer-friendly Highnote platform, product and engineering teams at digital enterprises of all sizes can easily and efficiently embed virtual and physical payment cards (commercial and consumer prepaid, debit, credit, and charge), ledger, and wallet capabilities into their existing products. This creates compelling value while growing revenue and building a unique and differentiated brand.

We leverage Cloud Spanner, BigQuery, and Google Kubernetes Engine (GKE) to create a unified and highly secure PCI DSS-compliant platform with GraphQL APIs that provide rapid and flexible money transfers. This gives us a reliable platform to deliver and test customer experiences, respond to outcomes, and make better business decisions. Powered by Google Cloud, our data models and application domains are architected to support configurations and customizations that unlock a diverse set of new use cases across industries, including retail, travel, logistics, healthcare, and sustainable wage access programs.

We are especially proud to highlight our enablement of sustainable wage access, as this program helps the 50% of Americans living paycheck to paycheck. Embedding this program within payroll systems provides a viable alternative to payday lenders who often charge exorbitant fees and interest rates. In real world terms, this means Highnote helps people access earned wages before payday at no cost.

The other customer we just went live with was Tillful, and their Tillful card helps small businesses build their business credit. This program will help new and emerging businesses as well as underrepresented owners of small businesses by making the credit ecosystem accessible. Highnote’s platform is designed to support multiple use cases across many industries. For example, we also help the trucking and logistics companies to develop fleet and fuel cards, and spend management companies who are looking to uplevel offerings.

Delivering high-performance transactions with Cloud Spanner

Building one of the world’s most modern card platforms would not have been possible without Cloud Spanner. We needed a solution that would keep our massive petabyte databases from buckling and more securely deliver data anywhere in the U.S. Cloud Spanner does all this and more, as it routinely connects purchases from millions of customers to tens of thousands of vendors. We also wanted to reduce overhead by 80% by eliminating manual sharding, partitioning, and optimization of data. These processes are automatic with Cloud Spanner so we can operate at maximum efficiency.

We specifically selected Cloud Spanner as our distributed SQL database management and storage solution because of its outstanding availability, zero plan maintenance downtime, security certifications, and the highest consistency guarantees of any scale-out database. We continue to optimally scale without any downtime or compromises to the integrity or security of our data. This is key for us because we can address unexpected spikes, long-term growth, and new services without costly rearchitecting.

Highnote is designed to perform over billions of transactions on Cloud Spanner, and the average latency of less than 250 ms is a testament to the robustness of Google Cloud services.

Enabling actionable customer insights at scale

BigQuery is another key Google Cloud solution that we rely on to deliver deep insights and visibility for our customers on a highly secure and scalable platform. When building Highnote, we knew we needed a cost-effective solution that excelled at data analytics. This is particularly critical for accurately measuring the performance—whether profitability or efficacy—of any program or card.

Using BigQuery, we successfully run analytics at scale with as much as a 34% lower three-year TCO than cloud data warehouse alternatives. Over the past year, BigQuery has enabled our customers to unlock data-rich capabilities with a ledger that tracks money in real time and serves up complete debit and credit entries for every event across their accounts. Companies also access real time balances for revenue, fees, customer accounts, and available funds management without complicated spreadsheets.

To quickly and efficiently roll out Highnote to our customers, we needed a simple way to automatically deploy, scale, and manage Kubernetes. When selecting a Kubernetes management tool, our top priorities were rapidly spinning up and securely scaling across multiple sites. As part of Google Cloud’s expansive ecosystem, Google Kubernetes Engine (GKE) was the top choice due to seamless and automatic Kubernetes scaling and management.

We quickly got off the ground with single-click clusters and scaled up by using the high-availability control plane—including multi-zonal and regional clusters—to easily accommodate multiple active-active regions (which other solutions cannot do). As an embedded finance platform, stringent security protocols were obviously a key consideration for us. GKE is secure by default and runs routine vulnerability scans of container images and data encryption. Further security assistance was provided by Google Cloud partners 66degrees and DoiT International to help us rapidly validate VPC PCI compliance and ensure the uninterrupted performance of thousands of transactions per second.

Winning in fintech with Google for Startups

Building the industry’s first end-to-end embedded finance platform would have been extremely challenging without the extensive Google Cloud support. By working closely with our Startups team and Google partners, we had access to Google Cloud services to more easily validate VPC PCI compliance and address most issues before we exited stealth. Their responsiveness is incredible and stands out compared to support services we’ve seen from other technology providers.

Our participation in the Google for Startups Cloud program has been instrumental to our success. With Google Cloud, we are making embedded payments accessible to our customers without a big budget price tag. By doing so, we help unleash the creativity of emerging enterprises by enabling them to innovate with payment services and rewards programs to reach new markets and customers. If companies can dream, we can enable them to realize it on Highnote. Our platform really is that flexible. We’re excited where we can go and grow with Google Cloud.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

In the United Kingdom, the popularity of online grocery shopping is expected to surge from about 6% of the market today to 9% by 2021, according to market research firm Mintel. One of the pioneers of online-only grocery retailing is Ocado, based in Hatfield, Hertfordshire in the U.K. Since starting commercial deliveries in 2002, the company has grown to 600,000 active customers, 260,000 weekly orders, and £1.39 billion in annual revenue.

Ocado takes supermarket trips out of the equation by enabling shoppers to purchase items online through its convenient web and mobile applications. Items are then picked and packed in automated warehouses and shipped directly to customers in a one-hour time slot of their choosing. Ocado’s delivery punctuality is 95%, order accuracy is 99%, and its service footprint now reaches more than 70% of the U.K. population.

“Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

—Paul Clarke, Chief Technology Officer, Ocado

The company achieved its success by building in-house almost all the technology and automation that powers its end-to-end e-commerce, fulfillment, and logistics platform. Ocado also developed a new platform, the Ocado Smart Platform (OSP), which offers large brick-and-mortar grocery retailers around the world access to a best-in-class solution for online grocery.

Democratizing machine learning

The shopping journey for online grocery retailing differs significantly from other e-businesses. Customers often buy dozens of products at once, a single household may have multiple buyers using multiple devices, and product shelf life may only be a couple of days.

“We often say that having built an end-to-end platform that can do online grocery scalably and profitably, we can do other forms of online retail; but the reverse does not necessarily follow,” says Paul Clarke, Chief Technology Officer at Ocado. “Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

The Ocado business model takes advantage of consumers’ shifting preferences and the links between digital technology and shopping experiences.

“Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

—Paul Clarke, Chief Technology Officer, Ocado

The company has been building machine learning into its systems for over five years. Until recently, Ocado machine learning applications required specialist data scientists, typically with PhDs in machine learning, who would build these solutions from the ground up. It also required the specialist who set up the system and costly on-premises infrastructure to train and run these systems.

However, working with Google as a private alpha testing site for Google Cloud Machine Learning Engine accelerated its adoption of artificial intelligence (AI).

“We’ve been talking about how the cloud could democratize AI for some time,” says Paul. “Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

TensorFlow is an open source software library for machine learning developed by the Google Brain team. Ocado developers, engineers, and data scientists now use TensorFlow for many of their machine learning projects. They deploy the models they build on Google Cloud Machine Learning Engine, which lets them train models faster across servers, desktop computers, and mobile devices through a single application program interface (API). Additionally, Google Cloud Machine Learning Engine integrates easily with the other Google Cloud Platform products used widely at Ocado.

What do customers really want?

One of the first TensorFlow models Ocado created was a machine learning algorithm that tags and categorizes customer emails and then prioritizes them for response.

The contact center receives thousands of emails each day and Ocado wanted to automate determining which ones needed to be answered immediately and which ones could wait.

For example, a first-time customer expressing their delight in using Ocado doesn’t need to be responded to with the same urgency as a customer who is missing an item from their order or who won’t be home to receive the delivery.

“Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

—James Donkin, General Manager, Ocado

“We get a lot of emails from customers saying, ‘Our service was great,’ or ‘The driver was very courteous,'” says James Donkin, General Manager, Ocado. “But when issues like weather or road conditions potentially affect delivery, we often get surges of urgent questions. Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

Using Google Cloud Machine Learning Engine, TensorFlow, and a large data set culled from several years’ worth of manually categorized customer emails, Ocado experimented on which kind of neural network architecture would best prioritize emails. After testing its models, Ocado implemented the highest-performing one and has been able to respond to urgent messages four times faster. The company also discovered that 7% of its emails don’t require a response at all, which means call center representatives now have more time to devote to higher priority messages.

“Without Google Cloud Machine Learning Engine, it would have been a lot harder to succeed on a project like email classification,” says Roland Plaszowski, who has recently managed several big data projects and initiatives at Ocado.

“Even if we invested significantly in infrastructure, it would be difficult to manage because of the computational intensity. It’s challenging and expensive to run machine learning projects at the same time without infrastructure that you can scale easily.”

Ocado also uses machine learning to predict customer behavior and improve experiences. By analyzing order data, Ocado makes shopping as frictionless as possible. For example, the ordering system can pre-populate customers’ shopping carts with items they are most likely to purchase, remind customers about items they may have forgotten, and notify them of multi-buy offers they haven’t completed, for example, only buying one of a buy one, get one free offer. Based on machine learning from previous purchase data, the Ocado system can also offer new products that are likely to delight customers.

“You will regularly see items that are more personally relevant to you instead of items that are being promoted more generally,” says James. “I’m a vegetarian, so I’m offered specials for vegetarian products that I normally buy and new ones that I’ve never bought. I’m also less likely to see things that I’m not interested in.”

Machines and machine learning

Within the Internet of Things (IoT), Ocado is looking to enhance its warehouse robots with machine learning. An integral part of the OSP, thousands of robots continually stream data into Google Cloud Storage and Google BigQuery.

Ocado data scientists apply machine learning to create a type of swarm intelligence that enables warehouse robots to work cooperatively to achieve a common goal. Projects include modules to search robot telemetry data, such as whether a battery pack is operating within standard tolerances or whether firmware has been successfully loaded, and use it to optimize maintenance schedules or detect patterns in wear and tear.

“Another challenge we’re looking at is how to embed machine learning directly into robots so they become smarter in terms of self-testing, exception handling, and error recovery,” says Paul. “This is a challenging combination of IoT, data analytics, and machine learning that we believe Google BigQuery and Google Cloud Machine Learning are particularly well suited to helping Ocado achieve.”

Scaling for new business

Scalability is also a major reason behind some of Ocado’s cloud initiatives, including the migration of all its on-premises data to the cloud. Ocado wanted to improve customer experiences, empower business teams with greater insight, and reduce IT overhead, so it consolidated onto Google Cloud Platform.

“The old databases just weren’t fast enough,” says Paul. “We needed a solution that could scale with the amount of data we generate and how we use it. Google Cloud Storage and Google BigQuery now provide the backbone, from a data point of view, for the Ocado Smart Platform.”

Ocado estimates its business, product, and transaction data is approaching two petabytes. Combining customer and supply chain data helps both internal Ocado operations and the company’s ambitions to commercialize OSP.

“When compared with other options for expansion internationally, selling OSP as a managed service lets us turn companies that could have been competitors into customers,” says Paul. “We want to build OSP once and then turn it on for multiple business-to-business customers.”

Each time Ocado adds a new hosting customer to OSP, it will launch a customized instance to fit that customer’s requirements. The capacity and performance of each new OSP instance must be able to scale quickly as the backend platform for established retailers with large numbers of products, customers, and transactions.

Ocado’s first OSP customer, Morrisons, is already benefiting from this first-of-a kind solution. Morrisons is one of the UK’s four largest supermarkets and uses OSP to power its online retail business. Using Google Cloud Platform, Ocado has stored, processed, and analyzed terabytes of Morrisons’ data using a dedicated data lake and Google BigQuery.

In addition to using Google Cloud Platform for OSP, Ocado also adopted it for its own online grocery retail business operation. Ocado originally used the Apache Spark and Apache Hadoop open-source frameworks on Google Compute Engine for its data platform. Moving to Google BigQuery frees Ocado business analysts from the complex query setup and workflows associated with Spark and Hadoop. Plus, it lets Ocado share data analytics with suppliers and partners.

Google BigQuery is well integrated with TensorFlow on Google Cloud Machine Learning Engine and Google Cloud Dataproc, the Apache Spark and Apache Hadoop service that lets Ocado use open source data tools for batch processing, querying, streaming, and machine learning. Google Cloud Dataflow and Google Cloud Dataproc handle cluster management, and provide an easy-to-use framework so developers can spend less time and money on administration and more time on delivering valuable business features.

Switching from Hadoop to Google BigQuery revealed a series of cost and performance improvements. For example, Ocado no longer needed to decide how many instances to bring up in a cluster or wait for the instances to spin up. Google handled everything.

“We simply ran our queries and paid for the resources that we use,” adds Roland. “One big win with Google BigQuery is we don’t have to do maintenance. Best of all, we saw Google BigQuery outperform our Hadoop cluster by over 80 times on our largest dataset, and for only two-thirds the cost.”

We’ve been saying it for years, the benefits and potential of the cloud abound. 

And yet, more than 80% of respondents in a survey of 753 business leaders point to managing cloud spend as their top organizational challenge, and these same respondents estimate that nearly 1/3 of their cloud spend is inefficient or wasted (Flexera, 2023). Many organizations are new to optimizing cloud costs and ensuring resources are used efficiently.

As your organization digitally transforms you may be realizing what other organizations are realizing too: When it comes to business value, simply migrating to the cloud isn’t enough. Achieving the full benefits of cloud requires fundamental changes to both mindset and behaviors around existing financial-management practices. It requires changing the way your disparate teams work together. 

Enter the Cloud FinOps Building Blocks

Cloud FinOps is a framework, discipline, and cultural shift combining people, processes, and technology to drive financial awareness and accountability. FinOps practices align engineering, finance, technology and business leaders and teams under a primary objective: to maximize business value from the cloud. With Cloud FinOps practices, every business stakeholder is charged not only to take responsibility for their spending and costs, but also to optimize them. These practices enable businesses to manage consumption and make sound, data-informed cloud-spend decisions. Cloud FinOps is comprised of five building blocks:

1. Accountability and enablement
   Establishing governance and policies to manage cloud spend and realize business value.
2. Measurement and realization
   Driving financial accountability and value realization with a defined set of KPIs and success metrics.
3. Cost optimization
   Providing financial visibility and recommendations of IT resource usage to optimize cloud spend.
4. Planning and forecasting
   Modernizing budgeting, forecasting, and chargeback methods to allow for iterative, innovative and cost effective development practices.
5. Tools and accelerators
   Deploying and integrating a set of cloud cost tooling to effectively manage and track cloud spend. Learn more here. 

For a general overview of the Cloud FinOps framework and more on the five building blocks, check out these resources:

• Video | What is FinOps and 3 reasons why you should care about it.
  This 5-minute video provides an overview of FinOps, the 5 building blocks, and how they can benefit your organization. 
• Podcast | FinOps with Joe Daly
  In this podcast, Joe Daly of the FinOps Foundation shares about the key principles of FinOps, which he refers to as financial DevOps. Daly discusses how this framework is helping companies make better and more efficient financial decisions while taking advantage of the cloud.
• Blog | Decoding Cloud FinOps to accelerate digital transformation
  This blogpost discusses the critical role of FinOps in a successful digital transformation. It outlines key metrics to help measure and track business value and to increase visibility into the effects of digital transformation on top-line revenue. 
• Article | Cloud FinOps: The secret to unlocking the economic potential of public cloud
  This Forbes article profiles OpenX, the first major advertising exchange platform to migrate entirely to the cloud. It details the 5 key pillars of the Cloud FinOps framework, which OpenX leveraged in their digital transformation strategy. In just 9 months, they reduced their per-unit costs by more than 60%.  

Importantly, Cloud FinOps isn’t about saving money; it’s about making money. It’s about promoting a cost-conscious culture, financial accountability, and business agility in the cloud. Whatever stage of the cloud journey you’re at, cloud FinOps practices will help you get the most value out of Google Cloud. This framework can help to remove blockers, implement the building blocks, and empower your teams to make better business decisions. 

The Cloud FinOps Journey 

Implementing Cloud FinOps is neither a destination nor a box your organization will check then archive. Rather, Cloud FinOps is an ongoing journey and discipline. It’s inherently iterative. As such, growth and maturity across processes, capabilities, and domains requires action, repetition, and continuous learning. 

Across the five FinOps building blocks, we’ve identified 50 subprocesses to best understand organizations’ FinOps proficiency, capabilities, practice domains, and blind spots. We scale them from 1 to 5 and categorize them in one of three phases of maturity: Crawl, Walk, or Run. Organizations in the Crawl phase tend to focus on technical problem solving and cloud-cost visibility. Organizations in the Walk phase emphasize strategic improvements such as employing cost visibility dashboards to realize better business value. And organizations in the Run phase are focused primarily on transformational change and strategic innovation, factoring cost considerations into both processes and cloud architecture. 

Through this “crawl, walk, run” maturity model, we can evaluate proficiency, establish a benchmark, and recommend a targeted action plan for FinOps adoption. And whatever your level of maturity, your organization can take quick scalable action not only to foster improvement but also to evaluate outcomes and gain insights. 

The key here is that regardless of your organization’s Cloud FinOps maturity level, you can take small steps now toward continuous improvement. Here are some common focus areas and several more resources organized by maturity level that you can access.

Crawl phase 

Improve cloud-cost visibility. 

• Whitepaper | Drive Cloud FinOps at scale with Google Cloud Tagging
  Tags and labels can be useful and flexible tools to help your organization segment cloud spend and allocate costs. This whitepaper introduces Google Cloud Tags and best practices for implementing them. It differentiates tags, which offer reliable reporting and governance features, from labels, which can be prone to problems, including poor coverage and a lack of integrity in data labeling. 
• Whitepaper | Unlocking the value of Cloud FinOps with a new operating model
  This white paper unpacks the details of the FinOps operating model, including roles, organizational alignment, and driving culture change. It details how to establish strong financial governance and a cost-conscious culture. 
• Whitepaper | Cloud FinOps: Shared services cost allocation
  In this whitepaper, you’ll explore the elements of cost allocation as well as the complexities and challenges associated with shared-services cost allocation. While some of these concepts and models are interchangeable between legacy and cloud environments, this whitepaper focuses primarily on cloud computing and associated services.

Walk phase

Improve business-value realization. 

• Blog | 5 key metrics to measure Cloud FinOps impact in your organization in 2022 and beyond
  To drive business growth and topline revenue, business leaders must be able to connect cloud investments to business outcomes. As such, traditional IT metrics and KPIs must continue to evolve. In this blogpost, we’ll explore five key business-value metrics aligned to the five Cloud FinOps building blocks. 
• Whitepaper | Maximize business value with Cloud FinOps
  The cloud introduces new complexity and challenges to traditional IT financial management. As such, it requires strategic financial governance, processes, and partnership across the organization. This whitepaper explains how Cloud FinOps helps enterprises that have invested in cloud to drive financial accountability and accelerate business value.

Run phase 

Improve strategic cloud innovation. 

• Whitepaper | Unit costing: The next frontier in cloud
  In this whitepaper, you’ll explore the nature of and need for cloud unit costing, the standard by which FinOps practitioners obtain full business context for their cloud costs. It features examples from cloud-first organizations that have pioneered FinOps practices. Additionally, it examines several cloud forecasting and budgeting methods, ranging from least to most rigorous. 
• Blog | You get what you pay for: Principles for designing a chargeback process
  Chargeback, a crucial Cloud FinOps capability, is the process of mapping cloud consumption to internal users within an organization. It provides transparency, facilitates accountability,  enables recovery of cloud costs, and fosters a culture of fiscal responsibility. This blogpost will walk you through some best practices in designing an effective chargeback process in Google Cloud. 

Success with Cloud FinOps

As global markets continue to face challenges, there’s never been a better time to increase the return on your cloud investments. Adopting and implementing FinOps practices will help. For some real-world examples of how organizations across a range of FinOps maturity levels have collectively saved millions of dollars on their overall cloud spend, check out these customers’ stories. 

• Video | Next 2022: Top 10 ways to lower your costs on Google Cloud with General Mills
  In this video, which highlights ten leading cloud cost optimization practices, hear how General Mills, which is on pace to increase their cloud footprint by 60%, has approached the discipline of cost savings and accelerated their adoption of Cloud FinOps to drive waste out of their cloud usage. 
• Video | How Nuro optimized their costs on Google Cloud
  In this video, you’ll get an overview of the Google Cloud FinOps framework, a deep dive on cost-optimization best practices, and hear about how startup Nuro AI has adopted their own cost-savings discipline and Cloud FinOps practice. 
• Video | How OpenX reduce per unit costs by 60%
  In this video, you’ll learn how to establish a cost center of excellence within your cloud practice, explore several cost-optimization recommendations, and hear from OpenX about how they reduced their costs on Google Cloud. 
• Case Study | How Sky saved millions with Google Cloud
  In this case study, read how a few years into their cloud adoption journey, media and entertainment company, Sky Group discovered over $1.5 million in savings and optimized costs with BigQuery, Compute Engine, and Cloud Storage. 
• Case Study | Etsy: Doing more with less cost and infrastructure
  In this case study, read how after migrating their data center and ecommerce platform to the cloud, Etsy realized more than 50% savings in compute energy and leveraged committed use discounts (CUDs) to reduce their compute costs by 42%. 

It’s important to remember that FinOps success looks different for different organizations. It’s neither a one-time fix nor a destination reached by way of a single path. But for every organization, success requires small actions, refinement, and continuous improvement. As you leverage Google Cloud FinOps resources and tools, your organization can:

• Drive financial accountability and visibility.
• Optimize cloud usage and cost efficiency.
• Enable cross organizational trust and collaboration.
• Prevent cloud-spend sprawl.
• Break down departmental silos.
• Accelerate innovation. 

Getting started with Cloud FinOps

At Google, we have a team of experts in leading FinOps practices dedicated to helping you create an actionable plan to optimize cloud spend and drive cost efficiency. We’ve created numerous resources to help you get started from any stage in the FinOps journey. 

Whitepaper | Maximize Business Value with Cloud FinOps
This whitepaper outlines steps to help your organization implement FinOps. It details required teams and processes as well as the optimal behaviors, approaches, and outcomes to help maximize your investment on Google Cloud. 

With Google Cloud FinOps, your organization can also accelerate business value in the cloud. To find out more, join us on the Google Cloud Twitter channel twice a month for open Twitter Spaces discussions or reach out to your Google Cloud Sales Representative for a 1:1 discussion.