As lead for analytics and AI solutions at Google Cloud, my team works with startups building on Google Cloud. This puts us in the fortunate position to learn from founders and engineers about how early-stage startups’ investments can either constrain them or position them for success, even at the seed level. In this post, I want to share a few of the best practices to keep in mind as you’re building. 

Understand your value proposition before diving into a technology stack

If you’re launching a startup in the cloud, you’re no doubt thinking about a technology stack, but it’s important to step back a bit and think carefully about the major value proposition that your startup offers to your customers. That value proposition is going to fundamentally drive the kind of technology that you should pick.

For example, does your system need processing in real time, or can it be done in a batch mode? Can you rely on once-a-day insights or do the insights have to come in as events happen?

Additionally, what kind of latency will your customers face? That latency makes your value proposition either usable or unusable. Early on in Google’s development, leaders realized that no one was going to wait more than a few hundred milliseconds for a web page to show them their results, and that realization drove the technology decisions that have allowed Google to scale from being a startup in a garage to being a trillion dollar company. Your startup needs to define its value to customers with this level of specificity before it can build a technology stack suited to its needs. 

Focus on customer interactions

A few companies have gracefully pulled off big IT pivots that reshaped their value proposition. Netflix, for example, moved from mostly sending DVDs through the mail to becoming a streaming service and major content producer. That’s a huge shift in the user experience and the technology stack necessary to support it, even if the underlying value proposition (i.e., get content to customers) was broadly the same. But it’s also an outlier. If you’re planning for potential changes of this magnitude, rather than focused on getting your value proposition to users, you probably need to sharpen what that value proposition is.

Specifically, you need a clear vision of how customers will access and interact with your business. Typically, they’ll do so over a website or a mobile app, but there are still so many variables. 

Are customers going to transmit documents? If so, in what format? Is handwriting supported or is input limited to typing? Can they use images for optical character recognition? Will it mostly be forms? Will the data be structured or unstructured? If all that sounds  a little overwhelming, don’t worry, it’ll seem simpler by the end of this article—but also be aware: we’re just getting warmed up.

Imagine that most of your customers will access your business via voice, so you know you’ll want to prioritize conversational workflows. That’s a start—but dig deeper.  Even if we suppose you’re usingDialogflow, a Google Cloud conversational AI platform that lets you build and deploy virtual agents, we’re still not really seeing the value proposition.  How will all this work, from the beginning of a typical full customer interaction to the resolution? How many interactions will have to be facilitated over low-bandwidth connections, for example? When it comes to user interactions, make sure you can see an end-to-end use case.

Another example: you’re building a retail website, and one of your end-to-end use cases involves the customer asking if a certain amount of a given product is in stock, whether it’s one unit of the product, ten or hundreds. If the product is not sufficiently stocked, you want your app to offer similar items that are. Will your technology stack support this end-to-end use case?

These considerations are not an argument for premature optimization. There’s value in moving fast, getting minimum viable products to users, and then iterating. But in the early stages, you only get one chance to start on the right foot—and how you navigate that chance will influence a lot of dollars and effort down the road. You need to make sure you have business use cases, not just an idea, before you can start designing a technology stack.  

Here’s how to get in the right frame of mind. Pick three use cases: two that are “bread and butter” and one that is technologically complex.  Make sure your proposed technology stack can support all three, end to end. 

Default toward higher levels of abstraction

Now that we’re in the right frame of mind, we’re ready to think about the technology stack more directly. 

As a startup, you’ll need to conserve resources, and to do that, you’ll want to build at the highest level of abstraction possible for your value proposition. For example, you probably don’t want your people setting up clusters. You don’t want them configuring things if they can use a fully managed service. You want them focused on building your prototype, not managing infrastructure.

This focus has definitely informed how we create products at Google Cloud, as our canonical data stack—Pub/Sub, Dataflow, BigQuery, and Vertex AI—consists of auto-scaling and serverless products.

But management of infrastructure is not the only place where you should err toward a less-is-more philosophy. 

When it comes to architecture, choose no-code over low-code and low-code over writing custom code. For example, rather than writing ETL pipelines to transform the data you need before you land it into BigQuery, you could use pre-built connectors to directly land the raw data into BigQuery. That’s no code right there. Then, transform the data into the form you need using SQL views directly in the data warehouse. This is called ELT, and it is low code. You will be a lot more agile if you choose an ELT approach over an ETL approach. 

Another place is when you choose your ML modeling framework. Don’t start with custom TensorFlow models. Start with AutoML. That’s no-code. You can invoke AutoML directly from BigQuery, avoiding the need to build complex data and ML pipelines. If necessary, move on to pre-built models from TensorFlow Hub, HuggingFace, etc. That’s low-code. Build your own custom ML models only as a last resort.

Focus on getting your vision to market, not chasing technology hype  

The goal is to pick the right technology stack for bringing your vision to market, generating value for customers, conserving resources, and maintaining flexibility for growth. Early IT investments should usually gravitate toward things that preserve flexibility, such as managed services built on standard protocols or open APIs, but they needn’t always rush to the flashiest technologies.  The answer isn’t always ML, for example. The answer might be heuristics to start, with a path to ML once you have collected enough data. You want to make sure that your intelligence layer has enough abstraction so you can mark it up with simple rules at first, but then replace it with a more robust system as you go along. 

Launch and iterate fast with these principles 

The preceding discussion is a reminder that your most expensive resource is your people—and that you really want them to be focused on building your prototype, minimum viable product or production app  You want to launch fast and iterate fast, and the only way you can do that is by focusing on the things that differentiate you. 

But regardless of the technologies you use, the bottom line is the same: follow these four principles. 

• Figure out your major value proposition and design your tech stack around it. 
• Be very careful about user interactions. User experience is super important; you need to make sure you deliver the kind of experience that your customers have grown to expect.
• When you’re building, pick the highest possible level of abstraction possible—the most fully managed tools and no-code/low-code frameworks that give you the functionality that you need. 
• Instead of choosing new or flashy technologies, consider if you can build a “good enough” minimum viable product quickly and come back to a better implementation later. 

To learn more about why startups are choosing Google Cloud, click here.

Google Cloud’s partnership with Sky Group, one of Europe’s largest media and entertainment companies, dates back more than four years to when Sky first became a Google Cloud customer moving diagnostic data from millions of its Sky Q TV boxes to its Google Cloud data platform.

In June 2019, a few years into their cloud adoption journey, Sky was faced with a challenge they had anticipated from the start. Their recent bill across all major cloud providers had been increasing rapidly, reaching their planned yearly budget after only six months. Sky wasn’t sure if they’d undershot their forecasts, if they were overspending, or both.

“In the beginning, we were given a brief to investigate internal cloud spend with the aim of finding out where we could make savings, but in reality we didn’t know what we would expect to find,” said Nathan King, a cloud architect in the Cloud Enablement Center and now Head of Cloud Financial Management (FinOps) at Sky since the start of 2020.

Nathan assembled a small team who started to explore Google Cloud spend using the Cloud Billing tool. At first, they drilled into their biggest Google Cloud cost categories and discovered some immediate cost optimizations with BigQuery, Compute Engine and Cloud Storage. Over the course of the next six months, through careful analysis, they managed to find over $1.5m in immediate savings, exceeding expectations.

Yet they soon realized this was just the tip of the iceberg—it was clear there were millions of pounds more savings to be made, but actually achieving them at scale would require careful planning. “We formed a FinOps function to target these savings, but with 600 to 700 projects for Google Cloud alone, spanning four Google Cloud organizations, it would have been a manual process and difficult for teams to digest our recommendations,” Nathan said.

After attending a Google-led FinOps workshop and shaping their FinOps strategy, Nathan’s team focused on iterating through the FinOps lifecycle phases of Inform, Optimize, Operate and generating savings over time. Here’s how they did it:

Inform: Make Information Visible

The first step was focused on developing a clear vision for cost allocation and recharge, which required partnering closely with the finance, procurement and tax teams (particularly for international and affiliates) to understand the supporting business logic and processes. With a lot of hard work, the team managed to break down barriers to implement and embed new processes into broader business functions like finance.

WIth the recharge model in place, the team ran a number of pilots to find the right FinOps tooling to meet their needs. They ran a number of pilots, including using Data Studio and visualizing BigQuery exports. Given their ambitions to scale across the enterprise globally, the team chose Google Cloud’s Looker to realize their vision, building intuitive dashboards to visualize spend and recommendations across all cloud providers. “We wanted one view across all clouds, where customers can dynamically see cloud spend and intelligent optimization recommendations in just one place,” Nathan said.

After less than three weeks of development, the Looker dashboards were ready to go and have been a game changer ever since. “The moment our leadership and different departments started seeing the Looker dashboards, the value we were adding as a FinOps team became immediately clear,” Nathan said.

There are different report pages for each stakeholder group, each custom developed and automated using Looker and BigQuery. The BigQuery Optimization page, for example, provides insights on Slots consumed across the organization, down to granular query data like the cost of each query, how it was written, who submitted it and number of slots utilized. The dashboards also highlight potential areas of optimization, like BigQuery datasets without retention policies set or where data isn’t partitioned.

A recent breakthrough has been building pages for business teams, showing the related cloud spend contributing to a business unit of value, such as the cost per live stream or per subscriber in Sky’s case. Although this is an inherently difficult metric to capture, the opportunity has been made possible with the FinOps team’s progress and is starting to drive business investment decisions.

Optimize: Drive Cloud Efficiency

The second stage of the FinOps lifecycle focuses on delivering optimizations. As Sky’s FinOps dashboards were operationalized and highlighted savings opportunities, they enabled users to generate more than $3 million in Google Cloud savings alone in 2020 and over $800,000 in other cloud providers.

The team began with focusing on the top four products by spend: BigQuery, Compute Engine, Cloud Dataflow and Cloud Storage. Working with their Google account team and studying Google whitepapers and blog posts like Cloud cost optimization: principles for lasting success, they developed their own best practice guidance and embedded recommendations into the dashboards.

Creating their own recommenders and leveraging Google Cloud’s recommenders, the team discovered a plethora of cost optimization opportunities. “Key examples were overly expensive queries, storage buckets set without retention policies, and VMs without autoscaling enabled,” Nathan said. Teams were then empowered to make their own savings, like the NowTV business unit that had been forecast to overspend for the year until they received their dashboard with thousands of optimization recommendations. After just three weeks, the team had implemented more than 90% of recommendations and brought their spend under budget for the year, saving more than 50%.

The FinOps team still searches for new recommendations every day and have been collaborating with Google product managers to take their insights to the next level. “We’ve loved partnering with Google product managers, who encourage us to give feedback on new features before they go to market. We’ve also shared some of our in-house recommenders to influence the features being developed by Google, including the Idle VM and Idle Persistent Disk Recommenders as part of Active Assist,” Nathan said.

Operate: Embed FinOps & Drive Self-Sufficiency

Now that teams could visualize their cloud spend and make real-time decisions based on cost optimization recommendations, the FinOps team has begun working on embedding processes, leveraging machine learning, and improving efficiency in their own ways of working.

Looker’s extensive capabilities continue to play a role in this. “Before we started using Looker, our most popular report was an electricity bill showing customers’ detailed monthly cloud spend, previous month comparisons and forecasts for months ahead,” Nathan said. “This report took days, sometimes weeks to run. With Looker, we’ve automated the entire process and brought that time down to just minutes.”

More teams are embedding the dashboards into their own processes, like finance, which now uses the interactive dashboards in meetings instead of static report snapshots, or in-house Google Cloud architects, who use the recommendations to optimize their cloud spend before deploying any technology.

As the FinOps team continues to operate like a product function, designing with CX/UX in mind and iteratively releasing new features like anomaly reporting, budget alerts, and forecasting based on machine learning, it’s becoming clear that Cloud Financial Management is a key capability and mindset that can impact wide-reaching parts of the business at scale.

Elevating Sky’s FinOps journey to the next level
Indeed, as more business teams collaborate with the FinOps function, the opportunities are growing. “The FinOps team has changed the way we view and manage cloud spend, enabling us to partner with finance and show digestible reports to the CFO. We’re now looking further to broaden our range of insights, like elevating our dashboards to understand how using Google Cloud is supporting Sky’s Net carbon zero ambitions by incorporating Google’s data center sustainability metrics,” says Vince Marco, Architecture Manager at Sky.

So, after being unsure of drivers for their increasing cloud spend in 2019, 18 months later Sky is far more confident about its investment decisions. The team knows that every dollar spent is being used optimally and driving maximum value for its investment.

If you’re an enterprise using cloud, but want to better manage cloud costs, consider setting up a FinOps capability and creating a FinOps mindset. Looker can help you get started by providing reporting and insights into cloud expenditures to identify initial savings. As you learn more and scale, empower teams to make their own savings utilizing built-in actionality for monitoring and customizing for business billing activity nuances and department-specific chargebacks. Reimagine how cloud finances can be managed and optimized as Sky is doing.

To learn more about Looker’s Cloud Cost Management Block visit Looker Marketplace.

Google Cloud today announced that it has been selected by the Israeli government to provide public cloud services to help address the country’s challenges within the public sector, including in healthcare, transportation, and education. 

Following a thorough public tender process, Google Cloud was selected for this four-phase project known as “Project Nimbus.” The agreement will deliver cloud services to all government entities from across the state, including ministries, authorities, and government-owned companies. The agreement is also available for higher education, health maintenance organizations and municipalities. The project will run for an initial period of 7 years, and the Israeli government may extend the engagement for up to 23 years in total. 

As part of the agreement, Google Cloud will work with the public sector on the formulation of best practices for cloud migration, integration and migration to the cloud, and optimisation of cloud services. Google Cloud will also provide training to the country’s technical government employees and senior leaders to enhance digital skills. Google Cloud announced last month that it will open a Google Cloud region in Israel to make it easier for customers to serve their own users faster, more reliably and securely. The region in Israel will be available to serve not only the government and related entities but also private commercial companies, just like any other Google Cloud region. 

The Accountant General of Israel, Mr. Yali Rothenberg, congratulates Google on their winning bid in the first tender of “Project Nimbus”, a multi-year flagship project led by the Israeli Government Procurement Administration, that is intended to provide a comprehensive framework for the provision of cloud services to the Government of Israel.

We are delighted to have been chosen to help digitally transform Israel. This builds on the continued success we are seeing with the public sector globally.

Google Cloud region in Israel

In April, we announced that a new Google Cloud region is coming to Israel to make it easier for customers to serve their own users faster, more reliably and securely. Our global network of Google Cloud regions are the foundation of the cloud infrastructure we’re building to support our customers in the Middle East and around the world. With cloud’s 25 regions (and forthcoming Middle East regions in Qatar and Saudi Arabia) and 76 zones around the world, we deliver high-performance, low-latency services and products for Google Cloud’s enterprise and public sector customers.

Looking back on the past year, I see challenges—but also reinvention. Reinvention in how children are educated. Reinvention in how medical professionals provide care. Reinvention in how customers purchase products. This reinvention was made possible by all of the IT leaders around the world who had a vision about what could be possible with technology.

Technology has allowed people to work and complete critical activities safely outside of their standard locations. But, it has also enabled transformation in ways we have not seen before: in how people collaborate, in how businesses operate, and most important to me, in how organizations innovate.

Rethinking what it means to transform

The leading indicator for organizations that are accelerating their innovation during this time is how they are thinking about transformation. Instead of asking infrastructure questions about where their apps and services should run, they are asking transformation questions about how to build an environment that enables every person, process, and technology to adapt in order to bring the highest level of innovation to the business. 

Innovative companies have moved beyond migrating their data centers to the cloud, changing not only where their business is done but, more importantly, how it is done. For example, Papa John’s recently announced they are building a digital platform that looks at real-time data across the business to improve its loyalty programs, website, and customer and partner experiences. Albertsons Companies is transforming itself by reinventing grocery shopping, both the digital and physical aisle, through shoppable maps, AI-powered conversational commerce, and predictive grocery list building. Airbus is reimagining their work environment to embrace the hybrid work reality. And Siemens is partnering with Google Cloud to reinvent industrial manufacturing with AI to empower employees, automate mundane tasks, and improve product quality.  

Here, transformation is made possible with technologies that enable new innovations for their customers versus decisions about where infrastructure should be run. And this aligns with a recent study by Forrester that states the top two IT initiatives for the next 12 months are to increase innovation and to invest in technology that helps employees do their jobs better.¹

Some of the best conversations I’ve had with customers are focused on how to:

• Accelerate transformation while also maintaining the freedom to adapt to market needs.
• Make every employee—data scientists to sales associates—smarter with real-time data to make the best decisions.
• Bring people together and enable them to communicate, collaborate, and share with each other when they can not meet in person.
• Protect everything that matters to us—our people, our customers, our data, our customers’ data, and each transaction we undertake.

This new customer thinking is driving new technology requirements—requirements that can be solved through a transformation cloud. A transformation cloud accelerates an organization’s digital transformation through app and infrastructure modernization, data democratization, people connections, and trusted transactions. The result is an organization – and its workers –  that can take advantage of all of the benefits of cloud computing to drive innovation.

The new requirements for innovation

Organizations want to work with multiple cloud providers to choose the best technology for each of their apps and services while also mitigating against inevitable cloud outages and vendor lock in. They value the flexibility of open source based solutions and look to our multicloud platforms like Google Kubernetes Engine and Anthos to instill freedom in how they innovate and drive differentiated customer experiences. It is no surprise that, in a recent Google-commissioned IDG research study, 78% of Global IT leaders stated that multi/hybrid cloud support is a major consideration when selecting a cloud provider and 74% preferred open source cloud solutions.² Customers like MLB, DenizBank, and Macquarie Bank understand the necessity of a multicloud strategy and are taking advantage of Google Cloud’s open, hybrid architecture to give them the maximum flexibility to run their business how and where they want.

These organizations also want to use data to better understand their customers, enhance their products, and improve inventory accuracy in order to make real-time decisions—and bring it together into a cohesive data cloud. They value how analytics solutions democratize access to data for all employees and how embedded AI helps them predict and automate the future. The Forrester study mentioned above also shows that companies focused on improving their use of data for better decision-making, are taking actions to improve data self-service capabilities and make access to data and insights more democratic.³ Customers like Twitter, PayPal, The Home Depot, HSBC, and Stanford Medicine, unify their data across their organizations to power deeper AI-driven business insights, make better real-time decisions, and build and run their data-driven applications. 

And while technology is driving many of the transformations we see, so are an organization’s people. Workers are finding new ways to strengthen human connections, deepen their impact, and serve customers while transforming how work happens—as shown in the fact that 59% of organizations in the IDG study accelerated or newly introduced remote working and collaboration capabilities in 2020.⁴ Customers like Kia Motors, Cambridge Health Alliance, and PwC are using Google Workspace to enable teams of all sizes to connect, create,  collaborate, and to drive innovation from any device, and any location.

Finally, the innovation that we see in every digital transaction is matched with new ways to protect and secure the business. Organizations want to protect their employees, customers, and partners against emerging threats, analyze massive amounts of data to secure infrastructure, and build a long term strategy for strategic governance of their assets regardless of their location. Getting this right is essential as organizations see security as a top pain point impeding innovation.⁵ Customers like Equifax and Evernote are using Google Cloud’s secure platform and security products to extend customer confidence anywhere their systems may operate.

Google Cloud technologies are already powering customers’ transformation clouds

Supporting our customers’ reinventions are our top priority and we believe that together, we can pave the way for what is next. With our investments in multicloud and AI/ML, to sustainable infrastructure, industry solutions, and technology that improves our communities, such as COVID-19 vaccine distribution, we are proud that our customers trust Google Cloud solutions to digitally transform their business.

We have lots more to tell you about in the coming months, starting with our Data Cloud Summit on May 26th. Between this event, and the multiple other events we have this summer, you will learn about how our industry leadership and collaboration with our partners are enabling our customers to build powerful transformation clouds to support their continued reinvention.

^{1. Forrester Analytics Business Technographics® Priorities And Journey Survey, 2021
2. IDG Communications, Inc: “No Turning Back: How the Pandemic Has Reshaped Digital Business Agendas”, 2021
3. Of the companies that prioritize data in decision making, 37% are improving data self-service capabilities and 30% are making access to data and insights more democratic (Forrester Analytics Business Technographics® Priorities And Journey Survey, 2021)
4. IDG Communications, Inc, “No Turning Back: How the Pandemic Has Reshaped Digital Business Agendas”, 2021
5. 33% of organizations stated Security risks & concerns as a top pain point impeding innovation (IDG Communications, Inc, “No Turning Back: How the Pandemic Has Reshaped Digital Business Agendas”, 2021)}