Cloud

Cloud Bigtable is a NoSQL database service that can achieve single-digit millisecond latency for reads and writes. However, if you’re developing applications with complex infrastructure, global customers and millions of interactions, there is potential for unexpected latency. Latency in large scale systems can cause missed business opportunities, poor customer experiences, and disrupted data pipelines. 

Because Bigtable is a managed service, you can monitor its resources with several real time dashboards and tools. You’ll see throughput, resource utilization, error rates, and more, which will help you quickly deploy, debug and recognize issues. We have added several new client-side metrics as part of the Bigtable client libraries for Java to improve the monitoring experience. Based on common support questions, we identified metrics to increase the transparency of the entire remote procedure call (RPC.) Now, you can find out more quickly whether latency is coming from issues on your end – like from a new feature or an anti-pattern in your code – or if service downtime is causing the issues and needs escalation.

In this blog post, we’ll look at how to get deeper insights into your applications with the new Bigtable client-side metrics. Then we will examine a few scenarios to learn how to debug and address the issues with the metrics.

Understanding the new metrics

There are eight new metrics available when you use either the Bigtable client library for Java or the HBase client available for customers migrating from HBase. Most of the metrics are data points that will provide valuable transparency into your system. 

Previously, you may have been able to see the overall latency of a request but couldn’t investigate the individual layers of it, which made debugging some scenarios difficult. Now, you can quickly identify a variety of issues on your own and reach out to Support with more informed questions if needed.

The diagram below shows a simplified request lifecycle. Each of the new metrics are labeled, so you can see which layer of the request it is measuring. The lifecycle begins with a client application making a request to the Google frontend, which gets routed to the Cloud Bigtable server. If the request receives a retryable error, the client schedules a retry and resends the request.

If you can identify where the latency is coming from, then you’ll have some clear steps to begin troubleshooting. There are full descriptions of each metric available in the Cloud Bigtable documentation, so use this quick reference to get started:

Client-side Metric Setup

To enable the new client-side metrics in your Java client, follow the steps in the Cloud Bigtable documentation. The change to your code just needs to be added wherever your code connects to the Bigtable client.

After 15 minutes, the new metrics will be accessible via the Cloud Monitoring UI and the Cloud Monitoring API. We’ll look at the metrics via the Cloud Console in the Metrics Explorer. 

Using the metric selector, search for bigtable.googleapis.com/client to find the client-side metrics.

You can view additional metrics in one graph by clicking “add another metric” at the bottom. You can also create a dashboard with each metric by clicking “save chart” and adding the chart to your dashboard. We have an example dashboard available in a JSON file and instructions for how to upload and install it to the Metrics Explorer. This example shows all the new metrics in a dashboard that you can customize.

Debugging common problems

As we’ve explored, there are a variety of reasons latency could be different than expected. Let’s look at three examples of high latency exposed by the client-side metrics, their cause and how to fix them.

High attempt latency: Incorrectly sized channel pool

Our first application is doing very low queries per second (QPS) reading from Bigtable with around one request per second. Perhaps this is a dashboard that reads large amounts of data per request, but isn’t receiving frequent throughput traffic. 

The latency graph looks like this:

The operation latencies are above 3 seconds or 3000 ms, but the Bigtable server latencies are all less than 25ms, so I know my query isn’t causing any issues and something else might be going wrong here. We can look at our custom Bigtable client-side metrics dashboard which contains retry count and connectivity error for more context.

The retry count metric shows that the requests are being retried multiple times. There are also thousands of connectivity errors per minute, indicating that something is wrong with my connection. 

Thankfully, this is a simple fix in this scenario – after reading about connection pools in the documentation, we realize that we need to change the default settings since this is a low-throughput scenario. The idle channels are getting disconnected, and constantly reestablishing the connections is causing the high latencies. We can calculate and set the appropriate size channel pool (a minimum of two channels is recommended) and see the low latency as expected.

High application blocking latency: Processing live read row stream

In another application, perhaps we are doing a prefix scan over several rows and processing each of them. A snippet of the code looks like this:

The 50th percentile (P50) for Bigtable server latencies are low at around 4ms, but the P50 attempt latencies are around 275ms. We can see that this latency comes from the processing time of the responses, and if we look at the metrics we’ll see most of this latency comes from the application blocking latencies, which are also around 275ms. 

To reduce the overall latency or the attempt latency in this scenario, we can cache the rows and then process them:

In this example we’re not going to see a significant difference in the overall application performance since each row still takes 5ms to process, but it is a useful way to verify that the larger latency is expected or remove it as a factor altogether. The graphs below shows the original latencies and then the decrease once the code was updated:

High server latency: Cross-region application servers and Bigtable

In this last example, we’ve solved all our latency problems and want to add an additional Bigtable cluster in a different region because we’re expanding globally. When we check the latencies, we see the attempt latencies are around 200 ms while the Bigtable server latencies are still low around 2.2 ms.

The server latencies are around 147 ms though, which indicates the high attempt latencies are coming from the application communicating with the Bigtable server. This makes sense since there is a round trip call across regions. We can add an additional machine to our application server in the region of our new Bigtable cluster and should see this round-trip cost eliminated.

Next steps

In this article, you’ve learned how to get increased observability and transparency into your systems with Cloud Bigtable’s new client-side metrics, seen how to enable them, and explored a few common scenarios that could be impacting your overall system latency. 

Read up more on the new client-side metrics to further optimize your Bigtable system.

Set up some monitoring dashboards

Learn about Bigtable performance overall to ensure your database design isn’t causing high Bigtable server latency and you’re getting the best usage out of your tools

Add alerts for your newly monitored metrics to ensure you can quickly react to any latency changes

Editor’s note: Google Cloud partner commercetools shares how modern technologies like composable commerce, cloud-native infrastructure and artificial intelligence/machine learning (AI/ML) will lead the way in business-to-business (B2B) digital commerce this year.

Digital commerce in B2B has been predicted as the next big thing for years; yet, at the start of COVID-19, 60% of B2B companies had zero or limited eCommerce capabilities. The pandemic accelerated digitization and eCommerce has finally taken off: As of February 2022, 65% of B2B companies offered eCommerce capabilities. 

The behavior of B2B buyers is also changing: Consumer-like expectations are at the heart of successful B2B commerce, and this is how manufacturers, distributors and wholesalers will shape their customer experiences. Today, 73% of B2B buyers want a personalized business-to-consumer or B2C-like experience. 83% prefer ordering or paying through digital commerce and 72% are eager to purchase across channels. 

With digital commerce dictating how B2Bs will grow in 2023 and beyond, what trends will spur digital transformations across this business model? Here’s what the team at commercetools expects to unfold in B2B eCommerce this year.  

#1 B2B firms are switching to cloud-native, composable commerce 

B2B players still plagued with manual processes and siloed backend systems will move away from monolithic platforms and choose composable commerce. In a nutshell, composability enables businesses to select best-of-breed components, such as search, cart or checkout, and “compose” them into a custom application.

B2B firms will modernize their commerce backend, interoperating siloed systems like Configure Price Quote solutions (CPQs) for sales and enterprise resource planning solutions (ERPs) for order entry with an API-first and composable commerce stack. They will also pivot from on-premise deployments to cloud-native architectures as the baseline for auto-scaling capabilities instead of pre-provisioning online capacity during traffic peaks. That way, B2Bs can customize customer-centric experiences to boost revenue while reducing the complexity and cost of in-house IT infrastructure, as well as gaining operational efficiencies.

B2Bs will maximize the cross-section of composable commerce and cloud-native infrastructure by leveraging a commerce backend like commercetools Composable Commerce hosted on Google Cloud. This combined solution provides commercetools’ ready-to-use components built as microservices and exposed as APIs, such as product information management (PIM) and unified cart, integrated through the Google Cloud Marketplace. 

#2 Strong focus on data quality and personalization 

Focusing on data quality continues to be a big trend in 2023. B2B buyers expect product, pricing, inventory and shipping data points to be accurate across every touchpoint so they can make better purchasing decisions, such as when to order products and calculate quantities.

With so many data points to capture throughout the customer journey — product, inventory, pricing and customer data — we’ll see more B2B companies reorganizing their vast information pools to elevate customer experiences. They will pivot to modular and API-first solutions, plus flexible data models, so they can break data silos from legacy monolithic platforms and access such data when needed. 

We also expect to see more customer analytics to unlock data on buyer behavior. By understanding what customers see, click and add to their shopping lists, B2B businesses get valuable insights into how buyers behave, using this data in the shopping journey according to product interests. That way, it’s possible to offer personalized experiences across touchpoints without hassle. 

“It is important for B2B companies to look at their data as if it is one of their products; invest in its upkeep and integrity while finding ways to continuously improve it. Using advanced analytics powered by AI and ML to identify patterns from large amounts of data, B2B companies can activate insights into customer decision journeys to maintain loyalty, personalize experiences to improve satisfaction and boost revenue, while also finding ways to optimize costs. For example, with analytics, enterprises can streamline spend to focus on the highest-performing channels and reduce waste.” — Carrie Tharp, Google Cloud VP of Retail and Consumer  

With data-driven tools coming into play like Google Cloud’s Discovery AI, Recommendations AI and Vision Product Search connected with composable commerce, B2B players can boost customer analytics to personalize experiences, improve customer satisfaction and reduce churn.  

#3 The B2B customer experience will be redesigned

B2B players are taking a page out of the B2C playbook to elevate experiences throughout the customer journey. While intense work needs to happen in the backend commerce engine, B2B players will also redesign their digital frontends. That means boosting website performance, while mobile responsiveness and personalization will be at the forefront of these advanced digital initiatives. 

More than ever, B2B companies are looking for digital storefronts delivered as progressive web applications (PWAs) for optimized performance and responsiveness across devices, as well as fast-loading and responsive experiences to boost your digital presence, SEO rankings and conversion rate. B2Bs can further streamline frontend development with solutions natively connecting to Google Cloud Marketplace, which supports a variety of storefront providers, including commercetools Frontend. 

Leveraging Google Cloud’s unique capabilities, such as PWA web app development, Google Cloud Discovery AI solutions that include Retail Search and Vision API Product Search, among many others, B2B companies are well positioned to boost digital commerce in the years to come.   

What’s next in 2023? 

2022 was already a turbulent year; for better or worse, 2023 is expected to have a similar fate. For B2Bs, even the ones with tight budgets, investing in digital commerce can help future-proof businesses for whatever’s happening this year. To dive deeper into all predictions and insights by commercetools in collaboration with Google Cloud, read the guide Pivotal Trends and Predictions in B2B Digital Commerce in 2023.

We have been going through a phenomenal amount of technological advances in the space of big data and cloud, driven by the organizations’ needs to get value out of data. It’s a given that data drives innovation, but business needs are changing more rapidly than processes can accommodate, resulting in a widening gap between data and value. Luckily, there are proven strategies that organizations can utilize to close the data-value gap. According to McKinsey, top performing organizations who see the highest returns from Artificial Intelligence (AI) are more likely to follow strategy, data, models, tools, technology and talent best practices. 

Our new Modern Data Strategy paper focuses on aligning data experiences, data economy and data ecosystems as part of the decision making process so that every organization can maximize ROI from their data and AI investments.

Data experiences to leave no user behind

Data experiences are key for organizations to not just democratize the reach of data and AI but also enable organizations to get the most out of their people. For example, a leading social networking service allowed a whole new group of less technically inclined users, including data analysts and product managers, to get insights from data using Google Cloud tools such as BigQuery and Looker. Users can access the data they need with self-service, adding agility across the business.

According to IDC, by 2026, 7 PB of data will be generated per second globally. At the same time, only 10% of the data generated each year is original, while the remaining 90% is replicated. This is because the organizational culture isn’t changing together with the new capabilities — this is holding back the outcomes and results that organizations expect. Users don’t have access to useful data experiences that match their maturity level.

To help solve these challenges, the Modern Data Strategy paper suggests different methods you can implement to help build personalized and self-service data experiences, giving everyone a chance to make data-informed decisions.

Data economy to capitalize on the value of data

Even though data platforms have evolved, the organizational model for becoming data driven, making data accessible and using it effectively has not evolved. Organizations and ways of working are trying to keep up with this rapid change to close the data-value gap. Applying a DevOps mentality to data helps closing this gap. Managing data as a product with clear ownership and SLAs allows organizations to get more value out of it.

Data consumers at Delivery Hero used considerable time figuring out where the data was, how to access it, and understanding the quality and policies around usage and sharing. This was due to a fragmented setup where teams were building their own data silos. Delivery Hero developed their new data platform on Google Cloud using data product thinking and settled on a global catalog that documents all available data and its meaning, quality and source. Teams discover, use and build on datasets from other teams with ease and speed, reducing the time to access data from 48 hours to having access to live data at all times. This has helped teams develop models to deliver growth and value across the business, from route management for drivers and order predictions in logistics to better recommendations and personalization on the website.

Data ecosystem to foster innovation

Closing the data-value gap can have a huge impact on an organization’s competitiveness. According to IDC, by 2025, at least 90% of new enterprise application releases will include embedded AI functionality. From a technology perspective, data platforms support these ambitions already. Choosing the right data ecosystem can improve the scalability, usability and time to insight of your analytical teams.

A modern data ecosystem is key to organizations becoming more effective and efficient. BigQuery is at the heart of Google Cloud’s analytics lakehouse with a strong ecosystem of tools around it. By having such an open, unified and intelligent platform, your teams do not need to spend time reinventing the wheel or spending time in data plumbing tuning underlying infrastructure.

For example, Mercado Libre was able to build a new solution that provided near real-time data monitoring and analytics for their transportation network and enabled data analysts to create, embed, and deliver valuable insights. This solution prevented them from having to maintain multiple tools and featured a more scalable architecture. 

Turn data into a competitive advantage with the right strategies

Data is the heart of digital transformation and offers incredible opportunities for organizations to accelerate the most strategic business outcomes, such as:

increasing revenue by understanding customer preferences and offering personalized experiences,

enhancing workforce productivity by making data-derived insights easily accessible to each worker to foster data-informed decision making.

Primarily, organizations need to be guided by a robust data strategy. This in turn will enable them to get value from data and turn it into competitive advantage. The bottom line is that data-driven companies innovate faster. First, they are able to continuously optimize their operational efficiencies regardless of the size and complexity of their organizations, and as a result they keep costs down. Second, they can adapt quicker as market conditions change. 

Here are four potential ways to drive new value using data: 

Applications: Accelerated product development and shorter time to market

Analytics: Greater organizational and operational efficiency, agility, and pace to execute innovative programs. 

Visualizations: Increased productivity by using right intuitive tools to allow doing advanced analytics and AI.

Predictions: Creating differentiated solutions driven by Data and AI

Benefits of your data strategy

A data strategy helps you create the necessary alignment across your organization.

These are some examples of activities that your data strategy should drive:

Principles and processes to guide the organization toward faster decision-making and continued alignment with business goals and objectives

Continuous review of recommended systems and tools that align with your strategy’s vision while avoiding a one-size-fits-all approach

Clear and consistent policies and procedures for managing data securely throughout its lifecycle, from creation to disposal

Ensuring that data is used ethically and responsibly, in compliance with relevant laws and regulations

Creating and enabling a culture of data-driven decision-making through the use of accessible, governed data to drive business value

The responsible use of AI across your organization

A framework for building a modern data strategy

Organizations can follow the three pillars below for building a modern data strategy:

Data experiences: Productive user experiences enabling all users to access and create value from relevant data

Establish a data university to advance data literacy for everyone in your organization

Pivot your organization from role-oriented to product-oriented with cross-functional teams

Define data principles for your organization that align with priorities and provide clarity in decision making

Data economy: Principles and practices to ensure that data can be published, discovered, built on, and relied on

Staff a data platform team that is obsessed with the developer and analyst experience

Make sure you get external/enterprise source data into the data economy early, such as customers, transactions, and product data

Find teams that depend on each other for data and help them share their data as a data product

Implement basic data governance practices — stewardship, discovery, quality, reliability, and privacy 

Data ecosystem: A unified, open and intelligent platform with end-to-end data capabilities for all users and needs

Choose a data ecosystem that provides your organization with all the capabilities you need out of the box, with open standards to plug in other components where needed

Make a plan to enable everyone in the organization to apply AI in their work using the capabilities in the ecosystem

Keep your operational work to a minimum by choosing serverless tools for the capabilities in your data ecosystem

Getting started

These are some important questions to ask yourself before designing a modern data strategy for your organization:

Does everyone in the organization have positive experiences working with data?

Do you have dedicated learning paths for different personas in your organization to use data effectively?

Do you have democratized access to data products to build the data economy?

Does it support all your data people, including app developers, business analysts, data scientists and line of business users?

Can you enable the next generation of data driven solutions including Data Science / Machine Learning (ML) at scale?

Are you effectively using data and AI to fast-track your strategic business objectives?

Can your data platform provide the technological ecosystem that enables data processing at scale with modern tools?

Can you share data internally and externally in a governed way?

Read our new paper, Three pillars for building a modern data strategy, to find out how to get started and learn more about how to define a modern data strategy for your business.

It was an honor and privilege to work on this with Thomas de Lazzari, Sina Nek Akhtar, David Montag, Andreas Ribbrock, Zara Wells for support, work they have done and discussions.

Search abandonment—when a shopper searches for a product on a retailer’s website but doesn’t find what they are looking for—costs retailers more than $2 trillion annually globally1, and more than $234 billion in the United States alone. 

Today, we’re releasing new data about the impact of search abandonment, including its role in brand loyalty and shopper sentiment as uncovered by a Google Cloud-commissioned Harris Poll survey of nearly 13,500 adults over the age of 18 in 14 countries who have visited a retail website and used the search function in the past six months.

Good [and bad] search experiences have immediate impacts for retailers

Google Cloud first studied search abandonment and explored its impacts on shoppers and retailers in 2021. Our latest round of research, conducted by the The Harris Poll during the peak shopping season of 2022 (Black Friday and Cyber Monday), found that subpar search experiences on retailers’ websites remain costly, both in terms of lost sale opportunities and shopper sentiment.

When U.S. consumers have what they feel is a successful search on a retail website, the vast majority (92%) purchase the item they were searching for. More than three-quarters (78%) also purchase at least one item in addition to the one they originally searched for. On average, three additional items were purchased after a successful search.  

In contrast, after an unsuccessful search using the search function or search box on a retail website, more than half (53%) of U.S. consumers say they typically abandon their carts and go elsewhere if there’s at least one item they can’t find on a website. Globally, a similar proportion agree (53%), with those consumers in India (63%) most likely to abandon their carts.

The search bar is a retailer’s most important online asset

U.S. shoppers depend on the search function or search box when shopping online—it’s the most common way they search for products on retail websites (69%), followed closely by general website browsing (63%). Unsurprisingly, nearly 9 in 10 consumers (88%) say a good search function is very important or absolutely essential when visiting a retail website.

But consistency is lacking in search experiences. Only 1 in 10 U.S. shoppers say they get exactly what they are looking for (12%) or good alternatives (11%) every time they use the search function on a retailer’s site.

When surveyed in November – December 2022, more than three in four U.S. consumers (76%) said they have used the search function or search box on a retail website and it did not provide the item they were looking for; on average this happened four times that month. Globally, this number varies greatly. For example, shoppers in Japan experience unsuccessful searches around two times in a given month, while consumers in India experience far more unsuccessful searches on retail sites at five times per month.

Gone are the days where retailers can expect a customer to search, filter and refine repeatedly to find what they want. People around the world now expect site search engines to understand their intent deeply, return relevant results and help them discover new products easily with personalized recommendations. 

For example, when a shopper searches for a “long floral dress with short sleeves and comfortable fit” on an ecommerce site, they expect to immediately get results for precisely that—rather than adjusting their search multiple times, or worse, giving up their shopping journeys. But it’s still a struggle for many retailers’ search engines to understand exactly what shoppers are looking for, especially without capabilities like understanding natural language. And failing to understand what shoppers are looking for comes at a cost.

Search abandonment remains pervasive, but loyalty can be won

The majority of consumers in the United States (78%) and globally (72%) now say they are less loyal to a brand when it’s hard to find what they want on a website. 

Around 8 in 10 (U.S.: 81%, global: 80%) agree that after an unsuccessful search experience on a retail website they are more likely to leave the site and purchase the item elsewhere. Around 8 in 10 (U.S.: 82%, global: 77%) say they avoid websites where they have experienced search difficulties in the past. 

Fortunately for retailers, 86% of consumers say it’s possible to win back their loyalty after an unsuccessful search, most commonly by giving them a discount or promotion (46%), upgrading their website (45%), or sending an email with the correct item they searched for (36%).

Retailers can tap into Google Cloud’s AI technology to give shoppers the search and browsing experience they want. And with Google Cloud’s Discovery AI now integrated into Shopify’s Commerce Components, even more retailers worldwide can easily update their digital shopping experiences with Google-quality search capabilities. 

Download our ebook to see the full survey results, and visit our website to learn more about Google Cloud’s solutions for retailers to improve customer experiences and combat search abandonment.

1. Local currencies in each country were converted to USD using conversion rates from March 16, 2023

We are excited to announce the opening of our new cloud region in Turin, Italy. This new region joins the recently opened Google Cloud region in Milan to provide highly available and sustainable cloud services with data sovereignty and residency functionality to meet the needs of the Italian economy. It also joins 35 other Google Cloud regions throughout the globe, giving Italian businesses faster access to the global network of Google Cloud infrastructure. 

Organizations everywhere need the capacity to run mission-critical applications at the speed their customers expect. The new Turin Google Cloud region will help accelerate the digitization of local companies, public administrations and global organizations, and help Italy build for the future. A recent independent economic impact report by the University of Turin highlights that the Piedmont and Lombardy regions could potentially generate up to €3.3 bn by 20251. 

“With the availability of the Turin Google Cloud region, that follows the one of Milan, the path that will allow Intesa Sanpaolo to fully exploit the potential of cloud technology and further accelerate the process of digitization of its services, is completed,” said Enrico Bagnasco, Executive Director Information Systems of Intesa Sanpaolo. “A cloud hyperscaler like Google with two regions on the national territory will facilitate all Italian companies and the Public Administration in accessing the cloud by providing greater security and simplifying business continuity and data protection. In parallel, the Opening Future initiatives will promote both the dissemination of digital skills in schools, SMBs and start-ups in the territories and a sustainable growth, with employment benefits and social and economic repercussions.”

A growing ecosystem of technology partners will also benefit from new Google Cloud regions in Italy. An independent study by the Digital Innovation Observatories and the Energy & Strategy group of the Milan Politecnico calculates that, thanks to the Google Cloud regions of Milan and Turin, projects across the supply chain will generate a new market for partners estimated at around €1.904 billion in the three-year period 2023-20252.

“Our partnership with Google Cloud is important for the digitalization process of companies, public administrations and citizen”, commented Elio Schiavo, Chief Enterprise and Innovative Solutions Officer of TIM. “The two regions represent an important result for this collaboration and are a distinctive asset that enables the offering of low latency, connectivity and data sovereignty functionalities. It is therefore a strategic infrastructure that will combine large economic benefits with important advantages for the entire industrial sector on a national scale.”

The new region launches with three cloud zones and our standard services which include Compute Engine, Google Kubernetes Engine, Cloud Storage, Persistent Disk, CloudSQL, and Cloud Identity, among others. Our customers will also benefit from critical features such as data residency controls, default encryption, organizational policies and VPC Service Controls. 

Now with two cloud regions in-country, Italian customers can expect improved business continuity planning with the unique option to distribute workloads across geo-redundant infrastructure to meet IT and business requirements for disaster recovery.

Whether you’re a traditional enterprise, a startup, or a digital native, our transformation cloud will help you become:

Smarter: The power of data is helping companies leverage more data and deeper insights to help catalyze transformation. Google Cloud makes it easy to get more value from structured or unstructured data to solve your most important challenges, make data-driven decisions, and deliver better business outcomes. 

Open: Google Cloud’s design principles are deeply rooted in the belief that a secure, open approach is the best way to empower you to drive differentiated experiences. Our commitment to multicloud, hybrid cloud, and open source unlocks innovation so you can build faster, in any environment. 

Connected: Digital transformation goes beyond technology — it requires the right people and culture. As work continues to shift outside of physical locations, Google Cloud provides the tools to help people be more innovative, productive, and able to make faster decisions. 

Trusted: Google Cloud offers a zero-trust architecture to protect data, applications, and infrastructure and a shared responsibility model where we have an active stake in our customers’ security outcomes. We are also a trusted partner of European businesses and governments, so you can transform faster while also meeting digital sovereignty requirements.

Sustainable: Google operates the cleanest cloud in the industry, helping customers reduce the carbon emissions of their digital infrastructure. With solutions to measure and manage supply chain, climate and materials risk, we help organizations meet their sustainability targets. 

Visit our locations page for more details about Google Cloud regions and updates on the availability of additional services and regions.

1. The Economic and Employment Impact of Milan Data Centers; Francesco Carbonero (University of Turin) Aldo Geuna (University of Turin) Luigi Riso (Catholic University of Milan); May 2022.
2. Le Google Cloud region di Milano e Torino generano un nuovo mercato per i partner del valore di 1.9 miliardi di Euro

Digital sovereignty continues to be a top priority for organizations working to advance or begin their digital transformation efforts. Over the past few years, Google Cloud has worked extensively with customers, partners, policy makers and governments around the world to understand evolving sovereignty requirements. In Europe, this has resulted in our “Cloud. On Europe’s Terms” initiative and a broad portfolio of Sovereign Solutions we have already brought to market to help support customers’ current and emerging needs as they bring more workloads to the cloud.

One thing remains clear from our ongoing discussions in the market: Designing a digital sovereignty strategy that balances control and innovation is challenging because: 

Foundational concepts are not always well-understood, including regulatory requirements, legal safeguards, and risk management.

Many organizations struggle to articulate specific requirements, particularly when it comes to how sovereign strategies enable digital transformation. 

Choosing the best potential technologies and solutions for your organization’s needs can be difficult. 

A shortage of advisory capacity and expertise in the market can make these challenges even harder to overcome.

Google Cloud’s Digital Sovereignty Explorer is designed to help you make progress on all of these issues. Initially focused on the needs of European organizations, the Explorer is an online, interactive tool that takes individuals through a guided series of questions about their organizations’ digital sovereignty requirements. It seeks to simplify terms and explain important concepts. 

At each step along the way, the Explorer aims to help you to clarify key considerations and tradeoffs in your digital sovereignty options, to understand available solution options, and to make more informed choices about potential solutions for your particular cloud workloads. 

Upon completing the Explorer, you will receive a personalized report summarizing your responses with additional reference material  and recommendations on potentially applicable solutions from Google Cloud and our European partners.

Google Cloud recognised the need for resources like the Digital Sovereignty Explorer. Industry Analysts agree. IDC, who advises global organizations on digital sovereignty strategies, notes:

“The relatively new concept of cloud data sovereignty is still in its evolutionary stages where users are only just starting to understand all the implications it will have on their cloud strategies. Defining what sovereignty actually means from the outset is vital if buyers are able to make informed decisions,” said Rahiel Nasir, Associate Research Director, European Cloud and Global Lead, Worldwide Digital Sovereignty, IDC.

Exploring Digital Sovereignty

Digital Sovereignty discussions often focus only on data residency. The Digital Sovereignty Explorer aims to broaden the discussion in line with Google Cloud’s vision of sovereignty covering three distinct pillars: 

Data sovereignty (including control over encryption and data access), 

Operational sovereignty (visibility and control over provider operations), and 

Software sovereignty (providing the ability to run and move cloud workloads without being locked-in to a particular provider, including in extraordinary situations such as stressed exits). 

The Explorer delves into each pillar with a series of questions designed to surface both clearly perceived and less obvious requirements. For example, the guided exploration of data sovereignty covers topics such as regional data residency, administrative access controls, third-party access restrictions, and encryption key management strategies. Operational sovereignty delves into topics such as preference for local partner operations, including technical support staffing options, and the desired scope of partner oversight. Software sovereignty explores topics such as workload-specific needs for on-premises storage, infrastructure, or disconnected operations, and the potential tradeoffs involved. 

Through the Explorer, for example, a manufacturer could determine which safeguards they might employ to limit insider access to intellectual property. A government agency could learn about options for disconnected operations for cloud infrastructure processing their most sensitive data. The interactive nature of the tool helps organizations to develop the right control strategies for their cloud workloads based on the sensitivity of their data, operations, policies, and regulatory obligations.

How to get started

The Digital Sovereignty Explorer is available and accessible today at no charge to any organization that may benefit from its guided exploration of this critical topic. The Explorer is available in English, French, and German, with Italian and Spanish versions coming soon in April. 

We plan to evolve the Explorer to incorporate additional capabilities and regions as new solutions become available. We hope it will help strengthen your understanding of digital sovereignty and that you will find it useful in developing requirements and a solution roadmap to accelerate your cloud transformation.