While some traditional financial services companies have more slowly transitioned to the cloud, capital markets firms have embraced cloud computing across their entire value chains — front-, middle-, and back-office. We wanted to understand the dynamics behind this rapid adoption, the most common use cases, and the types of technology most in use, particularly as it relates to market data. Google Cloud commissioned Coalition Greenwich to survey 102 institutional capital markets professionals — at exchanges, trading systems, data aggregators, data producers, asset managers, hedge funds, and investment banks — in the United States, Canada, France, Germany, Italy, the Netherlands, Switzerland, and the United Kingdom. 

Our research found that while there are many drivers, demand for easier accessibility is fueling widespread adoption of cloud-based market data services, and associated trading infrastructures, across the buy side and sell side. In fact, 68% of sell-side and buy-side users find it critical for market data providers to offer public cloud-based data services. At the same time, exchanges, market data providers, aggregators, and trading systems are embracing the cloud as a delivery model by offering access to data directly via their own cloud services, APIs or partners.

Here were five noteworthy takeaways from the study: 

1. Cloud services are becoming ubiquitous for data delivery. Today, the cloud is pervasive, with 93% of exchanges, trading systems and data providers offering cloud-based data and services, according to surveyed executives. Moreover, 100% of those surveyed intend to offer new cloud-based services, such as derived data, in the next 12 months.

2. Commercial and investment banks are offering additional connectivity, real-time data feeds, and trading applications delivered via the cloud,demonstrating that it’s not only exchanges, trading systems, and data providers that are moving rapidly to the cloud. Internal use cases abound as well, with 67% of those surveyed consuming cloud-deployed market data, primarily for data analytics. 88% of surveyed sell-side firms intend to consume cloud-based market data services, with digital transformation, data science and quant research as the top use cases.

3. Buy side firms will consume even more cloud-deployed data. Today, 90% of surveyed buy-side firms are consuming cloud-deployed market data, mostly for portfolio management. 70% of buy-side firms intend to consume more public cloud-based market data services in the next 12 months, adding services such as compliance and regulatory reporting.

4. AI/ML, powered by cloud, is moving out of the pilot phase and into mainstream use. Today, 50% of exchanges, trading systems, and data providers are offering data products or services powered by AI/ML, and of those, 42% intend to offer AI-powered trade execution and trading analytics services in the next 12 months. Within commercial and investment banks, 55% said they are currently using AI/ML in the cloud, and while that was true for only 14% of overall buy-side respondents, 44% of large buy-side respondents are using it.

5. Exchanges, trading systems, and data providers are prioritizing public cloud for internal insights. 71% of these firms are using the public cloud, mostly for data transmission, processing, analysis, and long-term data storage. Over the next 12 months, 33% of new public cloud workloads will focus on data mining, data insights and advanced analytics, while 28% of new AI/ML tooling and infrastructure investments will focus on faster analytics and risk reviews, and 27% on data quality maintenance.

“We see new, dramatic shifts on the adoption of cloud across market data,” said David Easthope, Senior Analyst for Coalition Greenwich. “And we expect further proliferation of cloud-based services and greater consumption across the trading and investing lifecycle.”

Conclusions and future predictions

Based on the survey results, Coalition Greenwich predicts five following trends over the next 12 months:

1. Exchanges and trading systems will continue to launch a wide array of new cloud-based and possibly cloud exclusive data services across derived data, end of day data, reference data and pricing data.
2. Data providers will launch new data products such as pre-trade analytics powered by AI/ML in the cloud.
3. Commercial and investment banks will offer additional connectivity, real-time data feeds, and trading applications delivered via the cloud.
4. Buy-side firms will consume even more cloud-deployed data, including real-time market data, portfolio management data, and risk analytics.
5. Exchanges, trading systems and data providers will explore proof-of-concepts around core systems on the cloud. Improvements to AI/ML tooling or infrastructure will ramp up as firms seek more rapid responses to risk initiatives.

To learn more about these findings, download our two full reports, The Future of market data: Distribution and consumption through cloud and AI and Exchanges and data providers: Prioritizing the cloud and AI for internal insights or our short infographic.

Research methodology

The survey was conducted online by Coalition Greenwich on behalf of Google Cloud from March 2021 to April 2021 among 102 executives in North America (n=82), EMEA (n=17) and other (n=3) who are employed full-time and who are participants or influencers in decisions around cloud and/or senior management with a role at a company which is an institutional asset manager, hedge fund, alternative investment manager, exchange and/or trading system, information provider, information aggregator, or other asset manager/asset owner. The survey included wide perspectives from a range of firm size and asset class focus, including equity, fixed income, FX, commodities, multi-asset, and other asset classes.

Foot Notes

1.  We defined market data as direct feeds, consolidated feeds, terminal and desktop products, security and reference data, pricing data, historical data, alternative data, and index data.

Natural language understanding (NLU) is getting increasingly better at solving complex problems and these language breakthroughs are creating big waves in Artificial Intelligence. For example, new language models are enabling Everyday Robots to create more helpful robots that can break down user instructions and have even enabled people to generate imaginative visuals from complex text prompts. 

These leaps in NLU are powered by neural networks trained to understand human language. This technology has greatly advanced since the introduction of Google’s Transformer architecture in 2017 with the introduction of large models trained on massive amounts of data like GPT-3 and, even more recently, with GLaM, LaMDA, and PaLM. This latest generation of models are called Large Language Models (LLMs) because of their sheer size and the vast volumes of data on which they are trained, and they can be applied to a range of tasks to create more powerful digital assistants, generate better search results and product recommendations, enforce smarter platform curation and safety features, and much more. 

For these reasons, we’re pleased to announce we’ve updated the Google Cloud Natural Language (NL) API with a new LLM-based model for Content Classification. 

With an expansive pre-trained classification taxonomy, the newest version of Content Classification from the Natural Language API leverages the latest Google research to improve customer use cases spanning actionable insights on user trends, to ad targeting, to content-based filtering. In this article, we’ll explore the NL API’s new capabilities, which are the first of many efforts we’ll be making to bring the power of LLMs to Google Cloud. 

How LLMs help machines understand human language 

As Google Cloud VP and General Manager of AI and Industry Solutions, Andrew Moore has argued, if computer systems become more conversant with natural human languages, they become a foundation for more sophisticated use cases, able to not only understand user intent but also create complex bespoke solutions. Google has been a leading research force in this space, with LLM projects like LaMDA, PaLM and T5 contributing to the Cloud NL API’s improved v2 classification model.

Parsing language is a difficult AI task for machines due in part to the contextual and individual interpretation of words or phrases. The word “server,” for example, could refer to a computer, a restaurant employee, or a tennis player. To understand the word, a model needs to be trained around not only a basic definition but also the context and positioning of the word within a sentence or conversation and its evolving connotations. Because they process voluminous training data via Transformers, LLMs are well-suited to this type of work. 

Thanks to the integration of Google’s latest language modeling technology, and an updated and expanded training data set, the next generation of the Content Classification API not only has over 1,000 labels (up from around 600 previously), but now also supports 11 languages (with Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russia, Spanish, and Dutch joining previously-available English)—and does so with improved accuracy.

​​AI raises questions about the best way to build fairness, interpretability, privacy, and security into these new systems in order to benefit people and society. At Google, we prioritize the responsible development of AI and take steps to offer products where a responsible approach is built in by design. For Content Classification, we limited use of sensitive labels and conducted performance evaluations. See our Responsible AI page for more information about our commitments to responsible innovation. 

Get Started 

Today’s announcement is just the first step in bringing LLM capabilities to Google Cloud AI products, and we’re excited to see how our more powerful Natural Language API helps developers, analysts and data scientists generate insights and offer superior experiences. Our early adopters are implementing the API to improve user recommendations, display ad targeting, and insights about new trends.

If you’re ready to get started with this major leap in Google Cloud language services, visit our NL API documentation, and to learn more about Google Cloud’s AI services, visit our AI and machine learning products page.

As we think about economic recovery from COVID-19—both inside Google and outside through working with Google Cloud customers—we’ve made many important observations. Among them is the recognition that the ways software developers and IT practitioners work together will shift in the post COVID-19 world. Our economic recovery today will look different than past recoveries, and on a fundamental level, the way we innovate will be different than it’s ever been before.

Right now, we’re entering a new phase of cloud computing, where businesses have shifted from making tactical infrastructure decisions, to making larger IT decisions with an eye towards enabling transformation throughout the company. Data, and what we can do with that data, is key to this transformation. And how companies put data in the hands of every employee to help catalyze transformation and solve the most important and impactful opportunities in their industries is at the core.  

A recent Google-commissioned study by IDG highlighted the role of data analytics and intelligent solutions when it comes to helping businesses separate from their competition. The survey of 2,000 IT leaders across the globe reinforced the notion that the ability to derive insights from data will go a long way towards determining which companies win in this new era.

Data analytics and intelligence were prioritized during COVID-19

The results of the IDG study show a separation amongst those organizations that embrace the capabilities of today’s data analytics and AI/ML tools and those that do not. When COVID-19 hit, many organizations cancelled IT initiatives, with 55% of respondents delaying or cancelling at least one technology project. However, 32% of respondents accelerated or introduced initiatives around building out or improving the use of data analytics and intelligence. IT leaders realize how critical data is to their future success, even when resources are scarce.

Digital-focused companies are faster to embrace advanced intelligence tools

Furthermore, enthusiasm for big data analytics, AI, and ML technologies is highest among companies who are further along in their digital transformation journeys. Fifty-four percent of companies who identify as “Fully digitally transformed” or “Digital native” are using or considering using these tools, vs. the global average of 37%. And, these same organizations are embracing the promise of AI more than their peers. Forty-eight percent felt that “Embedded AI across our full stack of cloud solutions will be critical” vs 39% of digital conservatives. These companies realize these digital tools enable them to be more resilient, agile, and prepared for whatever the future brings.

Companies are turning to cloud to maximize insights from data

As companies tap into the promise of data analytics and AI/ML, they are turning to cloud for help. When considering which cloud providers to work with, 78% of respondents said big data analysis is a “must have” or a “major consideration,” which placed this capability at the top of the list of consideration factors. This is not surprising, as cloud solutions address the most common pain points and barriers to innovation. Three of the respondents’ top four areas impeding innovation are addressed by cloud: Insufficient IT & developer skill sets (1st), security risks and concerns (2nd), and legacy systems and technologies (4th). Plus, cloud makes it easier to quickly launch a project, scale up or scale down, and pay for only what you use.

COVID-19 changed the very nature of business, and of IT. It forced IT leaders to decide where to put their scarce resources and big data analytics and AI/ML were, understandably, at the top of the list. To learn more about the findings, download the IDG report “No turning back: How the pandemic reshaped digital business agendas.”

At Google Cloud, we believe you get most benefits from the cloud when you scale infrastructure based on changing demand. Compute Engine allows you to configure autoscaling to save costs during periods of low demand, and add capacity to support peak loads. 

When you use a managed instance group (MIG), you can have an autoscaler automatically create or delete virtual machine (VM) instances based on increases or decreases in load. However, if your application takes several minutes to initialize, creating VMs in response to growing load might not increase your application’s capacity quickly enough. For example, if there’s a large increase in load (like when users first wake up in the morning), some users might experience delays while your application is initializing on new instances.

A good way to solve this problem would be to create VMs ahead of demand so that your application has enough time to initialize beforehand. This requires knowing upcoming demand. If only we could predict the future… Well, now we can!

Introducing predictive autoscaling

Predictive autoscaling uses Google Cloud’s machine learning capabilities to forecast capacity needs. It creates VMs ahead of growing demand allowing enough time for your application to initialize.

How does it work?

Predictive autoscaling uses your instance group’s CPU history to forecast future load and calculate how many VMs are needed to meet your target CPU utilization. Our machine learning adjusts the forecast based on recurring load patterns for each MIG. 

You can specify how far in advance you want autoscaler to create new VMs by configuring the application initialization period. For example, if your app takes 5 minutes to initialize, autoscaler will create new instances 5 minutes ahead of the anticipated load increase. This allows you to keep your CPU utilization within the target and keep your application responsive even when there’s high growth in demand. 

Many of our customers have different capacity needs during different times of the day or different days of the week. Our forecasting model understands weekly and daily patterns to cover for these differences. For example, if your app usually needs less capacity on the weekend our forecast will capture that. Or, if you have higher capacity needs during working hours, we also have you covered.

Why should you try it?

Predictive autoscaling continuously adapts forecasted capacity to best match upcoming demand. Autoscaler checks the forecast several times per minute and creates or deletes VMs to match its prediction. The forecast itself is updated every few minutes to match recent load trends so if your growth rate is higher or lower than usual we will adjust the forecast accordingly. This gives you capacity needed to cover peak load while saving on cost when demand goes down. 

You can start using predictive autoscaling without worry as it’s fully compatible with the current autoscaler. Autoscaler will calculate enough VMs to cover both forecasted as well as real-time CPU load—whichever is higher. This works with other autoscaling features as well: you can scale based on schedule, your Load Balancer request target or Cloud Monitoring metrics. Autoscaler provides enough capacity to all of your configurations by taking the highest number of VMs needed to meet all your targets.

Getting started

You can enable predictive autoscaling in the Google Cloud Console. Select an autoscaled MIG from the instance groups page and click Edit group. Change predictive autoscaling configuration from Off to Optimize for availability.

To better understand whether predictive autoscaling is good for your application, click the link See if predictive autoscaling can optimize your availability. This will show you a comparison of the last seven days with your current autoscaling configuration vs. with predictive autoscaling enabled.

In the above chart, 

• Average VM minutes overloaded per day shows how often your VMs exceed your CPU utilization target. This happens when demand is higher than available capacity. Predictive autoscaling can reduce this by starting VMs ahead of anticipated load. 
• Average VMs per day is a proxy for cost. This shows how much additional VM capacity you need to keep your CPU utilization within the target you have set. You can optimize your cost by adjusting Minimum instances andCPU utilization as explained below. 

Optimizing your configuration

Make sure your Cool down period reflects how long it takes for your application to initialize from VM boot time until it’s ready to serve the load. Predictive autoscaling will use this value to start VMs ahead of forecasted load. If you set it to 10 minutes (600 seconds) your VMs will start 10 minutes before the load is expected to increase.

Review your autoscaling CPU utilization target and Minimum number of instances. With predictive autoscaling you no longer need a buffer to compensate for the time it takes for a VM to start. If your application works best at 70% CPU utilization you don’t need to set target to a much lower value as predictive autoscaling will start VMs ahead of usual load. A higher CPU utilization and lower Minimum number of instances allows you to reduce the cost as you don’t need to pay for additional capacity to prepare for growing demand.

Try predictive autoscaling today

Predictive autoscaling is generally available across all Google Cloud regions. For more information on how to configure, simulate and monitor predictive autoscaling, consult the documentation.

Editor’s note: To kick off the new year and in preparation for NRF The Big Show, we invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that continues to see tremendous change. Please enjoy this entry from our partner.

If you put a pot of water on the stove, it doesn’t heat up instantly. It simmers slowly at first, eventually picking up steam to reach a rolling boil. The retail landscape is not so different. Capgemini’s research shows the sector has evolved over four generations, from the early days of fragmented outlets to omnichannel and customer-centric focuses, with a fifth generation on the horizon that promises to be centered on consumption.

• Generation 1: Fragmented outlets
• Generation 2: Chain concentration
• Generation 3: Omnichannel
• Generation 4: Consumer-centric
• Generation 5: Consumption-centric

Although incremental change allows companies to experiment and iterate during their digital journeys, the COVID-19 pandemic rapidly accelerated the evolution of online and contactless shopping. Evolution became a revolution, with most Consumer Product and Retail (CPR) companies still mastering the omnichannel generation of their digital transformation to create a seamless shopping experience. Companies that are more digitally mature are already aspiring to the consumer-centric phase, embracing opportunities made possible by technology such as personalization and automation.

What consumers want

Customers have more choices in how they shop and engage with brands. This has made it harder for brands to predict and anticipate needs across customer journeys. And that’s convincing some companies to innovate more quickly as consumer demand drives the need for speed and scale.

Think about your own online behavior. Say you’re shopping for an item and searching online for the closest store in your neighborhood. Google is likely your go-to for finding that information. Looking to troubleshoot an issue with a product or seek out a service? Again, you’ll likely hit up Google first, not even considering going directly to a brand’s website for answers.

Both scenarios point to a disconnect between virtual and physical worlds, a gap technology can bridge in numerous ways such as breaking down silos and integrating fragmented media channels. Interestingly, though, not everything will be centered online all the time. In our recent study on consumer behavior, The great consumer reset, Capgemini discovered 57 percent of shoppers plan to return to brick-and-mortar stores post-pandemic, which is basically unchanged from the 59 percent who often interacted with physical stores before.

But business as usual? Not even close. Consumers have come to expect a frictionless shopping experience (buy online, pick up in store) or an immersive one (products displayed online using augmented reality), and are not content to return to in-store lineups, empty shelves, or a one-size-fits-all approach. Moreover, customers want personalized interactions while ensuring their data and privacy are protected.

So the role of the store is changing. In fact, many online-only brands are opening brick-and-mortar establishments to drive customer experience. In our research on “smart stores,” we found the majority of consumers (66 percent) believe automation can improve their shopping experience by solving the challenges they face at retail stores.

From personalization to serendipity

Retailers must recognize that they have to win consumer trust and confidence. Many consumers believe retailers’ use of tech is focused on reducing costs rather than easing friction. And they’re right. That same Capgemini research found that only one-third (35 percent) of retailers consider “solving customer pain points” as the most important criteria when deciding which automation use cases to implement.

“Retailers are largely in the early stages of adopting automation, and that’s an opportunity to rethink how they’re using technology, not just to smooth out friction and engender consumer trust but to build unexpected consumer benefits,” says Neerav Vyas, Head of Customer First, Co-Chief Innovation Officer, Insights & Data, North America, Capgemini. “We’re trying to move towards this idea of delivering serendipitous experiences to bridge the physical and digital divide.”

The focus is not solely on shoppers seeking out a specific product. “When consumers are in an exploratory mood, retailers can recommend products and services customers didn’t even know they wanted,” says Vyas. For example, business teams that use personalization platforms as part of an integrated media strategy can optimize algorithms against outcomes such as improving conversion and driving engagement.

Vyas says the elevated experience from “personalization to serendipity” fosters trust in the ability of recommendation architectures to persuade and influence consumers’ choices in beneficial ways. A case in point: our research found that half (52 percent) of spending by millennials goes towards experience-related purchases. As always, the key is to meet consumers where they are. Even better, according to Vyas, is to anticipate and understand when signals like customer intent are changing.

How to create value throughout the customer journey

One solution companies can implement right now is an integrated media spend platform that incorporates reporting, planning, and strategy across the entire customer journey. This offers value throughout the customer journey by using technology to reduce friction along the way. Think of it as starting with the customer looking for a product (search and discovery), moving on to the purchase (omnichannel basket, “shoppable” screens) and pick up/delivery (QR code scan in store), and through to post-purchase engagement with the retailer (Google Contact Center AI).

Such a holistic approach also accelerates data acquisition, integration, and reporting using advanced analytics to break down silos and emphasize the importance of privacy and first-party data. This in turn guides end-to-end interventions across customer journeys that enable optimized media spend, empowers businesses to analyze their spend distribution, fine-tunes owned and paid tactics with agencies, and promotes stewardship to support audit efficacy.

A culture of experimentation

With this data-driven focus, CPRs can create a 360-degree perspective of the customer. That intelligence can be used to enhance and humanize automated shopping experiences by putting the customer in control, whether online, in store, or across company brands. Moreover, by using Google Cloud’s emerging technologies such as artificial intelligence (AI), we help companies accelerate value across the spectrum, from supply-chain optimization and customer innovation to consumer experience.

Building a culture of experimentation is a team effort. “It’s not ever just one person who had a big idea. It’s all incremental steps,” says Jennifer Marchand, Google Cloud COE Leader, Capgemini. “Finding the right use case and timing is everything.” Take Google Glass Enterprise, she continues. For greater consumer experience, it can enable in-store associates to better serve with hands-free checkout, customer personalization and recommendations, and special offers. At the same time, Computer Vision and Smart Shelves can help prioritize tasks for employees, notifying them of low stock or a spill in the store.

Marchand points out that companies and consumers alike might not be ready to fully adopt some technology like facial recognition, but since almost everyone has a smartphone these days, they can benefit from automation with ease. “What’s interesting,” she adds, “is the way Google thinks about these types of problems, solving them for the long term.”

The store of tomorrow

Imagine a truly frictionless shopping experience, where state-of-the-art computer vision and AI identifies the products you pick up, put back, and keep, allowing you to head home, completely bypassing the checkout, with a 99 percent accuracy rate and receipts sent directly to your mobile app. That utopian experience is already taking shape at CornerShop, Capgemini’s live experimental store in London, UK.

Jamie MacLoud, Transformation & Strategy Consultant at frog, part of Capgemini, describes the retail space, which runs on Google Cloud, as the store of tomorrow, and not the distant future. It’s an experiential space where retailers and brands can explore, develop, and test technological shopping innovations in real-time. The outcome is a clearer understanding of how digital innovation can enable new ways to progress the customer experience, improve in-store operations, and help consumers to rediscover the joy of in-person retail through new ways to shop and engage with brands.

“We build, test, and learn about store concepts of tomorrow that we believe could be implemented into actual stores in the next one to two years. Getting these experiences in front of real customers in the CornerShop allows us to generate tangible learnings that we can share with our clients and use to shape future store strategy” says MacLeod. CornerShop was opened to the public in two eight-week stints, which allowed real-time testing to see what technologies resonated with customers, which brands can adapt and scale. Frictionless checkout, not surprisingly, was a big win for customers, but the technology underpinning the “virtual try-on” of clothing was deemed more suitable for the store of the future.

So, unlike an innovation lab, CornerShop lets companies experiment risk free, speeding up the process from hypothesis to full-scale implementation. It’s also another step toward solving the challenges customers face, while delivering those serendipitous experiences that build brand loyalty and longevity.

Learn more about how Capgemini is partnering with Google Cloud to help retailers create next-generation shopping experiences today.

We would like to acknowledge Jamie MacLoud, Transformation & Strategy Consultant at frog, part of Capgemini and Neerav Vyas, Head of Customer First, Co-Chief Innovation Officer, Insights & Data, North America at Capgemini who supported with invaluable insights and subject matter expertise in the writing of this blog post.