In recent years, the grocery industry has had to shift to facilitate a wider variety of checkout journeys for customers. This has meant ensuring a richer transaction mix, including mobile shopping, online shopping, in-store checkout, cashierless checkout or any combination thereof like buy online, pickup in store (BOPIS).  

What’s more, in the past year and a half alone grocers have had to enable consumers new ways to shop for essentials. This has included needing to rapidly integrate or build on-demand delivery apps, offer curbside pickup with near-instant fulfillment as well as support touchless and cashless checkout experiences. Searches on Google Maps for retailers in the US with curbside pickup options have increased by 9000% since March 2020, and we believe these trends from 2020 will continue to define the future of grocery shopping.

The future of grocery will require agility and openness

Firstly, the need to rapidly adapt to changing consumer habits will be the new normal. Grocers will increasingly look to digitally transform legacy retail systems and modernize point of sale (POS) platforms to deliver and scale omnichannel experiences as quickly as possible. This necessitates a more agile and open architectural approach to technology – one built on microservices and leverages APIs so that new applications and experiences can be built, integrated and delivered faster.

Automation and data-driven retailing will be table stakes

In order for retailers to blend what they’re offering in the store with digital experiences more efficiently, they will also need to automate more. For example, with automation and business intelligence, grocers can take labor that might have been tied up with tender operations and checkout and redistribute those resources to restocking shelves, curbside pick-up or improving customer experiences. 

Automation and access to real-time in-store inventory & supply chain data can also help grocers avoid the supply chain challenges seen in the early days of COVID-19. Grocers will need to find ways to leverage automation to ingest, organize, and analyze data from physical store networks, digital channels, distribution centers to better forecast demand and manage future fluctuations.

How NCR and Google Cloud are helping grocers adapt to disruption with operational agility

Helping grocers improve operational agility to address changing consumer shopping habits and to thrive during times of disruption is something that NCR and Google Cloud have teamed up to do. NCR has over 135 years of experience in retail, having invented the cash register and are continuing to help grocers innovate. NCR Emerald builds upon the company’s leadership in POS software and has turned it into a unified platform that helps grocers operate the entire store from front to back. The solution supports cashier-led checkout, self-checkout, integrated payments, merchandising, and enables regional managers and corporate employees access to the analytics and tools needed to optimize loyalty programs and promotions.

NCR has invested in a comprehensive, agile, and API-led retail architecture that lets grocers continually innovate and design new experiences as customers and the industry evolve. By running Emerald on Google Cloud, NCR can offer the solution on a subscription basis, helping grocers lower upfront capital expenditures and ensuring scalability. What’s more, NCR can tap into Google Cloud’s strength in data, analytics, and openness to deliver three key imperatives. Let’s take a look at each of these below.

Run the way grocers need to while leveraging Google Cloud as a single source of logic

Traditionally the POS system lived in the store. If disaster strikes, people still need access to food and essentials so the grocery store still needs to operate. It hardly gets more mission-critical than that. NCR Emerald is built on microservices, leveraging Kubernetes for front-of-house compute, and VMs (See graphic 1 below). This makes it easy to support lightweight clients accessible by store employees via any range of mobile devices, computer terminals, self-service kiosks, peripheral devices like receipt printers as well as legacy applications.

What’s unique is that because Emerald runs on Google Cloud, it supports all those in-store and digital touchpoints mentioned above, but also allows grocers to run lean. Emerald leverages Google Cloud as a single source of truth and operates a lot of what it does out of logic. Every sales transaction coming from every channel, including e-commerce, can be logged via NCR’s Hosted Service and centralized in BigQuery and Bigtable as a transaction data master. This enables the grocer to manage any transactional use case very consistently, whether it e supporting customers who want to purchase in one store and return in another, offering digital receipts or the ability to exchange online purchases in store. Emerald on Google Cloud can help retailers extend capabilities through the power of the cloud but not need to live exclusively in the cloud. In other words, the solution allows grocers the ability to run the way they need to.

Enable data-driven and real-time decision making for grocers

Store managers, regional managers, category managers, and others all require different cuts of the data to do their jobs effectively. However, data silos persist and how data is formatted and arranged can still remain pretty static. Therefore allowing users with different roles the ability to view and analyze that data quickly and in different ways continues to be a challenge. 

As mentioned above, Emerald leverages Google Cloud data management solutions as the central repository for transactional, behavioral, and merchandising data. Every transaction from every store and every channel can be stored via NCR Hosted Service on BigQuery and Bigtable. NCR Analytics then harnesses the advanced analytical and data visualization capabilities of Looker to help grocers get a consolidated view of their business across all channels and then allow employees to slice and dice the data they way they need to. NCR Analytics also leverages the power of Google Cloud AI and machine learning to add another level of intelligence to the retailer’s data. For example, store managers can visualize how well they’re using their real estate and see how productive lanes 1-3 are compared with 7-10 or compare self-service versus manned lanes. By mapping to the retailer’s own catalog, they can also break down category-level performance and trends.

NCR Analytics takes advantage of Google Cloud’s data pipeline to reduce processing time, with scaling and resource management provided out of the box. By letting the cloud store and process the data, NCR is providing the ability for retailers to analyze their data in near real-time across all platforms – a real game changer in the grocery business.

Open APIs let grocers continually enrich the retail experience

Finally, Emerald is built on an API-first architecture managed through Apigee. It uses the power of Apigee as an open API platform to expose how Emerald can work with other NCR applications like loyalty and promotions, and third party applications like mobile ordering and order delivery to enrich the grocery experience for employees and customers. Every API that Emerald uses is available on Apigee, allowing them to share code samples and giving developers the ability to run scripts. This approach can allow retailers the ability to innovate in a fraction of the time and cost, speeding up 3rd party integrations up front and as businesses grow. 

Take, for example, Northgate Market, a chain of 40 stores in California, that were able to transform its digital operations and enable experiences that set it apart from competitors – quickly and simply with Emerald. It took less than 6 months to go from contract to live deployment in the first store. Since then, Northgate Market has been able to extend their intelligence by leveraging the power of Looker and NCR Analytics.

Learn more about how NCR has been able to leverage an open, cloud-enabled architecture to help customers innovate across the retail, hospitality, and banking industries on the webinar “Role of APIs in Digital Transformation”. You can also learn more about how Northgate uses e-commerce to transform customer experience and gain consumer insights.

Investment management is a heavily data-driven industry—portfolio managers and investment researchers require a large number of data sources to guide them in shaping their investment strategies. 

New cloud capabilities and technologies enable investment managers to process data faster than ever before and iterate on ideas quickly to fuel innovation in the signal generation process and gain a competitive edge. 

Using the cloud for investment research workflows makes it easier to onboard data from data providers, spin up large compute workloads in the midst of market volatility or during heavy research cycles, and manage complex machine learning or natural language workflows to gain market insights.

We hear from industry leaders that they’re exploring new ways to run investment research. “Differentiated investment strategies require new types of information sources, and new ways to process that information,” David Easthope, senior analyst, Market Structure and Technology, Greenwich Associates. “And that, of course, relies heavily on having access to reliable and scalable storage, computational, and AI / ML resources. More specifically, quantitative strategies can benefit from the computational platforms and embedded AI/ML capabilities the cloud can offer.” 

Google Cloud gives investment managers essential components to work and operate faster as they bring their investment research workflows to the cloud. Here are the key highlights:

1. Simplify, speed up your data acquisition, discovery, and analytics

The foundation of any investment strategy starts with data—acquiring it, detecting patterns, and analyzing it for insights. Enabling data providers to easily share large datasets such as tick history within a high-performance analytics engine can greatly reduce the data engineering overhead when possible.

Once data is onboarded, you can tag business and technical metadata related to your datasets and provide portfolio managers the ability to discover these datasets via a search interface.

We further review analytics options for various scenarios, including aggregating massive datasets, creating dashboards, and incorporating streaming analytics workloads.

2. Take advantage of burst compute workloads

Data engineers and researchers require ready access to burst compute capabilities to perform backtesting, portfolio simulations and run risk calculations. Cloud works well for these workloads due to its elasticity, consumption-based models, and hardware evolution.

Many investment managers are shifting to a container-based strategy along with a Kubernetes-based scheduler for greater consistency, scaling and efficiency in environments with a large number of researchers. Cloud managed services and a rich suite of CI/CD tools can make this vision a reality while improving security and developer productivity.

3. Tackle machine learning (ML) and model deployment with the help from cloud

Quantitative researchers scour vast amounts of market and alternative data sources searching for signals and correlations, while ML engineers have the challenge of taking these signals and moving them to production.

Google Cloud empowers users to create and operationalize their models without wasting valuable time with a comprehensive set of MLOps tools. 

In this paper, we explore multiple solutions for ML and model deployment. Those capabilities reduce the amount of time operationalizing ML models, so quants and data scientists have more time to devote to differentiating activities. 

4. Get the data you need in less time with Natural Language and Document AI

Thousands of financial filings, news articles, and sell-side research reports are generated every day, and it’s difficult for humans alone to process this volume of information. These documents are often generated in many languages and the ability to do entity recognition, sentiment or syntactical analysis in those languages, or perhaps translate them into the language of the portfolio manager is of critical importance. Google Cloud provides these capabilities through pre-trained models, or allows you to train high-quality models with your own datasets.

Getting started

There are plenty of emerging technologies, tools, and approaches available to help investment managers today. At Google Cloud, we can help you access, organize, and utilize these essential components to make your research faster, reliable, and more valuable.

To learn more about these four keys to better investment research, check out our whitepaper for more.

“Any device, anytime, anywhere.” A cohort of CIOs within Airbus believed that the cloud, combined with new ways of working, could provide the foundation for this vision. Google Workspace and Google Cloud have played a pivotal role in helping Airbus realize this new path, transforming security, data management, and collaboration along the way.

Adopting a secure-by-design approach

In adopting Google Workspace and Google Cloud Airbus needed to ensure a robust, zero-trust security model that works across the entire organization, even when employees are working outside the office. Google Workspace provides a single login that enables secure access to data, based on device and user information, as well as contextual inputs that inform the security risk of each login and user action. Airbus admins also use Google Workspace to define trust rules that govern what information and files can be shared within and outside the organization, making it easy for employees to comply with best practices from anywhere.

Encryption also plays a central role in keeping information secure and private. By default, Google Workspace uses the latest cryptographic standards to encrypt all data at rest and in transit. Google Workspace also offers client-side encryption, which Airbus uses for their most sensitive projects, giving them authoritative control over their data as the sole owner of their encryption keys.

And to ensure the organization is protected against hackers, Airbus has implemented sharding as a standard practice, thereby splitting data across multiple servers and data centers. Because the company works with incredibly sensitive information—including government and military information—the ability to locate data all within European data centers continues to be a necessity.

Powerful data management

Managing an enormous volume and variety of data, Airbus needs to ensure complete compliance with internal policies, as well as with external standards, like the General Data Protection Regulation (GDPR). Given this context, Airbus requires a solution that has strong built-in governance controls. Airbus also leverages the Drive labels feature, along with manual classification, to ensure that every file added to Google Drive is tagged and labeled correctly. In turn, these labels define the loss-prevention policies assigned to each file.

Staying connected during the pandemic

Before the pandemic, nearly every employee spent their workdays at an Airbus facility. When remote work became mandatory, the company made the pivot to Google Chat and Google Meet as an essential part of supporting real-time and asynchronous collaboration. Gmail also played a significant role in secure, anywhere-anytime communication, with its built-in anti-spam and anti-malware protections. Customizable filters let administrators protect against suspicious attachments, untrustworthy links, and countless other forms of malicious content. While Gmail blocks more than 99.9% of spam and phishing messages from ever reaching users’ inboxes, more advanced security measures like sandboxing can be put in place for specific use cases.

Protecting more than just data

Google Cloud’s sustainability efforts are as equally important to Airbus as data security. Google Cloud has been working to keep its climate footprint and those who use its services as low as possible, with all of Google currently carbon neutral and with a goal to run on carbon-free energy 24/7 at all of our data centers by 2030. And with our smarter, more efficient data centers, we’re already on that path with more than six times the computing power for the same amount of electrical power we used 5 years ago.

Building the future of work

By combining Google Workspace with Google Cloud, Airbus has been able to live up to its vision of “any device, anytime, anywhere.” The new model is a core foundation for its evolving future of work. Not only has Airbus adopted a zero-trust model across the organization, it’s also transformed how data is secured, managed, and accessed by employees working across a broad range of locations. The new flexible approach has also led to changes in how collaboration happens. Google is deeply gratified to have supported Airbus as they implement these changes and we continue to be proud that we’re running the cleanest cloud in the industry.

Want to learn more about how Google Workspace helps businesses like yours do more while keeping your data secure? Read this whitepaper to find out about our zero-trust model and other ways we protect organizations.