Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. 

Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

Week of May 24-May 28 2021

• Google Cloud for financial services: driving your transformation cloud journey–As we welcome the industry to our Financial Services Summit, we’re sharing more on how Google Cloud accelerates a financial organization’s digital transformation through app and infrastructure modernization, data democratization, people connections, and trusted transactions. Read more or watch the summit on demand.
• Introducing Datashare solution for financial services–We announced the general availability of Datashare for financial services, a new Google Cloud solution that brings together the entire capital markets ecosystem—data publishers and data consumers—to exchange market data securely and easily. Read more.
• Announcing Datastream in Preview–Datastream, a serverless change data capture (CDC) and replication service, allows enterprises to synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency to support real-time analytics, database replication, and event-driven architectures. Read more.
• Introducing Dataplex: An intelligent data fabric for analytics at scale–Dataplex provides a way to centrally manage, monitor, and govern your data across data lakes, data warehouses and data marts, and make this data securely accessible to a variety of analytics and data science tools. Read more. 
• Announcing Dataflow Prime–Available in Preview in Q3 2021, Dataflow Prime is a new platform based on a serverless, no-ops, auto-tuning architecture built to bring unparalleled resource utilization and radical operational simplicity to big data processing. Dataflow Prime builds on Dataflow and brings new user benefits with innovations in resource utilization and distributed diagnostics. The new capabilities in Dataflow significantly reduce the time spent on infrastructure sizing and tuning tasks, as well as time spent diagnosing data freshness problems. Read more.
• Secure and scalable sharing for data and analytics with Analytics Hub–With Analytics Hub, available in Preview in Q3, organizations get a rich data ecosystem by publishing and subscribing to analytics-ready datasets; control and monitoring over how their data is being used; a self-service way to access valuable and trusted data assets; and an easy way to monetize their data assets without the overhead of building and managing the infrastructure. Read more.
• Cloud Spanner trims entry cost by 90%–Coming soon to Preview, granular instance sizing in Spanner lets organizations run workloads at as low as 1/10th the cost of regular instances, equating to approximately $65/month. Read more.
• Cloud Bigtable lifts SLA and adds new security features for regulated industries–Bigtable instances with a multi-cluster routing policy across 3 or more regions are now covered by a 99.999% monthly uptime percentage under the new SLA. In addition, new Data Access audit logs can help determine whether sensitive customer information has been accessed in the event of a security incident, and if so, when, and by whom. Read more.
• Build a no-code journaling app–In honor of Mental Health Awareness Month, Google Cloud’s no-code application development platform, AppSheet, demonstrates how you can build a journaling app complete with titles, time stamps, mood entries, and more. Learn how with this blog and video here.
• New features in Security Command Center—On May 24th, Security Command Center Premium launched the general availability of granular access controls at project- and folder-level and Center for Internet Security (CIS) 1.1 benchmarks for Google Cloud Platform Foundation. These new capabilities enable organizations to improve their security posture and efficiently manage risk for their Google Cloud environment. Learn more.
• Simplified API operations with AI–Google Cloud’s API management platform Apigee applies Google’s industry leading ML and AI to your API metadata. Understand how it works with anomaly detection here.
• This week: Data Cloud and Financial Services Summits–Our Google Cloud Summit series begins this week with the Data Cloud Summit on Wednesday May 26 (Global). At this half-day event, you’ll learn how leading companies like PayPal, Workday, Equifax, and many others are driving competitive differentiation using Google Cloud technologies to build their data clouds and transform data into value that drives innovation. The following day, Thursday May 27 (Global & EMEA) at the Financial Services Summit, discover how Google Cloud is helping financial institutions such as PayPal, Global Payments, HSBC, Credit Suisse, AXA Switzerland and more unlock new possibilities and accelerate business through innovation. Read more and explore the entire summit series.
• Announcing the Google for Games Developer Summit 2021 on July 12th-13th–With a surge of new gamers and an increase in time spent playing games in the last year, it’s more important than ever for game developers to delight and engage players. To help developers with this opportunity, the games teams at Google are back to announce the return of the Google for Games Developer Summit 2021 on July 12th-13th. Hear from experts across Google about new game solutions they’re building to make it easier for you to continue creating great games, connecting with players and scaling your business. Registration is free and open to all game developers. Register for the free online event at g.co/gamedevsummit to get more details in the coming weeks. We can’t wait to share our latest innovations with the developer community. Learn more.

As Google’s productivity adviser, I’ve been closely following the evolving discussion around the future of work. Long before the pandemic, employees, business leaders, and analysts were already thinking about how work needed to evolve, but the crisis brought the future cascading  into the present. Virtually overnight, millions of companies and workers went remote and stayed that way for over a year, pushing their collaboration and productivity tools to the test across living rooms and time zones. While some organizations were able to launch new solutions or adapt existing ones to keep their people connected and productive, many struggled. 

The organizations that struggled quickly discovered their tools weren’t complete, scalable, secure, or built for the cloud era. Meanwhile, across all industries and business types, employees reported spikes in burnout, feelings of disconnection, and frustration with finding the latest information or using unfamiliar tools.

With many businesses headed into a hybrid model, the world of work has been transformed, possibly forever. There’s a fresh and fast-moving conversation about how organizations can succeed within the evolving future of work. 

But at the center of that discussion are two familiar topics: collaboration and productivity. How will they evolve in an era of distributed teams and surging employee demand for  flexibility? And how will businesses meet expectations to innovate quickly and deliver on rising customer expectations while navigating the new future of work? 

Although 2020 was a major inflection point, a closer look reveals that many of the technologies, trends, and cultural norms shaping the future of work have been around for some time. For years, forward-thinking organizations have been wrestling with how to maximize collaboration, productivity, and wellbeing among their employees, and they’ve been developing the tools to make it happen at scale. By that measure, the future of work has been here for some time; it just hasn’t been evenly distributed or easily visible.

With this context in mind, we’re sharing our new guide Create what’s next: The future of collaboration and productivity, which highlights three areas of focus for organizations wanting to catch up with those leading the charge to empower the future of work.

1. Making work-from-anywhere a reality with flexible solutions
2. Giving people helpful tools to maximize their impact
3. Enabling knowledge sharing and human connection

Armed with these three strategies, businesses can improve productivity and encourage innovation while better meeting the needs of their customers and their employees—now and in the years ahead.

We share some highlights from the guide in the infographic below (click to enlarge). You can also download the full guide now.

Time to go outside again, I guess. I’ll need a sun hat. Sunscreen. Maybe some new sandals? What else?

With the Recommendations AI service, I might be reminded to grab a reusable water bottle and a swimsuit. Or some after-sun aloe lotion. Good thing, cause I’ll need it.

Recommendations AI is a solution that uses machine learning to bring product recommendations to their shoppers across any catalog or client list. This service is part of our full suite of Retail solutions. When you integrate with the Retail API, you get the benefit of Google’s Product Discovery. Integrating once to reap the benefits over and over. Recommendations is the starting point, and you can easily extend into Retail Search and Vision Product Search in the upcoming future. 

The Recommendations solution is fully managed, global-scale and powered by deep learning, so you can focus on a great shopping experience and let someone else worry about the infrastructure.

Compared to baseline recommendation systems used by customers, Recommendations AI showed double digit uplift in conversion and clickthrough rates in A/B experiments controlled by the customers. You can optimize for click-through, conversion or session revenue, and fine tune the models to make sure you omit out-of-stock items or duplicates, for example.

So how does it work, and how do you get started? Read on, and we’ll walk you through the pipeline, starting with the data you already have to placement in your online store. 

Formula: Data -> Model -> Placement

You start with your catalog, the list of all the things (postcards, movies, pie recipes) that you want to show your customers. Then you ingest your PII-redacted user events -this is the historic event data like home page views, add to cart events and more along with real time user events. This user event is joined with the product catalog and items that allows us to construct the sequence of shoppers’ activity, thus being able to predict what the shopper has a high propensity to purchase next. The user events can come from both online activity across devices or offline store purchases

The recommendation model will return a list of products, which are the recommendations. The brains of the operation, if you will. This model is trained using all the data that you ingest, using the latest neural network models and techniques that Google has built expertise over the years in flagship products like Youtube and News, that allows us to uncover shopper intent,  so it can best predict the right recommendations to show to the right people.

Every model outputs a list of product identifiers, but where do they go? They go into placements, the spots, panels, carousels on your customer’s journey interacting with your brand that you’ve set aside to highlight recommendations. A model can send recommendations to one or more placements, but each placement only receives information from one recommendation model. Your pages will then need to render the products with the right images, text or other metadata, using the product ID that is returned by the model.

What do recommendations look like?

Let’s start by browsing our postcard-selling website, where I’ve been buying some vintage California postcards already. The recommendations algorithm has caught on to my interest, showing me other potential cards to purchase based on my history:

Put your data to work

To get started we need to bring your data into the recommendation model, so it can understand your customers, your inventory, and your sales patterns. 

The model takes in the product catalog you use, and metadata about those products to better understand nuances in assortment, pricing and variables like size and style. You might already have this data stored in BigQuery or Merchant Center, and hence we provide easy integrations that you can leverage to get started even faster.

As for the user events, don’t worry if you already have systems in place to capture web and mobile activity. We make it easy to bring in your real time event logs by providing seamless integrations with Google Tag Manager, Javascript pixel, or even historic events from Cloud Storage, BigQuery or using inline API or JSON, so you can immediately train the models on this imported  data. All this allows you to kickstart integrating with Recommendations AI in a matter of days.

The models then construct a sequence of activities that the user went through and joins with the products that the user engaged with. Once your data is ready to go, it takes a few days to train the model. Next onto making the data work for you.

Quickly customize your model

Setting up your own recommendations project in the console gives you the ability to choose what sort of model to train (based on what recommendations you want to generate) and your objective. Are you optimizing for click-through rate–more people click on the recommendation links or products–or for conversion rate–more people choose or buy what was suggested or revenue ?

Different models can be optimized for different optimization goals.; the GCP console explains what each one can do and how you can choose to optimize it.

Let’s unpack some of this terminology real quick.

We’ve got three model types:

• Recommended for you – Means we think these are items you’ll want to buy, based on your history; this is usually used on a home page to showcase items.
• Others you may like – Means if you’re browsing the page of a water bottle, we will recommend  alternative brands of water bottles that you may like as well as a backpack, based on your engagement  history.
• Frequently bought together – Means that when anyone buys sunscreen, we notice that they often also buy aloe lotion, so we will surface those items when someone adds any one of them to their cart.

And then we have three business objectives that the models optimize for:

• Click-through rate – How frequently did somebody click on a recommended item?
• Conversion rate– How frequently did somebody add a recommended item to their cart?
• Revenue per session – How much money did the recommendations generate for you?

Deliver anywhere along the journey

Now that you’re all set up in the Retail AI console, you can test out the recommendations in the console, even before you deploy to production.

You can integrate Recommendations into your frontend by calling the Predict APIt. The placements of recommendations will report data back into the dashboard and you can analyze and measure success for future iterations. 

On top of that you can use the recommendations for other parts of your customer’s journey. Email promotions, storefront kiosks, display ads or follow-up notifications can include recommendations based on past activity and cart contents. The model gives you useful product recommendations for a wide variety of touchpoints and steps in the purchasing process.

More best practices, and guides, are available inside our documentation.

How to get started

Training your own models can be tedious, time-consuming, and expensive. On top of that it requires deeper data science expertise to set up. Let us do it instead!

You can see how IKEA Retail uses Recommendations AI in this recent talk and blog from the Google Cloud Retail Summit..

To get started today you’ll need to make a Cloud project and enable the Retail API, which then allows you to access all the recommendation tools in one menu. Bring in your catalog and purchasing data, define a placement or two, and you can start putting recommendations on your site in a matter of days.

As business leaders evaluate their hybrid work models, employee productivity is top of mind. They’re trying to understand how to balance organizational needs for productivity with employee needs for autonomy, flexibility, and work/life balance. In my role as Google’s Productivity Advisor, I help organizations and employees realize that being productive means working smartly and efficiently — not overworking. And one key to productivity is developing strong time management skills.

Recent research found that 82% of workers don’t have a proper time management system, however. Instead, they rely on making lists, referencing their inbox, or simply winging it. And the result is telling: 21% of people say “their work is never under control or only under control one day per week.”

Everyone works differently, and one of the best things about hybrid work is that it empowers people to work in their own way, at their own pace. However, if your employees are managing their time inefficiently over the long term, their risk of burnout increases — which can be bad news for employee wellbeing, morale and productivity.

In order for any hybrid work plan to be effective and your teams to be healthy, your employees need to build their time management muscle. You can help them do so with a three-step process:

1. Understand how your employees are currently managing their time
2. Encourage your employees to identify time management strengths and areas for improvement
3. Empower your employees to maximize their time management practices

Step 1: Understanding your employees’ work habits

Before you can help your employees manage their time more effectively, you first need to understand how they spend it. To start, identify habits that might be preventing your employees from establishing productive workflows. For example, some employees might be working too much outside of business hours or spending the majority of their working hours in meetings.

While observation may help you glean some of this information, it’s best to go straight to the source. To find out if your employees feel they’re managing their time efficiently, simply ask them! You can distribute an anonymous questionnaire using a survey tool, like Google Forms, with questions like:

• Are you having trouble getting all your work done?
• Do you feel like you have enough independent working time?
• Do alerts and notifications interrupt or enhance your workflow?
• Which productivity and collaboration tools do you find the most and the least useful?
• How do you communicate best with your coworkers when working on- and off-site?

One major hurdle to effective time management can be too many meetings. To learn if meeting fatigue is plaguing your teams, include a survey question asking employees if they feel like they devote too much time to meetings. Help them get specific by encouraging them to use a tool like Time Insights. With this feature, employees can see exactly how much time they spend in meetings and how much focus time they schedule each week to accurately answer your survey.

Fielding an anonymous questionnaire is a great way to start your time management audit. The learnings that emerge can help you identify patterns in your workplace and among your teams — but they won’t reveal how individual employees work. For that, you’ll need to reach out to them directly.

Step 2: Identifying individual strengths to harness and areas for improvement

Once you have a general idea of how your team works, you can dig deeper into your individual employees’ time management methods and working styles. With this information, you can then help them identify the practices that work — and don’t work — for them.

Suggest to your employees that they spend a week observing their productivity patterns and time management methods. You might encourage people to look at:

• Their level of energy at different times during the day and/or on different days of the week
• The different types of work, such as strategic or administrative, they like doing and when
• How much time they spend doing work versus communicating about work
• How they feel during and after group meetings
• The amount of time they spend in deep-focus versus time spent switching among different tasks

If your organization uses digital timesheets, your employees can consult built-in, automatic time-tracking insights to see where they’re spending their time. Otherwise, you can easily create a custom time-tracking app using a no-code solution like AppSheet to help your employees measure the time they devote to their various responsibilities.

Once everyone has completed their weeklong study, set up dedicated meetings to review your employees’ observations with them individually. Armed with this new understanding, you and your employees can clarify when and how each person does their best work, identify areas for improvement, and start adjusting accordingly.

Step 3: Empowering your employees to flex their time management muscle

Now that you have clarity on how your employees spend their time, you can work with them to develop personal time management practices. To set them up for success, look for opportunities that take advantage of their strengths and offset their challenges — no matter how they like to work.

It’s important to remember there’s no one-size-fits-all solution to using time productively. Some workers love to tackle the most difficult tasks first thing in the morning; others feel more energized at the end of their day. Some people think best in conversation while others prefer to be alone with their thoughts. To help your employees develop time management practices that fit their working style, they need to feel empowered to decide how they can use their time and tools productively.

When it comes to time management, calendars are your best friend, so encourage your employees to take advantage of simple features. Google Calendar lets everyone see availability; by glancing at a shared calendar, coworkers will know when to reach out to someone (and when not to). You can also prompt your employees to block off quiet time during windows when they feel most productive, and use tools like Focus time to protect the time they need for heads-down work.

Email and chat are important, but they can also become a distraction if not used intentionally. Most people open email first thing in the morning, leave it open all day, and check it often. Instead, when you and your employees need to focus, try closing the email tab or using Gmail features to snooze and star emails to stay on task. The same approach applies to chat: check it strategically and turn off notifications when you need deep focus.

Finally, be intentional about building and maintaining trust with your teams as they develop new time management skills. If you decide to use Google Meet for regular check-ins, partner with your employees to develop meeting agendas directly inside Calendar invites to ensure your shared time will be used efficiently.

Valuing impact over output

Hybrid work offers an opportunity for business leaders to rethink their perspective on employee time management and what it means to be productive. Remember, the goal of building effective time management skills isn’t always to become more efficient so one can take on additional work. Often times, taking a step back helps us simplify how we work in order to deepen our impact. When you empower your employees to create a personal time management system, I think you’ll find they will be happier and healthier, with more to contribute to your organization.