How many times has a shopper searched for a product on a store’s website only to get results that aren’t relevant—or worse, provided no search results at all? While most ecommerce sites have search capabilities, few accomplish their ultimate goal: making it easy for customers to discover the products they want.

Some 94% of U.S. consumers abandoned a shopping session because they received irrelevant search results, according to a 2021 survey conducted by The Harris Poll and Google Cloud. It’s a phenomenon known as “search abandonment.” Indeed, poor product discovery experiences can stop a purchase in its tracks and leave shoppers frustrated. Retailers miss out on a staggering $300 billion each year due to search abandonment in the U.S. alone.

With the announcement today of our latest discovery solution, Retail Search, now generally available, retailers worldwide can supercharge their websites and mobile apps with Google-quality search. Built on Google’s technologies that understand user intent and context, the solution helps businesses improve the search and overall shopping experience across all of their digital touchpoints.

Already, early adopters like Lowes, Fnac Darty, and Casas Pernambucanas have been using Google Cloud’s discovery solutions to increase sales conversions, boost basket sizes, and improve customer engagement.

Understanding user intent in search is a hard problem

While we’ve come a long way from the days when search was largely based on keywords and boolean rules, shoppers still struggle to find what they’re looking for. They often have to come up with a perfectly-worded query that a retailer’s site search engine will understand, sometimes rephrasing several times before getting the results they want—if they find anything relevant at all.

Traditional search technologies don’t work in the modern age of online retail, where tens or even hundreds of thousands of items are available on a single ecommerce site.

Today, people expect search engines to understand their intent more deeply, return relevant results faster, and help them discover new products easily with personalized recommendations.

Fortunately, product discovery experiences on retail sites can now offer superior, contextual experiences for consumers, combining marketing and data science insights along with advanced search technologies based on machine learning and artificial intelligence.

Now, through the power of Retail Search, when a shopper searches for a “long black dress with short sleeves and comfortable fit” on an ecommerce site, they should immediately get results for precisely that—rather than refining their search multiple times, or worse, giving up their shopping journey.

Helping retailers solve their search experience woes

Retail Search transforms the shopping experience and makes it easier for shoppers to find relevant products by surfacing results that are intuitive and contextual.

This fully managed service is easily customizable, enabling organizations to craft shopper-focused search experiences. Our site search solution builds upon decades of Google’s experience and innovation in search indexing, retrieval, and ranking. Retailers can make product discovery even easier for shoppers, while optimizing for their business goals with advanced capabilities, including:

• Advanced query understanding that produces better results from even the broadest queries, including non-product searches.
• Semantic search to effectively match product attributes with website content for fast, relevant product discovery.
• Optimized results that leverage user interaction and ranking models to meet specific business goals.
• State-of-the-art security and privacy practices that ensure retailer data is isolated with strong access controls and is only used to deliver relevant search results on their own properties.

Retailers can now shape product discovery into a shopping experience that is timely, relevant, and personalized—all powered by Google Cloud.

Early adopters seeing immediate impact

Several leading retailers around the world have been able to quickly create better online shopping experiences for their customers and capture more digital and omnichannel growth using Google Cloud’s product discovery solutions.

“With limited customer signals and no historical data, descriptive long-tail searches are some of the most challenging queries to understand,” said Neelima Sharma, senior vice president, technology, e-commerce, marketing and merchandising at Lowe’s. “We have been partnering with Google Cloud to give our customers relevant results for long-tail searches and have seen an increase in click-through and search conversion and a drop in our ‘No Results Found’ rate since we launched.”

“Making constant improvements to our website search engines has always been a priority for us as we aim to give our customers a simpler, more customized and enhanced online shopping experience,” said Olivier Theulle, Fnac Darty’s chief ecommerce and digital officer. “As we implement Google Cloud’s site search solution on our Fnac Darty websites and are the first French retailer to do so, we expect the solution to deliver increased conversion rates while also offering greater customer satisfaction.”

“Google Cloud has helped improve the indexing and quality of search results on Pernambucanas’ digital platforms, providing a better experience to our customers and improving sales conversions,” said Fabiano Rustice, chief information officer at Pernambucanas. “On Black Friday in November 2021—the largest retail date in Brazil—we saw a 20% reduction in search refinements per user, for which Retail Search was instrumental. We are proud to be an early adopter and to have Google Cloud as a strategic innovation partner.”

In addition to Google Cloud’s efforts working directly with customers, our ecosystem of key partners, including GroupBy, Lucidworks, GridDynamics, SpringML, and others are also leveraging Google Cloud discovery solutions to provide value added services to their customers.

“In the fast paced and extremely competitive ecommerce environment, we are seeing that forward-looking and innovative retailers are prioritizing product discovery,” said Roland Gossage, CEO of GroupBy. “GroupBy has proudly partnered with Google Cloud to launch its product discovery solutions across several client environments. Already we have seen a more than 10% increase in online revenues, which equates to millions of dollars, and this is just the beginning.”

To learn more, visit Discovery Solutions for Retail or contact your Google Cloud field sales representative.

Not long ago, building AI into recommendation engines was a daunting, expensive task that could take years to get off the ground. But as Bazaarvoice has shown, with the help of cloud services, the time from AI investment to business outcomes is shorter than ever. 

Bazaarvoice is the leading provider of product reviews and user-generated content (UGC) solutions that help brands and retailers understand and better serve customers. Its 2019 acquisition of Influenster.com, a community of consumer reviewers 6.5 million strong, expanded the Bazaarvoice portfolio with a platform where consumers can share their candid opinions — and share they have, over 54 million times. 

After the acquisition, Bazaarvoice expanded the site’s product diversity by 53%, to more than 5.4 million unique products. To keep user engagement high, Influenster must be seen as both a source of trusted, transparent reviews and a place for customers to discover useful, relevant products for the first time. By introducing shoppers to new products Influenster not only provides value to customers but also helps brands collect consumer insights. 

Influenster started out as a place where people gathered to share their honest thoughts on beauty products but quickly expanded to nearly every category, from Art to Wearables. Because of the much smaller scope, the site started and flourished under a rules-based recommendation engine. However, as Influenster expanded its scope under Bazaarvoice, a more robust recommendation system became necessary. In its earliest days, Influenster was successful because of the human perspective it offered: For every product there was a litany of reviews and images that made users feel as if they were getting an endorsement on a product from a friend. 

The Bazaarvoice engineering team asked themselves how they could keep that same feeling of personalization with an ever-growing catalog of items and categories. They needed recommendations that could scale with the site, rather than requiring more rules be constructed each time a new product category was introduced. They also needed to ensure the Influenster experience would remain performant even towards unknown members.

Bazaarvoice tested out several recommendation engines, benchmarking each against their current rules-based system. In the end they decided on Google Cloud’s Recommendations AI because of its transparent billing, ease of integration and setup, and naturally, its proven results.

Transparent Billing

“Part of what the engineers loved was they knew exactly what it was going to cost as it scaled” says Nick Shiftan, SVP, Content Acquisition Services Product Unit for Influenster. The goal was to build once and innovate rather than leave a wake of technical debt only to be tackled when costs grew unexpectedly out of control. Google Cloud’s straightforward and pay-as-you-go billing allowed them to anticipate how costs would grow as user interactions did and plan accordingly.

Ease of integration

“I’m positively surprised how Google packed such a complex system in a very easy-to-use API” remarks Eralp Bayraktar, the Software Engineering team lead overseeing the project. Because the original team was made of just one full-time engineer the ease of integration became an even more critical feature. Not only does Recommendations AI pull from years of suggestion expertise in Google Search and YouTube, but in combination with Ad’s Merchant Center, it also creates a streamlined process for importing product metadata. From there, creating a model becomes a matter of picking the preferred recommendation type and then the business objective to optimize for. Once the model is created and the API integrated into the website, the code is already deployed at the global scale: There are no further architectural considerations to ensure recommendations are available to users worldwide. For Bazaarvoice, this meant going from ideation to production in one month.

Proven Results

“We have used it for product recommendations and off-loaded our DB-tiring business logic to Recommendations AI, which resulted in overall faster response times and much better recommendations as proven by our A/B tests,” Eralp continues.  

Bazaarvoice began by A/B testing Recommendations AI against their rules-based system. Early on in the experimental phase they noticed a clear and consistent 60% increase in the click-through rate over their original recommendation system. 

Even more impressive was the performance on Unknown Members. For every person that signs up for an account on Influenster.com there are many other visitors that come to the website and leave without fully registering. This is typically referred to as the “cold start” problem in the industry — how do you figure out what to recommend to those people without their history, behavior, or preferences? Recommendations AI gives you the option to input and train on unknown users, and by providing metadata on products, it can provide high-quality suggestions to registered members and first-time users alike. 

With a mind to the future, Eralp concludes his thoughts on Bazaarvoice’s experience: “It enables discovery by adding an adjustable percentage of cross-category products [for] healthier [traffic distribution] across all our catalog. We are investing in data science and having the Recommendations AI as the baseline is a good challenge for us to thrive.” 

To learn more about Recommendations AI and how it can help your organization thrive, check out our recently published 4 part guide which kicks off with an overview on “How to get better retail recommendations with Recommendations AI.” This series also covers data ingestion, modeling, as well as serving predictions & evaluating Recommendations AI. You can also easily get started with our Quickstart Guide.

Businesses around the globe are realizing the benefits of replacing legacy data silos with cloud-based enterprise data warehouses, including easier collaboration across business units and access to insights within their data that were previously unseen. However, bringing data from numerous disparate data sources into a single data warehouse requires you to develop pipelines that ingest data from these various sources into your enterprise data warehouse. Historically, this has meant that data engineering teams across the organization procure and implement various tools to do so. But this adds significant complexity to managing and maintaining all these pipelines and makes it much harder to effectively scale these efforts across the organization. Developing enterprise-grade, cloud-native pipelines to bring data into your data warehouse can alleviate many of these challenges. But, if done incorrectly, these pipelines can present new challenges that your teams will have to spend their time and energy addressing. 

Developing cloud-based data ingestion pipelines that replicate data from various sources into your cloud data warehouse can be a massive undertaking that requires significant investment of staffing resources. Such a large project can seem overwhelming and it can be difficult to identify where to begin planning such a project. We have defined the following principles for data pipeline planning to begin the process. These principles are intended to help you answer key business questions about your effort and begin to build data pipelines that address your business and technical needs. Each section below details a principle of data pipelines and certain factors your teams should consider as they begin developing their pipelines.

Principle 1: Clarify your objectives

The first principle to consider for pipeline development is clarify your objectives. This can be broadly defined as taking a holistic approach to pipeline development that encompasses requirements from several perspectives: technical teams, regulatory or policy requirements, desired outcomes, business goals, key timelines, available teams and their skill sets, and downstream data users. Clarifying your objectives clearly identifies and defines requirements from each key stakeholder at the beginning of the process and continually checks development against these requirements to ensure the pipelines built will meet these requirements.This is done by first clearly defining the desired end state for each project in a way that addresses a demonstrated business need of downstream data users. Remember that data pipelines are almost always the means to accomplish your end state, rather than the end state itself. An example of an effectively defined end-state is “enabling teams to gain a better understanding of our customers by providing access to our CRM data within our cloud data warehouse” rather than “move data from our CRM to our cloud data warehouse”. This may seem like a merely semantic difference, but framing the problem in terms of business needs helps your teams make technical decisions that will best meet these needs. 

After clearly defining the business problem you are trying to solve, you should facilitate requirement gathering from each stakeholder and use these requirements to guide the technical development and implementation of your ingestion pipelines. We recommend gathering stakeholders from each team, including downstream data users, prior to development to gather requirements for the technical implementation of the data pipeline. These will include critical timelines, uptime requirements, data update frequency, data transformation, DevOps needs, and security, policy, or regulatory requirements by which a data pipeline must meet.

Principle 2: Build your team

The second principle to consider for pipeline development is build your team. This means ensuring you have the right people with the right skills available in the right places to develop, deploy, and maintain your data pipelines. After you have gathered your pipeline requirements, you can begin to develop a summary architecture that will be used to build and deploy your data pipelines. This will help you identify the human talent you will need to successfully build, deploy, and manage these data pipelines and identify any potential shortfalls that would require additional support from either third-party partners or new team members.

Not only do you need to ensure you have the right people and skill sets available in aggregate, but these individuals need to be effectively structured to empower them to maximize their abilities. This means developing team structures that are optimized for each team’s responsibilities and their ability to support adjacent teams as needed.

This also means developing processes that prevent blockers to technical development whenever possible, such as ensuring that teams have all of the appropriate permissions they need to move data from the original source to your cloud data warehouse without violating the concept of least privilege. Developers need access to the original data source (depending on your requirements and architecture) in addition to the destination data warehouse. Examples of this are ensuring that developers have access to develop and/or connect to a Salesforce Connected App or read access to specific Search Ads 360 data fields.

Principle 3: Minimize time to value

The third principle to consider for pipeline development is minimize time to value. This means considering the long-term maintenance burden of a data pipeline prior to developing and deploying it in addition to being able to deploy a minimum viable pipeline as quickly as possible. Generally speaking, we recommend the following approach to building data pipelines to minimize their maintenance burden: Write as little code as possible. Functionally, this can be implemented by:

1. Leveraging interface-based data ingestion products whenever possible. These products minimize the amount of code that requires ongoing maintenance and empower users who aren’t software developers to build data pipelines. They can also reduce development time for data pipelines, allowing them to be deployed and updated more quickly. 

• Products like Google Data Transfer Service and Fivetran allow for managed data ingestion pipelines by any user to centralize data from SaaS applications, databases, file systems, and other tooling. With little to no code required, these managed services enable you to connect your data warehouse to your sources quickly and easily.
• For workloads managed by ETL developers and data engineers, tools like Google Cloud’s Data Fusion provide an easy-to-use visual interface for designing, managing and monitoring advanced pipelines with complex transformations.

2. Whenever interface-based products or data connectors are insufficient, use pre-existing code templates. Examples of this include templates available for Dataflow that allow users to define variables and run pipelines for common data ingestion use cases, and the Public Datasets pipeline architecture that our Datasets team uses for onboarding.

3. If neither of these options are sufficient, utilize managed services to deploy code for your pipelines. Managed services, such as Dataflow or Dataproc, eliminate the operational overhead of managing pipeline configuration by automatically scaling pipeline instances within predefined parameters.

Principle 4: Increase data trust and transparency

The fourth principle to consider for pipeline development is increase data trust and transparency. For the purposes of this document, we define this as the process of overseeing and managing data pipelines across all tools. Numerous data ingestion pipelines that each leverage different tools or are not developed under a coordinated management plan can result in “tech sprawl”, which significantly increases the management overhead of data ingestion pipelines as the quantity of data pipelines increases. This becomes especially cumbersome if you are subject to service-level agreements, or legal, regulatory, or policy requirements for overseeing data pipelines. Preventing tech sprawl is, by far, the best strategy for dealing with it by developing streamlined pipeline management processes that automate reporting. Although this can theoretically be achieved by building all of your data pipelines using a single cloud-based product, we do not recommend doing so because it prevents you from taking advantage of features and cost optimizations that come with choosing the best product for your use case. 

A monitoring service such as Google Cloud Monitoring Service or Splunk that automates metrics, events, and metadata collection from various products, including those hosted in on-premise and hybrid computing environments, can help you centralize reporting and monitoring of your data pipelines. A metadata management tool such as Google Cloud’s Data Catalog or Informatica’s Enterprise Data Catalog can help you better communicate the nuances of your data so users better understand which data resources are best fit for a given use case. This significantly reduces your pipeline’s governance burden by eliminating manual reporting processes that often result in inaccuracies or lagging updates.

Principle 5: Manage costs

The fifth principle to consider for pipeline development is manage costs. This encompasses both the cost of cloud resources and the staffing costs necessary to design, develop, deploy, and maintain your cloud resources. We believe that your goal should not necessarily be to minimize cost, but rather maximizing the value of your investment. This means maximizing the impact of every dollar spent by minimizing waste in cloud resource utilization and human time. There are several factors to consider when it comes to managing costs:

• Use the right tool for the job – Different data ingestion pipelines will have different requirements for latency, uptime, transformations, etc. Similarly, different data pipeline tools have different strengths and weaknesses. Choosing the right tool for each data pipeline can help your pipelines operate significantly more efficiently. This can reduce your overall cost, free up staffing time to focus on the most impactful projects, and make your pipelines much more efficient.
• Standardize resource labeling –  Implement and utilize a consistent labeling schema across all tools and platforms to have the most comprehensive view of your organization’s spending. One example is requiring all resources to be labeled by the cost center or team at time of creation. Consistent labeling allows you to monitor your spend across different teams and calculate the overall value of your cloud spending.
• Implement cost controls – If available, leverage cost controls to prevent errors that result in unexpectedly large bills. 
• Capture cloud spend – Capture your spend on all cloud resource utilization for internal analysis using a cloud data warehouse and a data visualization tool. Without it, you won’t understand the context of changes in cloud spend and how they correlate with changes in business.
• Make cost management everyone’s job – Managing costs should be part of the responsibilities of everyone who can create or utilize cloud resources. To do this well, we recommend making cloud spend reporting more transparent internally and/or implementing chargebacks to internal cost centers based on utilization.

Long-term, the increased granularity in cost reporting available within Google Cloud can help you better measure your key performance indicators. You can shift from cost-based reporting (i.e. – “We spent $X on BigQuery storage last month”) to value-based reporting (i.e. – “It costs $X to serve customers who bring in $Y revenue”). 

To learn more about managing costs, check out Google Cloud’s “Understanding the principles of cost optimization” white paper.

Principle 6: Leverage continually improving services

The sixth principle is leverage continually improving services. Cloud services are consistently improving their performance and stability, even if some of these improvements are not obvious to users. These improvements can help your pipelines run faster, cheaper, and more consistently over time. You can take advantage of the benefits of these improvements by:

• Automating both your pipelines and pipeline management: Not only should data pipelines be automated, but almost all aspects of managing your pipelines can also be automated. This includes pipeline/data lineage tracking, monitoring, cost management, scheduling, access management and more. This helps reduce long-term operational costs of each data pipeline that can significantly alter your value proposition and prevent any manual configurations from negating the benefits of later product improvements.
• Minimizing pipeline complexity whenever possible: While ingestion pipelines are relatively easy to develop using UI-based or managed services, they also require continued maintenance as long as they are in use. The most easily maintained data ingestion pipelines are typically the ones that minimize complexity and leverage automatic optimization capabilities. Any transformation in a data ingestion pipeline is a manual optimization of the pipeline that may struggle to adapt or scale as the underlying services improve. You can minimize the need for such transformations by building ELT (extract, load, transform) pipelines rather than ETL (extract, transform, load) pipelines. This pushes transformations down to the data warehouse that is use a specifically optimized query engine to transform your data rather than manually configured pipelines.

Next steps

If you’re looking for more information about developing your cloud-based data platform, check out our Build a modern, unified analytics data platform whitepaper. You can also visit our data integration site to learn more and find ways to get started with your data integration journey.

Once you’re ready to begin building your data ingestion pipelines, learn more about how Cloud Data Fusion and Fivetran can help you make sure your pipelines address these principles.

Data science can be applied to business problems to improve practices and help to strengthen customer satisfaction. In this blog, we address how the addition of Looker extends the value of your Google Cloud investments to help you understand your customer journey, unlock value from first-party data, and optimize existing cloud infrastructure.    

Data is a powerful tool 

While everyone doesn’t need to understand the nuts and bolts of data technologies, most people do care about the value data can create for them — how it can help them do their jobs better. Within the data space, we are seeing a trend of ongoing failure to make data accessible to “humans” –   the industry still hasn’t figured out how to put data into the hands of people when and where they need it, how they need it. 

What if everyone in your organization could analyze data at scale, and make more-informed, fact-based decisions?   Data and insights derived from data are valuable but only if your users see it.  We think Looker helps solve that. 

Solutions tell a larger story of how it all fits together 

Across all verticals and industries,  businesses benefit from knowing and understanding their customers better. Many business goals are to increase revenue by improving product recommendations and pricing optimization, improving the user experience through targeted marketing and personalization, and reducing churn while improving retention rates.  To help reach  these goals, key strategies should focus on understanding customer needs, their motivations, likes and dislikes, and using all available data – in other words, put yourself in the shoes of your customer.    

Our goal with Looker solutions is to offer the right level of out-of-box support that allows customers to get to value quickly, while maintaining the necessary flexibility. We aim to offer a library of data-driven solutions that accelerate data projects. Many solutions include Looker Blocks (pre-built pieces of code that accelerate data exploration environments) and Actions (custom integrations) that get customers up and running quickly and lets you build business-friendly access points for Google Cloud functionality like BQML, App Engine and Cloud Functions.  

Below, you’ll find a sampling of the newest Looker solutions. 

Listening to customers by looking at the data

Looker’s solution for Contact Center AI (CCAI), helps businesses gain a deeper understanding and appreciation of their customers’ full journey by unlocking insights from all their company’s first-party data. Call centers can converse naturally with customers and deliver outstanding experiences by leveraging artificial intelligence. CCAI‘s newest product —CCAI Insights — reviews conversations support agents are having, finding and annotating the data with the important information, and identifying the calls that need review. We’ve partnered with the product teams at CCAI to build Looker’s Block for CCAI Insights, which sets you on the path to integrating the advanced insights into your first-party data in Looker, overlaying business data with the customer experience.

Businesses can better understand contact center experiences and take immediate action when necessary to make sure the most valuable customers receive the best service. 

Realizing full business value of First-Party data

Looker for Google Marketing Platform (GMP) provides marketers the power to unlock the value of their company’s first-party data to more effectively target audiences.  The Looker Blocks and Actions for GMP offer interactive data exploration, slices of data with built-in ML predictions and activation paths back to the GMP.  This strategic solution continues to evolve with the Looker Action for Google Ads (Customer Match), the Looker Action for Google Analytics (Data Import) and the Looker Block for Google Analytics 4 (GA4).

• The Looker Action for Customer Match allows marketers to send segments and audiences based on first-party data directly into Google Ads. Reach users cross-device and across the web’s most powerful channels such as Display, Video, YouTube, and Gmail.  The entire process is performed within a single screen in Looker, and is able to be completed in a few minutes by a non-technical user. 
• The Looker Action for Data Import can be used to enhance user segmentation and remarketing audiences in Google Analytics by taking advantage of user information accessible in Looker, such as in CRM systems or transactional data warehouses.
• The Looker Block for Google Analytics 4 (GA4) expands the solution’s support with out-of-the-box dashboards and pre-baked BigQuery ML models for the newest version of Google Analytics.The Looker Block offers up reports with flexible configuration capabilities to unlock custom insights beyond the standard GA reporting. Customize audience segments, define custom goals to track and share these reports with teams who do not have access to the GA console.

From clinical notes to patient insights at scale

Taking a look at the Healthcare vertical, the Looker Healthcare NLP API Block serves as a critical bridge between existing care systems and applications hosted on Google Cloud providing a managed solution for storing and accessing healthcare data in Google Cloud. The Healthcare NLP API uses natural language models to extract healthcare information from medical text,  rapidly unlocking insights from unstructured medical text and providing medical providers with simplified access to intelligent insights.  Healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, such as medications, procedures, conditions, clinical history, and begin to link this to other clinical data sources for downstream AI/ML.   

Specifically, the natural language processing (NLP) Patient View (pictured below) allows you to review a single selected patient of interest, surfacing their clinical notes history over time. It informs clinical diagnosis with family history insights, which is not currently captured in claims, and captures additional procedure coding for revenue cycle purposes.

The dashboard below shows the NLP Term View which allows users to focus on chosen medical terms across the entire patient population in the dataset so they can start to view trends and patterns across groups of patients. 

This data can be used to: 

• Enhance patient matching for clinical trials 
• Identify re-purposed medications
• Drive advancements for research in cancer and rare diseases
• Identify how social determinants of health impact access to care

Managing Costs Across Clouds

Effective cloud cost management is important for reasons beyond cost control — it provides you the ability to reduce waste and predictably forecast both costs and resource needs. Looker’s solution for Cloud Cost Management offers quick access to necessary reporting and clear insights into cloud expenditures and utilization. 

This solution brings together billing data from different cloud providers in a phased approach: get up and running quickly with Blocks optimized for where the data is today (Google Cloud, AWS or Azure) as you work towards more sophisticated analysis for cross-platform planning and even cloud spend optimization with the mapping of tags, labels and cost centers across clouds.

The Looker Cloud Cost Management solution provides operational teams struggling to monitor, understand, and manage the costs and needs associated with their cloud technology with a comprehensive view into what, where and why they are spending money.

Making better decisions with Looker-powered data

Leading companies are discovering ways to get value from all of that data beyond displaying it in a report or a dashboard. They want to enable everyone to make better decisions but that’s only going to happen if everyone can ask questions of the data, and get reliable, correct answers without using outdated or incomplete data and without waiting for it. People and systems need to have data available to them in the way that makes the most sense for them at that moment.  

It’s clear that successful data-driven organizations will lead their respective segments not because they use data to create reports, but because they use it to power data experiences tailored to every part of the business, including employees, customers, operational workflows, products and services.   

As people’s way of experiencing data has evolved, more than ever before, dashboards alone are not enough. You can use data as fuel for data-driven business workflows, and to power digital experiences that improve customer engagement, conversions, and advocacy. 

From Nov. 9 – 11th, Looker is hosting its annual conference JOIN, where we’ll be showing new features, including how we help to:

• Build data experiences at the speed of business
• Accelerate the business value with packaged experiences 
• Unleash more insights for more people in the right way – Deliver data experiences at scale

There is no cost to attend JOIN.  Register here and learn how Looker helps organizations build and deliver custom data-driven experiences that goes beyond just reports and dashboards, scales and grows with your business, allows developers to build innovative data products faster, and ensures data reaches everyone.

Cloud developers — you’ve got it all. You can code in your choice of languages, enjoy portability with containers, minimize complexity with serverless, and manage the entire software lifecycle by following DevOps principles. But let’s face it, building and onboarding new cloud applications still requires a lot of manual planning, synthesis, and, yes, hard work. You need to research and plan your deployment, create a workable, secure architecture, and of course, you need to write the actual code,

For the last several decades, the cloud has been primarily a “do it yourself” model with volumes of options that have made development more complicated. The cloud went from overwhelmingly exciting to…. a bit overwhelming.

What if we all could bring that excitement back? What if you had some help that was available whenever and wherever you needed it? 

Say hello to Duet AI for Google Cloud

Powered by Google’s state-of-the-art generative-AI foundation models, Duet AI for Google Cloud is an always-on AI collaborator that provides help to users of all skill levels where they need it. With Duet AI, we’re on a mission to deliver a new cloud experience that’s personalized and intent-driven, and can deeply understand your environment to assist you in building secure, scalable applications, while providing expert guidance. 

As we evolve Google Cloud with Duet AI, we are looking to build a cloud platform that is more human-centric, holistic, and helpful, with responsible AI at the center of the experience:

• Human-centric: With Duet AI, we are making Google Cloud more accessible and personal to any type of user at any skill level by providing them with support whenever they need it, from code recommendations for developers, to prompt-based data insights for data engineers, to chat-based app creation for business users.
• Holistic: With generative AI at the center of the cloud experience, cloud development can be more cohesive, with fewer silos across functions, services, and tech stacks, providing a holistic picture in the format you want, wherever you are in Google Cloud.
• Helpful: To deliver smarter, contextual recommendations for building and operating apps with Google Cloud, we pre-trained Codey, one of the foundation models that powers Duet AI, with Google Cloud-specific content like documentation and sample code, and fine-tuned it based on Google Cloud  user behaviors and patterns. 
• Responsible: Our AI Principles set out our commitment to developing technology responsibly. Your code and recommendations will not be reused for any model learning and development. This helps ensure the privacy of your data and code, and also the integrity of the knowledge space from which our AI models are trained.

New capabilities available in Duet AI for Google Cloud

Here are some of the new capabilities available to get us started on our mission to deliver a new personalized and intent-driven cloud experience:

• Code assistance provides AI-driven code assistance for cloud users such as application developers and data engineers. It gives code recommendations as they type in real time, generates full functions and code blocks, and identifies vulnerabilities and errors in the code, while suggesting fixes.

Code assistance auto-generates code for creating a Google Cloud Storage bucket

Code assistance will be available through multiple products and services across Google Cloud, such as in Cloud Workstations, our fully-managed secure development environment, and other code-editing experiences in the Google Cloud Console. Developers will also find code assistance in Cloud Shell Editor or via our Cloud Code IDE extensions for VSCode and JetBrains IDEs. It supports multiple languages including Go, Java, Javascript, Python, and SQL.

• Chat assistance allows people to use simple natural language to get answers on specific development or cloud-related questions. Users can engage with chat assistance to get real-time guidance on various topics, such as how to use certain cloud services or functions, or get detailed implementation plans for their cloud projects. It can also provide architectural or coding best practices, helping to reduce the need to go searching for relevant documents.

Use chat assistance to get the detailed steps for deploying an app on Cloud Run

Chat assistance will also be available across multiple Google Cloud surface areas, for example IDEs, the Cloud Console, and through products and services. Whether you’re a developer, operator, data engineer, or security professional, you’ll be able to leverage chat assistance to help get more work done faster. 

Looking to optimize these features further for developers specialized in one particular area? With Generative AI support in Vertex AI, enterprises can fine-tune Codey using their own code base. They can consume these customized Codey models directly from Vertex AI today, and later this year, they will be able to connect it to the built-in Duet AI experience. And don’t worry, if you choose to train Codey with your code, your private data is kept private, and not used in the broader foundation model training corpus. You will have transparency and control over where data is stored and how or if it is used. 

• Duet AI for AppSheet will let users create intelligent business applications,  connect their data, and build workflows into Google Workspace via natural language.  With no coding required, users will be able to build apps by describing their needs in a chat guided by AI-powered prompts. This makes app creation accessible to more users, which can allow developer teams to focus their time on other high-impact work.

Create business applications with Duet AI for AppSheet using natural language

Experiment with Duet AI for Google Cloud today

We believe that having an assistant who is constantly evolving by your side will not only reduce an already overwhelmed developer’s workload, but also bring back the excitement of cloud development. With Duet AI, you can navigate the cloud with more confidence, ease, and — dare we say it — fun. 

And this is just the beginning. The future of the cloud experience that we are shaping with Duet AI is full of possibilities. We believe the future of developer productivity is more targeted personalized assistance. Check here to see our vision for Duet AI for Google Cloud – the redefinition of productivity in the workplace through unique end-to-end AI assisted technologies. 

These early features of Duet AI for Google Cloud are available today for limited users and we will be expanding access very soon. Sign up here to join Google Cloud’s AI Trusted Tester Program.