Editor’s note:  With Google Cloud’s datasets solution, you can access an ever-expanding resource of the newest datasets to support and empower your analyses and ML models, as well as frequently updated best practices on how to get the most out of any of our datasets. We will be regularly updating this blog with new datasets and announcements, so be sure to bookmark this link and check back often.

August 2021

New dataset: Google Cloud Release Notes

• Looking for a new way to access Google Cloud Release Notes besides the docs, the XML feed, and Cloud Console? Check out the Google Cloud Release Notes dataset. With up-to-date release notes for all generally available Google Cloud products, this dataset allows you to use BigQuery to programmatically analyze release notes across all products, exploring security bulletin updates, fixes, changes, and the latest feature releases. 
• Access the dataset

July 2021

Best practice: Use Google Trends data for common business needs

• The Google Trends dataset represents the first time we’re adding Google-owned Search data into Datasets for Google Cloud. The Trends data allows users to measure interest in a particular topic or search term across Google Search, from around the United States, down to the city-level. You can learn more about the dataset here, and check out the Looker dashboard here! These tables are super valuable in their own right, but when you blend them with other actionable data you can unlock whole new areas of opportunity for your team. To learn how to make informed decisions with Google Trends data, keep reading.
• Access the dataset

New dataset: COVID-19 Vaccination Search Insights

• With COVID-19 vaccinations being a topic of interest around the United States, this dataset shows aggregated, anonymized trends in searches related to COVID-19 vaccination and is intended to help public health officials design, target, and evaluate public education campaigns. Check out this interactive dashboard to explore searches for COVID-19 vaccination topics by region.
• Access the dataset

June 2021

New dataset: Google Diversity Annual Report 2021

• Since 2014, Google has disclosed data on the diversity of its workforce in an effort to bring candid transparency to the challenges technology companies like Google face in recruitment and retention of underrepresented communities. In an effort to make this data more accessible and useful, we’ve loaded it into BigQuery for the first time ever. To view Google’s Diversity Annual Report and learn more, check it out.
• Access the dataset

New dataset: Google Trends Top 25 Search terms

• The most popular and surging Google Search terms are now available in BigQuery as a public dataset. View the Top 25 and Top 25 rising queries from Google Trends from the past 30-days, including 5 years of historical data across the 210 Designated Market Areas (DMAs) in the US. Keep reading.
• Access the dataset

New dataset: COVID-19 Vaccination Access

• With metrics quantifying travel times to COVID-19 vaccination sites, this dataset is intended to help Public Health officials, researchers, and Healthcare Providers to identify areas with insufficient access, deploy interventions, and research these issues. Check out how this data is being used in a number of new tools.
• Access the dataset

Best practice: Leveraging BigQuery Public Boundaries datasets for geospatial analytics 

• Geospatial data is a critical component for a comprehensive analytics strategy. Whether you are trying to visualize data using geospatial parameters or do deeper analysis or modeling on customer distribution or proximity, most organizations have some type of geospatial data they would like to use – whether it be customer zipcodes, store locations, or shipping addresses. However, converting geographic data into the correct format for analysis and aggregation at different levels can be difficult. In this post, we’ll walk through some examples of how you can leverage the Google Cloud platform alongside Google Cloud Public Datasets to perform robust analytics on geographic data. Keep reading.
• Access the dataset

Get the metadata and try BigQuery sandbox 

When you’ve learned about many of our datasets and pre-built solutions from across Google, you may be ready to start querying them. Check out the full dataset directory and read all the metadata at g.co/cloud/marketplace-datasets, then dig into the data with our free-to-use BigQuery sandbox account, or $300 in credits with our Google Cloud free trial.

Be it marketing, sales, or even security, most departments in large organizations today have a range of SaaS tools at their disposal to help make their work more efficient. But people in corporate finance and accounting have been underserved in that respect. Their days are still spent on routine, mundane tasks that keep them away from more stimulating work. Auditoria.AI aims to change that by automating those functions with AI and natural language technology.

We strive to improve the lives of finance and accounting professionals by automating the routine, repetitive, and laborious parts of the finance function, such as copy-and-pasting data, validating documents, and checking for errors on spreadsheets, freeing these teams to focus instead on providing valuable, strategic insights to the business. 

To that end, the problems we’re solving affect three major finance functions:

• Accounts Payable, responsible for sending money out of the company, such as bill payments. 
• Accounts Receivable, responsible for bringing money into the company, such as invoicing for services. 
• General accounting, a broader term consisting of functions of the general ledger team and the CFO, including closing books. 

Historically, making these processes more efficient entailed dedicating more personnel to them. But this didn’t necessarily mean more work was done faster and to the highest standards. Many finance professionals are often overworked, dedicating extra hours, weekends, and sometimes holidays to process invoices, collect payments, and close the books on time. 

We created solutions for these three finance functions, with our SmartBots taking care of the back-and-forth communications between finance teams, vendors, and customers. In large companies, these micro-transactions add up to thousands per day, resulting in finance professionals spending entire days reading inquiries, interpreting requests, looking for relevant information, and answering as many as possible. But with our AR helpdesk, for example, accounts receivable tasks, such as a request for a copy of an invoice, get automated. Our technology reads emails and attachments to understand what is being requested. Then it connects to the Enterprise Resource Planning (ERP) software to grab the relevant information and attach it to the email, so the recipient gets a response within 60 seconds. 

Building the smart assistant that finance teams need

In our automation flow, we constantly handle different types of documents, from invoices and tax forms to receipts and email messages. But processing the interaction between computers and human language is complex. You must detect intent and facts, and understand the context before finding the specific slots of information extraction that may be relevant to specific processes and requests. Our solution adds value by extracting the right information in the right context, from the right document, for the relevant finance function. Instead of building everything from scratch, we turned to Google Cloud’s Document AI to support extracting data from unstructured documents to understand and analyze them. 

Document AI comes with pre-built models that help analyze specific parts of our post-production lifecycle. For example, Invoice Parser extracts text and values from invoices, including invoice number, supplier name, invoice amount, tax amount, and invoice due date, all of which are necessary for our SmartBots to execute an extraction workflow. These out-of-the-box features significantly accelerate our own product development process and time-to-market, which are critical for the performance of a startup such as ours. 

To ensure a high quality of information extraction, we used to do document readings in-house. Having automated some of that with Document AI, we’re at 85% accuracy extracting files, and with some additional customization efforts, we will achieve 95%+ extraction accuracy.

Meanwhile, we have now streamlined internal processes, which ultimately translates into faster services for our customers. For example, assuming all the information has been provided, it generally took up to 15 minutes to process a tax form. We now do that in seconds. 

The value of automation doesn’t stop there. Using DocumentAI to automate structured data extraction from documents, we have managed to:

• Speed up the collection of general ledger entries by 90%+
• Reduce errors and omissions by 85%+
• Close books 20% faster
• Improved the productivity of full-time employees by 60%+
• Reduce process workload by 75%+
• Improve vendor serviceability by 75%+
• Reduce vendor risk and fraud by 50%+

Leveraging automation to focus on more innovation

Automating some of our processes with Document AI also means we have more time to focus on developing new features and further improving our solution. 80-90% of the time used for extracting custom fields from documents has now been automated with an OCR metadata library. 

With Document AI taking care of standard extraction, we focus on the intelligence we add to post-extraction. For example, when an invoice comes in from a vendor, our application needs to figure out which vendor it is to match it to the correct records in the ERP. But variations in the documents could interfere with that extraction process. The vendor’s trading name might be slightly different from the company’s name registered in our internal system, delaying the process. With more time on our hands, we’re now working on features enabling our models to leverage logos and other elements extracted from documents to swiftly match them to the correct company registered in our systems.  

With the benefits we’ve seen thus far, we look forward to accelerating our international growth. We’ll be relying on Google Cloud’s Document AI to automate operations, potentially in different languages, as we continually remove friction from the work lives of finance and accounting people worldwide.

According to a National Academy of Medicine discussion paper, social determinants of health (SDoH) account for upwards of 80% of a population’s health outcomes. Breaking this down further, 50% come just from socioeconomic and physical environment factors such as education, employment, income, family and social support, community safety, air and water quality, and access to housing and transit. SDoH determine access to and quality of healthcare, and are contributors to a system of disparate access to care.

We believe that to more efficiently provide comprehensive healthcare access to more people, providers will leverage technology to blend in-person and virtual modes of care delivery.

In this article, Amwell and Google Cloud examine five ways telehealth – as part of a provider’s overall care model – can help democratize access to healthcare. (graphic url) 

Remove distance as a barrier to care

8.6 million Americans live more than 30 minutes from their nearest hospital. Long drives can deter patients from seeking care or maintaining routine visits. On the flip side, 92% of Americans nationwide have access to wired broadband in the home or through mobile broadband. Therefore, having a virtual visit just a click away can help remove barriers to care, like distance.

Eliminate the risk of unnecessary exposure

Virtual care means that patients don’t have to worry about potential exposure in transit, while sitting in waiting rooms, or from direct interactions during in-person health visits. This is particularly relevant when it comes to those with chronic diseases or underlying conditions. Telehealth provides patients who may be more susceptible to diseases with access to continuous healthcare without putting them at higher risk for developing more severe symptoms. Virtual visits can occur in the comfort –and safety– of patients’ homes.

Extend access to specialized care

55% of preventable hospitalization or mortality in rural settings is due to lack of access to specialty care. With telehealth, physical proximity to specialized services–typically in urban areas–can be reduced as a limiting factor. Virtual solutions grant everyone access to top specialists, regardless of location.

Save time and money

By augmenting in-person visits with telehealth applications, providers can benefit from greater efficiencies in scheduling, helping to improve their bottom line, and add more flexibility to their workday. Meanwhile, patients can spend less on travel and childcare, limit time taken off work, and save on other costs associated with in-person visits — for a savings of $35 to $690 per visit. In a survey by the COVID-19 Healthcare Coalition, 76% of the 2,000+ patients surveyed across the US responded that transportation was removed as a barrier, 65% reported they no longer had to take time off from work for an appointment, and 67 percent reporting lower costs than an in-person visit.

Combat physician shortage and fatigue

Research shows there will be a shortage of more than 100,000 doctors by 2030. In the midst of a physician shortage, telehealth can help improve care delivery and make it more efficient, ensuring more people can still have their healthcare needs addressed. Additionally, by integrating intelligence such as case triaging along with telehealth into a virtual care model, providers can help reduce clinician burnout.

Amwell and Google Cloud are partnering to deliver transformative telehealth solutions that will make it easier for more patients to receive care and improve patient and clinician experiences across the continuum of care. One example includes embedding real-time captioning and translation services powered by Google Cloud’s AI and NLP technologies within the Amwell platform to increase health access and understanding for more people.

As physicians, we are excited that the future of healthcare will continue to blend cloud technologies like EHR-integrated telehealth platforms, AI, healthcare-trained virtual agents along with in-person care to create an integrated hybrid care model that will improve patient outcomes and unburden providers, all while expanding access to broader patient populations.

To learn more, download the whitepaper “Healthcare’s Virtual Transformation,” written in conjunction with Becker’s Hospital Review.

^{1: “Social Determinants of Health 101 for Health Care: Five Plus Five,” National Academy of Medicine, October 2017}

One of the largest telecommunications companies in the world, Vodafone is at the forefront of building next-generation connectivity and a sustainable digital future.

Creating this digital future requires going beyond what’s possible today and unlocking significant investment in new technology and change. For Vodafone, a key driver is the use of artificial intelligence (AI) and machine learning (ML), enabling predictive capabilities in enhancing the customer experience, improving network performance, accelerating advances in research, and much more.

Following 18 months of hard work, Vodafone has made a huge leap forward in advancing its AI capabilities at scale with the launch of its “AI Booster” AI / ML platform. Led by the Global Big Data & AI organization under Vodafone Commercial, the platform will use the latest Google technology to enable the next generation of AI use cases, such as optimizing customer experiences, customer loyalty, and product recommendations.

Vodafone’s Commercial team has long focused on advancing its AI and ML capabilities to drive business results. Yet as demand grows, it is easier said than done to embed AI and ML into the fabric of the organization and rapidly build and deploy ML use cases at scale in a highly regulated industry. Accomplishing this task means not only having the right platform infrastructure, but also developing new skills, ways of working, and processes.

Having made meaningful strides in extracting value from data by moving it into a single source of truth on Google Cloud, Vodafone had already significantly increased efficiency, reduced data costs, and improved data quality. This enabled a plethora of use cases that generate business value using analytics and data science. The next step was building industrial scale ML capability, capable of handling thousands of ML models a day across 18+ countries, while streamlining data science processes and keeping up with technological growth.

Knowing they had to do something drastically different to scale successfully, along came the idea for AI Booster.

“To maximize business value at pace and scale, our vision was to enable fast creation and horizontal / vertical scaling of use cases in an automated, standardized manner. To do this, 18 months ago we set out to build a next-generation AI / ML platform based on new Google technology, some of which hadn’t even been announced yet.

“We knew it wouldn’t be easy. People said, ‘Shoot for the stars and you might get off the ground…’ Today, we’re really proud that AI Booster is truly taking off, and went live in almost double the markets we had originally planned. Together, we’ve used the best possible ML Ops tools and created Vodafone’s “AI Booster Platform” to make data scientists’ lives easier, maximise value and take co-creation and scaling of use cases globally to another level,” says Cornelia Schaurecker, Global Group Director for Big Data & AI at Vodafone.

AI Booster: a scalable, unified ML platform built entirely on Google Cloud

Google’s Vertex AI lets customers build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified platform. Built upon Vertex AI, Vodafone’s AI Booster is a fully managed cloud-native platform that integrates seamlessly with Vodafone’s Neuron platform, a data ocean built on Google Cloud.

“As a technology platform, we’re incredibly proud of building a cutting-edge MLOps platform based on best-in-class Google Cloud architecture with in-built automation, scalability and security. The result is we’re delivering more value from data science, while embedding reliability engineering principles throughout,” comments Ashish Vijayvargia, Analytics Product Lead at Vodafone

Indeed, while Vertex AI is at the core of the platform, it’s much more than that. With tools like Cloud Build and Artifact Registry for CI/CD, and Cloud Functions for automatically triggering Vertex Pipelines, automation is at the heart of driving efficiency and reducing operational overhead and deployment times. Today, users simply complete an online form, and then, within minutes, receive a fully functional AI Booster environment with all the right guardrails, controls, and approvals.

Not long ago it could take months to move a model from a proof of concept (PoC) to launching live in production. By focusing on ML operations (MLOps), the entire ML journey is now more cost-effective, faster, and flexible, all without compromising security. PoC-to-production can now be as little as four weeks, an 80% reduction.

Diving a bit deeper, Vodafone’s AI Booster Product Manager, Sebastian Mathalikunnel, summarizes key features of the platform: “Our overarching vision was a single ML platform-as-a-service that scales horizontally (business use cases across markets) and vertically (from PoC to Production). For this, we needed innovative solutions to make it both technically and commercially feasible. Selecting a few highlights, we:

• completely automated ML lifecycle compliance activities (drift / skew detection, explainability, auditability, etc.) via reusable pipelines, containers, and managed services;
• embedded security by design into the heart of the platform;
• capitalized on Google-native ML tooling using BQML, AutoML, Vertex AI and others;
• accelerated adoption through standardized and embedded ML templates.”

For the last point, Datatonic, a Google Cloud data and AI partner, was instrumental in building reusable MLOps Turbo Templates, a reference implementation of Vertex Pipelines, to accelerate building a production-ready MLOps solution on Google Cloud.

“Our team is devoted to solving complex challenges with data and AI, in a scalable way. From the start, we knew the extent of change Vodafone was embarking on with AI Booster. Through this open-source codebase, we’ve created a common standard for deploying ML models at scale on Google Cloud. The benefit to one data scientist alone is significant, so scaling this across hundreds of data scientists can really change the business,” says Jamie Curtis, Datatonic’s Practice Lead for MLOps.

Reimagining the data scientist & machine learning engineer experience

With the new technology platform in place, driving adoption across geographies and markets is the next challenge. The technology and process changes have a considerable impact on people’s roles, learning, and ways of working. For data scientists, non-core work now is supported by machines in the background—literally at the click of a button. They can spend time doing what they do best and discovering new tools to help them do the job.

With AI Booster, data scientists and ML engineers have already started to drive greater value and collaborate on innovative solutions. Supported by instructor-led and on-demand learning paths with Google Cloud, AI Booster is also shaping a culture of experimentation and learning.

Together We Can

Eighteen months in the making, AI Booster would not have happened without the dedication of teams across Vodafone, Datatonic, and Google Cloud. Googlers from across the globe were engaged in supporting Vodafone’s journey and continue to help build the next evolution of the platform.

Cornelia highlights that “all of this was only possible due to the incredible technology and teams at Vodafone and Google Cloud, who were flexible in listening to our requirements and even tweaking their products as a result. Alongside our ‘Spirit of Vodafone,’ which encourages experimenting and adapting fast, we’re able to optimize value for our customers and business. A huge thank you also to Datatonic, who were a critical partner throughout this journey and to Intel for their valuable funding contribution.”

The Google & Vodafone partnership continues to go from strength to strength, and together, we are accelerating the digital future and finding new ways to keep people connected.

“Vodafone’s flourishing relationship with Google Cloud is a vital aspect of our evolution toward becoming a world-leading tech communications company. It accelerates our ability to create faster, more scalable solutions to business challenges like improving customer loyalty and enhancing customer experience, whilst keeping Vodafone at the forefront of AI and data science,” says Cengiz Ucbenli, Global Head of Big Data and AI, Innovation, Governance at Vodafone.

Find out more about the work Google Cloud is doing to help Vodafone here, and to learn more about how Vertex AI capabilities continue to evolve, read about our recent Applied ML Summit.