Notified is a leading communications cloud for events, public relations, and investor relations to drive meaningful insights and outcomes. They provide communications solutions to effectively reach and engage customers, investors, employees, and the media.

One of Notified’s Public Relations solutions is the ‘Media Contact Database’ that allows customers to discover media and influencers in a unique media database powered by AI and human-curated research. 

The goal of the initiative is to expand the scope of the AI driven, dynamically discovered influencers, and analyze online news articles using AI/ML technologies to extract entities and classify content. The prior process to extract insights from news articles provided only 30-40% of the desired results, and there were accuracy and stability issues that resulted in a lot of manual intervention.

Journalist Beat

A key outcome of the AI driven process is to identify the ‘Journalist Beat’. A Journalist Beat essentially summarizes the individual’s area of focus such as a sports writer, financial journalist etc. 

Three options were evaluated for the AI/ML process to generate the Journalist Beats :

Option 1:  Topic ML

Unsupervised ML approach to determine the commonly used terms.

• Pro: Common approach to grouping documents and determine similar text
• Con: Unbounded list of text

Option 2: ML Classification

Build classification models (supervised) to map reference articles to ‘Beats’ 

• Pro: Aligns to ‘Research Analytics’ existing processes
• Con: Time to build and maintain ML models for hundreds of beats.

Option 3: GCP Context Classification

Leverage GCP’s Natural Language API for initial classification and as input to Notified single model

• Pro: Aligns to ‘Research Analytics’ without building ML models.

Ultimately the GCP Natural Language API solution was chosen because of the speed of execution and a high level of accuracy with the pretrained models. The Notified team was able to launch the product feature within a few weeks, without ever needing to do extensive data collection and train the models. 

Here is the high level process that was implemented for Journalist Beats.

Since Notified supports curated media contacts globally, news articles were instantly translated to English using GCP Translation API. GCP Natural Language API’s solution to classify text was used to analyze the translated text and generate the list of content categories.

Solution Architecture

Here is a sample solution architecture for the ‘Discovered Journalist’ process.

Three core principles guided the above architecture – Serverless & Fully Managed, Scalability & Elasticity for flexibility and to optimize costs, API led real-time processing.

In addition to the GCP Natural Language API and Translation API below are a few serverless GCP products that were part of the automated solution:

• BigQuery is Google Cloud’s fully managed, petabyte-scale, and cost-effective analytics data warehouse that lets you run analytics over vast amounts of data in near real time.
• Cloud Run is a fully managed serverless platform that can be used to develop and deploy highly scalable containerized applications.
• Cloud Tasks is a fully managed service that allows you to manage the execution, dispatch, and delivery of a large number of distributed tasks.

The powerful pre-trained models of the Natural Language API provide a comprehensive set of features to apply natural language understanding to applications such as sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis. 

Notified looks ahead to super-scaling

In an effort to even further improve its best in class ‘Media Contact Database’, Notified looks to super scale the above AI driven Influencer Discovery process to the order of 100+ million news articles per month. It plans to expand the scope of entities extracted from the news articles and provide a news exploration service for its customers by performing intelligent entity-based searches.To watch your markets evolve, see how competitors add AI insights. To actually stay in the market, make AI the main driver of your product road maps. GCP Natural Language API accelerated our ability to adopt AI at scale.Thomas Squeo, CTO, Notified

Acknowledgments

We’d like to thank our collaborators at Google and Notified for making this blog post possible. Thanks to Arpit Agrawal at MediaAgility for contributing to this blog post.

To learn more about how Google Cloud Natural Language AI can help your enterprise, try out an interactive demo and take the next step, visit the product overview page here.

While there is uncertainty about how much the climate will change in the future, we know it won’t look like the past. Extreme weather events will increase in frequency and severity; the world will continue to warm, and the cost of climate change will increase.

Government plays a vital role in understanding and responding to these changes quickly. Achieving this improved response time will require data insights to ensure informed decision-making—from local to global scales. The challenge is not only urgent; it’s one of the world’s biggest “big data” problems.

Fortunately, new technologies to help us monitor the Earth are proliferating. Thousands of satellites take millions of images of the planet every day. Sensors generate data about temperature, precipitation, wind, soil conditions, and more—as frequently as every second. We have more information about the planet’s systems than at any other time in history. And the data will only continue to grow. The problem is not the lack of data–it is harnessing this data to drive insights for decision makers to tackle climate change. That’s why Google Cloud has partnered with Climate Engine.

How Climate Engine and Google Cloud enable greater climate resilience

Climate Engine is a scientist-led company that works with Google to accelerate and scale the use of Google Earth Engine’s world-class geospatial capacities (in addition to those of Google Cloud Storage and BigQuery, among other tools) in support of climate action in the public sector. Powered by Google Cloud’s infrastructure, Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities, enabling scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth’s surface. 

With cloud-based technologies, we can leverage massive computing at a scale that generates actionable insights from Earth-based data. These insights help us better manage resources, understand risks, predict changes, and respond to disasters as we meet the challenge of climate change. Geospatial AI combines the power of artificial intelligence (AI) and machine learning (ML) with geospatial analysis. Google Cloud’s Geospatial AI solutions provide departments and agencies with a centralized system to collect, process, and deliver Earth-based data into decision-making contexts.

Climate Engine and Google Cloud provide specialists with the opportunity to go back in time and see how our landscapes have changed due to changes in climate and other human activities over the past few decades. Years of data can now be quantitatively analyzed and visualized in a matter of a few seconds, enabling government agencies to fulfill their mandates by drawing invaluable insights into how landscapes are changing, what physical and natural assets are at risk, and where the opportunities are for reducing emissions and increasing carbon sequestration. 

“This is game changing for natural resource managers and scientists at public institutions at all levels of government,” says Dr. Daniel McEvoy, regional climatologist, at the Desert Research Institute & Western Regional Climate Center, Nevada System of Higher Education.https://www.youtube.com/embed/aPGsi8bd_Zk?enablejsapi=1&

Use cases for geospatial climate information systems

The use cases for this technology are as varied as the climate challenges themselves. These include monitoring, predicting, and analyzing the risks of extreme weather events like floods, wildfire, drought, extreme heat, wind, and other climate hazards. Use cases also include tracking changes in ecosystems, disease vectors, water availability and quality, soil health, growing seasons, air pollution, and more. These use cases are some of the ways that Google Cloud and Climate Engine can help the public sector deliver on government mandates. These provide insights that are helpful for a wide range of departments, and that can be applied in spatial and temporal scales that are meaningful for governments to take action.

“Our planet is changing at a rate that we have never experienced,” says Forrest Melton of the NASA Western Water Applications Office. To respond to these changes, we must understand what is happening across a wide range of environmental variables and at geospatial scales that range from local to global. We now have access to more data about the planet than ever before. The big challenge is converting data into actionable insights and then rapidly integrating these insights into decision-making systems. Climate Engine and Google Cloud help resolve this problem through innovative analytical tools and effective use of cloud computing.” 

Climate change carries an existential risk to our current and future stability and security. Together, we are working to provide transformational technologies that help meet that risk and build a safer, more resilient future for all of us. 

Learn more about Google Cloud’s environmental initiatives here and here.

Powered by Vertex AI (Google Cloud’s platform for accelerating development and deployment of machine learning models into production), SAVI (Semi Automated Vision Inspection)1 is transforming surgical instrument identification and cataloging, leading to fewer canceled surgeries and easing pressure on surgery waitlists.

Max Kelsen, an analytics and software agency that specializes in machine learning, has worked closely with Google Cloud and Johnson & Johnson MedTech to create a system that can manage tens of thousands of individual devices, their characteristics, and how they apply to each set or tray used by a surgeon. SAVI does this while delivering a one in 10,000 real-world error rate, much faster and more accurately than manual processes currently in use across the industry. Implementing SAVI can also unlock end-to-end visibility and traceability across the surgical set supply chain and provide advanced analytics and insights.

Eliminating time-consuming manual processes

Surgeons need a large number of specialist instruments and devices to complete complex, delicate procedures. Because each tray of these instruments can typically cost more than $350,000, and having every type of set on shelf at every surgical facility is not feasible, manufacturers generally loan them to hospitals for procedures, such as inserting one of the manufacturers’ implants into a patient’s knee. Once a procedure is complete, the hospital returns the instrument tray to the manufacturer for storage and re-distribution to other hospitals as needed.

Each time a hospital returns a tray, the manufacturer needs to check that each instrument is there, correctly placed, cleaned, and fit for the purpose of the next procedure. As each set may hold more than 400 instruments, completing this process manually is complex and time-consuming. While each tray is checked before and after surgery at the hospital, and again when it arrives and leaves the manufacturer’s facility, Max Kelsen finds that 5% of surgeries can still be affected by missing, broken or bent instruments. This has a severe downstream impact on private hospitals in particular, directly affecting patient safety and outcomes; in Australia, for example, around 60% of surgeries are performed in private hospitals.

Johnson & Johnson MedTech has 60,000 surgical trays across the Asia-Pacific, and loans these trays out about 100,000 times per month. The manufacturer approached Max Kelsen to help design and develop a solution to make the supply chain more efficient, and to give more visibility into asset movement. As a Google Cloud Partner specializing in applying machine learning at scale in healthcare contexts, Max Kelsen had the expertise and track record to meet Johnson & Johnson MedTech’s need for a globally scalable solution that was engineered for quality and performance.

The first step was to establish a baseline for the project by determining how long the manufacturer’s team took to process each tray, and to set an efficiency number. We then spent six months determining and evaluating how to deliver a robust, accurate solution that outperformed current manual and labor-intensive methods in processing instruments and trays, globally. Our work included extensive technical feasibility research involving a representative sample for the variety and complexity of sets, trays, and devices needed for different types of surgery, including orthopedics, spinal trauma, and maxillofacial groups.

Working with Google Cloud to accelerate and de-risk the project

This is a familiar problem that is industry-wide. The issue has been widely explored and tried with a number of technologies over several years without producing the scalability and performance results required to make this an appropriate and feasible solution. Google Cloud partnered with Max Kelsen to accelerate and de-risk this large and strategic project for a mutual customer.

Technical feasibility took four months, prior to a year-long production pilot of SAVl in a distribution center in Queensland that services over 100 hospitals. After obtaining enough real-world data and experience to validate that the solution was as scalable and as accurate as needed, an Asia-Pacific rollout of the system commenced. SAVI is now live across Johnson & Johnson MedTech’s operations in Australia, New Zealand, and Japan, garnering recognition with a JAISA excellence award.

Google Cloud machine learning is integral to SAVI. Google Cloud’s technologies were a big differentiator for Max Kelsen’s engineering team in delivering the breakthroughs needed at scale, and in production, to meet Johnson & Johnson MedTech’s needs.

Reducing checking and documentation time

Running SAVI in Google Cloud has reduced the time Johnson & Johnson MedTech needs to check and document inspections of these surgical instrument sets by over 40%. The application also delivers consistent measurable quality that is often hard to measure at scale when using manual processes. During the pandemic, the application enabled Johnson & Johnson MedTech to operate with a lower headcount for the same volume output, enabling the organization to quickly service a backlog of waiting list surgeries.

In addition, the automation delivered with SAVI has reduced the time required to bring technicians up to speed on quality control processes, from eight to 12 months down to just three months, enhancing productivity and performance while delivering a more robust workforce.

So how does SAVI work in a real-world context? SAVI is deployed via a tablet and a web-based application incorporates an API to photograph the medical device trays, as shown below. Max Kelsen captures the photograph and sends it to a range of different services, via an API endpoint hosted on Google Cloud:

• Image information is stored in Cloud Storage
• Data relating to the trays is stored in Cloud SQL for PostgreSQL
• APIs and web UI components run in CloudRun
• Analytics data is stored within BigQuery

Once this tray and device onboarding stage is completed, the next step is to perform inferences from the images and data. By hosting online models with Kubeflow model serving on GKE, we enable a model to identify all the instruments in a tray at low latency.

Vertex AI Workbench notebooks are used for data exploration and modeling. Kubeflow training pipelines hosted on GKE are executed to produce machine learning models for specific surgical instrument sets. Several hundred machine learning models are then hosted with Kubeflow model serving on GKE, with state and analytics managed using Firebase. Using machine learning to infer from images whether any devices are incorrectly placed, dirty, or otherwise not fit for purpose, the data is then returned to the tablet for the user to respond accordingly.

Based on our success to date with SAVI, it is now available on Google Cloud Marketplace to help healthcare organizations achieve machine learning-powered efficiencies across a range of use cases, and ultimately improve patient safety and outcomes.

Not to be confused with the usage of Visual Inspection Model (Assembly) available in Vertex AI Vision

June is the month that holds the summer solstice, and some of us in the northern hemisphere get to enjoy the longest days of sunshine out of the entire year. We used all the hours we could in June to deliver a flurry of new features across BigQuery, Dataflow, Data Fusion, and more.  Let’s take a look!

Simple, Sophisticated, and Secure

Usability is a key tenant of our data analytics development efforts. Our new user-friendly BigQuery improvements this month include:

• Flexible data type casting
• Formatting to change column descriptions 
• GRANT/REVOKE access control commands using SQL

We hope this will delight data analysts, data scientists, DBAs, and SQL-enthusiasts who can find out more details in our blog here.

Beyond simplifying commands, we also recognize that it’s equally important to have more sophistication when dealing with transactions. That’s why we introduced multi-statement transactions in BigQuery.

As you probably know, BigQuery has long supported single-statement transactions through DML statements, such as INSERT, UPDATE, DELETE, MERGE and TRUNCATE, applied to one table per transaction. With multi-statement transactions, you can now use multiple SQL statements, including DML, spanning multiple tables in a single transaction. 

This means that any data changes across multiple tables associated with all statements in a given transaction are committed atomically (all at once) if successful—or all rolled back atomically in the event of a failure. 

We also know that organizations need to control access to data, down to the granular level and that, with the complexity of data platforms increasing day by day, it’s become even more critical to identify and monitor who has access to sensitive data. 

To help address these needs,  we announced the general availability of BigQuery row-level security. This capability gives customers a way to control access to subsets of data in the same table for different groups of users. Row-level security in BigQuery enables different user personas access to subsets of data in the same table and can easily be created, updated, and dropped using DDL statements. To learn more, check out the documentation and best practices.

Simple, Safe, and Smart

Beyond building a simpler, more sophisticated and more secure data platform for customers, our team has been focused on providing solutions powered by built-in intelligence. One of our core beliefs is that for machine learning to be adopted and useful at scale, it must be easy to use and deploy.  

BigQuery ML, our embedded machine learning capabilities, have been adopted by 80% of our top customers around the globe and it has become a cornerstone of their data to value journey.  

As part of our efforts, we announced the general availability of AutoML tables in BigQuery ML.  This no-code solution lets customers automatically build and deploy state-of-the-art machine learning models on structured data. With easy integration with Vertex AI, AutoML in BQML makes it simple to achieve machine learning magic in the background. From preprocessing data to feature engineering and model tuning all the way to cross validation, AutoML will “automagically” select and ensemble models so everyone—even non-data scientists—can use it.   

Want to take this feature for a test drive? Try it today on BigQuery’s NYC Taxi public dataset following the instructions in this blog! 

Speaking of public datasets, we also introduced the availability of Google Trends data in BigQuery to enable customers to measure interest in a topic or search term across Google Search.  This new dataset will soon be available in Analytics Hub and will be anonymized, indexed, normalized, and aggregated prior to publication. 

Want to ensure your end-cap displays are relevant to your local audience?  You can take signals from what people are looking for in your market area to inform what items to place. Want to understand what new features could be incorporated into an existing product based on what people are searching for?  Terms that appear in these datasets could be an indicator of what you should be paying attention to.

All this data and technology can be put to use to deploy critical solutions to grow and protect your business. For example,  it can be difficult to know how to define anomalies during detection. If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML. 

But what if you don’t know what kind of anomaly to expect, and you don’t have labeled data? Unlike typical predictive techniques that leverage supervised learning, organizations may need to be able to detect anomalies in the absence of labeled data. 

That’s why, we were particularly excited to announce the public preview of new anomaly detection capabilities in BigQuery ML that leverage unsupervised machine learning to help you detect anomalies without needing labeled data.  

Our team has been working with a large number of enterprises who leverage machine learning for better anomaly detection. In financial services for example, customers have used our technology to detect machine-learned anomalies in real-time foreign exchange data.  

To make it easier for you to take advantage of their best practices, we teamed up with Kasna to develop sample code, architecture guidance, and a data synthesizer that generates data so you can test these innovations right away. 

Simple, Scalable, and Speedy

Capturing, processing and analyzing data in motion has become an important component of our customer architecture choices. Along with batch processing, many of you need the flexibility to stream records into BigQuery so they can become available for query as they are written.  

Our new BigQuery Storage Write API combines the functionality of streaming ingestion and batch loading into a single API. You can use it to stream records into BigQuery or even batch process an arbitrarily large number of records and commit them in a single atomic operation.

Flexible systems that can do batch and real-time in the same environment is in our DNA: Dataflow, our serverless, data processing service for streaming and batch data was built with flexibility in mind.  

This principle applies not just to what Dataflow does but also how you can leverage it—whether you prefer using Dataflow SQL right from the BigQuery web UI, Vertex AI notebooks from the Dataflow interface, or the vast collection of pre-built templates to develop streaming pipelines.

Dataflow has been in the news quite a bit recently. You might have noted the recent introduction of Dataflow Prime, a new no-ops, auto-tuning functionality that optimizes resource utilization and further simplifies big data processing. You might have also read that Google Dataflow is a Leader in The 2021 Forrester Wave™: Streaming Analytics, giving Dataflow a score of 5 out of 5 across 12 different criteria.  

We couldn’t be more excited about the support the community has provided to this platform. The scalability of Dataflow is unparalleled and as you set your company up for more scale, more speed, and “streaming that screams”, we suggest you take a look at what leaders at Sky, RVU or Palo Alto Networks have already accomplished.

If you’re new to Dataflow, you’re in for a treat: this past month, Priyanka Vergadia (AKA CloudGirl) released a great set of resources to get you started. Read her blog here and watch her introduction video below!

https://youtube.com/watch?v=WRspZRG9e90%3Fenablejsapi%3D1%26

Simple structure that sticks together

We thrive to be the partner of choice for your transformation journey, regardless where your data comes from and how you choose to unify your data stack.  

Our partners at Tata Consultancy Services (TCS) recently released research that highlights the importance of a unifying digital fabric and how data integration services like Google Cloud Data Fusion can enable their clients to achieve this vision.

We also  announced SAP Integration with Cloud Data Fusion, Google Cloud’s native data integration platform, to seamlessly move data out of SAP Business Suite, SAP ERP and S4/HANA. To date, we provide more than 50 pipelines in Cloud Data Fusion to rapidly onboard SAP data.  

This past month, we introduced our SAP Accelerator for Order to Cash.  This accelerator is a sample implementation of the SAP Table Batch Source feature in Cloud Data Fusion and will help you get started with your end-to-end order to cash process and analytics. 

It includes sample Cloud Data Fusion pipelines that you can configure to connect to your SAP data source, perform transformations, store data in BigQuery, and set up analytics in Looker. It also comes with LookML dashboards which you can access on Github.

Countless great organizations have chosen to work with Google for their SAP data. In June, we wrote about ATB Financial’s journey and how the company uses data to better serve over 800,000 customers, save over CA$2.24 million in productivity, and realize more than CA$4 million in operating revenue through “D.E.E.P”, a data exposure enablement platform built around BigQuery.

Finally, if you are an application developer looking for a unified platform that brings together data from Firebase Crashlytics, Google Analytics, Cloud Firestore, and third party datasets, we have good news!  

This past month, we released a unified analytics platform that combines Firebase, BigQuery, Google Looker and FiveTran to easily integrate disparate data sources,  and infuse data into operational workflows for greater product development insights and increased customer experience. This resource comes with sample code, a reference guide and a great blog!  We hope you enjoy it. See you all next month!

https://youtube.com/watch?v=L25Vfzr2Ciw%3Fenablejsapi%3D1%26