Editor’s note: The post is part of a series highlighting our awesome partners, and their solutions, that are Built with BigQuery

In the field of producing engaging video content such as ads, many marketers ignore the power of data to improve their creative efforts to meet the consumers’ need for personalized messages. The demand for creative tech to efficiently personalize is real as marketers need personalized video Ads to reach their audience with the right message at the right time. Data, Insights and Technology are the main ingredients to deliver this value while ensuring security and privacy requirements are met. The Connected-Stories team partnered with Google Cloud to build a platform for Ad personalization. Google Data Cloud and BigQuery are at the forefront to assimilate data, leverage ML models, create personalized ads, and capitalize on real-time intelligence as the core features of the Connected-Stories NEXT platform.

Connected-Stories NEXT is an end-to-end creative management platform to develop, serve, and optimize interactive video and display ads that scale across any channel. The platform ingests first-party data to create custom ML models, measure numerous third-party data points to help brands develop unique customer journeys and create videos that their data signals can drive. An intelligent feedback loop passes real-time data back, enabling brands to make data-driven and actionable video ads that take the brand’s campaigns to the next level.

The core use case of the NEXT platform revolves around collecting user’s interaction data and optimizing for precision and speed to create an actionable Ad experience that is personalized for each user. The platform processes complex data points to create interactive data visualizations that allow for accurate analysis. The platform uses Vertex AI to access managed tools, workflows, and infrastructure to build, deploy, and scale ML models that have improved the accuracy to identify segments for further analysis.

The platform ingests 200M data events with peaks and valleys of activity. These events are processed to generate dashboards that enable users to visualize metrics based on filters in real-time. These dashboards have high performance requirements in terms of a responsive user interface under constantly changing data dimensions.

Google Cloud’s serverless stack coupled with limitless data cloud infrastructure has been the core to the NEXT platform’s data-driven innovation. The growing volume of data ingested, streamed and processed were scaled uniformly across the compute, storage and analytical layers of solution. A lean development team at Connected-Stories were able to focus all-in on the solution, while the serverless stack scaled, lowered attack service in terms of security and optimized the cost footprint through pay-as-you-go features.

BigQuery has been the backbone to support the vast amounts of data spreading over multiple geos resulting in workloads running at petabyte scale. BigQuery’s fully managed serverless architecture, real-time streaming, built-in machine learning and rich business intelligence capabilities distinguishes itself from a cloud data warehouse. It is the foundation needed to approach data and serve users in an unlimited number of ways. For an application with zero tolerance for failure, given its fully managed nature, BigQuery handles replication, recovery, data distributed optimization and management.

The platform’s requirements include the need for low maintenance, constantly ingesting and refreshing data and smart-tuning of aggregated data. These capabilities can be implemented by BigQuery’s materialized views feature. Materialized views are useful for precomputed views that regularly cache query results for better performance. These views possess the innate feature to read only the delta change from base tables and calculate the up-to-date aggregations. Materialized views impart faster outputs and consume fewer resources while reducing the cost footprint.

Some key considerations in using Google cloud and focusing on the Serverless stack include: quick onboarding to development, prototyping in short sprints and ease of preparing data in a rapidly changing environment. Typical considerations around low code / no code include data transformation, aggregation and reduced deployment time. These considerations are fulfilled through using serverless capabilities within Google Cloud such as PubSub, Cloud Storage, Cloud Run, Cloud Composer, Dataflow and BigQuery as described in the Architecture diagram below. The use of each of these components and services are described below.

1. Input/Ingest: At a high-level, microservices hosted in Cloud Run collect and aggregate incoming Ads events.
2. Enrichment: The output of this stage is a Pub-Sub message enriched with more attributes based on a pre-configured campaign.
3. Store: a Cloud Dataflow streaming job to create text files in Cloud Storage buckets.
4. Trigger: Cloud Composer triggers the spark jobs based on text files to process and group them to produce desired output as one record per impression, a logical group of events.
5. Deploy: Cloud Build is then used to automate all deployments.

Thus far, all Google cloud managed services work together to ingest, store and trigger the orchestration, all of which are scalable based on configurations including autoscaling capabilities.

1. Visualization: A visualization tool reads data from BigQuery to compute pre-aggregations required for each dashboard.
2. Data Model Evolution considerations: Though the solution served the purpose of creating pre-aggregations, as the data model evolved by adding a column or creating a new table, it led to recreating pre-aggregations and querying the data again. Alternatively, creating aggregate tables as an extra output of current ETLs seemed like a viable option. However, this would increase the cost and complexity of jobs. A similar situation to reprocess or update aggregated tables would occur as data is updated.

Precomputed views of data that is periodically cached are critical to reach the audience with the right message at the right time.

1. Performance: In order to increase the performance of the platform, we need to have regularly precomputed views of the data, cached .
2. Materialized Views: Consumers of these views needed faster response times, to consume fewer resources and output only the changes in comparison to a base table. BigQuery Materialized views were used to solve this very requirement. Materialized views have been highly leveraged to optimize the design resulting in lesser maintenance and access to fresh data with high performance with a relatively low technical investment in creating and maintaining SQL code.
3. Dashboards: Application dashboards pointing to the Materialized views are highly performant and provide a view into fresh data.
4. Custom Reports with Vertex AI Notebooks: Vertex AI notebooks directly read data from BigQuery to produce custom reports for a subset of customers. Vertex AI has been hugely beneficial to data analysts, where an environment with pre-installed libraries simplifies the readiness to use. Vertex AI Workbench notebooks are used to share these reports within the team allowing them to work always on the cloud without having the need to download data at any time. Besides, it increases the velocity to develop and test ML models faster.

The NEXT platform has yielded benefits such as customers having the ability to create unique consumer journeys powered by AI / ML personalization triggers, using first-party data and business intelligence tools to capitalize on real-time creative intelligence, which is a dashboard to measure campaign performance for cross-functional teams to analyze the impact of Ad content experience at a granular level. All of these while ensuring controlled access to data to enrich data without moving across clouds. The NEXT platform can keep up with increased demands for agility, scalability and reliability through the underlying usage of Google Cloud.

Partnering with Google, in the context of the Google Built with BigQuery program has surfaced the differentiated value in areas of creating interactive personalized Ads by using real-time data. In addition, by sharing this data across organizations as assets, ML models have fueled higher levels of innovation. Connected-Stories plan to deepen the penetration into the entire spectrum of services offered in the AI/ML area to enhance core functionality and provide newer capabilities to the platform.

Click here to learn more about Connected-Stories NEXT Platform capabilities.

The Built with BigQuery Advantage for ISVs

Through Built with BigQuery, launched in April ‘22 as part of Google Data Cloud Summit, Google is helping tech companies like Connected-Stories co-innovate in building applications that leverage Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs. Participating companies can:

• Get started fast with a Google-funded, pre-configured sandbox.
• Accelerate product design and architecture through access to designated technical experts from the ISV Center of Excellence who can share insights from key use cases, architectural patterns, and best practices encountered in the field.
• Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

The Google Data Cloud spectrum of products and specifically BigQuery give ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge and expanding partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in and exercise choice.

We thank the Google Cloud and Connected-Stories team members who co-authored the blog: Connected-Stories: Luna Catini, Marketing Director, Google: Sujit Khasnis, Cloud Partner Engineering

Japan is home to millions of vending machines installed on streets and in buildings, sports stadiums and other facilities. Vending machine owners and operators, including beverage manufacturers, stock these machines with different product combinations depending on location and demand. For example, they primarily display coffee and energy drinks in machines placed in offices and sports drinks and mineral water in machines at sports facilities. The combinations also vary by season: for example, owners and operators may display cold beverages in summer and hot beverages in winter. 

Traditionally, vending machine operators have relied on the intuition and experience of sales managers to determine the optimum product mix for each vending machine. However, in recent years, manufacturers such as Coca-Cola Bottlers Japan (CCBJ) have turned to data to analyze and make strategic decisions about when and where to locate products in machines.

CCBJ is the number one Coca-Cola bottler in Asia and vending machines comprise the bulk of its business. The organization operates about 700,000 machines across Tokyo, Osaka, Kyoto, and 35 prefectures. Minori Matsuda, Google Developer Expert and also Data Science Manager at CCBJ, says “The billions of data records collected from 700,000 physical devices are a great asset and a treasure trove we can take advantage of.”

Minori points out that when considering the mix of products in vending machines in sporting facilities, the managers naturally assume sports drinks would generally sell well. However, analysis of purchase data – including hot drinks and hot drinks plus sports drinks – found many parents purchased sweet drinks such as milk tea when they attended games or sessions involving their children.  “Analyzing data gives us new discoveries and, by using catchy storytelling techniques from exploratory data analysis, we are instilling a data culture within our company,” he says. “It’s worth creating by looking at facts rather than making assumptions!”

Minori believes that to analyze the vast amount of data collected from more than 700,000 vending machines, the business needs a powerful analytical platform. However, until recently, CCBJ had to extract data for analysis from its core systems, load this data into a warehouse it created and perform the required analyses.  The billions of records of data generated across the fleet – including transaction data – exposed some challenges for traditional analysis platforms. They could not efficiently process data at a considerable scale: it could take a day to return results and required extensive maintenance due to the size.

CCBJ considered building a machine learning (ML) platform as a layer on top of existing systems in August 2020 and opted for Google Cloud the following month.  “I feel that Google Cloud has an edge in all products and is very well thought out,“ says Minori, noting the scalability and cost of the platform allow the business to take a ‘trial and error’ approach to achieve the best outcomes from ML. Google Cloud also delivered the required visibility and flexibility to help the business deliver change every day against key performance indicators. 

MLOps platform streamlines ML pipeline development

CCBJ built its analysis platform using Vertex AI (formerly AI Platform) centered on a BigQuery analytics data warehouse, and partly using AutoML for tabular data. “We have created a prediction model of where to place vending machines, what products are lined up in the machines and at what price, how much they will sell, and implemented a mechanism that can be analyzed on a map,” says Minori, adding that building the platform with Google Cloud was not difficult. “We were able to realize it in a short period of time with a sense of speed, from platform examination to introduction, prediction model training, on-site proof of concept to rollout.”

The new data analytics platform of CCBJ consists of the following parts:

Data Sources

• The data collected from the vending machines are all stored on BigQuery.

Data Discovery and Feature Engineering

• Minori and other data scientists at CCBJ are using Vertex Notebooks, where they access the data on BigQuery by executing SQL queries directly from the Notebooks. This environment is used for the data discovery process and feature engineering. 

ML Training

• For ML training, CCBJ uses AutoML for Tabular data, Custom model training on Vertex AI, and BigQuery ML. AutoML gives model performance with AUC curves and also feature importance graphs.

ML Prediction and Serving

• For ML prediction, CCBJ uses Online Prediction for AutoML models and Online Prediction for custom models for real-time prediction when the salesperson finds the interesting point
• Batch Prediction is used for generating a large prediction map that covers the whole country
• The prediction results are distributed to sales managers’ tablets

CCBJ started constructing the platform in September 2020, and completed it within a month. The business has conducted proofs of concept at its base in Kyoto since February 2021, and since April, has rolled out the platform to sales managers in 35 prefectures in one metropolitan area. “Data analysis is built into the day-to-day routines of sales managers with 100% utilization,” says Minori. “They can utilize the prediction results on tablets that were able to achieve pretty high accuracy from the start.”

The hardest part was the education of sales managers in the field; having them understand the reasoning behind the ML prediction results for particular outcomes, so they could be convinced to make use of the results. “For example, regarding a new installation location predicted by the model, it seemed that there was no effective information for installation from the map information, but when I actually went there, there was a motorcycle shop and it was a place where young people who like motorcycles gathered,” says Minori. “Or there is a small meeting place where the elderly in the neighborhood are active. 

“In many cases, new discoveries that cannot be understood from map information alone can be derived from the data.”

Minori also points to a phenomenon whereby humans pursued and confirmed factors inferred by the model – meaning that once they experienced analysis and it worked effectively, they asked why the same type of analysis or prediction could not be undertaken next time. The resulting cycle of more inquiries generated, more information gathered and more data captured for analysis meant the accuracy of results was improved.

Minori describes Vertex AI as having a number of strengths in helping CCBJ build a ML data analysis platform. “One of the major merits of Vertex AI was that we were able to realize MLOps that streamlines the entire development life cycle from construction of the ML pipeline to its execution,” he says.

With near real-time data analysis through Google Cloud, CCBJ teams can spend time developing strategies rather than waiting for data requested from the IT systems department. Exploratory data analysis is also considerably easier as repeated trial and error has greatly improved the accuracy of analyses. Before we used Machine Learning, most machine placement processes were done by human senses, by looking at a map to find the suggestion points. By using Machine Learning to generate a massive number of placement point suggestions, the efficiency of routing of salespeople has been dramatically improved. 

In the future, CCBJ aims to automate the continuous training pipeline with Vertex AI. “CCBJ is a tech company that operates in the food industry,” says Minori. With the organization operating a vending machine network of 700,000 units, it would like to create new businesses based on utilization and analyzing data. Some of these businesses may be based on Sustainable Development Goals (SDGs) initiatives such as the utilization of recycled PET bottles, measures to prevent food loss and ways of using vending machines to contribute to local communities, which we have been working on for some time. It would be interesting if we could collaborate with Google Cloud on these in the future.”

The home lending journey entails processing an immense number of documents daily from hundreds of thousands of borrowers. Currently, home lending document processing relies on some outdated digital models and a high dependency on manual labor, resulting in slow processing times and higher origination costs. Scaling a business that sorts through millions of documents daily, while increasing efficacy and accuracy, is no small feat. When it comes to applying for a mortgage loan, consumers expect a digital experience that’s as good as the in-person one. Roostify simplifies the home lending journey for lenders and their customers.

No time to spare: Overcoming document processing challenges with AI

Roostify provides enterprise cloud applications for mortgage and home lenders. In order to empower its customers to deliver a better, more personalized lending experience, they needed to automate and scale their in-house document parsing functionality.

As a key component of its document intelligence service, Roostify is leveraging Google Cloud’s Lending DocAI machine learning platform to automate processing documents required during a home loan application process, such as tax returns or bank statements with multi-language support. This partnership delivers data capture at scale, enabling Roostify customers to automatically identify document types from the uploaded file and to extract relevant entities such as wages, tax liabilities, names, and ID numbers for further processing, and make things move faster in the cumbersome lending process. 

Roostify’s solutions leverage Google Cloud’s Lending DocAI, which is built on the recently announced Document AI platform, a unified console for document processing. Customers can easily create and customize all the specialized parsers (e.g., mortgage lending documents and tax returns parsers) on the platform without the need to perform additional data mapping or training. All Google Cloud’s specialized parsers are fine-tuned to achieve industry-leading accuracy, helping customers and partners confidently unlock insights from documents with machine learning. Learn more about the solution from the GA launch blog and the overview video.

Integrating Lending DocAI’s intelligent document processing capabilities into the Roostify platform means more innovation for their customers and tangible results: faster loan processing times, fewer document intake errors, and lower origination costs. Additional support in Google Lending DAI for other languages and more documents like global Know Your Customer (KYC) documents or payroll reports is in the near future.

Full integration of AI solutions

Working together with Roostify’s platform team, we were able to help them solve their document processing challenge through integration of various GCP products such as Lending DocAI (LDAI), Data Loss Prevention (DLP) for redacting sensitive data, BigQuery for data warehousing and analytics, and Firestore for API status. To make it very safe and secure, all data was encrypted end-to-end at Rest and in Transit. LDAI won’t require any training data to process. It is an easy plug and play API.

Here is a sneak peek in the high level deployment architecture for LDAI in Roostify environment:

Here are the steps for processing data:

1. Receives document processing request from the client.
2. API Function directs requests to the pre-processing service. For Async requests a processing ID is generated and returned to the caller.
3. Pre-processing service sends the request for further processing (Long/short PDF conversion), calling other microservices and receives back the responses. Any error in the response received is then sent to the response processing service. 
4. If the response is synchronous, the pre-processing service directs it to the LDAI Invoker service. 
   1. If the response is asynchronous, the pre-processing service feeds it into the Cloud Pub/Sub service.
5. Cloud Pub/Sub service feeds the response back to the LDAI Invoker service.
6. LDAI Invoker service routes the request to the Google LDAI API for classification if there are multiple pages in the document.
7. Document will be split based on LDAI response and then saved in a GCS bucket for temporary storage.
8. LDAI entity interface for single page processing and then LDAI Invoker sends LDAI results to LDAI Response Processing
9. If a request is a synchronous request the LDAI Response Processor sends results to the API Function so that it can complete the synchronous call and respond to the rConnect caller.
   1. If the request is an asynchronous request the LDAI Response Processor will respond to the caller’s webhook and complete the transaction.
10. Finally, Data stored in the GCP bucket will be deleted.

All the responses that come from the LDAI API can optionally feed into BigQuery via the Response Processor, after parsing it through Data Loss Prevention (DLP) API to redact the PII/sensitive information.  Throughout the processing of both asynchronous and synchronous requests all transactions are logged using Cloud Logging.  For asynchronous transactions, the state is maintained throughout the process using Cloud Firestore.

Roostify currently uses this technology to power two different solutions: Roostify Document Intelligence and Roostify Beyond™. Roostify Document Intelligence is a real-time document capture, classification, and data extraction solution built for home lenders. It ingests documents uploaded by borrowers and loan officers, identifies the relevant documents, and extracts and classifies key information. Roostify Document Intelligence is available as a standalone API service to any home lender with any digital lending infrastructure already in place. 

Roostify Beyond™ is a robust suite of AI-powered solutions that enables home lenders to create intelligent experiences from start to close. It combines powerful data, insightful analytics, and meaningful visualization to streamline the underwriting process. Roostify Beyond™ is currently available only to Roostify customers as part of an Early Adopter program and will be rolled out to the market later this year.

Through this partnership, Roostify has enabled its customers to adopt a data-first approach to their home lending processes, which will lead to improved user experiences and significantly reduced loan processing times.

Fast track end-to-end deployment with Google Cloud AI Services (AIS)

Google AIS (Professional Services Organization), in collaboration with our partner Quantiphi, helped Roostify deploy this system into production and fast-tracked the development multifold to generate the final business value.

The partnership between Google Cloud and Roostify is just one of the latest examples of how we’re providing AI-powered solutions to solve business problems.

As software-defined vehicles continue to advance and the quantity of digital services grows to meet consumer demand, the data required to provide these services continue to grow as well. This makes automotive manufacturers and suppliers look for capabilities to log and analyze data, update applications, and extend commands to in-vehicle software.

The challenges the automotive sector faces can be quantified. A modern vehicle contains upwards of 70 electronic control units (ECUs), most of which are connected to one or more sensors. Not only is it now possible to exactly measure many aspects of vehicle performance, but new options become available. Using LIDAR (light detection and ranging), for example, vehicles are achieving higher levels of autonomy; this leads to a data stream from such demanding applications that may reach 25 GB per hour. For the in-vehicle processing of data, 100 million lines of software code may be present — more than a fighter jet. This in-vehicle code will have to be maintained with updates and new functionalities.

Access to the data will allow manufacturers to gain valuable insights into operational details of their vehicles. The use of this data can help to reduce costs and risks, increase ROI, support ESG initiatives, and provide valuable insights to develop innovative solutions and shorten the time to value for Electric Vehicle innovations.

Sibros’ Deep Connected Platform (DCP) makes it possible for these manufacturers to build and launch new connected vehicle use cases from production to post-sale at scale by connecting and managing all software and data throughout every life cycle stage. A key component of this platform is the Sibros Deep Logger that provides capabilities like the following:

• Full configurability of what to record, when to record it, and how fast to record it.
• High resolution timestamps of all Controller Area Network (CAN) messages.
• Dynamic application of live log configurations to receive new data points without deploying new software.

For example, properly analyzed engine data enables true predictive maintenance for the first time, which creates the option to repair or replace components before failure happens. Another example would be the evaluation of data regarding the use of certain in-car features with the goal to redesign its interior.

Two other components of the DCP are software updates and remote commands to ECUs. The DCP on Google Cloud enables seamless integration with any vehicle architecture and provides OEMs and suppliers with the platform to manage connected vehicle data at rest and in transit using a proven and secure way on a global scale.

OEMs can pull data through APIs provided by Sibros into Google Data Cloud (including BigQuery) to gain access to the rich information data sets provided by the DCP within their environment and blend this data with their first party data sets to provide value insights for their business. Some of the Connected Vehicle insights that DCP information enables are:

• Damage prevention, improved operation, or development of the next generation of engines with insights from complex analyses that could consider parameters like model, engine type, mileage, overall speed, temperature, air pressure, load, services, and more.
• The combination of electric vehicle battery usage data like charging cycles, engine performance, and battery age with contributing factors as the use of the air conditioning to determine if such factors contribute to hazardous battery conditions and for improved battery development.
• Cross-organization collaboration in R&D by the provision of information on all these metrics and more from real-world driving, like engine knock data and even tire pressure.
• Google Cloud’s unified data cloud offering provides a complete platform for building data-driven applications like those from Sibros — from simplified data ingestion, processing, and storage to powerful analytics, AI, ML, and data sharing capabilities — integrated with Google Cloud. With a diverse partner ecosystem and support for multi-cloud, open-source tools and APIs, Google Cloud provides Sibros the portability and the extensibility they need to avoid data lock-in.

“Software has an ever increasing importance in the automotive world, even more so with electric vehicles and new mobility services. Google Cloud is partnering with Sibros to bring their award winning Deep Connected Platform to deliver high frequency, low latency over-the-air software updates, data logging & diagnostics capabilities to our automotive customers, leveraging the security and scale of Google Cloud. This is revolutionizing everything from development cycles to business models and customer relationships.” — Matthias Breunig, Director, Global Automotive Solutions, Google Cloud

Through Built with BigQuery, Google Cloud is helping tech companies like Sibros build innovative applications on Google’s Data Cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs.

“Sibros is looking forward to partnering with Google Cloud, which will enable vehicle manufacturers and suppliers to reach the next level in their use of data. Sibros solutions for Deep Data Logging and Updating on the Google Data Cloud, combined with Google BigQuery, will help them to mitigate risks, reduce costs, add innovative products, and introduce value-added use cases.” — Xiaojian Huang, Chief Digital Officer, Software, Sibros

Sibros and Google Cloud are driving Connected Mobility transformation to help our customers accelerate R&D innovation, power efficient operations, and unlock software-defined vehicle use cases with a full stack connected vehicle platform. Click here to learn more about Sibros on Google Cloud.