Editor’s note: Today’s post is by Mike Francomb, Senior Vice President of Technology, RecruitMilitary and a U.S. Army Veteran. RecruitMilitary is a wholly owned subsidiary of Bradley-Morris, Inc. (BMI), the largest military-focused recruiting company in the United States. RecruitMilitary uses Google Cloud Talent Solution to power its job search experience and connect more organizations with veteran talent.

For seven years, I served in the U.S. Army as a Field Artillery Officer, Military Occupation Code 13A. My time in service included a deployment to Operation Desert Shield / Desert Storm with the 24th Infantry Division out of Fort Stewart, GA, and a variety of front line artillery leadership roles, serving as a logistics officer for my unit and as an instructor teaching new officers how to be professional artillerymen. My day-to-day entailed leading teams of highly trained soldiers and managing logistics and materials to help those soldiers perform at a high level in stressful, fast-paced environments. It was my job to ensure we were ready to handle any circumstance. 

The hardest part about transitioning out of the Army in May 1996 as a highly trained artillery veteran was the fact that, though I felt prepared for any challenge ahead, I wasn’t sure I was making the right choice. I made a common mistake of transitioning veterans, I jumped right into an entrepreneurial venture. Looking back, I wish I’d had access to resources that displayed career options that would help translate my skills for the corporate world, it would have helped me be better prepared and know what my options were. I wasn’t ready to jump from the Army into running a business, and it was a long two years. 

Though my first job out of the Army was challenging, it taught me that I loved the start-up environment, and I joined RecruitMilitary in October 1998 when it was five months old. For the past 21 years, I have been fortunate enough to play an important role in helping RecruitMilitary grow to what it is today, the industry leader in connecting military veterans with organizations.  

RecruitMilitary connects organizations with veteran talent through over 30 products and services, all of which are fueled by our job board. Our job board, with over 1,400,000 members, is core to our business. In fact, if we don’t have an active and growing job board population, we don’t have the supply of veteran talent we need to deliver to our clients across our suite of services. 

With veteran unemployment at a 50-year low, it became increasingly challenging for RecruitMilitary to grow our veteran job seeker database and keep those veterans actively applying to client jobs. Being a data-driven company, we saw our existing search functionality was no longer producing the desired results for clients and began to receive client feedback about decreased candidate activity.

It was clear to us that we needed to begin adopting machine learning and more advanced search capabilities into our products and operations. The HR Tech space is shifting that way fast, and we want to be at the forefront. As we researched paths to take and learned of Google’s operating philosophy leading with AI, and that they were developing a tool for veteran job search, it made a lot of sense to go with a leader.

When Grow with Google announced its commitment to support veterans, we learned that we could add their military occupation code (MOS) translation feature to our job board through Cloud Talent Solution. This feature lets transitioning service members enter their military occupation codes (MOS, AFSC, NEC, or rating) directly into our search bar to see relevant civilian jobs available at client companies. We’re also using Cloud Talent Solution’s remote work functionality to provide an improved job search experience that allows our customers to make remote work opportunities in the U.S. more discoverable on their career sites. We’re excited about this feature, as it enhances our ability to deliver meaningful jobs to important members of our military community, military spouses, and veterans with limited mobility. 

The results of Cloud Talent Solution compared to our previous search are tremendous. Our job seekers are getting a much better experience, and to us that means more veterans are connected to jobs with our clients. We know this because our number of daily job applications has grown by 78 percent. And knowing that we now have a tool that’s going to learn and get better as more of our job seekers use it means that we will reap benefit for work done over time, and so will our clients and veterans who use our job board. That’s tremendous ROI to receive for a lean development staff. 

These are just a few of the types of tools I wish I’d had access to when I was considering my transition in 1996. With the help of technology and resources, like those from RecruitMilitary and Grow with Google, people in the military community, including veterans like myself, can prepare for and build meaningful careers.

With insights directing the bulk of today’s marketing decisions, leading marketers are driving growth by embracing three core mindset shifts. Marketing leaders are working toward a holistic view of consumers; they are investing in machine learning to support their activities; and they believe how they apply their data is crucial to success. Google partnered with MIT Sloan Management Review and MIT Technology Review for a deeper dive into the mindsets around these beliefs. Here’s what we found.

1. Leading marketers are working toward a holistic view of consumers.

• 63% of leading marketers agree they are using KPIs to develop a single integrated view of the customer.

• 66% of leading marketers agree they should build teams for end-to-end customer experiences and journeys, across channels and devices.

• Marketing leaders are 60% more likely than laggards to believe that marketing teams should own a data-driven customer strategy that supports all organizational stakeholders.

2.  Leading marketers are investing in machine learning to support their activities.

• Measurement-leaders are more than 2X as likely as their measurement-challenged counterparts to agree that their organization is already investing in automation and machine learning technologies to drive marketing activities.

• 75% of marketers who use machine learning to drive marketing activities said they were satisfied with how their KPIs inform and influence decision-making across their enterprise.

• 73% of marketing leaders who have invested in machine learning have shifted more than 10% of their time from manual activation to strategic insight generation.

3. Leading marketers believe how they apply their data is crucial to success.

• 66% of marketing leaders believe how companies apply their data will play a key role in their ability to thrive.

• 60% of leading marketers believe data-driven attribution is essential to understanding journeys of high-value customers.

• Marketing leaders are 53% more likely than laggards to say machine learning processes data signals to help marketers better understand consumer intent.

With the advent of the pandemic, contact center traffic has increased by as much as 300%, taxing center capabilities. To help handle the surge, and keep up with heightened customer demands, many customer experience providers have deployed automation in the form of virtual agents that serve as the first—and, sometimes the last—line of customer support. How effective these virtual agents are in delivering timely, complete customer service depends in large part on the contact center’s infrastructure and the power of the automation solution.

Customer support providers can now start to reimagine customer experience with Conversational AI and take advantage of the Google Cloud-Automation Anywhere partnership to modernize their operations. 

Bringing together the Google Cloud Contact Center AI (CCAI) solution and the Automation Anywhere cloud-native Automation 360 platform, the partnership helps contact centers remain competitive by improving their performance metrics and customer satisfaction to exceed the continuously growing expectations.

Information here, there, anywhere—with limited access

Over the years, many contact centers have accumulated a multitude of systems, often creating a disconnected infrastructure. As a result, both human and virtual agents alike are challenged to keep up with growing customers’ expectations for timely, accurate, and complete service. Without up-to-date software and infrastructure, human agents have to perform a “swivel-chair” maneuver, logging in to the different systems, sifting through records, copying the needed information, and deciding what the next action should be. This approach is not conducive to achieving lower average handle times (or AHT), reduced processing errors, or increased customer satisfaction.

In such a siloed environment, virtual agents may not connect to all the relevant data systems and applications. That also limits what they can do to support human agents. Typically, a virtual agent can handle basic customer requests, such as providing banking customers with their account balances. For more complex requests, such as applying for a line of credit, a virtual agent still must transfer customers to human agents, and the swivel-chair maneuver begins. 

Enter the RPA-assisted AI-powered virtual agent

The combination of Google Cloud Contact Center AI and Automation Anywhere’s Automation 360 RPA platform can help contact centers get the maximum benefit from utilizing virtual agents, enriching customer engagements. Further, by integrating automation, opening up APIs, and creating new processes for virtual agents, customer experience teams can help streamline operations by enabling users to quickly access the information they require. This helps minimize the need for the “swivel-chair” maneuver.

Automation 360 makes it possible for the CCAI virtual agents to access all systems and applications in both legacy and modern infrastructure, helping resolve every case quickly and easily. Not only can the virtual agents respond faster with answers, but they can complete more complex end-to-end requests. With RPA, customers are reporting 66% improved efficiency of contact center operations while exceeding their AHT reduction goals. 

This video illustrates how such automation can dramatically reduce the processing time of a customer service request, and you can read more about the details of contact center automation, as well.

Additionally, a comprehensive development platform, the Google Cloud CCAI Dialogflow, powers virtual agents—chatbots and voicebots—with Conversational AI to deliver lifelike customer experiences anytime a customer reaches out to a brand. CCAI Agent Assist helps human agents with turn-by-turn guidance, ready-to-deliver answers, and ready-to-send responses. CCAI Insights identifies key metrics such as call drivers and customer sentiment for workflow optimization. 

Living up to their potential

TELUS International, a leading digital customer experience provider and long-time partner of both Google Cloud and Automation Anywhere, has been leveraging virtual agents to enhance the employee and customer experience.  

“We are very proud of the enormous value that we can provide our clients through combining our expertise in customer experience and digital transformation alongside the innovative solutions of our technology partners,” Jim Radzicki, CTO of TELUS International, explains. “For instance, through leveraging Google Cloud Contact Center AI and Automation Anywhere’s RPA integration, we are able to expand the capabilities of virtual agents to process a wider variety of customer requests while allowing our team members to focus on the most critical conversations and creating a meaningful connection with every customer.” 

With CCAI and Automation 360, virtual agents can help contact centers deliver 24/7, comprehensive, accurate service, all of which helps eliminate wait times—even with heavy traffic. And this is just the start. 

With deeper integration planned between Automation 360 and CCAI, RPA can augment CCAI Agent Assist’s abilities to help human agents by bringing untapped case-sensitive information to their fingertips. Furthermore, Automation 360 Bot Insight can complement CCAI Insights’ as well as TELUS International’s Intelligent Insights, a tool-agnostic platform to monitor and manage RPA solutions and bots, with backend data access metrics.

At Google Cloud and Automation Anywhere, we’ll continue developing our contact center solution to extend automation capabilities for better customer service and greater customer satisfaction. Instead of constantly swiveling, agents can once again be at the center of the call center action, taking their work—and the experience of their customers—to the next level.

October 23rd (this past Saturday!) was my 4th Googlevarsery and we are wrapping an incredible Google Next 2021!

When I started in 2017, we had a dream of making BigQuery Intelligent Data Warehouse that would power every organization’s data driven digital transformation. 

This year at Next, It was amazing to see Google Cloud’s CEO, Thomas Kurian, kick off his keynote with CTO of WalMart, Suresh Kumar , talking about how his organization is giving its data the “BigQuery treatment”.

AS  I recap Next 2021 and  reflect on our amazing journey over the past 4 years, I’m so proud of the opportunity I’ve had to work with some of the world’s most innovative companies from Twitter to Walmart to Home Depot, Snap, Paypal and many others.   

So much of what we announced at Next is the result of years of hard work, persistence and commitment to delivering the best analytics experience for customers. 

I believe that one of the reasons why customers choose Google for data is because we have shown a strong alignment between our strategy and theirs and because we’ve been relentlessly delivering innovation at the speed they require. 

Unified Smart Analytics Platform 

Over the past 4 years our focus has been to build industries leading unified smart analytics platforms. BigQuery is at the heart of this vision and seamlessly integrates with all our other services. Customers can use BigQuery to query data in BigQuery Storage, Google Cloud Storage, AWS S3, Azure Blobstore, various databases like BigTable, Spanner, Cloud SQL etc. They can also use  any engine like Spark, Dataflow, Vertex AI with BigQuery. BigQuery automatically syncs all its metadata with Data Catalog and users can then run a Data Loss Prevention service to identify sensitive data and tag it. These tags can then be used to create access policies. 

In addition to Google services, all our partner products also integrate with BigQuery seamlessly. Some of the key partners highlighted at Next 21 included Data Ingestion (Fivetran, Informatica & Confluent), Data preparation (Trifacta, DBT),  Data Governance (Colibra), Data Science (Databricks, Dataiku) and BI (Tableau, PowerBI, Qlik etc).

Planet Scale analytics with BigQuery

BigQuery is an amazing platform and over the past 11 years we have continued to innovate in various aspects. Scalability has always been a huge differentiator for BigQuery. BigQuery has many customers with more than 100 petabytes of data and our largest customer is now approaching  an exabyte of data. Our large customers have run queries over trillions of rows. 

But scale for us is not just about storing or processing a lot of data. Scale is also how we can reach every organization in the world. This is the reason we launched BigQuery Sandbox which enables organizations to get started with BigQuery without a credit card. This has enabled us to reach tens of thousands of customers. Additionally to make it easy to get started with BigQuery we have built integrations with various Google tools like Firebase, Google Ads, Google Analytics 360, etc. 

Finally, to simplify adoption we now provide options for customers to choose whether they would like to pay per query, buy flat rate subscriptions or buy per second capacity. With our autoscaling capabilities we can provide customers best value by mixing flat rate subscription discounts with auto scaling with flex slots.

Intelligent Data Warehouse to empower every data analyst to become a data scientist

BigQuery ML is one of the  biggest innovations that we have brought to market over the past few years. Our vision is to make every data analyst a data scientist by democratizing Machine learning. 80% of time is spent in moving, prepping and transforming data for the ML platform. This also causes a huge data governance problem as now every data scientist has a copy of your most valuable data.  Our approach was very simple.  We asked:”what if we could bring ML to data rather than taking data to an ML engine?” 

That is how BigQuery ML was born. Simply write 2 lines of SQL code and create ML models. 

Over the past 4 years we have launched many models like regression, matrix factorization, anomaly detection, time series, XGboost, DNN etc. These  models are used by customers to solve complex  business problems simply from segmentation, recommendations, time series forecasting, package delivery estimation etc. The service is very popular: 80%+ of our top customers are using BigQueryML today.  When you consider that the average adoption rate of ML/AI is in the low 30%, 80% is a pretty good result!

We announced tighter integration of BQML with Vertex AI. Model explainability will provide the ability to explain the results of predictive ML classification and regression models by understanding how each feature contributes to the predicted result. Also users will be able to manage, compare and deploy BigQuery ML models in Vertex; leverage Vertex Pipelines to train and predict BigQuery ML models.

Real-time streaming analytics with BigQuery 

Customer expectations are changing and everyone wants everything in an instant: according to Gartner, by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.

The BigQuery’s storage engine is optimized for real-time streaming. BigQuery supports streaming ingestion of 10s of millions of events in real-time and there is no impact on query performance. Additionally customers  can use materialized views and BI Engine (which is now GA) on top of streaming data. We guarantee always fast, always fresh data. Our system automatically updates MVs and BI Engine. 

Many customers also use our PubSub service to collect real-time events and process these through Dataflow prior to ingesting into BigQuery. This is a streaming ETL pattern which is very popular. Last year,we announced PubSub Lite to  provide customers with a 90% lower price point and aTCO that is lower than any DIY Kafka deployment. 

We also announced Dataflow Prime, it is our next generation platform for Dataflow. Big Data processing platforms have only focused on horizontal scaling to optimize workloads. But we have seen new patterns and use cases like streaming AI where you may have a few steps in pipelines that perform data prep and then customers  have to run a GPU based model. Customers  want to use different sizes and shapes of machines to run these pipelines in the most optimum manner. This is exactly what Dataflow Prime does. It delivers vertical auto scaling with the right fitting for your pipelines. We believe this should lower costs for pipelines significantly.

With Datastream as our change data capture service (built on Alooma technology), we have solved the last key problem space for customers. We can automatically detect changes in your operational databases like MySQL, Postgres, Oracle etc and sync them in BigQuery.

Most importantly, all these products work seamlessly with each other through a set of templates. Our goal is to make this even more seamless over next year. 

Open Data Analytics with BigQuery

Google has always been a big believer in Open Source initiatives. Our customers love using various open source offerings like Spark, Flink, Presto, Airflow etc. With Dataproc & Composer our customers have been able to run various of these open source frameworks on GCP and leverage our scale, speed and security. Dataproc is a great service and delivers massive savings to customers moving from on-prem Hadoop environments. But customers want to focus on jobs and not clusters. 

That’s why we launched Dataproc Serverless Spark (GA) offering at Next 2021. This new service adheres to one of our key design principles we started with: make data simple.  

Just like with BigQuery, you can simply RUN QUERY. With Spark on Google Cloud, you simply RUN JOB.  ZDNet did a great piece on this.  I invite you to check it out!

Many of our customers are moving to Kubernetes and wanted to use that as the platform for Spark. Our upcoming Spark on GKE offering will give the ability to deploy spark workloads on existing Kubernetes clusters.  

But for me the most exciting capability we have is, the ability to run Spark directly on BigQuery Storage. BigQuery storage is highly optimized analytical storage. By running Spark directly on it, we again bring compute to data and avoid moving data to compute. 

BigSearch to power Log Analytics

We are bringing the power of Search to BigQuery. Customers already ingest massive amounts of log data into BigQuery and perform analytics on it. Our customers have been asking us for better support for native JSON and Search. At Next 21 we announced the upcoming availability of both these capabilities.

Fast cross column search will provide efficient indexing of structured, semi-structured and unstructured data. User friendly SQL functions let customers rapidly find data points without having to scan all the text in your table or even know which column the data resides in. 

This will be tightly integrated with native JSON, allowing customers to get BigQuery performance and storage optimizations on JSON as well as search on unstructured or constantly changing  data structures. 

Multi & Cross Cloud Analytics

Research on multi cloud adoption is unequivocal — 92% of businesses in 2021 report having a multi cloud strategy. We have always believed in providing customers choice to our customers and meeting them where they are. It was clear that all our customers wanted us to take our gems like BigQuery to other clouds as their data was distributed on different clouds. 

Additionally it was clear that customers wanted cross cloud analytics not multi-cloud solutions that can just run in different clouds. In short, see all their data with a single pane of glass, perform analysis on top of any data without worrying about where it is located, avoid egress costs and finally perform cross cloud analysis across datasets on different clouds.

With BigQuery Omni, we deliver on this vision, with a new way of analyzing data stored in multiple public clouds.  Unlike competitors, BigQuery Omni does not create silos across different clouds. BigQUery provides a single control plane that shows an analyst all data they have access to across all clouds. Analyst just writes the query and we send it to the right cloud across AWS, Azure or GCP to execute it locally. Hence no egress costs are incurred. 

We announced BQ Omni GA for both AWS and Azure at Google Next 21 and I’m really proud of the team for delivering on this vision.  Check out Vidya’s session and learn from Johnson and Johnson how they innovate in a multi-cloud world.

Geospatial Analytics with BigQuery and Earth Engine

We have partnered with our Google Geospatial team to deliver GIS functionality inside BigQuery over the years. At Next we announced that customers will be able to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and Google Maps Platform. 

Think about all the scenarios and use-cases your team’s going to be able to enable sustainable sourcing, saving energy or understanding business risks.

We’re integrating the best of Google and Google Cloud together to – again – make it easier to work with data to create a sustainable future for our planet.  

BigQuery as a Data Exchange & Sharing Platform

BigQuery was built to be a sharing platform. Today we have 3000+ organizations sharing more than 250 petabytes of data across organizations. Google also brings more than 150 public datasets to be used across various use cases. In addition to this, we are also bringing some of the most unique datasets like Google Trends to BigQuery. This will enable organizations to understand in real-time trends and apply to their business problems.

I am super excited about the Analytics Hub Preview announcement. Analytics Hub will provide the ability for organizations to build private and public analytics exchanges. This will include data, insights, ML Models and visualizations. This is built on top of the industry leading security capabilities of BigQuery.

Breaking Data Silos

Data is distributed across various systems in the organization and making it easy to break the data silo and make all this data accessible to all is critical. I’m also particularly excited about the Migration Factory we’re building with Informatica and the work we are doing for data movement, intelligent data wrangling with players like Trifacta and FiveTran, with whom we share over 1,000 customers (and growing!).  Additionally we continue to deliver native Google service to help our customers. 

We acquired Cask in 2018 and launched our self service Data Integration service in Data Fusion. Now Fusion allows customers to create complex pipelines with just simple drag and drop. This year we focused on unlocking SAP data for our customers. We have launched various SAP connectors and accelerators to achieve this.

At GCP Next we also announced our BigQuery Migration service in preview. Many of our customers are migrating their legacy data warehouses and data lakes to BigQuery. BigQuery Migration Service provides end-to-end tools to simplify migrations for these customers. 

And today, to make migrations to BigQuery easier for even more customers, I am super excited to announce the acquisition of CompilerWorks. CompilerWorks’ Transpiler is designed from the ground up to facilitate SQL migration in the real world and will help our customers accelerate their migrations. It supports migrations from over 10 legacy enterprises data warehouses and we will be making it available as part of our BigQuery Migration service in the coming months.

Data Democratization with BigQuery

Over the past 4 years we have focused a lot on making it very  easy to derive actionable insights from data in BigQuery. Our priority has been to provide a strong ecosystem of partners that can provide you with great tools to achieve this but also deliver native Google capabilities. 

With our BI engine GA announcement which we introduced in 2019, previewed earlier this year and showcased with tools like Microsoft PowerBI and Tableau, is now available for all to play with.

BigQuery + Data Studio are like peanut butter and Jelly. They just work well together. We launched BI Engine first with Data Studio and scaled it to all the users. More than 40% of our BigQuery customers use Data Studio. Once we knew BI Engine works extremely well we now have made it an integral part of BigQuery API and launched it for all our internal and partner BI tools. 

We announced GA for BI Engine at Next 2021 but we were already GA with Data Studio for the past 2 years. We recently moved the Data Studio team back into Google Cloud making the partnership even stronger. If you have not used Data Studio, I encourage you to take a look and get started for free today here!! 

Connected Sheets for BigQuery is one of my favorite combinations. You can give every business user in your organization the ability to analyze billions of records using standard Google Sheets experience. I personally use it everyday to analyze all our product data. 

We acquired Looker in Feb 2020 with a vision of providing a semantic modeling layer to our customers with a governed BI solution. Looker is tightly integrated with BigQuery including BigQuery ML. Our latest partnership with Tableau where Tableau customers will soon be able to leverage Looker’s semantic model, enabling new levels of data governance while democratizing access to data. 

Finally, I have a dream that one day we will bring Google Assistant to your enterprise data. This is the vision of Data QnA. We are in early innings on this and we will continue to work hard to make this vision a reality. 

Intelligent Data Fabric to unify the platform

Another important trend that shaped our market is the Data Mesh.  Earlier this year, Starburst invited me to talk about this very topic. We have been working for years on this concept, and although we would love for all data to be neatly organized in one place, we know that our customers’ reality is that it is not (If you want to know more about this, read about my debate on this topic with Fivetran’s George Fraser, a16z’s Martin Casado and Databricks’ Ali Ghodsi).

Everything I’ve learned from customers over my years in this field is that they don’t just need a data catalog or a set of data quality and governance tools, they need an intelligent data fabric.  That is why we created Dataplex, whose general availability we announced at Next.

Dataplex enables customers to centrally manage, monitor, and govern data across data lakes, data warehouses, and data marts, while also ensuring data is securely accessible to a variety of analytics and data science tools.  It lets customers organize and manage data in a way that makes sense for their business, without data movement or duplication. It provides logical constructs – lakes, data zones, and assets – which enable customers to abstract away the underlying storage systems to build a foundation for setting policies around data access, security, lifecycle management, and so on.  Check out Prajakta Damle’s session and learn from Deutsche Bank how they are thinking about a unified data mesh across distributed data.

Closing Thoughts

Analysts have recognized our momentum and, as I look back at this year, I couldn’t thank our customers and partners enough for the support they provided my team and I across our large Data Analytics portfolio: in March, Google BigQuery was named a Leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021.  And in June, Dataflow was named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report.

If you want to get a taste for why customers choose us over other hyperscalers or cloud data warehousing, I suggest you watch the Data Journey series we’ve just launched, which documents the stories of organizations modernizing to the cloud with us.

The Google Cloud Data Analytics portfolio has become a leading force in the industry and I couldn’t be more excited to have been part of it.  I do miss you, my customers and partners, and I’m frankly bummed that we didn’t get to meet in person like we’ve done so many times before (see a photo of my last in-person talk before the pandemic), but this Google Next was extra special, so let’s dive into the product innovation and their themes.

I hope that I will get to see you in person next time we run Google Next!