Millions of people annually view esports, as top players and teams compete in thrilling, fast-paced tests of reflexes, strategy, and teamwork. Fans are a diverse group, sharing a passion for action. A new, revolutionary project we’re working on with Call of Duty League just made that action a lot better.

ActivStat brings fans, players, and commentators the power of competitive statistics in real-time—stats that matter not only to the game at hand, but also for a full roster of competitors globally. Using ActivStat, live broadcasts will soon be enhanced with more depth and color-of-play while they’re happening, building excitement and adding to the overall experience.

Technically, ActivStat is an entirely new capability for esports. It’s a constantly updating catalog of statistics that is sourced, analyzed, updated, and delivered in an easy-to-consume way across global-scale computing systems, with a latency of milliseconds or seconds, rather than minutes or hours. By comparison, many of these stats today are available to fans after a day or more of processing.

Call of Duty League plans to begin rolling out ActivStat during the 2021 season. The initial rollout will include critical information like player and team standings and winning ratios across multiple aspects of virtual combat—including ultimately what these numbers mean for rankings. ActivStats are delivered both in raw statistics and via visualizations and graphics, providing commentators with fast access to the types of insights fans crave. 

For engineers at both companies, building the service has been an epic success all its own. Due to a sponsorship with Call of Duty signed last February, our dedicated game engineering team quickly innovated a new solution incorporating high-speed networking, data pipelines from multiple cloud sources, and data warehousing to create a user-friendly dashboard. Real data was flowing into commentator dashboards by April.

Esports are more complex to cover in many ways than regular sports. Instead of a well-defined physical playing field (often a simple rectangular space), multiplayer games involve complex and sprawling virtual environments that can be the size of a large campus or airport with multiple levels and hidden locations.   

Gameplay between competitors happens across many of these locations simultaneously. In addition, competitors also each choose their own configurations of equipment, known as “loadouts,” which can dramatically affect gameplay and strategy. All of this additional complexity in online gaming involves data that needs to be captured, analyzed, and communicated to the fans in insightful ways.

Two Google Cloud technology capabilities play central roles in the operations of ActivStat. BigQuery—a planet-scale data warehouse that can store and query petabytes of information in real time—is the foundation of the ActivStat platform for gathering and summarizing millisecond-level statistics. Looker, an intuitive analytic dashboard, surfaces those insights to commentators in an easy-to-use, real-time dashboard that enables the commentators to speak to compelling insights and statistics in sync with the gameplay as it is happening in the live broadcast.

While the initial release of ActivStat for this season of Call of Duty League provides compelling and powerful capabilities, it’s only the beginning of this Call of Duty League-Google Cloud co-innovation partnership. Our future vision is mapping gameplay hotspots on the field, and predicting where to place cameras with machine learning as the match evolves–enabling broadcast producers to build excitement for fans by always being in the middle of the best action. Call of Duty League and Google will also look to drive statistics and metrics directly into the broadcast feed.

The implications of the real-time statistical capabilities of ActivStat go beyond gaming and esports. Historically, gaming has been at the leading edge of what computer processing, computer graphics, wide-area networking, data analysis, and insight can do. The real-time data ingestion and output used in ActivStat could one day be useful powering live broadcasts in other types of sporting events, as well as blending live video feeds and data to support use cases in media & entertainment, healthcare, finance, manufacturing, and other verticals. We’re excited about the potential for this bold new solution and partnership between Call of Duty League and Google in esports and beyond.

To learn more about Call of Duty League-Google Cloud partnership, visit our press release.

Education data can inform strategy, enhance both teaching and learning, and help us better understand customer needs. But that data is only as useful as an organization’s ability to access, analyze, and act upon it. Education Technology (EdTech) companies with a strong data warehouse management policy can help to make the most of educational data at all levels.

Creating a data team

When an EdTech doesn’t have a dedicated data team, databases will likely be poorly maintained and data can be hard to find, it can lack critical security, or fail to meet a customer’s specific requirements. A dedicated data team can prevent this and better support an organization’s data needs.  There’s a correlation between the maturity of a data team and the success of an organization’s data utilization. When considering the three primary components of team, outlook, and results, we can better understand an organization’s maturity. By using this matrix from Looker’s Analytical Maturity Playbook, you can assess the maturity of your organization’s data team. Most would agree, the goal is for your operation to move from data chaos to data-driven. But, how can you do that? Think about your data this way: the intelligence you gain from your data is not only an asset…it’s a product. When your data team views your business intelligence as a product—one that’s just as important as the services you offer—they can set the stage for creating a successful data-driven company. Mature data organizations:

• Report functionality regularly
• Constantly assess potential improvements to existing systems
• Manage content rollout to internal employees

Redivis: Making data accessible, connected & secure

Redivis is a platform that connects academic researchers with data and the tools to understand education intelligence. Their customers, who are researchers, expressed how difficult it is to find new datasets for their studies. Once they found a dataset, they often had to access and work with it before they could tell if the information would be useful for their studies. Redivis Data administrators were also concerned about data security. Redivis built their platform on top of Google Cloud’s security infrastructure, making it a highly scalable data processing environment. 

Redivis developed a transparent, tiered access system for datasets that allows researchers to request separate access to a dataset’s documentation to determine whether they can use the dataset. Detailed audit logs (supported by Google Cloud Logging) and robust application-level security controls tighten data access. To offer more insights for their researchers, they securely connected their private datasets with public datasets hosted in BigQuery allowing researchers to run queries on billions of records in seconds, not hours.

With a data team in place, it’s time to visualize where to store and organize your data. Data is an asset. You want to invest in collecting, operationalizing, and storing it properly. “Inadequate data aggregation, harmonization, and processing impedes teams from making the right data-driven decisions and pivoting when needed,” according to Jesus Trujillo Gomez, a strategic business executive at Google Cloud. You can determine how mature your warehouse is by using this matrix to help you identify where your organization falls on the spectrum (least mature on the left, most mature on the right). A company’s data warehouse can be assessed in four areas: Technology, Pipeline, Quality, and Performance. Here are some suggested steps to bring your EdTech company one step closer to a data powerhouse. 

Classcraft: Using a data-driven approach to make learning fun

Launched in 2014, learning company Classcraft is reimagining the classroom, creating better learning environments with gamification. To support their mission, their team has created a complete data platform that uses data analytics to find new ways to engage students in the classroom. Classcraft became a Google for Education Build Partner to keep up with rising demand. They expanded their footprint to reach 6 million students and educators in 165 countries. 

They also migrated to Google Cloud, building on several integrations with Google Workspace for Education, Google Classroom, and Chrome for Education, allowing them to support customers ranging from individual teachers to entire school districts.  Classcraft migrated its database and analytics infrastructure to BigQuery and Cloud Storage. “We capture about 10 million new data points every month related to school culture and student performance,” says Chief Technology Officer and VP of Product Stéphane Guillemette. “With BigQuery and other Google Cloud tools, we can quickly deliver valuable insights to educators.”

The ecommerce playing field has been hard to navigate for most retailers, and Cart.com is on a mission to change that. Traditionally, retailers needing to run their online store, order fulfillment, customer service, marketing, and other essential activities have had to cobble together systems to get the capabilities they need – much less having access to analytics across these functions. The result is costly, siloed ecommerce operations that are difficult to manage and scale.

It’s clearly not a formula for success, yet that’s the reality facing most retailers. Cart.com, in contrast, has set out to democratize ecommerce by giving brands of all sizes the full capabilities they need to take on the world’s largest online retailers. Our end-to-end environment empowers retailers to keep more of their revenue, set up proven strategies for managing all aspects of their business, and act on valuable insights from customer data every step of the way.

Together with our talented team, we’re building a unified ecommerce platform that already provides value to many leading or up and coming brands including Whataburger, GUESS, Dr. Scholl’s, Rowing Blazers, and Howler Bros. 

We’re excited about the opportunity ahead as we reimagine traditional approaches to online sales, fulfillment, marketing, accessing growth capital, providing a unified view of all ecommerce and marketing analytics, and other activities. Expectations for Cart.com are high, and we are building a company that can scale to $100B in revenue and beyond. Supported by the Startup Program by Google Cloud and Google Cloud solutions, we’re establishing a technology platform to transform all aspects of ecommerce for brands worldwide. 

Partner in disruption

At Cart.com, we’re currently targeting an underserved market. Our ideal customer is beyond demonstrating product-market-fit and is now at an inflection point seeking a growth opportunity. Typically, those companies are generating between $1M and $100M in annual revenue. We’ve seen an enthusiastic response from brands and retailers as well as investors, with backing from investors in just over a year totaling $143 million in three funding rounds.

Our strategy is to build an integrated ecommerce model that combines best-of-breed solutions, many of which we gain through acquisitions and then build upon to provide a streamlined and fully integrated experience for our brands. We’ve made seven acquisitions so far to round out our online store, order fulfillment, marketing services, customer service, and we have launched some integral partnerships including easy access to growth capital through our relationship with Clearco and product protection for customers on every purchase with Extend. Instead of acquiring a data company, we’re building our data platform on Google Cloud, across each operating function for a single-view for brands to harness actionable data. We see Google Cloud as the leader for data management, analytics, machine learning (ML) and artificial intelligence (AI).

Other reasons why we’re building our business on Google Cloud include scalability, excellence, security, reach, and data analytics that are far superior to other environments.

We also feel a cultural and mission alignment with Google Cloud and envision leaning into a long-term partnership of marketing, selling, and disrupting the disruptors together. Equally important to us are the investments Google Cloud is willing to make in early-stage companies like ours. The support through the Google Cloud for Startups program has been outstanding.

Built on Google Cloud

A wide range of Google Cloud solutions provide the foundation for our platform. For instance, Cloud Pub/Sub keeps our services communicating with one another. We rely on fully managed relational databases, like Cloud SQL and Cloud Spanner, to securely handle the huge volume of brand and shopper data generated every day.

Cloud Run allowed us to develop inside of containers before our Kubernetes infrastructure was ready to go. Now, we are taking advantage of all the capabilities in Google Kubernetes Engine. BigQuery integrates with all Google Cloud solutions and offers true data streaming natively out of the box, along with Dataflow for advanced analytics. We also use Container Registry to store and manage our Docker container images. Right now, we’re testing Cloud Composer to evaluate using it for data workflow orchestration instead of Apache Airflow.

The openness of the Google Cloud environment is further enabled by Anthos, which we may deploy soon to perform data integrations quickly as we acquire more companies over the next year. For example, if we acquire a company using Azure, we can easily align it with our Google Cloud ecosystem.

Enabling ecommerce 2.0

Recently, our team has been experimenting with Google Cloud Vertex AI and the fully managed services of AI deployment and ML operations. The capabilities would save us substantial time in the management of the ML lifecycle which allows us to focus more on developing proprietary AI that will transform commerce at scale.

Because Google Cloud is so far ahead in data science, our teams benefit from deep Google Cloud expertise as we look to provide brands with unmatched insights into customers to improve services and revenue. We’re also planning to test Recommendations AI among other tools to deploy customer product recommendations and personalization as turnkey productized offerings. Moving forward, we will likely use Bigtable to aid in serving machine learning to hundreds of thousands of brands due to its low latency and scalability.

Fanatical about brand success

We know that our work with Google Cloud for Startups and use of Google Cloud solutions for best-in-class data management, analytics, ML, and AI will enable us to offer even more transformative services to brands.

We also see the opportunity to use our platform and customer insights to break down barriers between brands, enabling retailers to share information and work better together when it’s in their best interests. What we’re building today on Google Cloud is fundamentally changing what’s possible for retailers of any size everywhere. 

As a startup, when recruiting talent or working with prospective customers, it helps to share our success with Google Cloud. We view them as an extension of the Cart.com team. It also validates our business as we continue building a more integrated, holistic approach to commerce that opens new opportunities and drives growth for brands worldwide.

For more details about Cart.com’s vision for unified ecommerce, check out our video.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

As we engage with enterprises across the globe, one thing is becoming clear: Today’s businesses are solving complex business problems that are data-intensive.

But often, their data platform infrastructure is holding them back. Data platform architectures that were designed in the 1990s are not ready to solve business problems for 2020. We don’t have to tell you about the explosive data growth that’s going on for businesses around the world. If you’re managing data infrastructure today, you already know plenty about that data growth.

Ever faster and larger data streams, global business needs, and tech-savvy users are all putting the pressure on IT teams to move faster, with more agility.

Despite all these changes, it is often the legacy, traditional data warehouse where most of the data analytics tasks take place, and they’re underprepared for those demands.

When we talk to people working in IT today, we hear a lot about the constraints that come with operating legacy technology while trying to build a modern data strategy. Those legacy data warehouses likely aren’t cutting it anymore. Here’s why—and here’s what you can do about it.  

1. Business agility is hard to achieve with legacy tools. 
Business agility is the main goal as organizations move toward completely digital operations. Think of online banking, or retailers staying ahead of always-on e-commerce needs in a competitive environment. All these great, cutting-edge innovations reflect cultural and technical change, where flexibility is essential. A business has to be able to manage and analyze data quickly to understand how to better serve customers, and allow its internal teams to do their best work with the best data available.

We hear that lots of data warehouses running today are operating at 95% or 100%, maxing out what they can provide to the business.

Whether it’s on-premises or an existing data warehouse infrastructure moved wholesale to cloud, those warehouses aren’t keeping up with all the data requests users have. Managing and preventing these issues can take up a lot of IT time, and the problems often compound over time. Hitting capacity limits slows down users and ties up database administrators too.

From a data infrastructure perspective, separating the compute and storage layers is essential to achieve business agility. When a data warehouse can handle your scalability needs and self-manage performance, that’s when you can really start being proactive. 

2. Legacy data warehouses require a disproportionate degree of management. 
Most of the reports and queries your business runs are probably time-sensitive, and that sense of urgency is only increasing as users and teams see the possibilities of data analytics.

In our engagements with customers, we often observe that they are spending a majority of the time on systems engineering, so that only about 15% of the time is spent analyzing data. That’s a lot of time spent on maintenance work.

Because legacy infrastructure is complex, we often hear that businesses continue to invest in hiring people to manage those outdated systems, even though they’re not advancing data strategy or agility.

To cut time on managing a data warehouse, it helps to automate the system engineering work away from the analytics work, like BigQuery enables.

Once those functions are separated, the analytics work can take center stage and let users become less dependent on administrators. BigQuery also helps remove the user access issues that are common with legacy data warehouses. Once that happens, users can focus on building reports, exploring datasets, and sharing trusted results easily. 

3. Legacy data warehouse costs make it harder to invest in strategy. 
Like other on-prem systems, data warehouses adhere to the old-school model of paying for technology, with the associated hardware and licensing costs and ongoing systems engineering.

This kind of inefficient architecture drives more inefficiency. When the business is moving toward becoming data-driven, they’ll continue to ask your team for more data. But responding to those needs means you’ll run out of money pretty quickly. 

Cloud offers much more cost flexibility, meaning you’re not paying for, or managing, the entire underlying infrastructure stack. Of course, it’s possible to simply port an inefficient legacy architecture into the public cloud.

To avoid that, we like to talk about total cost of ownership (TCO) for data warehouses, because it captures the full picture of how legacy technology costs and business agility aren’t matching up.

Moving to BigQuery isn’t just moving to cloud—it’s moving to a new cost model, where you’re cutting out that underlying infrastructure and systems engineering. You can get more detail on cloud data warehouse TCO comparisons from ESG.

4. A legacy data warehouse can’t flexibly meet business needs. 
While overnight data operations used to be the norm, the global opportunities for businesses mean that a data warehouse now has to load streaming and batch data while also supporting simultaneous queries. Hardware is the main constraint for legacy systems as they struggle to keep up.

Moving your existing architecture into the cloud usually means moving your existing issues into the cloud, and we hear from businesses that doing so still doesn’t allow for real-time streaming.

That’s a key component for data analysts and users. Using a platform like BigQuery means you’re essentially moving your computational capabilities into the data warehouse itself, so it scales as more and more users are accessing analytics.

Unlimited compute is a pretty good way to help your business become digital. Instead of playing catch-up with user requests, you can focus on developing new features. Cloud brings added security, too, with cloud data warehouses able to do things like automatically replicate, restore and back up data, and offer ways to classify and redact sensitive data. 

5. Legacy data warehouses lack built-in, mature predictive analytics solutions. 
Legacy data warehouses are usually struggling to keep up with daily data needs, like providing reports to departments like finance or sales. It can be hard to imagine having the time and resources to start doing predictive analytics when provisioning and compute limits are holding your teams back.

We hear from customers that many of them are tasked with simplifying infrastructure and adding modern capabilities like AI, ML and self-service analytics for business users.

The best stories about digital transformation are those where the technology changes and business or cultural changes happen at the same time. One customer told us that because BigQuery uses a familiar SQL interface, they were actually able to shift the work of data analytics away from a small, overworked group of data scientists into the hands of many more workers.

Doing so also eliminated a lot of the siloed data lakes that had sprung up as data scientists extracted data one project at a time into various repositories to train ML models.

These large-scale computational possibilities save time and overhead, but also let businesses explore new avenues of growth. AI and ML are already changing the face of industries like retail, where predictive analytics can provide forecasting and other tasks to help the business make better decisions. BigQuery lets you take on sophisticated machine learning tasks without moving data or using a third-party tool. 

We designed BigQuery so that our engineers deploy the resources needed for you to scale. It means your focus can change entirely toward meeting the needs the business has put forth, and bringing a lot more flexibility.

BigQuery is fully serverless and runs on underlying Google infrastructure, so it integrates with our ecosystem of data and analytics partner tools. This architecture means you’re continually getting the most up-to-date software stack—analytics that scale, real-time insights, and cutting-edge functionality that includes geospatial and machine learning right from the SQL interface. 

Streamline your path to data warehouse modernization with BigQuery by learning about Google Cloud’s proven migration methodology and get started with your data warehouse by applying for our migration offer.