With Google Cloud, AirAsia is now a “data first” business that can capture, analyze, and report on rising volumes of data to address complex problems and increase revenue streams. The airline is also using AI-powered chatbots to streamline internal operations and provide a faster, more efficient service.

Google Cloud results

• Increases employee survey response rates by 30% and improved safety, scheduling, and employee orientation
• Enables movement towards a zero trust security model while reducing administration load on IT team
• Increases business agility by deploying faster and more frequently

Industries: Travel & Hospitality

Location: Malaysia

Become a digital airline powered by data and machine learning

AirAsia’s vision is simple: allow everyone to fly. Founded in 2001, the airline and sister company AirAsia X have grown to service 150+ destinations in 25 markets, using 274 aircraft to operate 11,000+ weekly flights from 23 hubs across the region. While the airline is known as a provider of low-cost airfares to locations across Asia, this is only one part of its value proposition. AirAsia also aims to deliver innovative, personalized products and services that meet the needs of each of its passengers.

Technology is key to AirAsia’s success and, with strong support from Group Chief Executive Officer Tony Fernandes, in 2016 the airline began a five-year program to become a data-first business and a digital airline.

“We wanted to better ensure we were using data correctly to become more agile, efficient, and customer oriented,” says Lye Kong Wei, Chief of Data Science, Group Head at AirAsia.

AirAsia needed technologies and services that could capture, process, analyze, and report on data, while delivering value for money and meeting its speed and availability requirements. The airline also wanted to minimize infrastructure management and system administration demands on its technology team.

Google Cloud the best fit

AirAsia realized only a cloud service could meet its needs and began evaluating the market. The airline then conducted a proof of concept and found Google Cloud was the best fit for its business. It was already familiar with Google Cloud, having deployed Google Workspace collaboration and productivity applications to its workforce in all countries except China. According to Kong Wei, products such as Forms, Docs, Sheets, and Gmail delivered a considerable improvement in collaboration between various departments, as well as streamlining and automating a range of processes.

The business was particularly excited by the potential of the BigQuery analytics data warehouse to power its digital transformation. “We knew data was a big part of making decisions in the future,” says Kong Wei. “So we needed a platform that could scale to meet our growing appetite for it. Google Cloud—in particular BigQuery—was ideal for this task.”

The AirAsia technology team was impressed by the ease and flexibility with which it could extract, transform, and load customer data from its systems, websites, and mobile applications into BigQuery for analysis. Data, reports, and dashboards were delivered and visualized through Looker Studio.

BigQuery also scaled seamlessly to support data growth and, as a managed service, required minimal administration from the airline’s technology team. “In addition, with BigQuery, we could process queries and requests much faster than previously and tackle more complex problems,” says Kong Wei. “As a result, we could be more innovative about realizing opportunities,” he adds, citing the benefits of being able to view and understand historical measures of booking curves—a measure of how long it took customers to book before a flight. This improves the airline’s ability to manage revenues.

A broad ecosystem

BigQuery and Looker Studio are just two components of a broad ecosystem—powered largely by Google Cloud services—deployed by AirAsia. Pub/Sub provides a scalable message queue that enables AirAsia developers to integrate systems hosted on Google Cloud or externally. Apache Airflow enables the business to create, schedule, and monitor workflows, while Cloud Composer manages the dependencies of PHP and related libraries. App Engine allows AirAsia personnel to develop and host web applications. “Thanks to App Engine, we’ve easily been able to create new applications, services, and APIs powered by the data we have been collecting,” says Kong Wei.

AirAsia is also running the middleware for its APIs in a managed Google Kubernetes Engine environment for increased scalability, resource optimization, and reliability, while Cloud Storage provides storage for data from a range of systems and sources. Dataflow enables the business to transform and process data in stream and batch modes from its website search page as customers look for flights.

Faster deployment and testing

The stability of Google Cloud service means AirAsia has a reliable base from which to launch new products and features. “If we have consistent reliability from our core systems—and Google Cloud incorporates monitoring tools such Cloud Monitoring that enable us to identify issues quickly—developers and product engineers can focus on turning ideas into reality,” says Kong Wei. “With a minimal number of people involved, we can very quickly transform an idea or thought process into a deliverable. Prior to Google Cloud, bringing those ideas to fruition would have been impossible.”

Robust security

AirAsia is also relying on Security Health Analytics, a product that integrates with Security Command Center, to identify misconfigurations and compliance violations in its Google Cloud resources and take action. Security Health Analytics ensures the airline’s budgets go to keeping customers’ travel costs low rather than recovering from security breaches.

The product enables AirAsia to check that resources are configured properly and are compliant with CIS benchmarks as its critical workloads run in Google Kubernetes Engine and App Engine.

“Being able to go to the new Security Health Analytics dashboard eliminates the guesswork of what we have running and if it is secure,” says Muhammad Faeez Bin Azmi, Information Security and Automation Solution Architect. “Now anyone on our team, even non-security professionals, can go to this dashboard and see a list of the misconfigured assets and compliance violations across all of our Google Cloud resources. We can also see the severity of misconfigurations, which helps us prioritize our response.”

“Security Health Analytics has really helped us reduce the amount of time we spend trying to figure out what’s wrong with our resources. It’s allowed us to use our time more effectively to identify and resolve more security issues than we could before.”

A new identity solution

AirAsia had also used a legacy on-premises directory for many years. However, as the company grew and expanded to new markets and regions, it had to manage multiple servers across a number of on-premises data centers and the public cloud, which proved costly and time-consuming.

Its Allstars—the airline’s name for its employees—needed to easily access a number of legacy on-premises apps in addition to a growing number of SaaS apps. As a business, it also needed a more seamless integration between its HR system of record and its identity solution for user provisioning and life cycle management. Solving these challenges with its existing on-premises directory was simply not feasible.

AirAsia brought up its identity concerns with the Google Cloud team, and after a number of conversations, decided to deploy Cloud Identity, Google’s cloud-based Identity and Access Management solution, to help address the identity challenges it was facing.

The airline chose Cloud Identity for a number of reasons—first, it was eager to move to the cloud as quickly as possible. Moving identity management to the cloud was a key enabler of this and the airline’s broader digital transformation. Managing identities from the cloud also enabled the airline to have a single identity and set of credentials for each employee, which they could use to access all the applications they need to be productive, both in the cloud and on-premises.

In addition, deploying Cloud Identity was a key step towards enabling the zero trust security model, which the airline felt was the best approach to strengthen its security posture and fight modern threats. Cloud Identity also integrated seamlessly with its existing technologies, which include not only Google Cloud products like Google Workspace and Chrome OS but also third-party tools like Citrix, Papercut, and others.

And finally, Cloud Identity offered significant cost and resource savings. With Cloud Identity in place, AirAsia’s IT department could spend less time worrying about managing multiple on-premises directory servers and and could instead focus on delivering value to Allstar employees.

Machine learning employed to increase ancillary revenue

In March 2018, AirAsia established the groundwork to use machine learning to optimize pricing for a range of services and began by using AI Platform to sort and predict demand for ancillary services such as baggage, seats, and meals. “By using AI Platform, we can sort based on data about history to predict the future,” says Kong Wei.

Dialogflow in wide use

With Google Cloud well established within the business, AirAsia is using Dialogflow, a voice and conversational interface development suite (and one of the core components of Contact Center AI) that enables businesses to create engaging AI powered voice- and text-based interfaces such as chatbots and voice apps—to streamline operations and reduce costs

“Tony Fernandes, our Group Chief Executive Officer, motivated us to create a range of metrics that would enable us to respond more quickly and efficiently to customers,” says Yuashini Vellasamy, Product Manager at AirAsia.

This resulted in the initial deployment of Dialogflow in AirAsia’s operational areas, from crew scheduling to internal business tasks.

With Dialogflow, AirAsia is able to provide pilots, crew, catering, and other teams with flight times, capacity and any other relevant information about their assignments. Another bot accepts medical certificates and updates from pilots and crew members unable to make rostered assignments and switches those assignments to other pilots and crews. “This improves our on-time performance and operational efficiency, as we are able to optimize the creation of crew schedules,” says Vellasamy.

AirAsia also uses Dialogflow to power a “safety bot” for airport ground staff. “We tried a lot of tools to encourage staff to advise us when they saw a safety issue—but employee usage was low,” says Vellasamy. “However, when we set up the safety bot, which asks users just four questions about an issue they have observed, we saw an increase in employee interaction, which helps ensure proper safety measures.

Beyond crew optimization and overall safety, AirAsia’s HR department in Asia uses a Dialogflow-powered bot to run a post-orientation engagement program for new employees. The bot follows up with employees about issues from parking to AirAsia’s onboarding buddy program, saving HR employees time from scheduling one-on-one meetings. Dialogflow also powered an employee satisfaction survey of 3,000 team members, resulting in a 30% increase in response rate compared to the previous year.

One of Dialogflow’s benefits is its language coverage, which the airline’s marketing team uses to target customers and prospects across a region with diverse languages and cultures. “For weekly campaigns and promotions, our sales and marketing teams simply update the campaign materials and the changes are reflected automatically in Dialogflow and consequently to users,” says Vellasamy. “This enables us to quickly scale and reach our customers globally.”

An upward trajectory

AirAsia is poised to reap further benefits from Google Cloud as its deployment matures. “On the data side, we’re more advanced, while on the application and programming side we have a long way to go,” says Kong Wei. “We’re on a trajectory upwards—we’re looking at a lot of new ideas and how to embrace and deploy them so we can further our data-first and digital airline agendas.”

Need a quick overview of Google Cloud analytics technologies? Quickly learn these 11 Google Cloud products—each explained in under two minutes.

BigQuery in a minute

Storing and querying massive datasets can be time consuming and expensive without the right infrastructure. This video gives you an overview of BigQuery, Google’s fully-managed data warehouse. Watch to learn how to ingest, store, analyze, and visualize big data with ease.

Firestore in a minute

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps, at global scale. In this video, learn how use Firestore and discover features that simplify app development without compromising security.

Cloud Spanner in a minute

Cloud Spanner is a fully managed relational database with unlimited scale, strong consistency, and up to 99.999% availability. In this video, you’ll learn how Cloud Spanner can help you create time-sensitive, mission critical applications at scale.

Cloud SQL in a minute

Cloud SQL is a fully-managed database service that helps you set up, maintain, manage, and administer your relational databases on Google Cloud. In this video you’ll learn how Cloud SQL can help you with time-consuming tasks such as patches updates, replicas, and backups so you can focus on designing your application.

Memorystore in a minute

Memorystore is a fully managed and highly available in-memory service for Google Cloud applications. This tool can automate complex tasks, while providing top-notch security by integrating IAM protocols without increasing latency. Watch to learn what Memorystore is and what it can do to help in your developer projects.

Bigtable in a minute

Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads. In this video, you’ll learn what Bigtable is and how this key-value store supports high read and write throughput, while maintaining low latency.

BigQuery ML in a minute 

BigQuery ML lets you create and execute machine learning models in BigQuery by using standard SQL queries. In this video, learn how you can use BigQuery ML for your machine learning projects.

Dataflow in a minute

Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing. In this video, learn how it can be used to deploy batch and streaming data processing pipelines.

Cloud Pub/Sub in a minute

Cloud Pub/Sub is an asynchronous messaging service that decouples services that produce events from services that process events. In this video, you’ll learn how you can use it for message storage, real-time message delivery, and much more, while still providing consistent performance at scale and high availability.

Dataproc in a minute

Dataproc is a managed service that lets you take advantage of open source data tools like Apache Spark, Flink and Presto for batch processing, SQL, streaming, and machine learning. In this video, you’ll learn what Dataproc is and how you can use it to simplify data and analytics processing.

Data Fusion in a minute

Cloud Data Fusion is a fully managed, cloud-native, enterprise data integration service for quickly building and managing data pipelines. In this video, you’ll learn how Cloud Data Fusion can help you build smarter data marts, data lakes, and data warehouses.

As more and more businesses turn to advanced data analytics to help them make smarter decisions, run real-time analytics, and improve business operations, an increasing number are modernizing their data warehouses to make it all possible.

For many businesses, knowing how to modernize means understanding where their data warehouse sits on the spectrum between traditional and cutting edge.

To help, we collaborated with TDWI to offer the data warehouse maturity assessment. By answering a few questions on their current warehouse environment, businesses can get a customized report with helpful tips and recommendations.

A report card for your data warehouse

The data warehouse maturity assessment evaluates five different categories, assigning your business a rating for each that spans from “traditional warehouse” to “advanced/visionary.”

1. Data diversity: How well does your warehouse support disparate data types, and does it provide users with a current and holistic view of data from a variety of internal and external sources?
2. Infrastructure agility: How well does your warehouse support the various needs of your organization by allowing access from anywhere, ingesting data in real time, and separating storage from compute to enable independent scaling?
3. Analytics support: How easy is it to adopt sophisticated analytical techniques at scale, such as machine learning, predictive analytics, deep learning, and IoT analytics?
4. Collaboration and sharing: Can teams within your organization and beyond share information and work together quickly and easily using data from your warehouse, and is your data strategy aligned with your business goals?
5. Security and governance: Are there tools and techniques in place to control data access, support data governance, and ensure compliance with data security standards?

In addition to the assessment you’ll also receive a detailed report with guidance and best practices that can help you determine your next steps in your modernization journey.

Getting started

Fill out a brief form to launch the survey, then take 15 minutes to answer a series of questions about the platforms, systems, and hardware you use to integrate, manage, and store business data for reporting and analytics. You’ll see your results right away.

As the Olympics kicked off in Tokyo at the end of July, we found ourselves reflecting on the beauty of diverse countries and cultures coming together to celebrate greatness and sportsmanship. For this month’s blog, we’d like to highlight some key data and analytics performances that should help inspire you to reach new heights in your data journey.

Let’s review the highlights!

BigQuery ML Anomaly Detection: A perfect 10 for augmented analytics

Identifying anomalous behavior at scale is a critical component of any analytics strategy. Whether you want to work with a single frame of data or a time series progression, BigQuery ML allows you to bring the power of machine learning to your data warehouse. 

In this blog released at the beginning of last month, our team walked through both non-time series and time-series approaches to anomaly detection in BigQuery ML:

• Non-time series anomaly detection
  • Autoencoder model (now in Public Preview)
  • K-means model (already in GA)
• Time-series anomaly detection
  • ARIMA_PLUS time series model (already in GA)

These approaches make it easy for your team to quickly experiment with data stored in BigQuery to identify what works best for your particular anomaly detection needs. Once a model has been identified as the right fit, you can easily port that model into the Vertex AI platform for real-time analysis or schedule it in BigQuery for continued batch processing.

App Analytics: Winning the team event

Google provides a broad ecosystem of technologies and services aimed at solving modern day challenges. Some of the best solutions come when those technologies are combined with our data analytics offerings to surface additional insights and provide new opportunities. 

Firebase has deep adoption in the app development community and provides the technology backbone for many organization’s app strategy. This month we launched a design pattern that shows Firebase customers how to use Crashlytics data, CRM, issue tracking, and support data in BigQuery and Looker to identify opportunities to improve app quality and enhance customer experiences.

Crux on BigQuery: Taking gold in the all-around data competition

Crux Informatics provides data services to many large companies to help their customers make smarter business decisions. While they were already operating on a modern stack and not on the hunt for a modern data warehouse, BigQuery became an enticing option due to performance and a more optimal pricing model. Crux also found advantages with lower-cost ingestion and processing engines like Dataflow that allow for streaming analytics.… when it came to building a centralized large-scale data cloud, we needed to invest in a solution that would not only suit our current data storage needs but also enable us to tackle what’s coming, supporting a massive ecosystem of data delivery and operations for thousands of companies.Mark Etherington
Chief Technology Office, Crux Informatics

Technology is a team sport, and Crux found our support team responsive and ready to help. This decision to more deeply adopt Google Cloud’s data analytics offerings provides Crux with the flexibility to manage a constantly evolving data ecosystem and stay competitive.

You can read more about Crux’s decision to adopt BigQuery in this blog.

Google Trends: A classic emerges a champion

Following up on the launch of our Google Trends dataset in June, we delivered some examples of how to use that data to augment your decision making. 

As a quick recap of that dataset, Google Cloud, and in particular BigQuery, provide access to the top 25 trending terms by Nielsen’s Designated Market Area® (DMA) with a weekly granularity. These trending terms are based on search patterns and have historically only been available on the Google Trends website.https://www.youtube.com/embed/9FJAXMF0ASc?enablejsapi=1&

The Google Trends design pattern addresses some common business needs, such as identifying what’s trending geographically near your stores and how to match trending terms to products to identify potential campaigns. 

Dataflow GPU: More power than ever for those streaming sprints

Dataflow is our fully-managed data processing platform that supports both batch and streaming workloads. The ability of Dataflow to scale and easily manage unbounded data has made it the streaming solution of choice for large workloads with high-speed needs in Google Cloud. 

But what if we could take that speed and provide even more processing power for advanced use cases? Our team, in partnership with NVIDIA, did just that by adding GPU support to Dataflow. This allows our customers to easily accelerate compute-intensive processing like image analysis and predictive forecasting with amazing increases in efficiency and speed. 

Take a look at the times below:

Data Fusion: A play-by-play for data integration’s winning performance

Data Fusion provides Google Cloud customers with a single place to perform all kinds of data integration activities. Whether it’s ETL, ELT, or simply integrating with a cloud application, Data Fusion provides a clean UI and streamlined experience with deep integrations to other Google Cloud data systems. Check out our team’s review of this tool and the capabilities it can bring to your organization.

Anyone who has ever coached or played on a sports team, or had a child involved in sports, knows how difficult scheduling and logistics can be. From game and practice schedules, to uniforms and who’s bringing the snacks, it can be a lot for coaches, administrators, parents, and players to manage.

It’s no wonder that TeamSnap, a sports team, club, and tournament management app, has exploded in popularity worldwide. By syncing events to everyone’s personal calendars and providing messaging and payment tracking, TeamSnap makes communication and organization easy.

TeamSnap markets its app to coaches, players, and clubs via targeted YouTube ads. It also uses Google AdWords and DoubleClick to advertise on search results and run programmatic campaigns. These methods have been highly effective, helping TeamSnap grow to millions of users worldwide and become one of the most popular apps in the iOS app store.

As its business and data grew, TeamSnap was challenged to track ROI and measure the customer journey across channels and devices over time. The company’s marketing budget grew quickly, making it even more important to spend wisely. With data in Google Analytics 360, DoubleClick Campaign Manager, Google AdWords, and Salesforce, TeamSnap needed a way to link and correlate those data sources in a scalable, timely, and cost-effective way to understand the true impact of its digital marketing across websites and mobile apps.

To avoid the painstaking manual process of pulling data from multiple sources, TeamSnap began using Google Analytics 360, which integrates with Google BigQuery, to provide a fully managed big data analysis service. TeamSnap analyzes the data using Tableau, which connects directly to Google BigQuery for fast analytics and helps the company share and collaborate on that information with self-service ease.

The combination allows TeamSnap to easily track the activity of millions of users with self-service ease, without worrying about the scalability or availability of the big data platform.

“Before Google Analytics 360, Google BigQuery, and Tableau, tracking our return on ad spend was difficult because we had so much data,” says Ken McDonald, Chief Growth Officer at TeamSnap. “We didn’t always have insights to make the best choices. We don’t have that problem anymore because we’ve moved to real-time reporting. We find additional revenue growth opportunities almost daily.”

Making Ad Dollars Work Harder

TeamSnap now automatically imports unsampled Google Analytics 360 logs into the Google BigQuery data warehouse. To import data from other sources such as Google AdWords, DoubleClick, and YouTube, TeamSnap uses Google BigQuery Data Transfer Service. With all relevant data consolidated in Google BigQuery, TeamSnap can use Tableau to perform advanced analytics on its digital marketing, executing ad-hoc analyses in seconds, while eliminating data sampling issues, to improve accuracy. These analyses can also be reused and shared with internal and external stakeholders via Tableau Online, promoting governed reuse and consistency.

“Using Google Analytics 360 and Google BigQuery with Tableau to track our return on ad spend is ideal,” says Ken. “It’s easy to use SQL to query the data or explore it with drag-and-drop ease.”

With Google BigQuery, Ken and his team can bring all the data from the TeamSnap billing systems, internal CRM, and other Google services into one straightforward dataset that everyone uses. With Tableau, users are able to perform self-service analytics on this data and provision analyses via shared dashboards that communicate the same consistent truth across the company. These dashboards provide a single view of the business to discover new patterns and questions worth analyzing.

All of this results in enormous time savings because no one is re-inventing the wheel. “Using these tools, we immediately reallocated $300,000 of ad spend that was performing poorly, generating 200% ROI in the first two days,” says Ken.

Ken now spends his time analyzing data instead of trying to pull it all together, identifying pockets of inefficient spend in real time and reallocating those marketing dollars toward better performing campaigns.

“Before, we could only focus on the largest campaign-level datasets because it was so time consuming to pull the data,” he says. “With Google BigQuery and Tableau, we can examine our advertising ROI much more granularly and reallocate more than $10 million in ad spend annually to grow the company faster and more efficiently.”

More Effective A/B Testing

To make sure it is delivering the best customer experiences, TeamSnap uses Google Optimize to run A/B tests on its website. It uses Google BigQuery and Tableau to verify and supplement these findings by measuring longer-term customer behavior across devices, spanning both web and mobile apps.

By pulling in data from Google Optimize, Google Analytics 360, Salesforce, and in-house billing and CRM systems, and understanding it with Tableau, TeamSnap has increased the accuracy and effectiveness of its A/B testing, gaining a more complete picture of customer onboarding and activity. In some cases, it found that short-term indicators it previously trusted were actually poor predictors of long-term behavior.

“Integration between Google Analytics 360 and Google BigQuery is seamless, giving us much more confidence in our A/B testing,” says Ken. “We’re constantly finding new and interesting ways to use our digital marketing data. Often, making a simple change can increase revenue by hundreds of thousands of dollars a year.”

Improving Product Quality

TeamSnap also uses Google BigQuery and Tableau to improve its own product, tracking customer activity at such a granular level that usability and functionality issues can be exposed and addressed faster. It’s also increasing customer engagement by verifying that potential customers are coming in through the right onboarding path—for example, a coach versus a player, or a consumer versus a club or other sports business. Using A/B testing to make sure customers are routed to the appropriate flow, TeamSnap drove $4 million in additional customer value each year.

“We initially chose Google BigQuery and Tableau to help with marketing, but we realized quickly that they could help us on the product side as well,” says Ken. “Most of the testing we do is about making things better and easier for our customers, and we’re accelerating that process with Google BigQuery and Tableau.”