AirAsia Leverages Google Cloud to Enhance Pricing, Revenue, and Customer Experience
1113
Of your peers have already read this article.
5:30 Minutes
The most insightful time you'll spend today!
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
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.”
4311
Of your peers have already watched this video.
20:00 Minutes
The most insightful time you'll spend today!
The New and Upcoming Infrastructure for Google Cloud’s AI and ML Solutions
How does Google manage to provide its customers a differentiated compute platform experience and define ways to fully leverage its infrastructure supporting its cutting-edge AI and ML offerings? Easy-to-use, scalable and ability to create innovative products and services to end-users at low cost of ownership is the narrative behind Google Cloud’s AI and ML solutions. Explore Google Cloud’s ML infrastructure and accelerator innovation for 2021.
Watch the video to find out how Google Cloud’s leadership in AI through Google research, Deep Mind and also practical application of AI within Google Products drive innovative platforms and offerings that cater to customers’ AI and ML use cases!
How BigQuery’s Unique Features Support Your Data at Petabyte-scale
4778
Of your peers have already read this article.
3:00 Minutes
The most insightful time you'll spend today!
Organizations rely on data warehouses to aggregate data from disparate sources, process it, and make it available for data analysis in support of strategic decision-making. BigQuery is the Google Cloud enterprise data warehouse designed to help organizations to run large scale analytics with ease and quickly unlock actionable insights. You can ingest data into BigQuery either through batch uploading or by streaming data directly to unlock real-time insights. As a fully-managed data warehouse, BigQuery takes care of the infrastructure so you can focus on analyzing your data up to petabyte-scale. BigQuery supports SQL (Structured Query Language), which you’re likely already familiar with if you’ve worked with ANSI-compliant relational databases.
BigQuery unique features
BI Engine – BigQuery BI Engine is a fast, in-memory analysis service that provides subsecond query response times with high concurrency. BI Engine integrates with Google Data Studio and Looker for visualizing query results and enables integration with other popular business intelligence (BI) tools.
BigQuery ML: BigQuery ML is unlocking machine learning for millions of data analysts. It enables data analysts and data scientists to build and operationalize machine learning models directly within BigQuery, using simple SQL.
BigQuery Omni – BigQuery Omni is a flexible, multi-cloud analytics solution powered by Anthos that lets you cost-effectively access and securely analyze data across Google Cloud, Amazon Web Services (AWS), and Azure, without leaving the BigQuery user interface (UI). Using standard SQL and familiar BigQuery APIs, you can break down data silos and gain critical business insights from a single pane of glass.
Data QnA: Data QnA enables self-service analytics for business users on BigQuery data as well as federated data from Cloud Storage, Bigtable, Cloud SQL, or Google Drive. It uses Dialogflow and enables users to formulate free-form text analytical questions, with auto-suggested entities while users type a question.
Connected Sheets -The native integration between Sheets and BigQuery makes it possible for all business stakeholders, who are already quite familiar with spreadsheet tools, to get their own up-to-date insights at any time.
Geospatial data – BigQuery offers accurate and scalable geospatial analysis with geography data types. It supports core GIS functions – measurements, transforms, constructors, and more – using standard SQL.
How does it work?
Here’s how it works: You ingest your own data into BigQuery or use data from the public datasets. Storage and compute are decoupled and can scale independently on demand. This offers immense flexibility and cost control for your business as you don’t need to keep expensive compute resources up and running all the time. As a result, BigQuery is much more cost-effective than traditional node-based cloud data warehouse solutions or on-premises systems. BigQuery also provides automatic backup and restore of your data.
You can ingest data into BigQuery in batches or stream real-time data from web, IoT, or mobile devices via Pub/Sub. You can also use Data Transfer Service to ingest data from other clouds, on-premises systems or third-party services. BigQuery also supports ODBC and JDBC drivers to connect with existing tools and infrastructure.
Interacting with BigQuery to load data, run queries, or create ML models can be done in three different ways. You can use the UI in the Cloud Console, the BigQuery command-line tool, or the API via client libraries available in several languages.
When it comes time to visualize your data, BigQuery integrates with Looker as well as several other business intelligence tools across the Google partner ecosystem.
What about security?
BigQuery offers built-in data protection at scale. It provides security and governance tools to efficiently govern data and democratize insights within your organization.
- Within BigQuery, users can assign dataset-level and project-level permissions to help govern data access. Secure data sharing ensures you can collaborate and operate your business with trust.
- Data is automatically encrypted both while in transit and at rest, ensuring that your data is protected from intrusions, theft, and attacks.
- Cloud DLP helps you discover and classify sensitive data assets.
- Cloud IAM provides access control and visibility into security policies.
- Data Catalog helps you discover and manage data.
How much does it cost?
The BigQuery sandbox lets you explore BigQuery capabilities at no cost and confirm that BigQuery fits your needs. With BigQuery you get predictable price-performance: you pay for storing and querying data, and for streaming inserts. Loading and exporting data are free of charge. Storage costs are based on the amount of data stored, and have two rates based on how often the data is changing. Query costs can be either:
- On-demand – you are charged per query by the amount of data processed
- Flat-rate – if you prefer to purchase dedicated resources
You can start with the pay-as-you-go, on-demand option and later move to flat-rate if that better suits your usage. Or, start with flat-rate, get a better understanding of your usage and move to the pay-as-you-go models for additional workloads.
To explore BigQuery and its capabilities a bit more, check out the sandbox; and when you’re ready to modernize your data warehouse with BigQuery then check out the documentation to streamline your migration process here. https://www.youtube.com/embed/So-tVyBQt8E?enablejsapi=1&
For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.
3129
Of your peers have already watched this video.
38:30 Minutes
The most insightful time you'll spend today!
Building Data Lakes on Google Cloud
A data lake is a centralized and a secure data platform for ingesting, storing, processing, and analyzing all your data–structured semi-structured and unstructured data–at scale.
“When we work with our customers we find out that the traditional approach for building data lakes, where you bring data from different sources and land them into a single central store has not worked. The traditional approach ended up creating swamps instead of lakes,” says Nitin Motgi, Product Manager, Google Cloud.
In this video, Motgi will talk about how to build data lakes correctly.
Google Cloud provides all the capabilities enterprises need to create and manage data lakes. Enterprises can use Google Cloud to aggregate their data and efficiently analyze it using cloud-native or open source tools irrespective of where the data is managed.
Also, learn how Google is making investments to simplify data lake deployment and management.
3134
Of your peers have already watched this video.
24:30 Minutes
The most insightful time you'll spend today!
How to Build a Global Marketing Data Hub
Data-driven marketing ensures that the right message is reaching the right customer at the right time through the right channel. However if an organization’s data resides in multiple systems and platforms it hampers its ability to deliver truly global data-driven campaigns, attribution analysis, and reporting.
See how Cloud Data Fusion with its growing plugins ecosystem can be leveraged to integrate multiple disparate data sources to build a comprehensive Marketing Data Warehouse. Learn how Cloud Data Fusion helped global CPG organizations build their Marketing DW.
Prateek Duble, Data Analytics Specialist, Google Cloud and Bhooshan Mogal, Product Manager, Cloud Data Fusion, Google Cloud, cover:
- How enterprises can build a robust global marketing data warehouse with Google Cloud including the business needs and technical challenge they encounter while building the holistic marketing data warehouses.
- How Cloud Data Fusion, Google Cloud Platform’s data ingestion and integration product, delivers critical capabilities to accelerate an enterprises’ journey to building marketing data warehouse.
- A reference architecture with Google Cloud products that shows how easily and quickly you can build marketing data warehouse in Google Cloud.
- A recent case study of a CPG business who built their marketing data warehouse with Google Cloud Platform.
This presentation also includes recent product launch announcements.
Google Cloud Tools Help U.S. Forest Department Generate Years of Insights into Earth’s Natural Resources
5003
Of your peers have already read this article.
3:00 Minutes
The most insightful time you'll spend today!
For 117 years, the U.S. Department of Agriculture’s Forest Service has been a steward of America’s forests, grasslands, and waterways. It directly manages 193 million acres and supports sustainable management on a total of 500 million acres of private, state, and tribal lands. Its impact reaches far beyond even that, offering its research and learning freely to the world.
At Google, we’re big admirers of the Forest Service’s mission. So we were thrilled to learn in 2011 that its scientists were using Google Earth Engine, our planetary-scale platform for Earth Science data and analysis, to aid its research, understanding, and effectiveness. In the years since, Google has worked with the Forest Service to meet its unique requirements for visual information about the planet. Using both historical and current data, the Forest Service built new products, workflows, and tools that help more effectively and sustainably manage our natural resources. The Forest Service also uses Earth Engine and Google Cloud to study the effects of climate change, forest fires, insects and disease, helping them create new insights and strategies.
Besides gaining newfound depths of insight, the Forest Service has also sped up its research dramatically, enabling everyone to do more. Using Google Cloud and Earth Engine, the Forest Service reduced the time it took to analyze 10 years worth of land-cover changes from three months to just one hour, using just 100 lines of code. The agency built new models for coping with change, then mapped these changes over time, in its Landscape Change Monitoring System (LCMS) project.
Emergency responders can now work better on new threats that arise after wildfires, hurricanes, and other natural disasters. Forest health specialists can detect and monitor the impacts of invasive insects, diseases, and drought. More Forest Service personnel can use new tools and products within Earth Engine, thanks to numerous training and outreach sessions within the Forest Service.
Researchers elsewhere also benefited when the Forest Service created new toolkits, and posted them to GitHub for public use. For example, there’s geeViz, a repository of Google Earth Engine Python code modules useful for general data processing, analysis, and visualization.
This is only the start. Recently, the Forest Service started using Google Cloud’s processing and analysis tools for projects like California’s Wildfire and Forest Resilience Action Plan. Forest Service researchers also use Google Cloud to better understand ecological conditions across landscapes in projects like Fuelcast, which provides actionable intelligence for rangeland managers, fire specialists, and growers, and the Scenario Investment Planning Platform for modeling local and national land management scenarios.
The Forest Service is a pioneer in building technology to help us better understand and care for our planet. With more frequent imaging, rich satellite data sets, and sophisticated database and computation systems, we can view and model the Earth as a large-scale dynamic system.
We are honored and excited to respond to the unique set of requirements of the scientists, engineers, rangers, and firefighters of the USFS, and look forward to years of learning about — and better caring for — our most precious resources.
*Image 1: The USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) uses science-based remote sensing methods to characterize vegetation and soil condition after wildland fire events. The results are used to facilitate emergency assessments to support hazard mitigation, to inform post-fire restoration planning, and to support the monitoring of national fire policy effectiveness. GTAC currently conducts these mapping efforts using long-established geospatial workflows. However, GTAC has adapted its post-fire mapping and assessment workflows to work within Google Earth Engine (GEE) to accommodate the needs of other users in the USFS. The spatially and temporally comprehensive coverage of moderate resolution multispectral data sources (e.g., Landsat, Sentinel 2) and analytical power provided by GEE allows users to create geospatial burn severity products quickly and easily. Box 1 shows a pre-fire Sentinel-2 false color composite image. Box 2 shows a post-fire Sentinel-2 false color composite image with the fire scar apparent in reddish brown. Box 3 shows a differenced Normalized Burn Ratio (dNBR) image showing the change between the pre- and post-fire images in Boxes 1 and 2. Box 4 shows a thresholded dNBR image of the burned area with four classes of burn severity (unburned to high severity), which is the final output delivered to forest managers.
*Image 2: Leveraging Google Earth Engine (GEE), the USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) and USFS Region 8, developed the Tree Structure Damage Impact Predictive (TreeS-DIP) modeling approach to predict wind damage to trees resulting from large hurricane events and produce spatial products across the landscape. TreeS-DIP results become available within 48 hours following landfall of a large storm event to allow allocation of ground resources to the field for strategic planning and management. Boxes 1 and 3 above show TreeS-DIP modeled outputs with varying data inputs and parameters. Box 2 shows changes in greenness (Normalized Burn Ratio; NBR) that was measured with GEE during the recovery from Hurricane Ida and is shown as a visual comparison to the rapidly available products from TreeS-DIP.
*Image 3: Severe drought conditions across the American West prompted concern about the health and status of pinyon-juniper woodlands, a vast and unique ecosystem. In a cooperative project between the USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) and Forest Health Protection (FHP), Google Earth Engine (GEE) was used to map pinyon pine and juniper mortality across 10 Western US States. The outputs are now being used to plan for future work including on-the-ground efforts, high-resolution imagery acquisitions, aerial surveys, in-depth mortality modeling, and planning for 2022 field season work.
Box 1 contains remote sensing change detection outputs (in white) generated with GEE, showing pinyon-juniper decline across the Southwestern US. Box 2 shows NAIP imagery from 2017 with, with box 3 showing NAIP imagery from 2021. NAIP imagery from these years shows trees changing from healthy and green in 2017 to brown and dying in 2021. In addition, box 2 and box 3 show change detection outputs from Box 1 for a location outside of Flagstaff, AZ converted to polygons (in white). The polygon in box 2 is displayed as a dashed line to serve as a reference, while the solid line in box 3 shows the measured change in 2021. Converting rasters to polygons allows the data to be easily used on tablet computers, as well as the ability to add information and photographs from field visits.
More Relevant Stories for Your Company
Can Users Just Ask Questions of Data in BigQuery and Get Answers?
At the root of every data insight lies a question. For a non-technical user, getting answers to an ad-hoc question, not found in existing dashboards or reports, has been the burden of BI teams for ages. Data QnA, a new service that empowers business users to simply ask questions of
Why and How to Migrate to Google BigQuery
Over the past few decades, organizations have mastered the science of data warehousing. They have increasingly applied descriptive analytics to large quantities of stored data, gaining insight into their core business operations. Conventional Business Intelligence (BI), which focuses on querying, reporting, and Online Analytical Processing, might have been a differentiating factor
What’s New in Retail: Bits from Google Cloud’s Retail & Consumer Goods Summit
Today we’re hosting our Retail & Consumer Goods Summit, a digital event dedicated to helping leading retailers and brands digitally transform their business. For me, this is a personally exciting moment, as I see tremendous opportunities for those companies that choose to focus on their customers and leverage technology to elevate
Home Depot’s Interconnected Retail Experience by Virtue of Google Cloud Migration for SAP Applications
With nearly 2,300 stores, The Home Depot is the world’s largest home-improvement chain — a brand that professional contractors and DIYers alike have come to depend on. The home improvement industry continues to experience unprecedented demand and dramatic increases in online ordering accompanied by expanding consumer expectations for things like
