As data environments become more complex, companies are turning to streaming analytics solutions that analyze data as it’s ingested and deliver immediate, high-value insights into what is happening now. These insights enable decision makers to act in real time to take advantage of opportunities or respond to issues as they occur.

While understanding what is happening now has great business value, forward-thinking companies are taking things a step further, using real-time analytics integrated with artificial intelligence (AI) and business intelligence (BI) to answer the question, “what might happen in the future?” Arkansas-based Tyson Foods has embraced AI/BI analytics to enable predictive insights that unlock new opportunities and drive future growth.

Creating a digital twin for connected intelligence company wide

Before using AI/BI, Tyson’s analytics capabilities consisted of traditional BI solutions focused on KPIs and simplifying data so that humans could understand it. Tyson wanted to leverage its data to uncover ways to improve current processes and grow its business. But with BI alone, Tyson struggled to use data to run the simulations and scenarios essential to make educated decisions. To keep growing, it had to embrace the complexity of its data, building ways to analyze it and use it to inform decision making. 

Tyson’s on-premises analytics solutions limited its ability to be aggressive and make intelligent, timely, prescriptive decisions. The solution was to create a digital twin to scale optimizations within business processes, moving from local optimizations to system-wide connected optimizations. Doing so meant shifting entirely to cloud computing, with an initial focus on building the ingestion component of the digital twin platform.

Investing in a digital twin enabled Tyson to accelerate new capabilities like supply chain simulation “what-if” scenarios, prescriptive price elasticity recommendations, and improvement of customer intimacy. 

Solving the ingestion problem for faster time to insights

Before its migration to Google Cloud, analytics projects that Tyson suffered from uncertainty over how to obtain the data. This problem was prolific and caused project times to be extended for weeks or even months due to the need to write and support one-off data ingestion processes at the front end. This problem also prevented the IT team from delivering analytics solutions fast enough for the business to take full advantage of them. 

To solve this analytics problem, the team created Data Ingestion Compute Engine (DICE). DICE is a Google Cloud-hosted, open-source, cloud-native ingestion platform developed to provide configuration-based, no-ops, code-free ingestion from disparate enterprise data systems, both internal and external. It is centered on three high-level goals:

1. Accelerate the speed of delivery of IT analytics solutions
2. Enable growth of IT capabilities to produce meaningful insight
3. Reduce long-term total cost of ownership for ingestion solutions

Creating DICE ingestion platform with Google Cloud services

Teams use DICE to set up secure data ingestion jobs in minutes without having to manage complex connections or write, deploy, and support their own code. DICE enables unbound scale, highly parallel processing, DevSecOps, open source, and the implementation of Lambda Data Architecture.

A DICE job is the logical unit of work in the DICE platform, consisting of immutable and mutable configurations persisted as JSON documents stored in Firestore. The job exists as an instruction set for the DICE data engine, which is Apache Beam running Dataflow to instruct which data to pull, how to pull it, how often to pull it, how to process it, when it changes, and where to direct it.

Two of DICE’s primary layers include the metadata engine and the data engine. The metadata engine is responsible for the creation and management of DICE job configuration and orchestration. It is made up of many microservices that interact with multiple Google Cloud services, including the job configuration creation API, job build configuration helper API, and job execution scheduler API.

The data engine is responsible for the physical ingestion of data, the change detection processing of that data, and the delivery of that data to specified targets. The data engine is Java code that uses the Apache Beam unified programming model and runs in Dataflow. It is comprised of streaming, jobs, and Dataflow flex template batch jobs. Logically, the data engine is segmented across three layers: the inbound processing layer, the DICE file system layer, and the target processing layer, which takes the data from the DICE file system and moves it to targets.

Rolling DICE for thousands of ingestion jobs each day

DICE was first deployed to a production environment in November 2019, and just two years later, it has more than 3,000 data ingestion jobs from more than a hundred disparate data systems, both internal and external to Tyson Foods. Most of these jobs run multiple times a day. On a daily basis the DICE environment sees more than 25,000 Dataflow jobs running and an average of 3.25 terabytes of new data being ingested.

DICE supports ingestion from many different types of technologies, including BigQuery, SQL Server, SAP HANA, Postgres, Oracle, MySQL, Db2, various types of file systems, and FTP servers. Additionally, DICE supports target platform technologies for ingestion jobs that include multiple JDBC targets, multiple file system targets, and BigQuery and queue-based store and forward technologies. 

The platform continues to see linear growth of DICE jobs, all while keeping platform costs relatively flat. With increasing demand for the platform, Tyson’s IT team is constantly enhancing DICE to support new sources and targets.

This intelligent platform keeps adding new value and makes it simple for Tyson to take advantage of its data. This innovation is a necessity in this fast-changing world of digital business in which companies must transform a high volume of complex data into actionable insight.

In 2006, online travel agency redBus introduced internet bus ticketing in India, unifying tens of thousands of bus schedules into a single booking operation. (Think of it as Expedia for bus booking.) Using BigQuery, redBus crunches terabytes of booking and inventory data in mere seconds and at a fraction of the cost of other big data services. BigQuery also helps engineers fix glitches quickly, minimize lost sales, and improve customer service.

Challenge

Executives at the Bangalore-based redBus needed a powerful tool to analyze booking and inventory data across their system of hundreds of bus operators serving more than 10,000 routes. They considered using clusters of Hadoop servers to process the data but decided the system would take too much time to set up and would require a specialized staff to maintain it. It also would not provide the lightning-fast analysis they needed.

“It would have taken at least a couple of hours to analyze anything,” says Pradeep Kumar, a technical architect at redBus. “Crunching very large data sets would have been a day’s job. We needed something more powerful to get the real-time analysis we were looking for.”

Solution

Kumar and his colleagues learned about BigQuery and realized it was the right match for their data processing needs. The web-based service, which enables companies to analyze massive datasets using Google’s data processing infrastructure, is easy to set up and manage since its simple, SQL-like query language doesn’t require complex technology or specialized personnel. It also has low overhead costs.

The redBus team uses BigQuery as part of an intricate data collection and analysis process. Applications hosted on a range of servers continually pump information related to customer searches, seat inventory, and bookings into a centralized data collection system. Engineers upload the data to BigQuery, which provides answers to complex queries within seconds. For example, BigQuery helps redBus staff:

• Learn how many times customers searched for seats and found none or very few available, indicating more seats should be added to a route
• Investigate decreases in bookings and notify engineers if a technical problem is the cause
• Identify server problems by quickly analyzing data related to server activity

Results

BigQuery provides near real-time data analysis capabilities at 20% of the cost of maintaining a complex Hadoop infrastructure. Queries that would have required a day to analyze on a Hadoop framework take less than 30 seconds using Google’s web-based service.

“We explored several data analytics solutions. Nothing comes remotely close to the sheer power of Google BigQuery,” Kumar says. “It made large-scale data collection and crunching possible with little effort, which has translated to a significant business advantage.”

Google Cloud Platform results

• Analyzes data sets as large as 2 terabytes in less than 30 seconds using a simple, SQL-like language
• Saves time analyzing technical problems and customer booking trends
• Spends 80% less than they would have on a Hadoop infrastructure and avoids setting up and maintaining a complex infrastructure in-house
• Strengthens the company by improving customer service and engineering quality

The fast insights gained through BigQuery are also making redBus a stronger company. By minimizing the time it takes staff members to solve technical problems, BigQuery has helped improve customer service and reduce lost sales.

“Getting to the root of problems used to be really time-consuming,” Kumar says. “By the time we figured it out, customers might have given up. Now if there are booking problems, BigQuery helps us understand the reason right away. Choosing Google BigQuery was the right decision for our company.”

May is Asian American Pacific Islander Heritage Month — a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights founder Georgene Huang and how her startup Fairygodboss uses easy to use Google Cloud tools to grow and innovate. 

Coming from a male-dominated industry like Wall Street, I never thought much about my gender. I was so focused on work that I didn’t feel like I could focus on my identity as a woman of color. That all changed when I found myself thrust into the job market while two months pregnant.

Traditionally, job-seeking sites are built by – and geared towards – men and their professional needs. There was no recruiting site that broke down what candidates who identify as women might expect from a company, let alone women of color like myself. I founded Fairygodboss to create a space where women can crowdsource information about opportunities and employers in order to make informed career decisions. 

Fairygodboss 

Fair•y•god•boss (noun): A person who elevates women at work.

Fairygodboss is the largest career community for women. In researching potential employers, I had many questions surrounding company culture, maternity leave, and women in leadership positions. While sites like Glassdoor provide high-level details for job seekers, I wanted more concrete examples and data than general salary information and anecdotes about work-life balance. 

Fairygodboss began as a job review site geared towards women, but has expanded significantly since our initial launch. In addition to providing free resources like career connections, job listings, virtual recruiting events, community advice, and real research on how companies treat women, we also proactively help companies improve gender diversity. Fairygodboss partners with hundreds of top employers to create a 360-degree recruiting and employer branding program designed to drive female applicants to job postings by showcasing a company’s track record of women in leadership within a company, and their diversity investments. 

Google Cloud + Fairygodboss: Data defined 

At our core, Fairygodboss is a data-driven company, and we rely on Google Cloud to help us process, store, and analyze user-driven events occurring on the site. As a highly scalable, serverless, and cost-effective data warehousing solution, BigQuery collect millions of events each day, helping us identify where traffic is coming from (and where it goes next), and it produces actionable insights to provide better recommendations, information, and opportunities for women visiting our platform. 

After seamlessly connecting our BigQuery data to Data Studio, we have been able to visualize and make sense of all the data we receive on our platform. Unlike some other data visualization tools, Data Studio allows us to analyze data in real time. With tools like BigQuery, we are now able to identify trends and gain a deep understanding for how our users interact with our site. 

Google for Startups Accelerator: Women Founders 

In order to provide more accurate job recommendations for our users, the Fairygodboss team wanted to build a machine learning prototype to classify our users and jobs into categories to facilitate more effective matching. So we applied for the Google for Startups Accelerator: Women Founders, a three-month digital accelerator program for high-potential Seed to Series A tech startups based in the U.S. and Canada. Along with tailored mentorship and product support from Googlers and industry experts, the Google for Startups Accelerator provided $100K in Google Cloud credits to scale our businesses. The mentorship we received from the Google Cloud technical experts as part of the Accelerator – special shout out to Peter Novig! – empowered us to integrate Cloud AutoML into our systems and ultimately curate more accurate job suggestions for our users.  

Not only did the Google for Startups Accelerator program help to achieve our business goals for the year, it was also extremely beneficial to be connected with a cohort of other women founders of color. While we are all building different kinds of businesses across different industries, the guidance around fundraising, scaling teams, and coping with the struggles of being a founder rang true for all of us. 

Advancing the community

It can be daunting to launch your own business – and even more so as an AAPI woman. Throughout my career, I am certain I have experienced unconscious bias around my race. Specifically, there have likely been “model minority” stereotypes about my demeanor and math abilities, assumptions that I may be mild-mannered or agreeable. While frustrating, these unfair tropes actually inspired me to see myself beyond others’ perceptions. Why be either analytical or creative, meek or brash, inspiring or agreeable? It’s the ors of life that prevent us from seeing ourselves as truly multi-dimensional individuals. I aspired to be more instead of or, and want my company to be as well. 

As an inclusion-focused business, it is extremely important that Fairygodboss mindfully engage with all underrepresented groups. Diversity can be sliced in many different ways, and these intersections lead to great opportunities for change. We have a team of volunteer ‘culture wizards’ at work that provide educational resources and videos for those wanting to learn more about different cultures and identities from members of under-represented groups. We also practice what we preach at Fairygodboss by prioritizing diverse slates and diversity in our own hiring process. We aim for the talent we hire to be representative of the diverse communities we support. It is imperative to take an intentional approach to hiring diverse talent, as it falls on all employers to promote equity and inclusion—there are always ways in which we can all improve. Fairygodboss looks forward to evolving with Google Cloud in 2021 and beyond as we work together to foster inclusive teams around the world. 

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.

Over the last year, COVID-19 presented unforeseen challenges for practically every type of business and organization—including schools, colleges, and universities. For educational institutions, the pandemic was an unapologetic agent of acceleration, shifting one billion learners from in-person to online learning within two months. 

The rapid transition to online learning exposed many schools’ lack of readiness for the new online learning environment. It also widened the learning equity gap for students, with fewer than 40% of students from low-income families having access to the tools required for remote learning.

For those who do have online access, today’s students expect everything from engaging and collaborative digital learning experiences to skills-based training for their roles in the future workforce. Expectations are also high for 24×7 multi-channel tech support across all learning devices, applications, and platforms.  

In these remarkable times, education technology companies have an important role to play in supporting academic institutions and students. Indeed, this is already happening, as the EdTech (Educational Technology) market is nearly tripling, with total global expenditures expected to reach $404 billion by 2025. However, the success of these EdTech companies depends on their performance in a number of areas, including:

• Content and products: How quickly can they generate new content and react to learner needs with new products to additional markets for broader adoption?
• Personalization: How effectively can they leverage artificial intelligence (AI) to provide a personalized experience to all types of learners?
• Trust and security: How trusted and secure are their services when educational organizations are suffering the highest number of data breaches since 2005?

Here are a few examples of how EdTech companies are successfully using AI and analytics to capture this opportunity and transform their businesses:

• Build better products: iSchoolConnect is an online platform that lets students explore schools, courses, and countries where they might study, and makes higher education admissions accessible to students around the globe. The company leverages AI services to help educational institutions optimize their academic operations by accelerating admission processing by greater than 90%, while saving significant costs.
• Launch in new markets faster: Classroom creativity tools provider Book Creator uses AI APIs to enhance accessibility and improve the learner experience. “The broad suite of intelligent APIs enables us to deliver richer experiences, faster and more easily, without having to be experts in machine learning, drawing recognition, map embeds, or other areas,” says VP of engineering Thom Leggett.
• Scale businesses securely: Using DevOps and CDN [content delivery network] services, Chrome browser recording extension creator Screencastify was able to support eight times growth in users overnight amid the COVID-19 pandemic, while maintaining consistent total cost of ownership. These technologies helped the company rapidly scale operations in response to the overnight increase in demand from consumers and assure student data privacy and security on a budget. “We know this is just the beginning, as more educators rely on technology to deliver richer, more interactive curricula to students,” says CEO James Francis. 
• Provide personalized learning and support: Smart analytics and AI can provide personalized support and recommendations for students, forecast demand, and predict shifts in learners’ preferences. Online learning platform Mindvalley uses cloud-based tools to understand and make decisions based on user activity and leverage machine learning to predict behavior. 

Google Cloud is partnering with many of these leading EdTech companies, as well as industry-leading consortiums like Ed-Fi and Unizin, to standardize educational common data models and best practices for more agile and cost-effective integration of EdTech into existing environments.

The education landscape is changing rapidly, and EdTech has a major role to play as institutions adapt to the massive shift in learners’ preferences and expectations. We’re committed to empowering EdTech companies with the tools and services they need to help expand learning for everyone, anywhere. 

Watch our Spotlight session with EdTechX to learn more.