With a data strategy and data warehouse in place, EdTechs are building a data culture that helps everyone – from educators and administrators, to employees in marketing and accounting – make more informed decisions with their data.

So how do you build a data culture in your organization? It starts by asking these questions:

• Are users able to find answers to their data questions from available data?
• Can they act based on available metrics?
• Can they tell a compelling story with the data at hand?

How well is your company using analytics? The matrix  from Looker’s Analytical Maturity eBook, shows ranges from “vanity” (nice to see) metrics to “optimization.” 

Adopting and rolling out your organization’s new data platform

As outlined in the previous blog in this series, From data chaos to data-driven: How dedicated data teams can help EdTechs influence the future of education, a comprehensive data team is essential for building a data warehouse. That data team should include a core education intelligence team. This team can align with analytically savvy members of each department they support. It’s critical that the Education Intelligence team understand each department’s reporting needs and can make adjustments based on user feedback.

Include these key components to ensure smooth adoption for internal teams:

• Share a roadmap: An effective rollout team always knows what’s next on their roadmap and communicates that to the entire organization. Launching analytics across an entire company at once usually leads to slow movement, miscommunication, and lack of adoption. A roadmap will ease this transition.
• Train everyone: This approach makes sure everyone is consistent during the data rollout. Show users how to make the most of the platform your data team has created, and be sure they know where they can go for help.
• Monitor and optimize: Monitor your organization’s analytics usage. Understand which individuals are using your education intelligence system to drive their day-to-day operations. These individuals can provide further insight into what is working. Identifying those who may need more guidance and support ensures they don’t miss out on benefits.

Guild Education: Creating a data culture

Guild Education started working with Google Cloud to build a data culture and implemented Looker as their data platform. 

The company transformed their student success program—in which coaches work directly with connected employees throughout their educational journey—with Looker. “As enrollments increased, even spreadsheets were not able to accurately keep track of caseloads,” Sean McKeever, Senior Business Intelligence Analyst, recalls. “We needed a singular source of truth that could be updated in almost real-time.” In response, they created “Student Rosters” to manage a coach’s student outreach.

Users were thrilled with the new tools, and when the coaching team grew from 20 to 90, Sean created ambassador groups consisting of data specialists to support each department. This sparked Guild Education’s “data-driven evolution.”

“Unexpectedly, the Student Success task force also became an engine for new BI work,” Sean says. “They blew my highest expectations out of the water and started owning virtually the entire process: building requirements, prototyping, testing, and deploying new dashboards and panels—with hardly any help from the BI team.”

These successes have set Guild Education on its way to becoming a fully data-driven company. Unexpectedly, the Student Success task force also became an engine for new BI work…they blew my highest expectations out of the water and started owning virtually the entire process…with hardly any help from the BI team.Sean McKeever
Senior Business Intelligence Analyst, Guild Education

Start your journey from data chaos to data-driven

We hope this three-part series has shown how data analytics has an important role to play in transforming the future of education. EdTech companies are unlocking the power of big data to serve their customers. “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,” says Jesus Trujillo Gomez, Strategic Business Executive, Education & Research, Google Cloud. 

Feeling inspired? Let’s meet your EdTech challenges together and transform your data culture. Visit Google Cloud for education technology.

Forrester Research has named Google Cloud a Leader in The Forrester Wave™: AI Infrastructure, Q4 2021 report authored by Mike Gualtieri and Tracy Woo. In the report, Forrester evaluated dimensions of AI architecture, training, inference and management against a set of pre-defined criteria. Forrester’s analysis and recognition gives customers the confidence they need to make important platform choices that will have lasting business impact. 

Google received the highest possible score in 16 Forrester Wave evaluation criteria: architecture design, architecture components, training software, training data, training throughput, training latency, inferencing throughput, inferencing latency, management operations, management external, deployment efficiency, execution roadmap, innovation roadmap, partner ecosystem, commercial model, and number of customers.

We believe that Google’s vision to be a unified data and AI solution provider for the end-to-end data science experience is recognized by Forrester, through high scores in the areas of architecture and innovation. We are focused on building the most robust yet cohesive experience to enable our customers to leverage the best of Google every step of the way. Here are four key areas where Google excels, among the many highlighted in this report. 

AI Infrastructure: Leverage the building blocks of innovation

When an organization chooses to run its business on Google Cloud, it benefits from innovative infrastructure available globally. Google offers users a rich set of building blocks such as Deep Learning VMs and containers, the latest GPUs/TPUs and a marketplace of curated ISV offerings to help architect your own custom software stack on VMs and/or Google Kubernetes Engine (GKE). 

Google provides a range of GPU & TPU accelerators for various use cases, including high performance training, low cost inferencing and large-scale accelerated data processing. Google is the only public cloud provider to offer up to 16 NVIDIA A100 GPUs in a single VM, making it possible to train very large AI models on a single node. Users can start with one NVIDIA A100 GPU and scale to 16 GPUs without configuring multiple VMs for single-node ML training.  Google also provides TPU pods for large-scale AI research with PyTorch, TensorFlow, and JAX.  The new fourth generation TPU pods deliver exaflop-scale peak performance with leading results in recent MLPerf benchmarks which included a 480 billion parameter language model.   

Google Kubernetes Engine provides the most advanced Kubernetes services with unique capabilities like Autopilot, highly automated cluster version upgrades, and cluster backup/restore. GKE is a good choice for a scalable multi-node bespoke platform for training, inference and Kubeflow pipelines, given its support for 15,000 nodes per cluster, auto-provisioning,  auto-scaling and various machine types (e.g. CPU, GPU, TPU and on-demand, spot). ML workloads also benefit from GKE’s support for dynamic scheduling, orchestrated maintenance, high availability, job API, customizability, fault tolerance and ML frameworks.  When a company’s footprint grows to a fleet of GKE clusters, its data teams can leverage Anthos Config Management to enforce consistent configurations and security policy compliance. 

Comprehensive MLOps: Build models faster and more easily without skimping on governance 

Google’s fully managed Vertex AI platform provides services for ML lifecycle management, from data ingestion and preparation all the way up to model deployment, monitoring, and management. Vertex AI requires nearly 80% fewer lines of code to train a model versus competitive platforms1, enabling data scientists and ML engineers across all levels of expertise to implement Machine Learning Operations (MLOps) so they can efficiently build and manage ML projects throughout the entire development lifecycle. 

Vertex AI Workbench provides data scientists with a single environment for the entire data-to-ML workflow, enabling data scientists to build and train models 5x faster than traditional notebooks. This is enabled by integrations across data services (like Dataproc, BigQuery, Dataplex, and Looker), which significantly reduce context switching.  Users are also able to access NVIDIA GPUs, modify hardware on the fly, and set up idle shutdown to optimize infrastructure costs. 

Organizations can then build and deploy models built on any framework (including TensorFlow, PyTorch, Scikit learn or XGBoost) with Vertex AI, with built-in tooling to track a model’s performance.  Vertex Training also provides various approaches for developing large models including Reduction Server to optimize bandwidth and latency of multi-node distributed training on NVIDIA GPUs for synchronous data parallel algorithms.  Vertex AI Prediction is serverless, and performs automatic provisioning and deprovisioning of nodes behind the scenes to provide low latency online predictions. It also provides the capability to split traffic between multiple models behind an endpoint. Models trained in Vertex AI can also be exported to be deployed in private or other public clouds.Google’s strengths in its current offering are in architecture, training, data throughput, and latency. Its sweet spot is in its product offering, Vertex AI, which has core AI compute capabilities and MLOps services for end-to-end AI lifecycle management.The Forrester Wave:™ AI Infrastructure, Q4 2021

In addition to building models, it is important to deploy tools for governance, security, and auditability. These tools are crucial for compliance in regulated industries, and they help teams to protect data, understand why given models fail, and determine how models can be improved. 

For orchestration and auditability, Vertex Pipelines and  Vertex ML Metadata tracks the inputs and outputs of an ML pipeline and the lineage of artifacts. Once models are in production, Vertex AI Model Monitoring supports feature skew and drift detection, alerting data scientists. These capabilities speed up debugging and create the visibility required for regulatory compliance and good data hygiene in general.For explainability, Vertex Explainable AI helps teams understand their model’s outputs for classification and regression tasks. Vertex AI tells how much each feature in the data contributed to the predicted result. Data teams can then use this information to verify that the model is behaving as expected, recognize bias in the model, and get ideas for ways to improve the model and training data.

These services together aim to simplify MLOps for data scientists and ML engineers, so that businesses can accelerate time to value for ML initiatives.

Security: Protect data while keeping ML pipelines flowing  

The Google stack builds security through progressive layers that deliver defense in depth. To accomplish data protection, authentication, authorization and non-repudiation, we have measures such as boot-level signature and chain-of-trust validation. 

Ubiquitous data encryption delivers unified control over data at-rest, in-use, and in-transit, with keys that are held by customers themselves. 

We offer options to run in fully encrypted confidential environments utilizing managed Hadoop or Spark with Confidential Dataproc or Confidential VMs.  

Partner Ecosystem: Work with world-class AI specialists 

Google works with certified partners globally to help our customers design, implement and manage complex AI systems. We have a growing list of partners with Machine Learning specializations on Google who have demonstrated customer success across industries, including deep partnerships with the largest Global System Integrators. The Google Cloud  Marketplace also provides a list of technology partners who allow enterprises to deploy machine learning applications on Google’s AI infrastructure.

Our dedication to being your partner of choice for ML Needs  

Leading organizations like OTOY, Allen Institute for AI and DeepMind (an Alphabet subsidiary) choose Google for ML, and enterprises like Twitter, Wayfair and The Home Depot shared more about their partnership with Google in their recent sessions at Google Next 2021.

Establishing well-tuned and appropriately managed ML systems has historically been challenging, even for highly skilled data scientists with sophisticated systems. With the key pillars of Google’s investments above, organizations can build, deploy, and scale ML models faster, with pre-trained and custom tooling, within a unified AI platform. 

We look forward to continuing to innovate and to helping customers on their digital transformation journey. To download the full report, click here. Get started on Vertex AI, learn what’s upcoming with infrastructure for AI and ML at Google here, and talk with our sales team.

In the United Kingdom, the popularity of online grocery shopping is expected to surge from about 6% of the market today to 9% by 2021, according to market research firm Mintel. One of the pioneers of online-only grocery retailing is Ocado, based in Hatfield, Hertfordshire in the U.K. Since starting commercial deliveries in 2002, the company has grown to 600,000 active customers, 260,000 weekly orders, and £1.39 billion in annual revenue.

Ocado takes supermarket trips out of the equation by enabling shoppers to purchase items online through its convenient web and mobile applications. Items are then picked and packed in automated warehouses and shipped directly to customers in a one-hour time slot of their choosing. Ocado’s delivery punctuality is 95%, order accuracy is 99%, and its service footprint now reaches more than 70% of the U.K. population.

“Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

—Paul Clarke, Chief Technology Officer, Ocado

The company achieved its success by building in-house almost all the technology and automation that powers its end-to-end e-commerce, fulfillment, and logistics platform. Ocado also developed a new platform, the Ocado Smart Platform (OSP), which offers large brick-and-mortar grocery retailers around the world access to a best-in-class solution for online grocery.

Democratizing machine learning

The shopping journey for online grocery retailing differs significantly from other e-businesses. Customers often buy dozens of products at once, a single household may have multiple buyers using multiple devices, and product shelf life may only be a couple of days.

“We often say that having built an end-to-end platform that can do online grocery scalably and profitably, we can do other forms of online retail; but the reverse does not necessarily follow,” says Paul Clarke, Chief Technology Officer at Ocado. “Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

The Ocado business model takes advantage of consumers’ shifting preferences and the links between digital technology and shopping experiences.

“Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

—Paul Clarke, Chief Technology Officer, Ocado

The company has been building machine learning into its systems for over five years. Until recently, Ocado machine learning applications required specialist data scientists, typically with PhDs in machine learning, who would build these solutions from the ground up. It also required the specialist who set up the system and costly on-premises infrastructure to train and run these systems.

However, working with Google as a private alpha testing site for Google Cloud Machine Learning Engine accelerated its adoption of artificial intelligence (AI).

“We’ve been talking about how the cloud could democratize AI for some time,” says Paul. “Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

TensorFlow is an open source software library for machine learning developed by the Google Brain team. Ocado developers, engineers, and data scientists now use TensorFlow for many of their machine learning projects. They deploy the models they build on Google Cloud Machine Learning Engine, which lets them train models faster across servers, desktop computers, and mobile devices through a single application program interface (API). Additionally, Google Cloud Machine Learning Engine integrates easily with the other Google Cloud Platform products used widely at Ocado.

What do customers really want?

One of the first TensorFlow models Ocado created was a machine learning algorithm that tags and categorizes customer emails and then prioritizes them for response.

The contact center receives thousands of emails each day and Ocado wanted to automate determining which ones needed to be answered immediately and which ones could wait.

For example, a first-time customer expressing their delight in using Ocado doesn’t need to be responded to with the same urgency as a customer who is missing an item from their order or who won’t be home to receive the delivery.

“Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

—James Donkin, General Manager, Ocado

“We get a lot of emails from customers saying, ‘Our service was great,’ or ‘The driver was very courteous,'” says James Donkin, General Manager, Ocado. “But when issues like weather or road conditions potentially affect delivery, we often get surges of urgent questions. Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

Using Google Cloud Machine Learning Engine, TensorFlow, and a large data set culled from several years’ worth of manually categorized customer emails, Ocado experimented on which kind of neural network architecture would best prioritize emails. After testing its models, Ocado implemented the highest-performing one and has been able to respond to urgent messages four times faster. The company also discovered that 7% of its emails don’t require a response at all, which means call center representatives now have more time to devote to higher priority messages.

“Without Google Cloud Machine Learning Engine, it would have been a lot harder to succeed on a project like email classification,” says Roland Plaszowski, who has recently managed several big data projects and initiatives at Ocado.

“Even if we invested significantly in infrastructure, it would be difficult to manage because of the computational intensity. It’s challenging and expensive to run machine learning projects at the same time without infrastructure that you can scale easily.”

Ocado also uses machine learning to predict customer behavior and improve experiences. By analyzing order data, Ocado makes shopping as frictionless as possible. For example, the ordering system can pre-populate customers’ shopping carts with items they are most likely to purchase, remind customers about items they may have forgotten, and notify them of multi-buy offers they haven’t completed, for example, only buying one of a buy one, get one free offer. Based on machine learning from previous purchase data, the Ocado system can also offer new products that are likely to delight customers.

“You will regularly see items that are more personally relevant to you instead of items that are being promoted more generally,” says James. “I’m a vegetarian, so I’m offered specials for vegetarian products that I normally buy and new ones that I’ve never bought. I’m also less likely to see things that I’m not interested in.”

Machines and machine learning

Within the Internet of Things (IoT), Ocado is looking to enhance its warehouse robots with machine learning. An integral part of the OSP, thousands of robots continually stream data into Google Cloud Storage and Google BigQuery.

Ocado data scientists apply machine learning to create a type of swarm intelligence that enables warehouse robots to work cooperatively to achieve a common goal. Projects include modules to search robot telemetry data, such as whether a battery pack is operating within standard tolerances or whether firmware has been successfully loaded, and use it to optimize maintenance schedules or detect patterns in wear and tear.

“Another challenge we’re looking at is how to embed machine learning directly into robots so they become smarter in terms of self-testing, exception handling, and error recovery,” says Paul. “This is a challenging combination of IoT, data analytics, and machine learning that we believe Google BigQuery and Google Cloud Machine Learning are particularly well suited to helping Ocado achieve.”

Scaling for new business

Scalability is also a major reason behind some of Ocado’s cloud initiatives, including the migration of all its on-premises data to the cloud. Ocado wanted to improve customer experiences, empower business teams with greater insight, and reduce IT overhead, so it consolidated onto Google Cloud Platform.

“The old databases just weren’t fast enough,” says Paul. “We needed a solution that could scale with the amount of data we generate and how we use it. Google Cloud Storage and Google BigQuery now provide the backbone, from a data point of view, for the Ocado Smart Platform.”

Ocado estimates its business, product, and transaction data is approaching two petabytes. Combining customer and supply chain data helps both internal Ocado operations and the company’s ambitions to commercialize OSP.

“When compared with other options for expansion internationally, selling OSP as a managed service lets us turn companies that could have been competitors into customers,” says Paul. “We want to build OSP once and then turn it on for multiple business-to-business customers.”

Each time Ocado adds a new hosting customer to OSP, it will launch a customized instance to fit that customer’s requirements. The capacity and performance of each new OSP instance must be able to scale quickly as the backend platform for established retailers with large numbers of products, customers, and transactions.

Ocado’s first OSP customer, Morrisons, is already benefiting from this first-of-a kind solution. Morrisons is one of the UK’s four largest supermarkets and uses OSP to power its online retail business. Using Google Cloud Platform, Ocado has stored, processed, and analyzed terabytes of Morrisons’ data using a dedicated data lake and Google BigQuery.

In addition to using Google Cloud Platform for OSP, Ocado also adopted it for its own online grocery retail business operation. Ocado originally used the Apache Spark and Apache Hadoop open-source frameworks on Google Compute Engine for its data platform. Moving to Google BigQuery frees Ocado business analysts from the complex query setup and workflows associated with Spark and Hadoop. Plus, it lets Ocado share data analytics with suppliers and partners.

Google BigQuery is well integrated with TensorFlow on Google Cloud Machine Learning Engine and Google Cloud Dataproc, the Apache Spark and Apache Hadoop service that lets Ocado use open source data tools for batch processing, querying, streaming, and machine learning. Google Cloud Dataflow and Google Cloud Dataproc handle cluster management, and provide an easy-to-use framework so developers can spend less time and money on administration and more time on delivering valuable business features.

Switching from Hadoop to Google BigQuery revealed a series of cost and performance improvements. For example, Ocado no longer needed to decide how many instances to bring up in a cluster or wait for the instances to spin up. Google handled everything.

“We simply ran our queries and paid for the resources that we use,” adds Roland. “One big win with Google BigQuery is we don’t have to do maintenance. Best of all, we saw Google BigQuery outperform our Hadoop cluster by over 80 times on our largest dataset, and for only two-thirds the cost.”