Key Highlights on Data Analytics to Smooth Your Organization’s Data Journey
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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.
Countries can Tackle Food Wastage Crisis Using Google’s Data Analytics
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With over ⅓ of the food in the USA ending up as waste according to the USDA, it is a compelling challenge to address this travesty. What will happen to hunger, food prices, trash reduction, water consumption, and overall sustainability when we stop squandering this abundance?
Beginning with the departure from the farm to the back of the store, the freshness clock continues to run. Grocers work very hard to purchase high quality produce items for their customers and the journey to the shelf can take a toll in both quality and remaining shelf life. Suppliers focus on delivering their items through the arduous supply chain journey to the store with speed and gentle handling. The baton is then passed to the store to unload and present the items to customers with care to sell through each lot significantly before the expiration or sell by date. This is to ensure that the time spent in the customer’s home is ample to ensure a great eating experience as well. Food waste is a farm to fork problem with opportunity at every step of the chain, but today we will focus on the segment that the grocery industry oversees.
With the complexities of weather, geopolitical issues, distribution, sales variability, pricing, promotions, and inventory management, it seems daunting to impact waste. Fortunately, data analytics and machine learning in the cloud is a powerful weapon in the fight against food waste. Data Scientists harness knowledge to draw meaning from data turning that data into decision driving information.
One key Google has been working on to accelerate value is to break down data silos and leverage machine learning to realize better outcomes, using our Google Data Cloud platform. This enables better planning through demand forecasting, Inventory management, assortment planning, and dynamic pricing and promotions.
That sounds great but how does it work?
Let’s walk through a day in the life journey to see how the integrated Google Data Cloud platform can change the game for good. Our friendly fictitious grocer FastFreshFood is committed to selling high quality perishable items to their local market. Their goal is to minimize food waste and maximize revenue by selling as much perishable fresh food as possible before the sell by date. Our fictitious grocer in partnership with Google Cloud could build a solution that will take a significant bite out of their food waste volume and better satisfy customers.
- Sales through the register and online are processed in real time with Datastream, Dataflow to keep an accurate perpetual inventory by minute of every single item.
- A Demand forecasting model using machine learning algorithms in BigQuery then identifies needs for back room replenishment, so Direct Store Delivery and daily store Distribution Centers manage ordering more efficiently to ensure just the right amount of each product each day.
- Realtime reporting dashboards in Looker with alerting capabilities enable the system to operate with strong associate support and understanding. The reporting suite shows inventory levels into the future, daily orders, and at risk items.
The pricing algorithm could also alert store leadership concerning any items that will not sell through and suggest real time in store specials resulting in zero waste at shelf and maximized revenue.
This approach is not just for perishable categories and is a pattern that works well for in-store produced items and center store items. The key point is that by bringing ML/AI to difficult business problems grocers are reinventing what is possible for both their profitability and sustainability.
The technical implementation of this design pattern in Google Cloud leverages Datastream, Dataflow, BigQuery and Looker products, it is detailed in a technical tutorial accompanying this blog post.
In partnership with Google Cloud, retailers can solve complex problems with innovative solutions to achieve higher quality, lower cost, and provide great customer experiences. To learn more from this and other use cases, please visit our Design Patterns website.
Curious to learn more?
We’re excited to share what we know about tackling food waste at Google, a topic we’ve been working on in the last decade as we’ve embarked on reducing our own food waste in our operations in over 50 countries in the world. The Google Food for Good team works exclusively on Google Cloud Platform with our partners on this topic. Two additional articles below.
Silos are for food, not for data – tackling food waste with technology
This business Cloud blog directly addresses information silos that currently exist across many nodes in the food system and how to break down cultural and organizational barriers to sharing.
“Unsiloing” data to work toward solving food waste and food insecurity
This follow-on technical Cloud blog articulates the path to setting up data pipelines, translating between data sets (not everyone calls a tomato a tomato!) and making sense of emergent insights.
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Sure, machine learning is becoming a business imperative, but how does it work in practice?
That’s the subject of a new step-by-step guide to solving business problems with artificial intelligence and ML, based on insights gathered by IDG Research Services.
Its publication comes at a time when technology leaders face growing pressure to embrace these emerging technologies, yet many have questions about how to get started.
It has real-life examples such as a health services company that used ML to reduce support ticket-resolution time from 48 minutes to six.
In another section, a financial services VP explains that cloud-based ML services enable his company to avoid spending money on computing resources that sit idle.
The guide also includes concrete tips for new ML adopters, provided by the CIOs and other IT leaders who participated in IDG’s research. For example, a real-estate CIO recommends the use of third-party tools that rely on AI and ML technologies, while a financial services VP highlights the challenge and potential of incorporating unstructured data into ML initiatives.
Download the guide now!
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Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions.
In a report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices.
Featuring strategic advice from Google experts, it answers questions such as:
- What’s driving businesses to consider the cloud for their data warehousing strategy?
- What are the advantages of a cloud-native data warehouse?
- How can you coordinate data warehouse modernization with other modernization projects?
- What’s the first step in migrating your data warehouse to the cloud?
- How will cloud data warehousing affect your business’s security posture?
Download the complete report to learn more about cloud data warehousing and how it can help your business transform with the times — and prepare for the future.
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Video: How AI is Helping Biologists Protect Wildlife
According to the World Wildlife Fund, vertebrate populations have shrunk an average of 60 percent since the 1970s. And a recent UN global assessment found that we’re at risk of losing one million species to extinction, many of which may become extinct within the next decade.
To better protect wildlife, seven organizations, led by Conservation International, and Google have mapped more than 4.5 million animals in the wild using photos taken from motion-activated cameras known as camera traps. The photos are all part of Wildlife Insights, an AI-enabled, Google Cloud-based platform that streamlines conservation monitoring by speeding up camera trap photo analysis.
With photos and aggregated data available for the world to see, people can change the way protected areas are managed, empower local communities in conservation, and bring the best data closer to conservationists and decision-makers.
Camera traps help researchers assess the health of wildlife species, especially those that are reclusive and rare. Worldwide, biologists and land managers place motion-triggered cameras in forests and wilderness areas to monitor species, snapping millions of photos a year.
But what do you do when you have millions of wildlife selfies to sort through? On top of that, how do you quickly process photos where animals are difficult to find, like when an animal is in the dark or hiding behind a bush? And how do you quickly sort through up to 80 percent of photos that have no wildlife at all because the camera trap was triggered by the elements, like grass blowing in the wind?
Watch this video to find out.
Google’s Intelligent Products Essentials Assist Manufacturers in Product Development Journey
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Expectations for both consumer and commercial products have changed. Consumers want products that evolve with their needs, adapt to their preferences, and stay up-to-date over time. Manufacturers, in turn, need to create products that provide engaging customer experiences not only to better compete in the marketplace, but also to provide new monetization opportunities.
However, embedding intelligence into new and existing products is challenging. Updating hardware is costly, and existing connected products do not have the capability to add new features. Furthermore, manufacturers do not have sufficient customer insights due to product telemetry and customer data silos, and may lack the AI expertise to quickly develop and deploy these features.
That’s why today we’re launching Intelligent Products Essentials, a solution that allows manufacturers to rapidly deliver products that adapt to their owners, update features over-the-air using AI at the edge, and provide customer insights using analytics in the cloud. The solution is designed to assist manufacturers in their product development journeys—whether developing a new product or enhancing existing ones.
With Intelligent Products Essentials, manufacturers can:
- Personalize customer experiences: Provide a compelling ownership experience that evolves over the lifetime of the product. For example, a chatbot that contextualizes responses based on product status and customer profile.
- Manage and update products over-the-air: Deploy updates to products in the field, gather performance insights and evolve capabilities over time with monetization opportunities.
- Predict parts and service issues: Detect operating thresholds, anomalies and predict failures to proactively recommend service using AI, reducing warranty claims, decreasing parts shortages and increasing customer satisfaction.
In order to help manufacturers quickly deploy these use cases and many more, Intelligent Products Essentials provides the following:
- Edge connections: Connect and ingest raw or time-series product telemetry from various device platforms utilizing IoT Core or Pub/Sub and enable deployment and management of firmware over-the-air and machine learning models with Vertex AI at the edge.
- Ownership App Template: Easily build connected product companion apps that work on smartphones, tablets, and computers. Use a pre-built API and accompanying sample app that can incorporate product or device registration, identity management, and provide application behavior analytics using Firebase.
- Product fleet management: Manage, update and analyze fleets of connected products via APIs, Google Kubernetes Engine, and Looker.
- AI services: Create new features or capabilities for your products using AI and machine learning products such as DialogFlow, Vision AI, AutoML, all from Vertex AI.
Enterprise data integration: Integrate data sources such as Enterprise Asset Management (EAM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) systems and others using Dataflow and BigQuery.
Intelligent Products Essentials helps manufacturers build new features across consumer, industrial, enterprise, and transportation products. Manufacturers can implement the solution in-house, or work with one of our certified solution integration partners like Quantifi and Softserve.
“The focus on intelligent products that Google Cloud is deploying provides a digital option for manufacturers and users. At its heart, systems like Intelligent Product Essentials are all about decision making. IDC sees faster and more effective decision-making as the fundamental reason for the drive to digitize products and processes. It’s how you can make faster and more effective decisions to meet heightened customer expectations, generate faster cash flow, and better revenue realization,” said Kevin Prouty, Group Vice President at IDC. “Digital offerings like Google’s Intelligent Product Essentials potentially go the last mile with the ability to connect the digital thread all the way through to the final user.”
Customers adopting Intelligent Products Essentials
GE Appliances, a Haier company, are enhancing their appliances using new AI-powered intelligent features to enable:
- Intelligent cooking: Help cook the perfect meal to personal preferences, regardless of your expertise and abilities in the kitchen.
- Frictionless service: Build smart appliances that know when they need maintenance and make it simple to take action or schedule services.
- Integrated digital lifestyle: Make appliances useful at every step of the way by integrating them with digital lifestyle services – for example, automating appliance behaviors according to customer calendars, such as oven preheating or scheduling the dishwasher to run in the late evening.
“Intelligent Products Essentials enhances our smart appliances ecosystem, offering richer consumer habit insights. This enables us to develop and offer new features and experiences to integrate with their digital lifestyle.“ —Shawn Stover, Vice-president Smart Home Solutions at GE Appliances.https://www.youtube.com/embed/zaWAJN8aKOw?enablejsapi=1&
Serial 1, Powered by Harley-Davidson, is using Intelligent Product Essentials to manage and update its next generation eBicycles, and personalize its customers’ digital ownership experiences.
“At Serial 1, we are dedicated to creating the easiest and most intuitive way to experience the fun, freedom, and adventure of riding a pedal-assist electric bicycle. Connectivity is a key component of delivering that mission, and working together to integrate Intelligent Product Essentials into our eBicycles will ensure that our customers enjoy the best possible user experience.”— Jason Huntsman, President, Serial 1.
Magic Leap, an augmented reality pioneer with industry-leading hardware and software, is building field service solutions with Intelligent Products Essentials with the goal of connecting manufacturers, dealers, and customers to more proactive and intelligent service.
“We look forward to using Intelligent Products Essentials to enable us to rapidly integrate manufacturers’ product data with dealer service partners into our field service solution. We’re excited to partner with Google Cloud as we continue to push the boundaries of physical interaction with the digital world.” — Walter Delph, Chief Business Officer, Magic Leap
Intelligent Product Essentials is available today. To learn more, visit our website.
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