Leverage the Power of Looker to Extract Data Value at Web Scale

4452
Of your peers have already read this article.
3:00 Minutes
The most insightful time you'll spend today!
Data science can be applied to business problems to improve practices and help to strengthen customer satisfaction. In this blog, we address how the addition of Looker extends the value of your Google Cloud investments to help you understand your customer journey, unlock value from first-party data, and optimize existing cloud infrastructure.
Data is a powerful tool
While everyone doesn’t need to understand the nuts and bolts of data technologies, most people do care about the value data can create for them — how it can help them do their jobs better. Within the data space, we are seeing a trend of ongoing failure to make data accessible to “humans” – the industry still hasn’t figured out how to put data into the hands of people when and where they need it, how they need it.
What if everyone in your organization could analyze data at scale, and make more-informed, fact-based decisions? Data and insights derived from data are valuable but only if your users see it. We think Looker helps solve that.
Solutions tell a larger story of how it all fits together
Across all verticals and industries, businesses benefit from knowing and understanding their customers better. Many business goals are to increase revenue by improving product recommendations and pricing optimization, improving the user experience through targeted marketing and personalization, and reducing churn while improving retention rates. To help reach these goals, key strategies should focus on understanding customer needs, their motivations, likes and dislikes, and using all available data – in other words, put yourself in the shoes of your customer.
Our goal with Looker solutions is to offer the right level of out-of-box support that allows customers to get to value quickly, while maintaining the necessary flexibility. We aim to offer a library of data-driven solutions that accelerate data projects. Many solutions include Looker Blocks (pre-built pieces of code that accelerate data exploration environments) and Actions (custom integrations) that get customers up and running quickly and lets you build business-friendly access points for Google Cloud functionality like BQML, App Engine and Cloud Functions.
Below, you’ll find a sampling of the newest Looker solutions.
Listening to customers by looking at the data
Looker’s solution for Contact Center AI (CCAI), helps businesses gain a deeper understanding and appreciation of their customers’ full journey by unlocking insights from all their company’s first-party data. Call centers can converse naturally with customers and deliver outstanding experiences by leveraging artificial intelligence. CCAI‘s newest product —CCAI Insights — reviews conversations support agents are having, finding and annotating the data with the important information, and identifying the calls that need review. We’ve partnered with the product teams at CCAI to build Looker’s Block for CCAI Insights, which sets you on the path to integrating the advanced insights into your first-party data in Looker, overlaying business data with the customer experience.

Businesses can better understand contact center experiences and take immediate action when necessary to make sure the most valuable customers receive the best service.
Realizing full business value of First-Party data
Looker for Google Marketing Platform (GMP) provides marketers the power to unlock the value of their company’s first-party data to more effectively target audiences. The Looker Blocks and Actions for GMP offer interactive data exploration, slices of data with built-in ML predictions and activation paths back to the GMP. This strategic solution continues to evolve with the Looker Action for Google Ads (Customer Match), the Looker Action for Google Analytics (Data Import) and the Looker Block for Google Analytics 4 (GA4).
- The Looker Action for Customer Match allows marketers to send segments and audiences based on first-party data directly into Google Ads. Reach users cross-device and across the web’s most powerful channels such as Display, Video, YouTube, and Gmail. The entire process is performed within a single screen in Looker, and is able to be completed in a few minutes by a non-technical user.
- The Looker Action for Data Import can be used to enhance user segmentation and remarketing audiences in Google Analytics by taking advantage of user information accessible in Looker, such as in CRM systems or transactional data warehouses.
- The Looker Block for Google Analytics 4 (GA4) expands the solution’s support with out-of-the-box dashboards and pre-baked BigQuery ML models for the newest version of Google Analytics.The Looker Block offers up reports with flexible configuration capabilities to unlock custom insights beyond the standard GA reporting. Customize audience segments, define custom goals to track and share these reports with teams who do not have access to the GA console.
From clinical notes to patient insights at scale
Taking a look at the Healthcare vertical, the Looker Healthcare NLP API Block serves as a critical bridge between existing care systems and applications hosted on Google Cloud providing a managed solution for storing and accessing healthcare data in Google Cloud. The Healthcare NLP API uses natural language models to extract healthcare information from medical text, rapidly unlocking insights from unstructured medical text and providing medical providers with simplified access to intelligent insights. Healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, such as medications, procedures, conditions, clinical history, and begin to link this to other clinical data sources for downstream AI/ML.
Specifically, the natural language processing (NLP) Patient View (pictured below) allows you to review a single selected patient of interest, surfacing their clinical notes history over time. It informs clinical diagnosis with family history insights, which is not currently captured in claims, and captures additional procedure coding for revenue cycle purposes.

The dashboard below shows the NLP Term View which allows users to focus on chosen medical terms across the entire patient population in the dataset so they can start to view trends and patterns across groups of patients.

This data can be used to:
- Enhance patient matching for clinical trials
- Identify re-purposed medications
- Drive advancements for research in cancer and rare diseases
- Identify how social determinants of health impact access to care
Managing Costs Across Clouds
Effective cloud cost management is important for reasons beyond cost control — it provides you the ability to reduce waste and predictably forecast both costs and resource needs. Looker’s solution for Cloud Cost Management offers quick access to necessary reporting and clear insights into cloud expenditures and utilization.
This solution brings together billing data from different cloud providers in a phased approach: get up and running quickly with Blocks optimized for where the data is today (Google Cloud, AWS or Azure) as you work towards more sophisticated analysis for cross-platform planning and even cloud spend optimization with the mapping of tags, labels and cost centers across clouds.

The Looker Cloud Cost Management solution provides operational teams struggling to monitor, understand, and manage the costs and needs associated with their cloud technology with a comprehensive view into what, where and why they are spending money.
Making better decisions with Looker-powered data
Leading companies are discovering ways to get value from all of that data beyond displaying it in a report or a dashboard. They want to enable everyone to make better decisions but that’s only going to happen if everyone can ask questions of the data, and get reliable, correct answers without using outdated or incomplete data and without waiting for it. People and systems need to have data available to them in the way that makes the most sense for them at that moment.
It’s clear that successful data-driven organizations will lead their respective segments not because they use data to create reports, but because they use it to power data experiences tailored to every part of the business, including employees, customers, operational workflows, products and services.
As people’s way of experiencing data has evolved, more than ever before, dashboards alone are not enough. You can use data as fuel for data-driven business workflows, and to power digital experiences that improve customer engagement, conversions, and advocacy.
From Nov. 9 – 11th, Looker is hosting its annual conference JOIN, where we’ll be showing new features, including how we help to:
- Build data experiences at the speed of business
- Accelerate the business value with packaged experiences
- Unleash more insights for more people in the right way – Deliver data experiences at scale
There is no cost to attend JOIN. Register here and learn how Looker helps organizations build and deliver custom data-driven experiences that goes beyond just reports and dashboards, scales and grows with your business, allows developers to build innovative data products faster, and ensures data reaches everyone.
Uniting Data and Analytics in One Place: Apache Iceberg and BigLake

1142
Of your peers have already read this article.
2:30 Minutes
The most insightful time you'll spend today!
When your data is siloed data across lakes and warehouses, it can be hard to transform outcomes with your data. Apache Iceberg is an open table format that provides data management capabilities for data hosted on object stores and enables organizations to run analytics and AI use cases over a single copy of data. A growing community of data engineers, customers, and industry partners are contributing, integrating, and deploying Iceberg, making it the standard for organizations building open-format lakehouses.
To help customers on this journey, we announced support for Iceberg through BigLake in October, 2022. Since its preview, many customers have started building lakehouse workloads using Apache Iceberg as their data management layer, and this support is now generally available.
Unify analytics, streaming and AI use cases over a single copy of data
You can use open-source engines to process and ingest data into Iceberg tables, and BigQuery can query those tables. Since the preview, customers have also used Spark, Trino and Flink to process Iceberg tables and make those tables available to their BigQuery users. Then, BigLake Metastore provides shared metadata for Iceberg tables across BigQuery and open-source engines, eliminating the need to maintain multiple table definitions. Further, you can provide BigQuery datasets and table properties when creating new Iceberg tables in Spark, and those tables become automatically available for the BigQuery user to query.

When implementing Iceberg lakehouse workloads, query performance is a top priority for data warehouse users. BigQuery natively integrates with the Iceberg transaction logs and leverages its rich metadata for efficient query planning. Query plans are designed to reduce BigQuery compute consumption by lowering the amount of scanned data, by optimizing joins, and by improving data-plane parallelism. The net result is that you get better query performance and lower slot usage when querying BigLake Iceberg tables.
This GA release also adds support to provide automatic synchronization of table schema in BigQuery when the table is modified through an open-source engine.
Engine-agnostic, industry-leading security and governance built-in
Customers have been telling us that building Iceberg lakehouses in a secure and governed manner is a top priority. BigLake support for Iceberg provides fine-grained access control, including row- and column-level security as well as data masking to simplify this. These features are designed to work independently of the query engine. During the preview, we expanded BigQuery to also support differential privacy for all tables including Iceberg.
You can also define security policies on a BigLake Iceberg table using BigQuery. Security policies are then enforced regardless of the query engine used — BigQuery natively enforces these policies at runtime, and open-source engines can securely access the data using the BigQuery Storage API. The BigQuery Storage API enforces the security policies at the data-plane layer, and is offered via pre-built connectors for Spark, Trino, Presto and TensorFlow. You can also use client libraries to build connectors for custom applications.
New use cases with multi-cloud Iceberg lakehouse
The open nature of Apache Iceberg lets you build multi-cloud lakehouses with uniform management of data. With this launch, you can now create BigLake Iceberg tables on Amazon S3, and query them using BigQuery Omni. BigLake’s performance and fine-grained access control features seamlessly extend to multi-cloud Iceberg tables, so you can securely perform cross-cloud analytics with BigQuery. We’ll extend similar support to Azure data lake Gen 2 in the coming weeks.
Apache Iceberg’s format uniformity across clouds also enables new data sharing use cases to help you share data with your customers, partners and suppliers, regardless of which cloud they are using. BigLake Iceberg tables on Cloud Storage or on Amazon S3 can be shared through Analytics Hub and consumed via BigQuery or OSS engines via BigQuery Storage Read API, providing an open sharing standard and the flexibility to use multiple query engines. A notable example of this is the recently announced Salesforce Data Cloud data sharing use case, which enables bi-directional cross-cloud data sharing between Salesforce and BigQuery and which is powered by Iceberg.
Getting started
To create and query your first Iceberg table, following the documentation. or run a quick proof of concept on our analytics lakehouse with the Iceberg jump start solution.
Data Culture Integral for Building Data Platforms in EdTech Firms

5025
Of your peers have already read this article.
2:00 Minutes
The most insightful time you'll spend today!
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.
2914
Of your peers have already watched this video.
36:00 Minutes
The most insightful time you'll spend today!
What’s New in BigQuery, Google Cloud’s Modern Data Warehouse
The demands that today’s enterprise has from data go far beyond the capabilities of traditional data warehousing. For many leaders, the need to digitally transform their businesses is a key driver for data analytics spending.
Businesses want to make real-time decisions from fresh information as well as make predictions from their data in order to remain competitive.
In this video, watch Sudhir Hasbe, Director of Product Management, Data Analytics, Google Cloud and Tino Tereshko, Product Manager, BigQuery, Google Cloud demonstrate what’s new in BigQuery, Google Cloud’s enterprise data warehouse, and hear about all the latest feature innovations and see some amazing demos.

4853
Of your peers have already watched this video.
38:32 Minutes
The most insightful time you'll spend today!
Everything You Need to Know About Google Cloud ML Engine 101
Machine learning is all around us today. But data scientists and IT teams tasked with creating models have a hard time bringing together the right mix of ingredients—from data, infrastructure, tools, and APIs—to do their jobs effectively.
Google’s Cloud ML Engine eases many of the challenges data scientists and IT teams face. It’s a managed service that allows businesses to build and deploy their own models using any type or any size of data.
With Google’s Cloud ML Engine, data scientists can create models for training and prediction. And it provides APIs for these two building blocks.
Nikhil Kothari, Senior Staff Software Engineer, Google Cloud, breaks down Google’s Cloud ML Engine. He shows you how to use it as a service, so that data scientists can focus on data and on building models instead of managing infrastructure.

Anthos for Manufacturing: Tackle DevOps Complexities and Drive Digital Transformation
DOWNLOAD E-BOOK6267
Of your peers have already downloaded this article
2:00 Minutes
The most insightful time you'll spend today!
More Relevant Stories for Your Company
Upgrade Your Contact Center with Knowlarity’s AI-powered Speech Analytics for Higher CX
Did you know, everyday about 56 million hours worth of phone conversations, equalling to 420 billion spoken words are handled by contact centers? Knowlarity, a renowned cloud business communication service provider with nearly 6,000 customers and over a million virtual users, leverages AI-powered speech analytics that offer insights to gauge

Delivering analysis-ready financial data at scale
Refinitiv, is a leading provider of financial market data. It serves over 40,000 institutions and operates in 190 countries. “We're constantly looking at solving some of the most difficult problems our users face when accessing new data sets as well as onboarding new sources that will be relevant for workflows

Post-COVID: Times Driven by Data Analytics and Intelligence
As we think about economic recovery from COVID-19—both inside Google and outside through working with Google Cloud customers—we’ve made many important observations. Among them is the recognition that the ways software developers and IT practitioners work together will shift in the post COVID-19 world. Our economic recovery today will look

How to Modernize Your Data infrastructure and Analytics: Case Study
Data modernization is key to digital transformation. Legacy systems are slow, expensive and cannot keep up with changing requirements. Data teams often spend more time on data pipelines than they do on data analysis or data science.in this video, Harish Ramachandraiah, Director Eng. & Analytics, Sunrun, speaks with Joel Mckelvey,







