ESG Did the Math: It’s Cheaper and Smarter to Migrate Enterprise Data Warehouses to Google BigQuery. Way Smarter - Build What's Next

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ESG Did the Math: It’s Cheaper and Smarter to Migrate Enterprise Data Warehouses to Google BigQuery. Way Smarter

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How to Build a BI Dashboard With Google Data Studio and BigQuery

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For as long as business intelligence (BI) has been around, visualization tools have played an important role in helping analysts and decision-makers quickly get insights from data.

In our current era of big data analytics, that premise still holds. To provide an integrated platform for building BI dashboards on top of big data, GCP offers the combination of Google BigQuery, a cloud-native data warehouse that helps you analyze petabytes of data quickly, and Google Data Studio, a free tool that helps you quickly create beautiful reports.

First, imagine that you work for an online retailer that bases important decisions on customer interaction data in the form of large amounts (multiple TBs) of usage logs stored as date-partitioned tables in a BigQuery dataset called usage. To get business value out of that data as quickly as possible, you want to build a dashboard for analysts that will provide visualizations of trends and patterns in your data.

The good news is that a connector is available that lets you query data stored in BigQuery directly from Data Studio.

Learn how to build a BI dashboard with Data Studio as the front end, and powered by BigQuery on the back end (with some help from Google App Engine for added efficiency). Download this how-to now.

Case Study

Seven-Eleven Japan Leverages Google Cloud’s Performance and Speed for Real-time Business Insights

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To cope up with rapid digitization led by smartphone proliferation and IT-vendor dependencies, businesses need to move away from legacy systems and infrastructure that limit the distribution, access and scalability of datasets. With Google Cloud platform and range of products, retail giant, Seven-Eleven Japan (SEJ) achieves targets with high speed responses, growth of its data cloud and business value.

With the rise of technologies like smartphones, retailers have felt the pressure to meet evolving consumer needs and expectations. Seven-Eleven Japan(“SEJ”) has long been on the forefront of this thanks to the way they develop and invest in IT. However, in recent years, Japan’s leading convenience store chain has struggled to maintain its complex legacy systems at the rate needed to keep up with today’s rapid digitization, spurred on by the increasing proliferation of smartphones and an IT vendor-dependent structure.  

Legacy systems limiting real-time responsiveness and innovation 

Since its early days, SEJ has been proactive in adopting information technology, mainly relying on technology solutions from Japan’s leading vendors. But as the systems have grown, key business issues have been resolved using a vendor-dependent structure rather than being driven by SEJ’s own needs.  

Datasets and business logic were combined and built into legacy environments, gradually leading to data silos. As a result, data was distributed across multiple systems, causing a variety of problems, including the inability to efficiently retrieve data when needed, delays in accessing data collected in individual stores, and difficulties taking measurements at the right time in business operations that require real-time responsiveness.

Connecting different systems also takes time and money, and the lead time for introducing new services—from planning to development and launch—has been longer than expected. 

To solve these problems, SEJ’s IT department built “Seven Central”—a new platform for practical data use launched in 2020 to support the company’s future IT strategies and digital transformation initiatives.

At its core, Seven Central’s ultimate purpose is to allow real-time data views. Versatile, real-time datasets—such as point-of-sale (POS) data from 7-Eleven stores—are consolidated into a centralized location in the cloud. They created a simple data mart that provides data via an API to enable them to respond more quickly to requests from individual departments. 

“In such uncertain times, it’s vital to use data to make quick decisions,” says Izuru Nishimura, Executive Officer and Head of ICT Department. “Each department across the entire company will be able to gain an immediate understanding of the situation based on the most up-to-date data and respond accordingly. This is why we built Seven Central.”

Google Cloud selected to help SEJ build and grow their data cloud

Today’s rapidly changing business environment has also highlighted the risk of IT support becoming a bottleneck. The long-term strategy is to gradually expand the datasets managed and collected in Seven Central according to business needs. 

In the first phase, SEJ collected POS data from all 21,000+ stores to enable real-time analysis. Moving forward, they would like to collect other relevant data—for example, unstructured data, such as images and videos, or master datasets that are currently stored externally. 

Google Cloud was already a top contender when SEJ started developing Seven Central in 2019. They compared various public cloud services besides Google Cloud, focusing on three main capabilities.

“We placed particular emphasis on service scalability to drive future digital transformation; security when handling data, which is the lifeline of our company; and finally, openness,” says Nishimura. He emphasizes that openness was perhaps the most important factor for choosing Google Cloud. Breaking away from the negative aspects of an entirely vendor-dependent system enabled them to build an agile development system with multiple vendors. 

Google Cloud technologies including BigQuery and API management platform, Apigee, play a vital role in Seven Central. BigQuery’s high-speed processing at petabyte scale and fully managed infrastructure helped keep costs low during development and verification.   

“Data is stored in a way that allows you to share it easily across organizations, which helps solve the issue of data silos from the perspective of scalability. I also like the fact there are some interesting features that could be used in the future—like BigQuery ML, which enables machine learning on BigQuery,” says Nishimura. 

Apigee allows SEJ to separate datasets and business logic, which is one of the key points of Seven Central. While the trend these days is to standardize interfaces using an API, the reality tends to involve many different APIs rather than the introduction of one unified API. With Apigee, SEJ provides a single unified API for all of its data cloud, and they can now understand what data is used thanks to Apigee’s API usage visualizations.  

“Right now, we collect data from all 21,000+ stores,” says Nishimura. “But in anticipation of a future expansion in business operations, we have designed a system that can scale up and run without issue, even if we were to have 30,000 stores, with 1,000 customers per store per day, purchasing five items per person.”

Real-time insights with BigQuery and Cloud Spanner

Real-time insights with BigQuery and Cloud Spanner.jpg

Google Cloud partner Cloud Ace came on board early in the planning phases. Based on their recommendations, SEJ decided to continue making full use of BigQuery to analyze data collected from all 21,000+ stores throughout Japan, while also using Cloud Spanner’s availability, near-unlimited scalability and transactional consistency to help achieve the real-time results needed for the project.

“Given that both the data and the regularity with which it is accessed are expected to steadily increase in the future, we chose Cloud Spanner as backend storage for data delivery via API. We consider it a good choice,” says Shota Kikuchi, General Manager, Consulting Department, Technology Division, Cloud Ace Co., Ltd.

Finally, they chose to use Google Cloud’s Stream Analytics Solutions messaging service for collecting POS data in real time, which can then be put to immediate use with Cloud Spanner and BigQuery. 

High-speed responses exceed targets and create new value 

Seven Central went live in September 2020 with surprising results. 

They initially set a target time of one hour from when a customer makes a purchase to the point when Seven Central can use that data. But when the final system was first tried—it took barely a minute. Moving forward they estimate that the latest inventory data from the service side will become available within a few minutes of being added to the system.  

“This is real innovation, and I must admit that I am quite surprised. As well as being able to solve existing issues, we also hope it will lead to new improvements and services that have been unimaginable up until now,” says Nishimura.

The team hopes to roll out the Seven Central platform in all companies affiliated with Seven & i Holdings—not just SEJ. They also plan to explore Google Cloud AI and machine learning technologies to take on challenges in new areas. For example, they are investigating the idea of clustering individual stores using BigQuery ML.

Seven Central has already attracted attention from many departments and received a lot of requests. Nishimura and his team say they hope to continue to grow Seven Central while still observing their fundamental principles—not including business logic, maintaining real-time results, and staying true to the uniqueness of SEJ.

Learn more about Google Cloud smart analytics solutions.

E-book

Upgrade Your Contact Center with Knowlarity’s AI-powered Speech Analytics for Higher CX

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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 customer preferences and emotions, campaign performance, agent’s effectiveness and much more. Knowlarity’s programmatic speech analytics platform is built with Google Cloud to optimize contact center performance by transcribing and analyzing millions of calls to impact savings, operations, CX, customer loyalty and retention, and revenue generation.

Download the e-Book to learn more about Knowlarity’s speech analytics for your business’ contact centers and elevate your agents’ performance by leveraging ML, natural language processing (NLP) and AI capabilities.

Whitepaper

Modernize the Data Stack to Transform the Data Experience

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As organizations look to revolutionize how they analyze and utilize data, modernizing the data-centric technology stack is critical to success.

Today, the traditional stack poses several challenges—too many steps, too many tools, and too many integrations—all leading to operational complexity, time delays, and high cost. Simplifying the data pipeline, data lifecycle, and data stack offers organizations improved efficiency as well as cost savings, and provides them more value by freeing up resources to focus on deriving insight through the analysis of data.

As organizations begin to transform their approach to analytics, modernizing the analytics stack is a top priority.

To address both the data supply and data demand, data teams must look for ways to simplify and optimize the data pipeline. That means transitioning traditional solutions, like data warehouses and business intelligence tools, to modern architectures built with scalability, agility, and availability in mind.

And what follows with a modernized stack is an ideal data experience rich in actionable insight, API-driven application integration, and the ability to address the real-time needs of a dynamic business.

To find out the advantages to modernizing data warehouses and how this can be accomplished, download, ESG’s report: Modernize the Data Stack to Transform the Data Experience.

Case Study

How Ather Energy is leveraging the Cloud to build and scale smart mobility solutions for India

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Ather Energy, India’s first-ever electric scooter, turns to Google Cloud to support the smooth running of its vehicles, lower costs, improve time to market, and create a great customer experience.

In 2013, long before the world was discussing clean energy and sustainable practices, two IIT Madras graduates — Swapnil Jain and Tarun Mehta — had an idea to develop India’s first-ever electrical scooter.

This was at a time when auto manufacturers were still focusing on fossil-fuel-driven vehicles and ‘eco-friendly’ mobility solutions were more a trendy alternative catering to a niche market.

The duo founded Ather Energy in 2013 and launched their first fully-electric scooter, the Ather S340, in Bengaluru in 2016. Since then, the company has released several new models into the market and is planning to expand to eight more cities by the end of the year.

To support the smooth running of their vehicles, lower costs, improve time to market, and create great customer experience, Ather turned to Google Cloud.

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