Case Studies AI platform thrives with huge data intake

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	AI platform thrives with huge data intake

AI platform thrives with huge data intake

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics Functional Applications - Remote Monitoring & Control Systems
Applicable Industries	Consumer Goods Software
Applicable Functions	Business Operation Product Research & Development Quality Assurance
Use Cases	Machine Condition Monitoring Predictive Maintenance Real-Time Location System (RTLS) Remote Asset Management
Services	Software Design & Engineering Services System Integration Training
Challenge	The data required to deliver a great user experience kept growing, as did costs and complexity, making it difficult to meet the demands of customers. Bixby’s deep-learning AI model understands the user’s voice and delivers more accurate results when user data accumulates. However, as Bixby required more data per user, the amount of data to analyze and manage increased, escalating data management costs to the development team. While the previously used cloud-based analysis system, Google Stackdriver and Google Cloud Operations supported simple search and debugging functions, the analysis team still had to go through additional processes and data format changes. There was room for improvement in producing meaningful results and enhancing customer experience, including debugging, data distribution, performance, and issue resolution. Read More
About Customer	Samsung Electronics Bixby is an artificial intelligence (AI) platform released in 2017. Bixby links to Galaxy, Samsung Electronics’ signature product line, and various gadgets and home appliances in the Galaxy ecosystem. Bixby leverages the user’s voice to perform daily tasks and control connected devices. Bixby’s deep-learning AI model understands the user’s voice and delivers more accurate results when user data accumulates. However, as Bixby required more data per user, the amount of data to analyze and manage increased, escalating data management costs to the development team. Read More
Solution	With the introduction of Sumo Logic, the overall quality and efficiency of the Bixby service operation improved, including cost savings from astutely maintaining and managing the data. Using log analysis and machine learning to address problems faster than simple searches, Sumo Logic improves debugging and performance issue resolution, leading to high-quality service delivery. Once up and running with Sumo Logic, the daily log ingestion volume averages 35 TB (terabytes). About 550 engineers and developers from the Samsung Electronics Bixby team use Sumo Logic; most of them are certified through Sumo Logic’s learning platform. By adopting Sumo Logic’s SaaS-analytics platform, the Samsung Electronics Bixby team successfully reduced management resources by improving the flexibility of the data analysis system. The developers, in particular, rated the platform highly because it does not require any additional storage space while offering powerful functions to analyze and process various forms of logs. This lets the developers generate more diverse insights for the service. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Functional Applications - Remote Monitoring & Control Systems

Applicable Industries

Consumer Goods

Software

Applicable Functions

Business Operation

Product Research & Development

Quality Assurance

Use Cases

Machine Condition Monitoring

Predictive Maintenance

Real-Time Location System (RTLS)

Remote Asset Management

Services

Software Design & Engineering Services

System Integration

Training

Challenge

The data required to deliver a great user experience kept growing, as did costs and complexity, making it difficult to meet the demands of customers. Bixby’s deep-learning AI model understands the user’s voice and delivers more accurate results when user data accumulates. However, as Bixby required more data per user, the amount of data to analyze and manage increased, escalating data management costs to the development team. While the previously used cloud-based analysis system, Google Stackdriver and Google Cloud Operations supported simple search and debugging functions, the analysis team still had to go through additional processes and data format changes. There was room for improvement in producing meaningful results and enhancing customer experience, including debugging, data distribution, performance, and issue resolution.

About Customer

Samsung Electronics Bixby is an artificial intelligence (AI) platform released in 2017. Bixby links to Galaxy, Samsung Electronics’ signature product line, and various gadgets and home appliances in the Galaxy ecosystem. Bixby leverages the user’s voice to perform daily tasks and control connected devices. Bixby’s deep-learning AI model understands the user’s voice and delivers more accurate results when user data accumulates. However, as Bixby required more data per user, the amount of data to analyze and manage increased, escalating data management costs to the development team.

Solution

With the introduction of Sumo Logic, the overall quality and efficiency of the Bixby service operation improved, including cost savings from astutely maintaining and managing the data. Using log analysis and machine learning to address problems faster than simple searches, Sumo Logic improves debugging and performance issue resolution, leading to high-quality service delivery. Once up and running with Sumo Logic, the daily log ingestion volume averages 35 TB (terabytes). About 550 engineers and developers from the Samsung Electronics Bixby team use Sumo Logic; most of them are certified through Sumo Logic’s learning platform. By adopting Sumo Logic’s SaaS-analytics platform, the Samsung Electronics Bixby team successfully reduced management resources by improving the flexibility of the data analysis system. The developers, in particular, rated the platform highly because it does not require any additional storage space while offering powerful functions to analyze and process various forms of logs. This lets the developers generate more diverse insights for the service.

Impact #1	Strengthening efficiency while reducing costs through a SaaS-based solution.
Impact #2	The developers rated the platform highly because it does not require any additional storage space while offering powerful functions to analyze and process various forms of logs.
Impact #3	Data tiering offers a mix of Continuous, Frequent, and Infrequent Tiers, which stores the data and provides a certain level of data access and search functions per tier.

Impact #1

Strengthening efficiency while reducing costs through a SaaS-based solution.

Impact #2

The developers rated the platform highly because it does not require any additional storage space while offering powerful functions to analyze and process various forms of logs.

Impact #3

Data tiering offers a mix of Continuous, Frequent, and Infrequent Tiers, which stores the data and provides a certain level of data access and search functions per tier.

Benefit #1	Reduced costs by 30% with data tiering.
Benefit #2	35TB average daily log ingestion volume.
Benefit #3	550 engineers and developers from the Samsung Electronics Bixby team use Sumo Logic.

Benefit #1

Reduced costs by 30% with data tiering.

Benefit #2

35TB average daily log ingestion volume.

Benefit #3

550 engineers and developers from the Samsung Electronics Bixby team use Sumo Logic.

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Overview

AI platform thrives with huge data intake

Operational Impact

Quantitative Benefit