The new GDPR regulation went into effect on May 25th. While a majority of conversations have revolved around the security and IT aspects of the law, marketing teams will play a crucial role in helping organizations meet GDPR standards and playing a strategic role across the organization . Join us to learn more, engage with your peers and get prepared.
This webinar will cover:
- How complying with the GDPR will drive better marketing and raise the standard of the quality of your customer engagement
- The GDPR elements marketers must know about
- The elements of PII that will be affected and what marketers need to do about it
- A deep dive on how GDPR regulations will affect your marketing channels - email, programmatic advertising, cold calls, etc.
- Tactical marketing updates needed to meet GDPR guidelines
AR and VR by the Numbers: A Data First Approach to the Technology and MarketTreasure Data, Inc.
The document discusses trends in the augmented reality (AR) and virtual reality (VR) markets. It notes that the combined AR and VR market is estimated to reach $120 billion by 2020, with AR's market estimated at $89.9 billion and VR's at $29.9 billion. While VR growth is clear, the exact size is unclear. The document outlines challenges like the need for improved headsets and continued developer investment outside of mobile. It emphasizes that AR currently focuses on using data to project context and enable interaction with the real world, and that collecting user data is important for defining the experience.
An overview of Customer Data Platforms (CDP) with the industry leader who coined the term, David Raab. Find out how to use Live Customer Data to create a better customer experience and how Live Data Management can give you a competitive edge with a 360 degree view of your clients.
Learn:
- The definition and requirements for Customer Data Platforms
- The differences between Customer Data Platforms and comparative technologies such as Data Warehousing and Marketing Automation
- Reference architectures/approaches to building CDP
- How Treasure Data is used to build Customer Data Platforms
And here's the song: https://youtu.be/RalMozVq55A
In this hands-on webinar we will cover how to leverage the Treasure Data Javascript SDK library to ensure user stitching of web data into the Treasure Data Customer Data Platform to provide a holistic view of prospects and customers.
We will demo the native SDK, as well as deploying the SDK inside of Adobe DTM and Google Tag Manager.
Hands-On: Managing Slowly Changing Dimensions Using TD WorkflowTreasure Data, Inc.
In this hands-on webinar we'll explore the data warehousing concept of Slowly Changing Dimensions (SCDs) and common use cases for managing SCDs when dealing with customer data. This webinar will demonstrate different methods for tracking SCDs in a data warehouse, and how Treasure Data Workflow can be used to create robust data pipelines to handle these processes.
Brand Analytics Management: Measuring CLV Across Platforms, Devices and AppsTreasure Data, Inc.
Gaming companies with multiple products often struggle to calculate accurate Customer Lifetime Value (CLTV) across their portfolio. This is because user data is often analyzed in silos so companies are unable to get a clear picture of ROI and CLTV across platforms, devices and apps.
In this webinar we’ll look at how you can apply a holistic and complete approach to your CLTV and ROI through the lens of gaming companies, though this technique is applicable for any company who has products spanning platforms.
We’ll also explore:
How the integral power of data in business has shifted over the past 10 years.
Discover the current technologies and processes used to analyze data across different platforms by combining multiple data streams, looking at examples in brand and portfolio-based LTV.
How to process and centralize dozens of varying data streams.
Nicolas Nadeau will speak from his extensive experience and show how leveraging data from multiple product strategies spanning many platforms can be highly beneficial for your company.
Do you know what your top ten 'happy' customers look like? Would you like to find ten more just like them? Come learn how to leverage 1st & 3rd party data to map your customer journey and drive users down a path where every interaction is personalized, fun, & data-driven. No more detractors, power your Customer Experience with data!
In this webinar you will learn:
-When, why, and how to leverage 1st, 2nd, and 3rd party data
-Tips & Tricks for marketers to become more data driven when launching their campaigns
-Why all marketers needs a 360 degree customer view
The reality is virtual, but successful VR games still require cold, hard data. For wildly popular games like Survios’ Raw Data, the first VR-exclusive game to reach #1 on Steam’s Global Top Sellers list, data and analytics are the key to success.
And now online gaming companies have the full-stack analytics infrastructure and tools to measure every aspect of a virtual reality game and its ecosystem in real time. You can keep tabs on lag, which ruins a VR experience, improve gameplay and identify issues before they become showstoppers, and create fully personalized, completely immersive experiences that blow minds and boost adoption, and more. All with the right tools.
Make success a reality: Register now for our latest interactive VB Live event, where we’ll tap top experts in the industry to share insights into turning data into winning VR games.
Attendees will:
* Understand the role of VR in online gaming
* Find out how VR company Survios successfully leverages the Exostatic analytics infrastructure for commercial and gaming success
* Discover how to deploy full-stack analytics infrastructure and tools
Speakers:
Nicolas Nadeau, President, Exostatic
Kiyoto Tamura, VP Marketing, Treasure Data
Ben Solganik, Producer, Survios
Stewart Rogers, Director of Marketing Technology, VentureBeat
Wendy Schuchart, Moderator, VentureBeat
The document discusses how marketers can better leverage customer data to improve the customer experience. It provides tips from various experts on developing a robust data strategy, asking the right questions of data to uncover insights, owning customer data to stay compliant with regulations, and how IoT can be used to inform and deploy customer experience solutions. The overall message is that marketers need to stop data from being fragmented and better connect customer touchpoints to deliver personalized experiences.
Harnessing Data for Better Customer Experience and Company SuccessTreasure Data, Inc.
As big data has exploded, the ability for companies to easily leverage it has imploded. Organizations are drowning in their own information, unable to see the forest through the trees, while the big players consistently outperform in their ability to deliver a great customer experience, faster, cheaper…As a result, the vast majority of companies are scrambling to catch up and become more agile, data-driven, to use their data more effectively so they can attract and retain their elusive customers...
In this joint deck by 451 Research and Treasure Data, you will learn how to enable your line of business team to own their own data (instead of relying on IT) to be able to:
- deliver a single, persistent view of your customer based on behavior data
- make that data accessible to the right people at the right time
- Increase organizational effectiveness by (finally) breaking down silos with data
- enable powerful marketing tools to enhance the customer experience
How to make your open source project MATTER
Let’s face it: most open source projects die. “For every Rails, Docker and React, there are thousands of projects that never take off. They die in the lonely corners of GitHub, only to be discovered by bots scanning for SSH private keys.
Over the last 5 years, I worked on and off on marketing a piece of infrastructure middleware called Fluentd. We tried many things to ensure that it did not die: From speaking at events, speaking to strangers, giving away stickers, making people install Fluentd on their laptop. Most everything I tried had a small, incremental effect, but there were several initiatives/hacks that raised Fluentd’s awareness to the next level. As I listed up these “ideas that worked”, I noticed the common thread: they all brought Fluentd into a new ecosystem via packaging.”
* 행사 정보 :2016년 10월 14일 MARU180 에서 진행된 '데이터야 놀자' 1day 컨퍼런스 발표 자료
* 발표자 : Dylan Ko (고영혁) Data Scientist / Data Architect at Treasure Data
* 발표 내용
- 데이터사이언티스트 고영혁 소개
- Treasure Data (트레저데이터) 소개
- 데이터로 돈 버는 글로벌 사례 #1
>> MUJI : 전통적 리테일에서 데이터 기반 O2O
- 데이터로 돈 버는 글로벌 사례 #2
>> WISH : 개인화&자동화를 통한 쇼핑 최적화
- 데이터로 돈 버는 글로벌 사례 #3
>> Oisix : 머신러닝으로 이탈고객 예측&방지
- 데이터로 돈 버는 글로벌 사례 #4
>> 워너브로스 : 프로세스 자동화로 시간과 돈 절약
- 데이터로 돈 버는 글로벌 사례 #5
>> Dentsu 등의 애드테크(Adtech) 회사들
- 데이터로 돈을 벌고자 할 때 반드시 체크해야 하는 것
Keynote on Fluentd Meetup Summer
Related Slide
- Fluentd ServerEngine Integration & Windows Support http://www.slideshare.net/RittaNarita/fluentd-meetup-2016-serverengine-integration-windows-support
- Fluentd v0.14 Plugin API Details http://www.slideshare.net/tagomoris/fluentd-v014-plugin-api-details
This document provides an introduction and overview of Hivemall, an open source machine learning library built as a collection of Hive UDFs. It begins with background on the presenter, Makoto Yui, and then covers the following key points:
- What Hivemall is and its vision of bringing machine learning capabilities to SQL users
- Popular algorithms supported in current and upcoming versions, such as random forest, factorization machines, gradient boosted trees
- Real-world use cases at companies such as for click-through rate prediction, user profiling, and churn detection
- How to use algorithms like random forest, matrix factorization, and factorization machines from Hive queries
- The development roadmap, with plans to support NLP
This document summarizes Johan Gustavsson's presentation on scalable Hadoop in the cloud. It discusses (1) replacing an on-premise Hadoop cluster with Plazma storage on S3 and job execution in containers, (2) how jobs are isolated either through individual JobClients or resource pools, and (3) ongoing architecture changes through the Patchset Treasure Data initiative to support multiple Hadoop versions and improve high availability of job submission services.
Muga Nishizawa discusses Embulk, an open-source bulk data loader. Embulk loads records from various sources to various targets in parallel using plugins. Treasure Data customers use Embulk to upload different file formats and data sources to their TD database. While Embulk is focused on bulk loading, TD also develops additional tools to generate Embulk configurations, manage loads over time, and scale Embulk using a MapReduce executor on Hadoop clusters for very large data loads.
John Hammink's Talk at Great Wide Open 2016. We discuss: 1.) the need for data analytics infrastructure that can scale exponentially and 2.) what such an infrastructure must contain and finally 3.) the need for an infrastructure to be able to handle un - and semi-structured data.
Treasure Data: Move your data from MySQL to Redshift with (not much more tha...Treasure Data, Inc.
This document discusses migrating data from MySQL to Amazon Redshift. It describes MySQL and Redshift, and some of the challenges of migrating between the two systems, such as incompatible schemas and manual processes. The proposed solution is to use a cloud data lake with schema-on-read to store JSON event data, which can then be loaded into Redshift, a cloud data warehouse with schema-on-write, providing an automated way to migrate data between different systems and schemas.
This document discusses migrating data from MySQL to Amazon Redshift. It describes MySQL and Redshift, and some of the challenges of migrating between the two systems, such as incompatible schemas and manual processes. The proposed solution is to use a cloud data lake with schema-on-read to store JSON event data, which can then be loaded into Redshift, a cloud data warehouse with schema-on-write, providing an automated way to migrate data between different systems and schemas.
This presentation describes the common issues when doing application logging and introduce how to solve most of the problems through the implementation of an unified logging layer with Fluentd.
Japan IBM Middleware User Community (JIMUC) 新春セミナーでの先進IT運用管理分科会の活動報告です。この分科会では Observability 製品:Instana / NewRelic / Datadog の3製品の機能比較をしています。今回はその中間結果をご報告しました。
This study aims to develop an interactive idea-generation support system that enables users to consider the potential side effects of realizing new ideas.
In idea generation, confirmation bias often leads to an excessive focus on ``convenience,'' which can result in the oversight of unintended consequences, referred to as the ``side effects of convenience.''
To address this, we explored methods to alleviate user biases and expand perspectives through system-supported dialogue, facilitating a broader consideration of potential side effects.
The proposed system employs a stepwise idea-generation process supported by large language models (LLMs), enabling users to refine their ideas interactively.
By dividing the ideation process into distinct stages, the system mitigates biases at each stage while promoting ideas' concretization and identifying side effects through visually supported dialogues.
Preliminary evaluation suggests that engaging with the proposed system fosters awareness of diverse perspectives on potential side effects and facilitates the generation of ideas that proactively address these issues.
1. Board Meeting
Treasure Agent
Presentation
Monitoring Service
August 15th, 2013 - 3:30PM PDT
Presented by
Hironobu Yoshikawa – CEO
Kazuki Ohta – CTO
Rich Ghiossi – VP, Marketing
Keith Goldstein – VP, Sales
Kengo Hirouchi – Director, Japan
Ankush Rustagi – Director, Marketing
Founder & CTO
太田 一樹
@kzk_mover
<[email protected]>
www.treasuredata.com
Copyright 2013
2. 会社概要
チーム概要
2011年12月創業、米国カリフォルニア州。
創業者は日本人3人組。2013年12月現在、社
員約30名。
Hiro Yoshikawa – CEO
Open source business veteran
Kaz Ohta – CTO
Founder of Hadoop Group Japan
Sadayuki Furuhashi – Chief Architect
MessagePack, Fluentd
ビッグデータの収集・保存・解析を一手に行
えるクラウドサービスを提供。他のサービス
と異なり、数日で始められるのが特徴。
サービスコンセプト
• すぐに使い始められる
• クラウドサービスとしての提供を行う
• シンプルな機能セット、手厚いサポート
• “Trend Setting Products” in Data for 2014
(Database Trends and Applications)
• “5 Hot Big Data Startups”
(Enterprise Apps Today)
Keith Goldstein – VP Sales & BD
VP, Business Devt, Tibco and Talend
Rich Ghiossi – VP Marketing
VP Marketing, ParAccel and HP
投資家概要
Sierra Ventures – (Tim Guleri)
Leading venture capital firm in Big Data
Bill Tai
Renown investor, GP Charles River Ventures
Jerry Yang
Founder, Yahoo!
Yukihiro “Matz” Matusmoto
Creator, “Ruby” programming language
James Lindenbaum
Founder, Heroku
5. 5
Project
• もともとはTreasure Data のデータコレクタ部分を、オープンソース化
• 2013年、国内外で広く浸透
• 世界中でデータを解析可能な形で収集するのをお手伝い
"We use Fluentd to collect massive data logs for our platforms. Having
developed a system based on Fluentd, we are now effectively monitoring and
analyzing our services in real-time. We are very much satisfied with its flexibility,
especially how easy it is to use in tandem with other systems."
"We utilize Fluentd to collect a very large amount of logs. The logs are
written into Hadoop HDFS clusters, and are also used to analyze
various service statuses in realtime. We also use many plugins from
rubygems.org to further enhance this mechanism."
Fluentd is very similar to Apache Flume or Facebook’s Scribe
[but] it’s easier to install and maintain and has better
documentation and support than either Flume or Scribe”
Fluentd オープンソースプロジェクトのユーザー例 (http://fluentd.org/)