Articles and Presentations
English
Adweek (Monthly Series since 10/2020):
1: Why Data Scientists Must Act as Mediators and Translators Between IT and Marketing
2: How to Eliminate Emotional Factors From Predictive Analytics
Target Marketing Magazine (Monthly Series 11/2013-9/2020):
"Big Data, Small Data, Clean Data, Messy Data" by Stephen H. Yu
1: "Big Data" Is Like Mining Gold for a Watch - Gold Can't Tell Time
2: The Future of Online is Offline
3: Who's Your Scapegoat?
4: Cheat Sheet: Is Your Database Marketing Ready?
5: Why Model?
6: Chicken or the Egg? Data or Analytics?
7: Data Deep Dive: The Art of Targeting
8: It's All about Ranking
9: Big Data Must Get Smaller
10: Beyond RFM Data
11: Free Form Data Are Not Exactly Free
12: Missing Data Can Be Meaningful
13: Not All Databases Are Created Equal
14: Smart Data - Not Big Data
15: How to Be a Good Data Scientist
16: Don't Do It Just Because You Can
17: Marketing and IT; Cats and Dogs
18: How to Outsource Analytics
19: Analytics Providers Should Know the Audience
20: Where Is the Data Movement Going?
21: Still Trapped in the 'Digital' Silo?
22: Personalization is about the Person
23: Perspectives Matter in Analytics
24: Road to Personalization
25: No One is One-Dimensional
26: Barriers to Personalization
27: Data Atrophy
28: Prescriptive Analytics at All Stages
29: Patients Aren't Ready for Treatment?
30: What Does Personalization Mean to You?
31A: Persona Marketing Tricks
31B: Collecting Rare Personas
32: Segments vs. Personas
33: Customer Journeys Don't Start on Your Website
34: Putting Data to Use
35: Facing the Future
36: Data Must Flow, But Not All of Them
37: Don't Hire Data Posers
38: Election Polls and the Price of Being Wrong
39: Why Buzzwords Suck
40: Watch the Attitude, Data Geeks
41: Personalization Framework
42: Key Elements of Complete Personalization
43: Evangelizing Analytics
44: Stop Blaming Marketing Problems on Software
45: Every Figure Must Be Good, Bad, or Ugly
46: Personalization: What Is in It for the Customers?
47: Data Mining: Where to Dig First?
48: The Art of Data Categorization
49: Creating a Persona Menu (for You)
50: Resistance Is Futile
51: The Human Factor (in the Age of Machines)
52: The Secret Sauce for B2B Loyalty Marketing
53: The Keyword in 'Customer Journey' Is 'Customer'
54: Ditch Rule-Based Segmentation
55: Data Geeks Must Learn to Speak to Clients
56: Lessons from the Facebook Fiasco
57: Don't Let Old Habits Dictate Your Marketing Thoughts
58: Wrestling the One-Time Buyer Syndrome
59: Marketing Success Metrics: Response or Dollars?
60: Marketing Success Sans 'Every Breath They Take, Every Move They Make'
61: Replacing Unskilled Data Marketers with AI
62: Use People-Oriented Marketing: Because Products Change, But People Rarely Do
63: Marketing Metrics Aren't Baseball Scores
64: How to Consider the Buyer's Journey, Not Just the Channel
65: Factors for Marketers to Consider in Attribution Rules
66: Don't Settle for Last-Touch Attribution in Marketing
67: Automation - with a Little Help from Good Machines
68: Why the Email Batch and Blast Practice Is an Addiction and How to End It
69: Don't Be a Data Hoarder - Why Data Governance Matters in Marketing
70: How Marketers Can Throw Away Data, without Regrets
71: Machine Learning? I Don't Think Those Words Mean What You Think They Mean
72: Even AI Needs Clean Data
73: Why Many Marketing Automation Projects Go South
74: When You Fail, Don't Blame the Data Scientist First
75: Data Scientists Aren't Magical: Analytics Is a Team Sport
76: Don't Ruin Good Models by Abusing Them
77: Marketers Find the Least-Wrong Answers via Modeling
78: Data Will Lead Marketers into a New World in 2020
79: 'Too Much" is a Relative Term for Promotional Marketing
80: Analytics Only Benefit You When Properly Applied
81: Don't Blame Personalization after Messing It up
82: Understanding What a Customer Data Platform Needs to Be
83: Beware of One-Size-Fits-All Customer Data Solutions
84: Know What to Automate with Machine Learning
85: Models Are Built, But the Job Isn’t Done Yet
86: The Biggest Obstacle to Personalization Is the Creative Element
John Miglautsch Show 4/2019 (Video Interview):
Marketing Attribution Challenges
DMA Webinar Presentation 5/2018 (Video of Presentation):
Ditch Rule-Based Segmentation
DMA Webinar Presentation 5/2017 (Video of Presentation):
Road to Personalization
Interview with Career in Analytics 6/2016:
Advice for Aspiring Data Scientists
DMA Webinar Presentation 3/2016 (Video of Presentation):
Art of Targeting and Personalization
insideBIGDATA Article 6/2015:
Data Refinement: The Dirty Side of Data Science
Print+Promo Interview 4/2015:
5 Ways to Tidy Up Your Data Strategy
DMA Webinar Presentation 4/2015 (Video of Presentation):
Big Data, Small Data, Clean Data, Messy Data
- The Importance of Analytics and Data Refinement
2014 Data Strategy Presentation (PDF File):
Big Data, Small Data, Clean Data, Messy Data
- The Art of Targeting and Data Refinement
TV Interview at the QIBRAS Conference in Sao Paulo 9/2014 (Video):
Success Factors of Data Strategy
The Troyanos Group Radio Show 2/2014 (Audio):
On the Big Data Movement
Target Marketing Magazine Article 12/2012:
How to Deal with Missing Data - Databases, Zeros, and Imputation
Target Marketing Magazine Article 1/2008:
Database: Close the Loop Properly
Korean
중앙일보 전문가 칼럼 (2019년 8월 - 현재)
"유혁의 데이터 이야기"
1회: 빅 데이터로 재미 좀 보셨습니까?
2회: 사장님, 데이터 가지고 그러시면 안 됩니다
3회: 데이터를 잘 다루는 사람이란?
4회: 기계에게 목적을 부여하는 인간
5회: 훌륭한 데이터 사이언티스트는 만들어진다
6회: 데이터 사용하려는 목적부터 분명히 하라
7회: 데이터는 도구이지 공포의 대상이 아니다
8회: 데이터는 제대로 분석해야 맛이 난다
9회: 데이터 분석 결과는 단순한 의견이 아니다
10회: 데이터 가지고 거짓말하면 안됩니다
11회: 데이터 기반의 의사결정을 잘하는 방법
12회: 알고리즘이 지배하는 세상
13회: 할 수 있는 일이라고 다 해선 안 된다
14회: 빅데이터에는 상명하복이 통하지 않는다
15회: 데이터 기반 마케팅의 주체는 소비자다
16회: 데이터 분석가는 통역가이자 중재자 돼야
17회: 정보도 편식하면 안됩니다
유혁의 "데이터를 잘 써먹을 수 있는 구체적인 방법들" 단행본과 더불어 발간된 요약본
한국정보화진흥원(NIA) 발행 빅데이터 실무서 "데이터를 잘 써먹을 수 있는 구체적인 방법들"
한국정보화진흥원(NIA) 발행 빅데이터 실무서 "빅데이터 - 질문을 명확히 하라" (요약본)
-- 요약본의 PDF 파일
IT Daily의 소개기사 (2017년 2월 22일)
유혁의 IT Daily와 컴퓨터 월드 월간 연재 칼럼 (2014년 2월 - 2015년 8월)
"데이터를 잘 써먹을 수 있는 구체적인 방법들"
1회: 연재를 시작하면서 - 빅 데이터에 관한 생각들
3회: 빅 데이터의 핵심은 분석이며 분석의 중심은 모델링
8회: 자유형(Freeform) 데이터는 결코 공짜가 아니다
17회: 예측적 분석을 통한 소비자와의 Relevancy 유지
18회: 연재를 마치며 - Data Movement의 미래는?
IT Daily/컴퓨터 월드 주최 지상 좌담회 2015년 1월
"빅 데이터와 데이터 사이언티스트에 대한 15인 15색의 목소리"
IT Daily/컴퓨터 월드 주최 좌담회 2014년 11월
"정보의 흐름을 아는 사람이 가치를 창출한다" (지상중계)
IT Daily/컴퓨터 월드 주최 빅 데이터 컨퍼런스 기조연설 2014년 11월
"빅 데이터 성공의 조건은 무엇인가" (관련기사)
IT Daily/컴퓨터 월드 주최 빅 데이터 컨퍼런스 기조연설 2013년 11월
"빅 데이터에 관한 생각들" (비디오)
"빅 데이터는 요술방망이 같은 존재가 아니다" (관련기사)
IT Daily/컴퓨터 월드 빅 데이터에 관한 대담 2013년 10월:
"빅 데이터는 단지 지나가는 유행이 아니라 접근법 간의 벽을 허무는 과정"
Stephen H. Yu Photo: IT DAILY/COMPUTER WORLD