1. Ubiquity of Data Opportunites

• Virtually every aspect of business is now open to data collection and often even instrumented for data collection

• This broad availability of data has led to increasing interesting in methods for extracting useful information and knowledge from data – a realm of data science

2. Example:

2.1 Hurricane Frances

What the unusually local demand for products at Wal-Mart were in areas under threat of an approaching hurricane?

Water?, flashlights? wood?, nails?, beer?
• No! These are the most common answers!

👉 – Strawberry pop-tarts
• No need to cook. Kids love to eat. Easy to store longer

2.2 Predicting Customer Churn

How the data science team should use MegaTelCo’s vast data resources to decide which customers should be offered the special retention deal prior to the expiration of their contracts

3.Data Science, Engineering,and Data-Driven Decision Making

Data-driven decision-making(DDD) refers to the practice of basing decisions on the analysis of data.

Two types of Decision in DDD

• ① 데이터 안에서 찾는 의사결정
  Decisions for which “discoveries” need to be made within data

• ② 데이터에 기반한 의사결정
  Decisions that repeat, especially at massive scale, and so decision-making can benefit from even small increases in decision-making accuracy based on data analysis

4. From Big Data 1.0 to Big Data 2.0

data를 통해 그전에는 할 수 없었던 것을 하는것..!

Summary

Web 1.0	Web 2.0
기본 웹 기술 도입, 웹 존재감 및 운영 효율성 향상	웹의 상호작용적 특성을 활용, 소비자의 목소리 반영 및 소셜 네트워크 사용 증가

Big Data 1.0	Big Data 2.0
데이터 처리 능력 구축으로 운영 효율성 지원	이전에는 불가능했던 새로운 능력과 더 나은 성과 추구

5. Data and Data Science Capability as a Strategic Asset

Fundamental principles of data science: data, and the capability to extract useful knowledge from data, should be regarded as key strategic assets.

6. Data-Analytic Thinking

6.1 데이터 기반 의사결정

6.2 데이터 기반 정책결정.

6.3 What is Data-Analytic?

• When faced with a business problem, you should be able to assess whether and how data can be improve performance.

• Businesses increasingly are driven by data analytics, so there is great professional advantage in being able to interact competently with and within such businesses.

• Firms in many traditional industries employ teams to bring advanced technologies to bear to increase revenue and to decrease costs.

• This requires a close interaction between the data scientists and the business people responsible for the decision-making.