Big Data Concept - M-Culture

Big Data Concept for Ministry of Culture

6 November 2018

Big Data – Fundamental Concept

“Big Data should be driven by Business Needs,

not Technology” Nimal Manuel Partner, McKinsey & Company Shilpa Aggarwal Associate Principal, McKinsey & Company

The Business Value to be captured is crucial from Big Data.

What is Big Data?

What is Big Data?

Data in Motion

Data at Rest

Data in Many Forms

Information

Ingestion

and

Integration

Landing Area,

Hadoop

Analytics Zone

and Archive

Raw Data

Structured Data Unstructured Data Text Analytics Data Mining

Entity Analytics Machine Learning

Real-time

Analytics

Video/Audio

Network/Sensor

Entity Analytics

Predictive

Stream

Processing Data Integration Data Federation Data Quality

Federation

Data Streams

Master Data Management

Matching and

Linking

Stewardship

Reference Data

Information Governance, Security & Business Continuity

Business

Intelligence

Data Exploration

& Visualization

Predictive

Analytics Big Data

Infrastructure

Programs

Agencies

Researchers

Administrators

Others

Internal

Exploration, Integrated Warehouse

& Marts Zone

Discovery

Deep Reflection

Operational

Predictive

Customer

Call Center Social CRM

Big Data & Analytics – Core Functions & Services

Credit to IBM Reference Architecture

Big Data Analytics – Technology Platform Hadoop & Open Sources

Example of Big Data Platform Architecture

Refer to NigelTebbutt1’s cone-tm-digital-marketing-principles-pdf

Big Data Analytics – Technology Platform Hadoop & Open Sources Hadoop Component Stack

Refer to NigelTebbutt1’s cone-tm-digital-marketing-principles-pdf

Big Data Analytics – Technology Platform Data Lake Reference Architecture

Big Data Analytics – Technology Platform Data Lake Reference Architecture

Big Data & Analytics – Core Functionalities Technology Perspective

Source Systems

Structured & Unstructured Content

(Big Data Content)

Data Quality

Data Security

EDW, GIS,

Data Lake,

Data Virtualization

ETL/ELT

Data on Demand

Information Sharing

Descriptive, Predictive,

Prescriptive & Cognitive Analytics

Streams Analytics

Persistent Relationship Awareness

Content & Sentiment Analytics

Analysis Repository

Workflow & Case Management Visualization &

Link Analysis

Trusted Information Layer Establish, Manage, Share & Deliver information that

is accurate, complete, in context and insightful.

- Data Management

- Data Integration

- Data Virtualization

- Data Quality

- Data Security & Privacy

- Data Governance

Analytics Layer Intelligence, Descriptive & Predictive Analytics against

structured, semi-structured and unstructured information

Visual Analysis & Collaboration Operational dashboards, workflow, case adjudication

Operational Dashboards

Information Exchange

Credit to IBM Reference Architecture

Data Virtualization

Big Data Analytics Landscape and Life Cycle

Big Data Analytics Tool - R Model (example)

• What is R? – Open Source Data Analysis Software R Language (Procedural Language e.g. If-then-else)

R Engine, R Library, PMML package for R (Predictive Model)

Open for integration: SAS, SPSS, Excel, SQL Server, Oracle, …

• R-Model Development Stats, Math, Data Science

Big Data Statistics In R

Distributed Computing on Hadoop

Advanced Analytics

• R-Model Development examples Linear Regression, Logistic Regression, Multiple Regression

ANOVA, ROC Curve

Principal Components Analysis (PCA)

Decision Trees, Random Forests

Support Vector Machines

Neural Networks

Markov Chain Monte Carlo

Social Network Modeling

Geo Location

Face Recognition

etc.

Note: R .vs. Python: Similar, Python-Object Oriented with easy-to-understand syntax, R's functionality is developed with statisticians in mind and strong data visualization capabilities.

Data Visualization with R Example – Cowplot CRAN package (ggplot2 add-on)

Comprehensive R Archive Network (CRAN) is the main repository for R packages.

Big Data Analytics – Data Visualization

Big Data Analytics – Data Visualization

Big Data Analytics – Data Visualization

Ministry of Culture – Big Data Sources Heritage Buildings

Ministry of Culture – Big Data Sources ศิลปินแหง่ชาติ

Ministry of Culture – Big Data Sources โบราณสถาน

Ministry of Culture – Big Data Sources Heritage Culture

Ministry of Culture – Big Data Sources Digital Technology for Heritage Culture

Ministry of Culture – Big Data Sources Digital Technology for Heritage Culture

Canadian Museum for Human Rights - Intangible Collections Transmedia Storytelling: watching films, playing games, reading texts, observing artefacts, being immersed in mixed-media environments, …

Ministry of Culture – Big Data Sources Digital Technology for Heritage Culture

Smartphone + GPS + Compass feature +high speed wireless network AR/MR

Sample Use Cases

Social Engagement &Text Analytics

• With Social Engagement & Text Analytics, we can know

who involved

the number of ‘check-in’s

Polarities of the messages

Video Analytics

• With Video Analytics, we can know:

Number of participants

Age ranges

Sex

Trends

Centralized Data Storage & Analysis

• CMS – Content Management System Enterprise Search Engine Archiving Engine Open Data APIs

• Web Content Analysis Retain audience Understand how the site measures up against the others Know where the content needs improvement

Discussion & QA