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Data Architect : Roles, Requirements and Values as Future Job

Sungwoo Chang Lead, Solution Engineering Oracle Korea

Global HR Forum 2018

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Agenda

Change in Data Management

Data Architect as Future Prospect

Roles and Requirements

Suggestions

2

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Change in Data Management

3

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Needs for Data Management and Analytics Everywhere

Product Requirements/ Specifications Mgmt

Opportunity Status

Delivery Order

6

Logistics Mgmt

Product Quality, Customer Claims & Warranty Mgmt

Planning

Product Lifecycle Mgmt

Marketing & Sales Mgmt

Pre-Sales and Sales Activities

Supply Chain & Production Planning

Production Mgmnt & Manufacturing

Transportation Plan and Execution

Delivery

After-sales Services

Finance & Accounting

Human Resources

Management

Yield Rate Improvement

Early detection of Product Defects

Product Usage Monitoring & Advices

Procurement Cost Reduction

VoC Analysis

Marketing & Sales Activities Optimization

Work-Style Reform

Demand/Sales Forecast

Quality Mgmt & Assurance

Maintenance Optimization & Equipment Failure Prevention

Product Failure Predicts with Proactive Service Actions

Productivity Improvement

Employee Satisfaction Improvement

Global Cost Mgmt

Best offer analysis

Social Listening Detailed Mgmt

Simulation

Decision Making Support

Increasing needs of decision making based on data everywhere Voice of Customer Voice of Product

Voice of Factory

Voice of Enterprise

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Data Architecture Considerations RDB Only RDB + NoSQL + Hadoop + α : “ Best Together ”

Relational Database Hadoop

Best Use for :

Business Data (Account, Customer etc)

Rigid transaction processing(ACID)

Guaranteeing logical consistency and physical safety for multiple users concurrent access

100% SQL Compliance

High cost

Best Use for :

Sparse data such as web/sensor logs

Parallel Batch Processing

no support for transaction processing

Fit for data collection and preprocessing

Low cost

NoSQL

Best Use for :

Data for SNS, Blog etc

Partial Consistency(Delay allowed)

Flexible and efficient

Designed for special purpose

Complimentary with RDBMS

Tips for TCO Reduction : Storing Data at Appropriate Systems based on the Characteristics of Data

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Future Data Architecture

`

Traditional Data

Customer Product

Revenue Order

Contract

Call Center ….

Emerging Data

SNS

Web

Mobile

Sensor

Log

Ext. Data

….

Realtim

e

Layer

Streaming

Ingestion

Real-Time

Machine learning Model

Real-Time Monitoring

Scala

Big Data Lake

Hadoop Hive Hbase Spark Impala Oozie Zookeeper

Solr

Hue

Landing Staging

내부

외부

기준 정보

분석/연계 모델

분석 레파지토리

Analytical Sandbox

Big Data SQL

Agile Mart Service Mart

AI (CDSW)

Machine/Deep Learning

GPU CPU

Batch

Layer

AI - Machine & Deep Learning based Data Platform

AI-enabled

• Adopting ML/DL on big data for prediction purpose

40G (전처리 결과)

Big Data Lake

Analytical Sandbox

Common Service Layer

• “Source Data Store “

• Landing Area to process Hadoop-based data suitable to analytical purpose

• “Free Analytical Space“

• For Analyst using Big Data SQL to access Hadoop and RDB in an integrated way

• “Space for getting business-oriented analytical information

• Statistical analysis is required

• Visualization is critical

• A number of highly trained people are required to design, implement and manage the data architecture

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Self-Driving

Self-Repairing

Self-Securing

Reduces human labor

Protects itself from attacks

Keeps business up & running

Data management also goes to ‘Autonomous’

New Trends : Autonomous Database

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Data Architect

: Roles and Requirements

8

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Changing Roles in Data Management

Free from Simple and Repetitive Jobs More Requirement for Creative Work

Reducing Maintenance Work Increasing Innovative Work

• Less time for infra management

• Less effort for patch and upgrade

• Less work for availability design

• Less work for system tuning

• More time for data architecture design

• More work for new application

development in very short time

• More labor for data analytics

• More investment to enhance data

collection and quality evaluation

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From DBA to Data Architect To-Be

“Data Architect”

Focusing on Business Support as Data Owner

• Designing optimized data

architecture including cloud

technology

• Defining security and ILM process

• Business support based on data

analytics

• Close work with developers

As-Is “DBA”

Focusing on

DB Maintenance

• Install, Operation,

Monitoring

• Backup/Recovery and

Trouble-shooting

• Relatively low portion of

business-related work

Transition

Trainings for

Transition

• EDA(Enterprise Data

Architecture) training

• Big data technologies -

Hadoop, Spark, NoSQL

• Demo & Hands-On capabilities

for various cloud services

• Emerging technologies such as

AI, Block-chain, IoT etc

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Requirements • Spectrum of technologies required to be a Data Architect

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Suggestions

Balanced Renovations in 4 Key Pillars

Technology, Architecture, Competency, Leadership

Sensemaking

Data Analytics with Humanities

12

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Four Areas where Renovations are Required - Technology, System, Personal Competency and Leadership

• Technology

– All-Data Architecture : RDB + NoSQL + Hadoop

Best Together

– Spark with AI for real-time analytics

• Architecture

– Micro Services Architecture with Cloud Adoption

• Personal Competencies

– Data Architect with Sensemaking

• Leadership

– Productive team coming up with good services based data analytics and customer understanding

– New Leadership : Google ‘Project Oxygen’

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Book Review : Sensemaking

• What is Sensemaking? – A way of getting practical knowledge based on Humanities

– Perceiving objects not just from themselves but from relationships of themselves

Understanding context of data is required as well as data themselves

• Five Ways of Sensemaking – Culture, not Individuals

– Thick data, not just thin data

– The Savannah, not the Zoo

– Creativity, not Manufacturing

– The North Star, not the GPS

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Summary - Data Architect is the Foundation of all in the age of Data -

Change in Data Management

New Data Architecture : RDB + NoSQL + Hadoop with On-Premise/Cloud mixture

Changing Roles in Data Management

From DBA with micro and system-focused jobs

To Data Architect with macro and business-oriented creative works

Suggestion

Not only personal competencies but also team work and new leadership