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NON- Invasive Workable Enterprise Data
Governance
By Bhaven [email protected]
Confidential | 2016
DISCLAIMER
Note: It is understood that the material in this presentation is intended for general information only and
should not be used in relation to any specific application without independent examination and
verification of its applicability and suitability by professionally qualified personnel. Those making use
thereof or relying thereon assume all risk and liability arising from such use or reliance.
Objectives ..Problem
● Understanding our data challenges and link them with our technological & architectural approaches to meet our business Enterprise Data (Information) Management modernization needs.
Confidential | 2016
Data Challenges ..Problem
● To understand our data challenges and find-out pathways to manage it○ Data is everywhere○ Data trust○ Data quality○ Multi-channel data (social media, web, clickstream, etc) : Velocity○ Many types of data from many sources: Variety○ Data volume ○ Data complexity
Confidential | 2016
Where we begin? ..Solution
● All must be workable… We ARE already govering data but we are doing it either informally or very vertical in nature.
We CAN formalize how we govern data by putting structure around what we are persently doing.
We CAN improve:
• How We Manage Data Risk and Secure Data
• Data Quality and Provide Quality Assurance
• Coordination, Cooperation, Communication Around Data
We DO NOT Have to spend A Lot of Money.
We NEED Structure. We should consider a Non-Invasive approach.
• Learning occurs when you see a change in thinking as a result of experience.
Confidential | 2016
How we proceed? ..Solution
● Before we start the term “Data Governance”, we have to start with what and where is governing happening. So, there are three interrelated and key concepts or terms that needs to be understood:
● Enterprise Information Management
1. EIM is the program that manages enterprise information assets to support the business and improve value.
2. EIM manages the plans, policies, frameworks, technologies, organizations, people, and process in an enterprise toward the goal of maximizing the investment in data content.
● Data Management
1. The function that develops and executes plans, policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information.
● Data Architecture
1. A Master set of data models and design approaches identifying the strategic data requirements and the components of data management, usually at an enterprise level.
Confidential | 2016
Governance – V● Definition: Data governance (DG) refers to the overall management of
the availability, usability, integrity, and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures.
Data Information, and content
life cycle
Confidential | 2016
Enterprise Information Management Framework ..Solution
Org
aniz
atio
n P
rogr
ams,
Pro
ject
s, A
pp
licat
ion
s
Org
aniz
atio
n A
cco
un
tab
ility
, & C
om
plia
nce
Business Principles, Rules, Policies
Definition, standards, location, context
Information everyone references- Asset, Customer, Users, Subscribers, Languages, country, etc.
Information everyone uses to get things done
OTLP AppsOperational Reporting
Digital Products
Analytical/BI
Au
dit
Bu
sin
ess
Met
adat
a C
atal
og
Ente
rpri
se A
rch
itec
ture
: So
luti
on
, Dat
a,
Inte
grat
ion
& B
I
Information Life Cycle Management
Dat
a Q
ual
ity/
Pro
filin
g
Confidential | 2016
Technology and InfrastructureInformation Integrity – Privacy, Security, Control
Business Environment, Drivers, Goals, Priorities
Business Environment, Drivers, Goals, Priorities
Business Principles, Rules, Policies
Definition, standards, location, context
Information Everyone References or Uses to Get Things Done: 3600 View of the Customer
Data Quality / Profiling
Business Metadata Catalog
Enterprise Architecture
Audit
Organizational Accountability &
Compliance
Org
aniz
atio
n P
rogr
ams,
Pro
ject
s, A
pp
licat
ion
s
Technology and Infrastructure, Information Integrity
Data Definition Process
• Process and rules for creating & maintaining Asset & customer data dictionaries
Data Monitoring & Measurement Process
• Establish rules and metrics for monitoring and improving customer batch data load performance
Data Access & Delivery Process
• Protocols for timing, maintenance and delivery of asset & customer data to /from external vendors and internal clients
Roles & Responsibilities
BUSINESS & TECHNOLOGY:
• Governing bodies for data governance
• Producers vs. Consumers of standards
Data Governance Training & Education
BUSINESS & TECHNOLOGY:
• Establish training process in standards and policies
Data Planning & Prioritization
BUSINESS:• Determine
Business Value & Urgency
TECHNOLOGY-Identify:• Determine
Technical Feasibility & System Impact
Organizational Change
Management
BUSINESS & TECHNOLOGY:
• Manage Data Governance Protocols for new initiatives, e.g. Kid’s project
Business Metadata Catalog
BUSINESS & TECHNOLOGY:• Asset DD• Customer Data
Dictionary• Customer Naming
ConventionsMaster (Reference)
Data Standards
• Which type of customer data (if any) should be referenced via master data?
Enterprise Architecture Solution
• Are governance standards in place to ensure consistency for data model and architectural designs and artifacts?
Technology & Tool Standards• BUSINESS: Are requirements established regarding
how data will be used, e.g. operational, analytical, predictive?
• TECHNOLOGY: What are the standards regarding
the right tool for the right client at the right time? What are the application versioning standards? What are the data integration tool standards?
Ente
rpri
se D
ata
Pla
tfo
rm:
Big
Dat
a In
itia
tive
s
Data Accessibility
BUSINESS: Which business area can
access Asset, customer?
TECHNOLOGY: Which data store owns
the master data and at what granular level
Data Availability
BUSINESS: What is the customer data
refresh frequency needed for the business?
TECHNOLOGY: How are upstream and
downstream customer refresh dependencies managed?
Data Quality
BUSINESS: Does the
customer data have business value? Are data quality controls in place?
TECHNOLOGY: What are
the customer data cleansing protocols? How is customer data persisted?
Data ConsistencyBUSINESS: Are new parties created across systems
following standardized conventions on a consistent basis?
TECHNOLOGY: Are customer related tables using
consistent naming conventions, default values,
truncate/load procedures, etc.
Data SecurityBUSINESS: Can Creative Services access film production
parties? Can Program Planning access contract licensor parties?
TECHNOLOGY: Does customer info require encryption
protocols and protection from unauthorized access?
Audit
BUSINESS: Does
customer related data need to comply with Sarbox requirements?
TECHNOLOGY: Does
customer related data require trace or logging tables, Sarbox rules, etc.?
Information Lifecycle Management
Enterprise Data Strategy and Design Framework …Solution
Confidential | 2016
ARM & SRMDRMBRM
End to End Process
Business Process
Detailed Process
Use Stories
Enterprise Data Subject Areas &
Data Flows
Conceptual data Model
Logical Data Model
Data Specifications
Major/Minor System portfolio
System inventory & process alignment
System Interface
Interface Specifications
Enterprise Services and functions
Explicit services & system specifications
Service Configuration details
Service Customization Requirements
Enterprise
Strategy
Enterprise
Design
Segment
Architecture
Solution
Architecture
Business Artifacts Data Artifacts Application Artifacts Technology Artifacts
FEA Reference Model
Zachman Principles
Strategy
Solutions
BRM-Business
Reference Model
DRM- Data
Reference Model
ARM-Application
Reference Model
SRM- Security
Reference Model
Confidential | 2016
Universal Data Layer- Architectural framework Sample for Discussion..
Dimensional Data Layer
TIME
Asset - Party
Product/Version Level
Format Level
Reference Data Layer
Prime
RDS
SeriesSeasonEpisode
Product
Version
FormatParty
GenreSynopsis
BlurbTitle
Synopsis
Award
Plot
Genre
RDS DIM
Asset Core
Asset Derived
Asset Party
Time
Broadcast
C2/Rights
Lowest GrainOperational/Analytical
UDL
Linear Schedule
Non-Linear Schedule
Ad-hoc
Available
NewIf needed /Future/Unknown
UDL Information Data HubUniversal Data Layer Presentation LayerRDS (Reference Data Store)
Asset Core PBL
Asset Episode
Party
Linear/Non-Linear
Service, Channel,Brand
Language, Org
Territory
Service Media
Service Media
Prime MindRpt
Comment
Offering Sup Role and Role Group
Burst
Category
Asset_burst_catgAsset Burst &
Category
Mobile
B2B/B2C
Self Service
Big Data Analytics
UDL
OthersAffiliate
Device
Available-Not used in UDL
Additional AssetsBundle
Promo
WIP
Rating/Advisory/CC
Party
Soap
Promo
Bundle
TowerRestrictions
ProdQry
Tactical
Higher GrainAnalytical UDL
Information
Data
DataData
Data Integration
OthersAffiliate/Device
Confidential | 2016
RDS Lifecycle Phase
Concept
Definition and Decision Processes
Discover Need
Idea generation
Initial Assesment
Preliminaryreview
In Depth Assesment
Detailed Review
text Prioritization
Rank against others ideas
text Allocate Resources
Identify funding and manage resources
text
text = Major Decision Point
NOTE: Phases process activities can be iterative, skipped, or sequential
Environment Scanning, Needs Assessment,
Scoping, and Prioritization
Buy, Build, or Integrate Release
Operate and Manage
Project Lifecycle Process
Kick Off/Initiate
Plan
Identify Requirements &
Schedules
Design
Determine Solution Architecture
text Implement
Build, Buy, or Integrate Solution
text Release
Provision and Launch
Test text
High Level Project Scoping
Validate
Production Support & Service Management Process
Service Management (e.g. customer relationship management, Customer Support, Lifecycle management)
Incident and Problem Management (e.g. Monitoring, Troubleshooting, Resolution, Root cause analysis)
Availability Management (e.g. Reliability, Capacity, Business Continuity, Security)
Configuration, Change & Release Mgmt. (e.g. Asset tracking, Upgrades, Change Control reviews)
Replace or Retire
Retire / Introduce Process
Validate Need Research Options
Research replace or retire options
Provision and Launch
Determine Approach text
Verify need to Retire / Introduce
Created Date:10/20/2014 Last Changed Date: 10/30/2014 By: Data Architect
Execution Strategy Framework (How?)…Solution
Confidential | 2016
Workable Data Governance Operating model of Roles & Responsibilities
TBD
Q&A
Confidential | 2016
Confidential | 2016