Workable Enteprise Data Governance

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Transcript of Workable Enteprise Data Governance

  • NON- Invasive Workable Enterprise Data

    Governance

    By Bhaven Chavanbhaven2001@yahoo.com

    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 ..SolutionO

    rgan

    izat

    ion

    Pro

    gram

    s, P

    roje

    cts,

    Ap

    plic

    atio

    ns

    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. Kids 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

    Forma