Data Management Technologies - SAS ?· SAS Data Management Technologies Supporting a Data Governance…

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  • SAS Data Management Technologies

    Supporting a Data Governance Process

    Dave Smith, SAS UK & I

  • Agenda

    Data Governance

    What it is

    Why its needed

    How to get started

    SAS technologies which can assist Data

    Governance programs

  • Data Governance Defined

    Formal orchestration of people, processes and

    technology to leverage data as a corporate asset

  • Why Govern Data?

    Regulation

    Risk

    Efficiency

    Opportunity

  • The interconnectedness of people

    process and technology

    Data Item

    Owner

    Physical

    Relationships

    Quality

    AttributesLineage

    Metadata

    Location

    Business

    Technical

    Status

    Links

    Definitions

    Importance

    Requirements

  • The Data

    Governance

    Journey

    ORCHESTRATING PEOPLE, PROCESSES, AND

    TECHNOLOGY

    IntegratedProactiveControlledManagedUnpredictable

    No awareness

    No accountability

    Nascent awareness

    and Data Stewards

    Assigned DS

    Accountability of

    business owners

    Proactive

    behaviours on DQ

    Recognized DS

    DG office

    DQ culture

    No common

    language

    Application-centric

    Business

    definitions

    DQ processes

    Shared business

    definitions and

    rules

    DG policies

    DQ embedded in

    processes

    Optimised cross-

    functional

    processes

    No tools

    DQ tools

    Metadata

    repositories

    Shared metadata

    Business glossary

    Analytical MDM

    DG process tool

    and dashboards

    Operational /

    collaborative MDM

    Data as a service

  • Data Management vs. Data Governance

    Data management is a by-product

    of data governance

    Effective data management needs

    to be governed

  • Data

    Governance

    THE QUESTIONS IT ADDRESSES

    Buying

    System

    Warehouse

    Mgt System

    Promotions /

    Marketing

    Campaign

    Mgt

    POS WEB

    CRM /

    Loyalty

    Program

    Cons.

    Marketing

    Customer

    Exp.

    Digital

    Marketing

    Finance &

    Risk

    Call

    Centre

    Market

    insight

    Disparate needs for data consumption

    PricingNew Product Introduction

    Promotion ManagementCustomer Registration

    Emailing Marketing Campaign

    Data silos / Application Centric Data Generation & Manipulation

    Unmanaged cross-functional processes

    Who owns the data?

    Who can author data

    and how?

    How are conflicting

    needs addressed?

    How is inaccurate

    information

    corrected?

    Who can decide

    about the

    changes?

    What does good

    data look like?

  • The role of

    Data

    Stewards

    ORCHESTRATING CROSS-FUNCTIONAL COLLABORATION

    ITBusiness

    Users

    Data

    Stewards

    Create & Consume

    Manage & Monitor

    Implement,

    Adapt & Extend

  • Common Data Governance Challenges

    Seen as an academic exercise

    The culture doesnt support centralized decision making

    Considered an IT issue

    The ROI isnt clear

    Definitions and explanations of data governance are varied and

    contradictory

    Nervousness about the G word

  • SAS Data Governance Framework

  • Top-down

    Bottom-up

    Where to Start?

    Data Quality

    Analysis

    Impact & root cause

    analysis

    DQ Standards

    definition

    Quick Wins

    Data Dictionary

    definition

    Vision &

    Roadmap

    Organizational

    framework

    Data

    Stewardship

    model

    DG & DQ

    Processes

    Business

    case / ROI

    Prioritization of

    DM initiatives

  • Other Best Practices

    Understand whats important to management now

    Work within your culture

    Understand your current state before making the pitch

    Choose sponsors based on initiative owners

    Corrections at source & available to real time processes

    Treat Data Governance as a project

    Rely on the big-bang approach

    Treat all data the same way

  • SAS Technologies for Data Governance

  • SAS Data Management Platform

  • Data Quality Process

    Define the terms and

    sourcesBusiness Owner

    Define the key entities

    Identify the sources and responsibilities

    1

    Discover & Profile

    the DataDQ Analyst

    Qualify & Quantify actual issues with the

    Data

    2

    Design data quality

    standardsBusiness Owner DQ Analyst

    Design the business rules to enforce data

    quality and data services

    3

    Apply injection and

    execution

    Operations and

    DI Experts

    Embed the DQ services and business rules into the operating

    systems and DI processes

    4

    Measure & Monitor actual vs. expected,

    identify trends, allocated tasks

    Monitor & Publish

    DQ measurement5

    Data

    Steward

    Business

    Owner

    Update & Improve systems and processes

    Remediate &

    Improve6Operations and

    DI ExpertsDQ

    Analyst

  • The Relationship service

    The Relationship Service collects and stores metadata

    Content from SAS and sources outside of SAS

    Processes that include resources used in data management,

    business intelligence, and data integration

    Consists of Resources and Relationships

    Resources are metadata representations of data assets or

    processes

    Relationships describe how two Resources are related

  • Relationship Types

    Is dependent on

    Is parent of

    Contains

    Is synonymous with

    Is associated with

    Is equal to

  • Lineage Viewer

    Acts as a viewer on the

    relationships database

    Allows different views of data

    lineage including governance

    and impact analysis

  • Business Data Network

    Central definitions of Terms

    across the organisation

    Links business and technical

    definitions to enable

    collaboration and clarity

  • Federation Server

    Create federated views of data

    from diverse sources

    Apply row and column access

    control, data encryption and

    masking to sources

    Enable detailed logging of data

    access

  • www.SAS.com

    Questions?

    http://www.sas.com/

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