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

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?