SAS Data Management Technologies Supporting a Data Governance - - PowerPoint PPT Presentation

SAS Data Management Technologies Supporting a Data Governance Process

Dave Smith, SAS UK & I

Agenda

• Data Governance
• What it is
• Why it’s 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 Attributes Lineage Metadata Location Business Technical Status Links Definitions Importance Requirements

The Data Governance Journey

ORCHESTRATING PEOPLE, PROCESSES, AND TECHNOLOGY

Integrated Proactive Controlled Managed Unpredictable

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

• f 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

Pricing New Product Introduction Promotion Management Customer 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

IT Business Users Data Stewards Create & Consume Manage & Monitor Implement, Adapt & Extend

Common Data Governance Challenges

• Seen as an academic exercise
• The culture doesn’t support centralized decision making
• Considered an IT issue
• The ROI isn’t 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 what’s 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 sources

Business Owner

Define the key entities Identify the sources and responsibilities

1

Discover & Profile the Data

DQ Analyst

Qualify & Quantify actual issues with the Data

2

Design data quality standards

Business 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 measurement

5

Data Steward Business Owner

Update & Improve systems and processes Remediate & Improve

6

Operations and DI Experts DQ 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?