1

Data Governance is critical to enabling Data-Driven SuccessAnalytics &Insights
Data Driven
Decisions

DigitialTransformationCloudModernization &Data LakeManagement

DataMonetization
Optimize DataValue

Customer 720
Internal & External
Customer View

Data Privacy
Consumer Privacyand Regulatory
ComplianceBusinessOperations
Enablement
Data
Democratization &Literacy

Data Governance Model
Ensures high quality, consistently available curated data

Without effective Data Governance the effort to execute these initiatives increases and lessens their value returned.
Data Governance should be a well-defined strategic component and not a deferred project.

Data Governance Framework

Metadata Management

Roles & Responsibilities

Establishment of a business glossary to define andlocate data, including information about business andtechnical processes, data rules, and logical and physicaldata structures

Identify the right people to steward, sponsor,
socialize and deliver a positive return on data

Data Quality Management

Change Management
Continuous communication to
stakeholders informing them of policies,
requirements, and data changes

Data
GovernanceValueLevers

Profile, measure and monitor data quality toincrease its value and opportunities to use it
throughout the organization

Master Data Management
Technology Alignment
Data governance helps streamline softwaredevelopment lifecycles and ensure IT is adequately
supporting the business goals* Aligned to Data Management Body of Knowledge

Deliver a single, consistent and accurate view of criticalbusiness data across systems, reduce the cost of data
integration, and improve digital experiences forcustomers, products, employees, etc.

Data Governance Outcomes

Implementing an efficient and effective data ecosystem will provide
organizations the ability to:
Enable implementation oforganization’s digitalstrategy at reduced cost

Build and deploy reusable,conformed, clean datasetsthat support improved
customer, vendor andemployee experiences
Higher quality data enables
self-service capabilities
without relying on tribal
knowledge

Greatly enhance security
and information accessmanagement

Enable data analytics
capabilities to drive
operational efficiencies anddiscover new business
opportunities

Instilling a mindset of data quality
throughout business processes and acrosstechnology solutions will improve businessoutcomes and productivity

Data governance is critical to the success oftechnology modernization by providing alayer of simplification of the data ecosystem tobuild onReduction in data storage footprint andoperational risk by removing redundantcopies of data and lessening movement ofdata across the organization

Data Governance Powered by Microsoft Purview
Microsoft Purview is a unified data governance service that helps you manage & govern your on-premise, multi-cloud, & SaaS data
Microsoft Purview allows you to:✓ Create a holistic, up-to-date map of your data landscape with automated data discovery, sensitive data classification, and end-to-end data lineage✓ Enable data curators to manage and secure your data estate✓ Empower data consumers to find valuable, trustworthy data

https://docs.microsoft.com/en-us/azure/purview/overview

Data Governance Areas and Process
1. Data Discovery and Classification Mapping

Kyndryl has identified 5 key data
governance areas where clients can
extract value with Microsoft Purview

2. Metadata Management and Lineage
3. Efficient Data Consumption
4. Risk and Compliance Management
5. Data Analytics and Insights

Assess

IterateKyndryl process for Data
Governance with Microsoft Purview

ContinuousImprovement

Define &EstablishImplementData Governance Powered by Microsoft Purview: Advisory Services
Kyndryl Advisory & Implementation Services for Data Governance are aligned to Microsoft best practices and enhance the effectiveness of yourimplementation of Microsoft Purview. We begin by helping your organization define and baseline your data governance strategy.
1 to 2+ weeks

2 to 3+ weeks

1 to 2+ weeks

Phase 1DISCOVERY

Phase 2ANALYSIS

Phase 3
RECOMMENDATION
7DISCOVERY•

Current state analysis
o Stakeholders
o Processeso Technology•

Internal client requirements•

External requirements•Maturity score

321

ANALYSISRECOMMENDATION•

Future state analysis•

Tailored advise based on 1•

Gap analysis•

Estimated level of effort•

Obstacles to achieving desired
future state•

Microsoft Purview Solutions* Phase wise timelines are indicative and for limited scope typically for a BU, Application or data project

and 2

Data Governance Powered by Microsoft Purview: Advisory Services
Factors involved in assessing the maturity level of anorganization in Data Governance w.r.t people,
process & technology
Interview
Regulations

KPIs &SLAs

Workshops

Assessment
Criteria

Existing
Documents

Level 0:UnawareThere is noownership,
security or
any systemdefined fordata in theorganization

Level 1:
Aware
Business &IT Leaders
start tounderstandandacknowledg
e the valueofinformationand EIM

Level 2:
Reactive

Level 3:
Proactive

Level 4:
ManagedLevel 5:
Effective

Sharing of
information
takes lace
between theteams.. The
level ofadherence
to theinformation
management system islow

Information
Management system is
accepted &adopted.
Datagovernancebecomes apart of every
project

EIM
standards &policies are
wellunderstoodandimplementedTheorganization
has reached
its goal interms ofinformation
management.

Vision &Mission

Profile Data

Architecture
review

Enable Data Governance for Informed Decision Making &Enhance Regulatory ComplianceData Governance Powered by Microsoft Purview: Implementation Services
Kyndryl Advisory & Implementation Services for Data Governance are aligned to Microsoft best practices for implementation of Microsoft Purview.
Our implementation process for Microsoft Purview follows a well-established framework
1 to 2+ weeks

2 to 3+ weeks

Phase 1PLANNING & DESIGN

Phase 2IMPLEMENTATION

1-2 weeks

Phase 3
HYPERCARE
9••123PLANNING & DESIGNIMPLEMENTATION

HYPERCARE

Current state analysis
o Stakeholders
o Processeso Technology
o Internal client requirementso External requirements•Implement Purview solution as per sprintwise•Complete Client UAT•

Production cutover•

Monitoring and break-fix support•

On-call support for technical issues andtraining

Create a high-level design and sprint-wise
implementation plan

* Phase wise timelines are indicative and for limited scope typically for a BU, Application or data projectData Governance Powered by Microsoft Purview: Implementation Services
Build, Test & Apply
Activities involved in the implementation phase are:
1. Build, Test and Apply the changes in the system
2. Monitor the implementation done by tracking KPIs/ Metrics as defined
3. Review and assess improvement in DG metrics on periodic basis and redefine action points

Below Data Governance Components can be implemented using Microsoft Purview

Business
GlossarySource
Integration

Data
Stewardship

Data Lineage

DataClassification

Data Security

Data
Collaboration

Data Privacy

Policies &Procedures

Data Discovery

Auto Tagging

Data Governance Powered by Microsoft Purview: Managed Services

11Kyndryl’s Managed Service offering
brings together well-establishedprocesses, tools, frameworks tomanage and continuously improveupon Data Governance KPI’s forclients

Operationalize

Enhance

ReviewData Governance Powered by MS Purview: Managed Services
Operationalize, review and enhance

Kyndryl services help you
monitor and review data
governance implementations
as well as refine and re-alignthe processes based on theresults.

Data reliability

Data governance

DataOps, data quality and observability

Governance, security and privacy➢ Monitoring data pipeline uptime andsuccess rate

➢ Managing data tagging, classification, masking
and ownership➢ Triggering service management flows forpipeline and job failures➢ Start, stop and monitor data crawlers/bots fordata discovery and tagging➢ Monitoring and validation of data quality
attributes like freshness, uniqueness,consistency and completeness➢ Managing configuration of data governancetools➢ Ensuring distribution of data as per
defined policies and standards➢ Monitoring of data volume across data
platforms and report unanticipatedchanges in data volume.

➢ Publishing data governance reports to business➢ Managing roles, policies and permissions in thegovernance tool
➢ Managing glossary assets➢ Maintain and update data quality rules tomeet set outcomes➢ Maintaining scripts and monitoring data
governance dashboards➢ Data quality incident categorization,prioritization and tracking➢ Identify gaps in data lineage and report to stakeholders for remediation➢ Reporting of data quality scores andmetrics➢ Manual data tagging➢ Maintaining and editing data catalogues