Data Monetization Through Governance Overview

Data governance has been majorly used to encompass data aspects as privacy, security,
and data compliance but not on monetization aspect. Ongoing data set governance
activities areaccordingly viewed as expenses and not investments, rather focus is given
to other technologies, processes, and frameworks.
Data monetization
Before we connect the data governance with the data monetization let us get a fair idea
about what is data monetization??
Enterprises are spread across regions, have subsidiaries, partners, branches and outlets.
This leads to multiple internal and external data sources, data consumers including
third party, technical applications set, big data sets and corresponding data quality issues.
Data monetization strategy includes to identify potential data sources, define the target
audience, evaluate data quality scorecard, determining the data weightage and accordingly
develop a marketing and sales plan of utilizing relevant dataset.
Even though data comprises around 20-25% of an organization’s enterprise value on
average, despite this figure, data still does not get the respect it deserves across companies
of every type. Data monetization and Governance are generally viewed as two independent
disciplines driving separate objectives.

1. Data monetization
through governance

2. 01
Abstract
Data governance has been majorly used to encompass data aspects as privacy, security,
and data compliance but not on monetization aspect. Ongoing data set governance
activities areaccordingly viewed as expenses and not investments, rather focus is given
to other technologies, processes, and frameworks.
Data monetization
Before we connect the data governance with the data monetization let us get a fair idea about what is data monetization??
Enterprises are spread across regions, have subsidiaries, partners, branches and outlets.
This leads to multiple internal and external data sources, data consumers including
third party, technical applications set, big data sets and corresponding data quality issues.
Data monetization strategy includes to identify potential data sources, define the target
audience, evaluate data quality scorecard, determining the data weightage and accordingly
develop a marketing and sales plan of utilizing relevant dataset.
Even though data comprises around 20-25% of an organization’s enterprise value on
average, despite this figure, data still does not get the respect it deserves across companies
of every type. Data monetization and Governance are generally viewed as two independent
disciplines driving separate objectives.
It is a structured approach to manage and utilize data in an organization which provides
the roles, collection of rules and processes that ensure privacy and compliance in an
organization's enterprise data management.
Governance can be done as macro and micro level, for any integrated technical architecture
the governance done on the individual application level is a micro one and the governance
deployed from source to target across data pipelines involving different applications is on
the macro level.
In this white paper we are going to recast data governance as a data monetization strategy
which is a platform that helps companies to monetise their internal data assets.
Let us start with the definition of the data governance framework.

3. There are few challenges of data monetization including business model, legal & regulatory,
security and privacy, organizational, and data management related.
Data monetization goes in sync with commercialization. Data commercialization is the
process of taking data and extracting value from it to generate revenue or create
cost savings. Data monetization, on the other hand, actively markets data to customers
as a product.
02
How governance supports data monetization
In this section let us understand the relevance of governance boosting data monetization.
As first step to generate additional revenue streams for the company there is need of organized data.
Domain specific data governance with the data marketplace platform supporting the
analytics capabilities should be considered as three data monetization business models.
Federated data governance is the appropriate approach to govern enterprise level big data
which involves data governance on regional level distributed across domains but with a
central control resolving common governance issues across regions.
Business benefits
Governance helps in resolving these aspects
1. Capture

Process

Retain

Publish

Archive
Data
Lifecycle
Monitor dataset at each stage
of its cyclic lifecycle
Create a recurring, repeatable process
leading to data monetization
Sales and Marketing gets insight into
customer preferences and behavior
Business users should be able to
control the governance
Legally compliance, data loss
prevention
Filtering of data silos reduce cost
Resolve data quality issues correcting
business intelligence results
Dataset version
Stakeholders
Unique risks
Regulatory
requirements
Business and
technical
considerations
Each stage of data lifecycle has its own...

4. 03
Governance boosts data monetization generating economic benefit by:
Monitoring dataset at each stage of its cyclic lifecycle
Every enterprise has a similar data life cycle, data gets loaded, get process, next it
gets store, further its published and gets archive ahead. At every step there is a
unique stakeholder, data version, specific risk and regulatory compliance required.
Governance committee monitors this data cyclic lifecycle
Data governance framework once setup can handle the recurring repeatable process leading to the data monetization advantage
Machine learning capabilities
Dataset stored across becomes historical data set applicable to be use for training by the machine learning capabilities of the Chainsys Smart data platform and suggest the insight into the customer preferences and behaviour to the sales and marketing team leading to the steps of the data monetization
Business controlling governance with legal team involvement
Tag technical data with business glossary making it easy to understand and use ahead
Smooth corporate-wide communications by data standardization
Business user to catch the governance easily and control it
Business legal team by making data compliant as per the regional regulations and preventing the data loss or any applicable fines
Improved data quality brings costs down as it
Controls de-duplication
Correct business intelligence results by not using corrupted data
Prevents incorrect data analysis in machine learning
Makes data suitable for sharing on marketplace within and across enterprises
Helps to avoid faulty data pipelines
Increase data efficiency

5. 04
Lower data management costs as it
List down connected dataset making it easy to understand
Easy data availability
Smooth data search results and its usability ahead
Appoints data custodian to be accountable for upcoming issues
Reducing Clients data storage cost with steps as
Differentiate between active and passive metadata
Discover data silos
Archive or delete the dataset as per there relevance and age
Analytics used by the sales and marketing team to promote dataset to target customers
Data access to data scientists, other analysts and business users
Correct data insights empowering business decisions
Proposed solution from ChainSys
One Platform for your
End to End Data Management needs
Chainsys provides unified services with data and analytics capabilities on cloud platform called as DataZAP, DataZen and DataZense making it a bundle of a smart data platform.
Data Migration Data Reconciliation Data Integration Data Quality Management Data Governance Analytical MDM Data Analytics Data Catalog Data Security & Compliance

6. 05
Having all these capabilities on the single platform makes the enterprise data management
including governance an easy task rather than governing multiple applications doing technical
task individually in the data pipeline, this stands as Chainsys differentiator.
Smart data platform
This data and analytics platform three services have technical capabilities as:
Connectors to different applications relevant for data ingress and egress
Support for data loading and transformation
Data migration support preferably from on premise to on cloud
Data quality rules designer including data cleansing, consistency, validation, enrichment
De-duplication of the dataset utilising the match and merge features
Structure and unstructured data support including transformation of the
unstructured data search as PDF, Document content to the structure format
Business process management designer to build workflow connected to how the data entry will happen on the User Interface or in the batch mode
Audit trail connected to every record including flow from data requester, next to the data reviewer and ahead to the data approver
Business process management also helps in designing the workflow directing the data quality rules set to be applied when the data gets loaded and what should be the next step as per the quality result
Comparison of different data versions
Disaster recovery and load balancing infrastructure
Securing the personal identity information utilising the data masking and encryption technologies
Platform has the machine learning support
Data visualization reports applicable to business insights
Data storage capability scalable to data lake for master, transactional dataset on the cloud

7. 06
Data catalog capabilities including metadata management which includes storing
entries about the user who has changed the data set, when was the data set changed,
how was the schema updated and other related details
Steps to data monetization
Smart data platform justifies to complete data governance requirement which includes
design of the data quality policies, implementation of the quality rules, tracking the
data quality scorecard of the monitored dataset, tagging the business glossary to the
technical schema, securing the personal identity information, the business insights on
the visualization reports, data lineage representation on the user interface, building up the
data marketplace between sources and consumers, business process management
designer to build up the workflow defining the data pipeline prospective flows and the
data catalog capability to store plus analyse the metadata information.
Hence building data governance framework using the ChainSys platform makes the
data monetization feasible including capabilities to:
Monitor dataset at each stage of its cyclic lifecycle
Machine learning defining next data strategies
Business team having governance control
Justify the legal team need to be regionally compliance on PII regulations
Improve data quality bringing costs down
Lower data management costs
Reducing data storage cost
Sales and Marketing team promote target customers as per the analytics
Data Classifications Business Metadata Data Quality
Risk Fields
High
Volume
No of PII Fields Identified
3
No of Fields Registered
16.67%
1
Registered Fields
Completeness
Validity
TimeframeNormal
Consider 100 80 60 40 20

8. 07
Best practices and KPIs
Governance Best Practices, KPIs via Smart Data Platform
Chainsys has been following best practices and KPIs as listed below in last data governance
implementations to achieve data monetization:
By tracking these KPIs, organizations can assess their progress, identify gaps,
drive continuous improvement in data governance
Follow top-bottom approach during planning phase and define the business objectives,
how the data governance framework is going to be effective to achieve the same
There should be a regular meeting on the status update as per the Sprint planning to track the data governance coordinating in the business returns as planned
Federated governance is the preferred mode as explained above
Engage regional business policy experts for inputs since every region has its own regulations
Agile methodology suits starting from the small and growing as per the learning on every step
Once the objectives are set start following the bottom top approach in which domain level stakeholders are going to define the local policies and the governance strategies to achieve the business objective
KPIs
Best Practices
Routing prioritization
and Notification
Measure business
values, set KPIs
Federated GovernanceStart small and grow
Engage Regional
Business policy experts
Audit TrailEducate Stakeholders
Clearly define
ownership
and accountability
Complete Data Pipeline rate
Golden Data KPI
Data Compliance KPI
Data Availability rate
Data Quality Scorecard on Accuracy,
Completeness, Uniqueness

9. 08
Educate the business stakeholders on their governance responsibilities since business
and technical team together compromise the governance committee
Audit trail is used to track and resolve the breakdown in any data pipeline
Route the responsibility within the team members to keep the data pipeline inflow
Prioritize data sources and the data set as per the business relevance
Send email notification to the stakeholder in the governance committee on data updates
Monitor key performance indicators (KPIs) indicating the status of the data pipelines, data set, the data quality dashboard, compliance check and take appropriate action if the indicator goes down
This all makes data monetization and commercialization applicable using Data Governance on ChainSys Smart Data Platform.
Bottom line
Governance team focus is on data itself, around data definitions, data lineage, and data policies. On other hand Business leaders are the experts in their respective departments but often look at data issues as challenges to work around – not through.
Break through this mindset by recasting governance committee having technical and
business stakeholders and unlock the Data Value to business gains.
IT, operations, change, and business leaders should work together in governance committee
with single objective on how the correct data will reduce costs, increase revenue, or improve
compliance.
Enabling these business outcomes will ensure that the data governance program is never
viewed as unnecessary bureaucracy – but rather an important strategic enabler.
Data ownership and accountability to stakeholders making them responsible on the
dataset management

10. 09
Contibutors
Contributors to this document include:
Document history
Updates to this whitepaper:
References
https://www.chainsys.com/
Sagar Jaswani, Enterprise Solution Architect, Chainsys
Change
Initial publication
Description Date
Data Monetization, first published Sept, 2023

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Data Management needs End to End
www.chainsys.com
Data Migration
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Data Integration Data Quality Management Data Governance Analytical MDM Data Analytics Data Catalog Data Security & Compliance