Data Monetization

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Scan any number of financial industry news publications, and stories regarding Wall Street’s hunger for new data sets to improve alpha abound. While this might seem like a new trend, monetizing data - or the ability to turn corporate data into revenue streams - has existed for decades. But both the supply side and the demand side have changed. On the supply side, the extreme variety of data that now exists (location-based, geospatial, socio-demographic, online search trends, pricing, etc.) combines with high computing power and new digital requirements to create a fertile data market environment. On the demand side, to remain competitive, companies in a wide variety of industries, not just the financial sector, are leveraging data in all forms to maintain an edge or be disruptive. During this session we’ll explore what data monetization is and the forms it can take; characteristics of data that could make it more valuable to external parties; and key considerations in making data products available to external parties. Intellectual property, data privacy, and contractual issues will also be explored.

Business

Dataversity
Turning Corporate Data
into Revenue Streams
CDO LLC
August 1, 2017

Copyright 2017 CDO, LLLC. All rights reserved.
Who We Are
❖ Micheline Casey,
@michelinecasey: Senior
executive with over 20 years'
experience to help companies
use data and analytics for
growth.
❖ Q McCallum, @qethanm:
Seasoned technology
consultant and writer with 20
years' experience applying data
and technology to business
problems.

Copyright 2017 CDO, LLLC. All rights reserved.
What We Do
❖ Data strategy
❖ Data monetization
❖ Data science
assessments
❖ Data ethics
CDOGuidance.com

Copyright 2017 CDO, LLLC. All rights reserved.
Data + Analytics = New Revenue Streams
❖ Increasing volumes of data
and digital exhaust over the
maturity lifecycle of a
company
❖ Demand for interesting data
sets across a variety of
industries and ecosystems

Copyright 2017 CDO, LLLC. All rights reserved.
Monetization Opportunities
New products/
services/
business models
New features to add
to existing products
Net new products
Wrap-around service
offerings
Increase CLV
Improve product
recommendations
Increase product/
service penetration
Improve ad
targeting
Improve proﬁt
margins
Funnel optimization
Price optimization
Accuracy of supply/
demand predictions
Data-as-a-service
Insights-as-a-
service
Answers-as-a-
service:
Data Products
INDIRECTDIRECT

Copyright 2017 CDO, LLLC. All rights reserved.
Strategic Questions
❖ What is the value
proposition?
❖ What business models and
use cases?
❖ Which technical enablers
need to be put in place?
❖ Which capabilities and
partnerships?

Copyright 2017 CDO, LLLC. All rights reserved.
Key Considerations
Market &
customer needs
The data
Product
delivery
Regulatory
items and
T&Cs
Legal and
intellectual
property

Copyright 2017 CDO, LLLC. All rights reserved.
Talk To Us - Q & A
❖ What can we
answer for you?

Thank you!
CDOGuidance.com
Micheline Casey,
m@cdoguidance.com
Q McCallum,
q@cdoguidance.com

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The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

Who Should Own Data Governance – IT or Business?

DATAVERSITY

It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

Data Management Best Practices

DATAVERSITY

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

MLOps – Applying DevOps to Competitive Advantage

DATAVERSITY

With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered. Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence. Topics you will hear addressed in this webinar: Data observability – what is it and how it can complement your data quality strategy Why now is the time to incorporate data observability into your DataOps strategy How data observability helps prevent data issues from impacting downstream analytics Examples of how data observability can be used to prevent real-world issues

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By consolidating data engineering, data warehouse, and data science capabilities under a single fully-managed platform, BigQuery can accelerate computation, reduce data analysis costs, and streamline data management. Following in-depth interviews with a security services provider and a telecommunications company, Nucleus Research found that customers moving to Google Cloud BigQuery from on-premises data warehouse solutions accelerate data processing by over 75 percent while reducing data ongoing administrative expenses by over 25 percent. As BigQuery continues to optimize its platform architecture for compute efficiency and multicloud support, Nucleus expects the vendor to see rapid adoption and further penetrate the data warehouse market.

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Data Monetization

1. Dataversity Turning Corporate Data into Revenue Streams CDO LLC August 1, 2017

2. Copyright 2017 CDO, LLLC. All rights reserved. Who We Are ❖ Micheline Casey, @michelinecasey: Senior executive with over 20 years' experience to help companies use data and analytics for growth. ❖ Q McCallum, @qethanm: Seasoned technology consultant and writer with 20 years' experience applying data and technology to business problems.

4. Copyright 2017 CDO, LLLC. All rights reserved. Data + Analytics = New Revenue Streams ❖ Increasing volumes of data and digital exhaust over the maturity lifecycle of a company ❖ Demand for interesting data sets across a variety of industries and ecosystems

5. Copyright 2017 CDO, LLLC. All rights reserved. Monetization Opportunities New products/ services/ business models New features to add to existing products Net new products Wrap-around service offerings Increase CLV Improve product recommendations Increase product/ service penetration Improve ad targeting Improve proﬁt margins Funnel optimization Price optimization Accuracy of supply/ demand predictions Data-as-a-service Insights-as-a- service Answers-as-a- service: Data Products INDIRECTDIRECT

6. Copyright 2017 CDO, LLLC. All rights reserved. Strategic Questions ❖ What is the value proposition? ❖ What business models and use cases? ❖ Which technical enablers need to be put in place? ❖ Which capabilities and partnerships?

9. Thank you! CDOGuidance.com Micheline Casey, m@cdoguidance.com Q McCallum, q@cdoguidance.com

Data Monetization

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