Governing Quality Analytics

The First Step in Information Management
www.firstsanfranciscopartners.com
Produced by:
MONTHLY SERIES
Brought to you in partnership with:
July 6, 2017
Governing Quality Analytics

Polling Questions
pg 2© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
 How effective is your organization in governing its analytics
processes and the responsible analytics teams?
− Very effective
− Somewhat effective
− Not effective
− This is not a focus for us today
 Is your organization taking advantage of analytics derived
from Big Data?
− Yes
− No
− Not sure

Topics for Today’s Analytics Webinar
 Degree of Governance Necessary to Use Analytics
Effectively
 Level of Organizational “Maturity” Required to Be
Effective (and be governed)
 Additional Capabilities Needed for Big Data
Governance
 Support Required to Leverage Insights Gained From
Big Data Analytics
Our assumption is all of the “analytics” we will mention are useful.

But First Some Definitions and Background …

Data Governance (DG)
 Data governance is the organizing
framework for establishing the
strategy, objectives and policy for
effectively managing corporate data.
 It consists of the processes, policies,
organization and technologies required
to manage and ensure the availability,
usability, integrity, consistency,
auditability and security of your data.
Communication
and Metrics
Data
Strategy
Data Policies
and Processes
Data
Standards
and
Modeling
A data
governance
program consists of
the inter-workings
of strategy,
standards, policies
and communication

pg 6
“Analytics”
 Is
− Algorithms and Artificial
Intelligence
− Data Science
− Data Mining
 i.e.
− Sophisticated analysis of data
and algorithms to extract new
insight about key elements of
your business or organization
 Isn’t
− A complex query
− Visualization
− Big Data
 i.e.
− You can do analytics within “small data”
− Different from BI and reporting, BUT on
the same spectrum of using your data
assets for business understanding,
decision-making and action
© 2016 First San Francisco Partners www.firstsanfranciscopartners.com

Degree of Governance Necessary
to Use Analytics Effectively

Effective Use vs. Degree of Data Governance
 Effective use means exploiting
data assets
 Degree of governance means
increasing oversight of the data
supply chain
− Quality
− Sourcing
− Distribution
− Meaning
Moreextensive
applicationofgovernance
More robust exploitation of data

data assets
supply chain
− Quality
− Sourcing
− Distribution
− Semantics
 For our discussion we will
consider three degrees of DG
Active
Federated Oversight
DG “lite”

data assets
supply chain
− Quality
− Sourcing
− Distribution
− Semantics
 For our discussion we will
consider three degrees of DG
Active -
• DG is acculturated, part of entire data and system life
cycles
Federated Oversight
• There is an acknowledged role of DG and some
central, high-level oversight
DG “lite”
• New function, limited engagement of DG

Review – Types of Data Analysis
Descriptive
Analytics:
• Aims to help
uncover
valuable
insight from
the data being
analyzed
• Answers the
question:
“What
happened?”
Predictive
Analytics:
• Helps forecast
the behavior
of people and
markets
• Answers the
question:
“What could
happen?”
Prescriptive
Analytics:
• Suggests
conclusions or
actions that
may be taken
based on the
analysis
• Answers the
question:
“What should
be done?”

Degrees of Data Governance by Analysis Types
Descriptive Predictive Prescriptive
DG “lite”
Quality – Not a concern
Sourcing – Some awareness
Distribution – Adhere to privacy
Meaning – Rarely a concern
Quality – Addressed by data volume
to an extent
Distribution – Wider access requires
more oversight
Meaning – Context required
More than DG “lite” required
Federated
oversight
Quality – Tighter data
Distribution – Wider access requires
more oversight
Quality – Volume dampening
Distribution – Wider requires more
oversight
Active
Quality –RDM, DQ profiles
Sourcing – Solid metadata
Distribution – Managed
Quality – DQ program, RDM
Sourcing – Data lineage
Distribution – Business function level
Meaning – Context, model
management

Level of Organizational “Maturity”
Required to Be Effective

Maturity of Organization
 What kind of analytics?
 Maturity is based on the use of data.

Review of Architecture Maturity
Recognized Data-Driven
Insight-
Driven
Audience
expands
Scattered
Analytics
Value has
limited
audience
Embedded
• Support tactical
operations
• Monetization
of data
Isolated
Your architecture will not be static.
Presenting only the ultimate future state is not practical.

Views of Data Maturity

Governance and Types of Maturity
Descriptive Predictive Prescriptive
Isolated
Not much of a role for
governance; in fact can be seen
as invasive – best to observe
and build relationships
Scope of DG depends on what is done with the results of
modeling; i.e., how significant the results are to the
organization. This is often where DG starts. Again, build
relationships with data science.
Recognized Governance needs to be present, at least in terms of data quality and ensuring consistency.
Data-Driven
DG, in addition to DQ and consistency, can also play a role in
aligning the business to new possibilities resulting from
analytics.
Governance needs to be part
of any definition of new
business processes, ensure
tracking of rules and machine
learning and, in general,
ensures business alignment.

Additional Capabilities Needed
for Big Data Governance

Data Governance to Remediate Architecture Challenge
 Secondary oversight of architecture
 Principles to guide decision-making
 Oversight of methods for architecture design
 Interaction with enterprise architecture
 Interaction with procurement
 Policies for approved usage of architectural
components

How to Use DG to Address Data-Driven
 Work with data scientists to diagnose possible impacts
 Determine organization tolerance for closed-loop activity
 Doublecheck with Compliance
 Work on the organization change issue of not liking the answer
Graphic: Deep Thought, from “Hitchhikers Guide to the Galaxy,” Douglas Adams, Film Version, 2005 Buena Vista Distribution

How to Integrate OCM Activities Into Current DG Program
 Place Chief Analytics Officer or data scientist on DG
council
 Appoint an “analytics steward”
 Update the formal (if there is one) Organization Change
Management plan
− Modify training and communications
− Update resistance guidelines
 Add taxonomies and algorithms to the glossary-type
stewardship activities or appoint algorithm custodians
 Analytics and Big Data is part of EIM, so all alignment
activities apply
Graphic: Deep Thought, from “Hitchhikers
Guide to the Galaxy,” Douglas Adams, Film
Version, 2005 Buena Vista Distribution

Data Governance and Big Data to
Remediate Compliance Challenge
 Tight integration with
Privacy Officer
 Specific policies addressing
privacy and compliance
 Identification of risk factors
related to compliance
− e.g., predicative and
prescriptive analytics to
identify breach pattern
Likelihood
of data
related
compliance
issue
Severity of data related compliance issue
FINRA
Market
Share
BCBS239
No DG oversight
mean all data-
related issues are
in red zone – no
preventative
measures in place
GDPR

Support Required to Leverage
Insights Gained From Big Data Analytics

Best Practices – Treat and View Your Data as an Asset
pg 24
TreatandViewYourDataas
anAsset
Information product
management function
Product Manager, quality
control, etc.
Define supply chain
Define the supply chain,
from source, production,
marketing and shipping]
Establish governance and
quality functions
The data scientists cannot
sustain the product without
good raw materials
There will be some
standards required for
source data
Integrate with data strategy
and vision for organization
90% of the same support
mechanisms are shared
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

Take-Aways and Q&A
 The application of data governance will vary based on
the types of architectures and analytics at play.
 Maturity is best used as a metric of capability vs.
accomplishment.
 While data quality and consistency are obvious
applications of data governance, oversight of business
alignment, architectural principles and establishing
data-driven business functions are becoming new
roles
 Q&A

Thank you!
Please join us Thursday, Aug. 3 for
the “Big Data Analytics” webinar.
John Ladley @jladley
john@firstsanfranciscopartners.com

Governing Quality Analytics

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