Data Governance and Data Science to Improve Data Quality

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Copyright © 2021 Robert S. Seiner – KIK Consulting & Educational Services / TDAN.com
Non-Invasive Data Governance™ is a trademark of Robert S. Seiner & KIK Consulting
#RWDG @RSeiner
Real-World Data Governance
Data Governance and Data Science to Improve Data Quality
Monthly Webinar Series Hosted by DATAVERSITY
Robert S. Seiner – KIK Consulting / TDAN.com
With Special Guest – Christopher Paskins – Western Digital®
August 19, 2021 – 11:00 a.m. PT / 2:00 p.m. ET

Myles Suer
Director, Solution Marketing
Data Governance and Data Science

2 | Proprietary & Confidential
Data Governance Matters to Data Scientists
Delivers an agile, modern data architecture
Creates data quality and trust
Detects, protects, and governs sensitive data
Speeds up operationalization of analytical models
Enables agile data processes including DataOps

Yet There is Work To Do
● Less than half of an organization’s
structured data is actively used in making
decisions
● Less than 1% of its unstructured data is
analyzed or used at all.
● More than 70% of employees have
access to data they should not
● 80% of analysts’ time is spent simply
discovering and preparing data

● A survey by Crowdﬂower found
that 19% of a data scientist time
was spent collecting data sets and
60% of their time was spent
cleaning and organizing data
● Kirk Boone asked whether data
scientists could be better called
data plumbers
Why Making Data Better Matters

Suggested Principles for Effective Data Governance
Program
People Centric vs Top, Down Forced
Autonomous--Takes the Labor out of Data Stewardship
Built on Continuous Improvement Principles
Accelerated Business Outcomes from Data

#RWDG @RSeiner
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Real-World Data Governance – Monthly Webinar Series
September 16, 2021 – Do-It-Yourself (DIY) Data Governance Framework
Third Thursday each Month @ 2pm EST – Register at TDAN.com, KIKconsulting.com, DATAVERSITY.net
Non-Invasive Data Governance Book
ISBN 9781935504856 / Technics Publications / Amazon.com
Speaking @ DATAVERSITY Live and Virtual Events
Data Governance & Information Quality Conference – December 6-10, 2021 – San Diego, California
Non-Invasive Data Governance / Metadata Governance Online Learning Plans
Available Now: Business Glossaries, Data Dictionaries and Data Catalogs (6 course learning plan)
DATAVERSITY Training Center
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Produced by DATAVERSITY – Subscribe for emails
KIK Consulting & Educational Services
Updated Site: KIKConsulting.com (“Knowledge is King”)
Home of Non-Invasive Data Governance™
Carnegie Mellon University (CMU)
Adjunct Faculty – Heinz College CDataO Program
Introduction – Bob Seiner

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Introduction – Chris Paskins
• Christopher Paskins
Global Head of Enterprise Data Governance and Data Quality
Western Digital Corp
Data Management executive with over twenty-five years of experience with deep
expertise in information systems management, data management, data analytics /
science, business intelligence and high-tech manufacturing engineering. A creative
problem solver with a unique mix of technical, business, and research proficiency
that lends itself to developing key strategies and world-class solutions that yield
significant impact. Chris believes in fostering and encouraging an environment for
learning and creative thinking that leads to innovative real-world solutions to
complex business and technical challenges.

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• In the webinar, Bob and Chris will share:
– The Relationship Between Data Governance and Data Science
– How Data Governance Can Leverage Data Science to Improve Data Quality
– Data Scientists’ Role in Data Governance
– Data Scientists’ Role in Improving Data Quality
– Combining the Three Concepts Into a “Tour de Force”
Abstract

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• Data Governance focuses on formalizing people’s behavior associated with managing data.
• Data Governance is the execution and enforcement of authority over the definition, production
and usage of data and data-related assets. Seiner
• Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and
systems to extract knowledge and insights from structured and unstructured data and apply
knowledge and actionable insights from data across a broad range of application domains. ACM
• Data Quality is the state of qualitative or quantitative pieces of information. Data is generally
considered high quality if it is "fit for intended uses in operations, decision making and planning.”
Redman
• Data Science & Data Quality require improvements in people’s behavior managing data.
The Relationship Between Data Governance and Data Science

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• Analogy: Running a race – The outcome will be a direct reflection of the training and preparation
that has been put forward.
– The ”outcome” would be the results of the predictions and prescriptions.
– The “training and preparation” would be the data quality.
• Not a luxury: Many businesses that have embarked into data science view data governance as a
luxury, that would be a mistake.
– While good data governance does many things for an organization, it helps to ensure data can
be used and cultivated to its full potential.
The Relationship Between Data Governance and Data Science

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• Organizations are investing in data science, informatics, machine learning, data mining, big data.
• They all have a common thread – dependency on data that people trust.
• Valuable Purpose for a Data Governance Program – To Use Strategic Data With Confidence.
• Organizations want to lead in data science, informatics, machine learning, data mining, big data.
• It is not enough to label people as Data Owners and then not help them to “own” the data.
• Senior Leadership needs help understanding that governed data is trusted data.
• Senior Leadership needs help understanding what governed data looks like.
How Data Governance Can Leverage Data Science to Improve Data Quality

#RWDG @RSeiner
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• Anomaly detection: Tell if my data is fresh, complete, etc.
• Rule prescription: Tell me what I should care about and should be measuring.
• Model data scoring: Is the data my model is using have the same data quality as my training set?
• Impact risk analysis: Are there any interruptions in my data supply chain?
How Data Governance Can Leverage Data Science to Improve Data Quality

#RWDG @RSeiner
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• Data Scientists are Data Governance Partners.
• Data Governance Partners: functions that already exist and already govern.
• Power Users: govern the use of data analytics.
• Require Confidence: in the data they use.
• Providing Requirements: for the analytical use of data.
Data Scientists’ Role in Data Governance

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• In some cases, the data scientists’ role can be overlooked on how it relates to data governance
– Some often used explanations given are:
• It is not their role in the company – they are not data stewards
• They don’t have direct impact or influence on data quality (they are usually aggregating
data from several different data domains for their models)
• The truth is that a data steward can be anyone who has a relationship with the data, that
includes of course data scientists. In most cases the data scientists can see data issues that arise
cross domain and are in an ideal role to make a huge impact.
Data Scientists’ Role in Data Governance

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• Data Scientists are a Data Quality Partner.
• Data Governance Partners: functions that already exist and already care about Data Quality.
• Power Users: govern the use of data analytics.
• Participate in Confidence: define confidence requirements for the data they use.
• Providing Quality Requirements: for the analytical use of data.
Data Scientists’ Role in Improving Data Quality

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• Let's break it down:
– Building the data architecture and pipelines: Data Analyst, Data Architect, Data Engineer
• These roles have a very high impact to data quality and should be considered stewards
– Using the data: Business Intelligence Analyst, Data Analyst, Statistician
• These roles are using the data that is available and have direct knowledge of the data
domain (subject matter experts), and therefore give direct feedback on the data quality.
Data Scientists’ Role in Improving Data Quality

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• Stewardship through formalized accountability.
• Governance through execution and enforcement of authority for analytics data.
• Partnership including coordination and cooperation.
• Communication that includes orientation, onboarding and ongoing.
• View Data Governance, Data Quality and Data Science as complementary disciplines.
• Data Science relies on Data Quality that is achieved with formal Data Governance.
Combining the Three Concepts Into a “Tour de Force”

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The name of the game is accelerated business value.
Business value can be gained without these three
working in concert, however it may take longer and
with much more frustration.
A few notes here: Data Governance does not just have
data stewardship in it’s core set of competencies, but it
is a major factor of success. Data Quality should be
part of a larger management process of continuous
improvement.
Combining the Three Concepts Into a “Tour de Force”
Use Data Science to
make humans more
effective and productive
Data Science

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• In the webinar, Bob and Chris shared:
– The Relationship Between Data Governance and Data Science
– How Data Governance Can Leverage Data Science to Improve Data Quality
– Data Scientists’ Role in Data Governance
– Data Scientists’ Role in Improving Data Quality
– Combining the Three Concepts Into a “Tour de Force”
Summary

#RWDG @RSeiner
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Questions & Answers

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Contact Information
• Robert S. Seiner
KIK Consulting & Educational Services – KIKconsulting.com
The Data Administration Newsletter – TDAN.com
rseiner@tdan.com @RSeiner
• Christopher Paskins
Western Digital®
christopher.paskins@wdc.com

Data Governance and Data Science to Improve Data Quality

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