IQ International is pleased to continue it tradition of celebrating world quality day (WQD) with a star studded panel addressing a theme from multiple angles. This year's WQD theme is "Making Operational Governance count" so we asked our subject matter experts to share their experiences in Data-Driven Digital transformation from the first principles of quality and governance. This will include best governance practices from the small & big data world covering analytics, master data, internet of things and data science. Jay Zaidi founded AlyData to provide advisory and execution services to business leaders on how they can leverage their most strategic asset - DATA - to gain deeper insights, optimize decisions and mitigate risks better. AlyData is based in Washington DC and specialises in Data Science and Data Management (i.e., Data Governance, Data Quality, Master Data Management, Data Privacy and Security, and Metadata Management). Prior to AlyData, Jay led Enterprise Data Management functions at Fannie Mae for 13 years, reporting directly to the Chief Data Officer. In July 2016, Jay authored two books on Data-Driven Leadership. One is titled "Data-Driven Leaders Always Win" and the second one is "Data-Driven Leadership: A New Leadership Paradigm in the Digital Age".

1. Taming the Digital Transformation Dragon
Big Data Governance Practices
Jay Zaidi
November 14, 2016

2. ABOUT ME
“Succeeding with data isn’t just a matter of putting Hadoop in
your machine room, or hiring some physicists with crazy math
skills. It requires you to develop a data culture that involves
people throughout the organization.”
- DJ Patil, Chief Data Scientist of the U.S.
● Founded AlyData in 2014. We specialise
in Data Management and Data Science.
Our mission is to transform
organizations, by helping them innovate
and gain a competitive advantage by
unleashing value from their data and
information assets.
● 13 Years at Fannie Mae. Last 5 years
was a direct report to the CDO.
● Have authored 2 books and over 80
articles on data management on
LinkedIn.
● Email - jayzaidi@alydata.com
● LinkedIn Profile -
https://www.linkedin.com/in/javedzai
di
● Twitter - @jayzaidi
#data-driven leadership 2

3. 3 MAJOR THEMES I WISH TO HIGHLIGHT -
1. Most important areas leaders focus on - Value Creation and Risk Management. “Data” is
foundational to that.
2. We are living in the Fourth Industrial Revolution or the “Age of Data”. Data practitioners must
focus on “What Data Does (value) and How to Govern It (risk)” not “What Data Is”. Value is
generated by Data Science by unleashing the power of data via better insights and Risk is
managed via robust Data Governance.
3. Data Governance is at the heart of Risk Management and Data Management (Master Data
Management, Data Quality, Metadata, Analytics, and Data Science). Build it into the Big Data
Lake, Data Science, and Small Data Repositories.
#create-value & #manage-risk3

4. #data-science 4

5. #data-governance 5

6. A FRAMEWORK FOR UNDERSTANDING DATA GOVERNANCE
6
Business
Imperatives
Risk
Management
Regulatory
Compliance
Legal
Data Security &
Privacy
Data
Controls
Data
Optimization
WHYWHAT
Data democratization, data decentralization, and tougher regulatory environment are driving data
governance as a critical risk management component.

7. “Governance is about changing culture and
implementing processes to ensure proper
oversight of data semantics and quality,
transparency into data related metrics,
accountability for data, and timely resolution
of data-related issues or questions”
#manage-risk 7

8. 8
BRIDGING THE DATA GOVERNANCE DIVIDE - SMALL AND BIG DATA
- Big data training about
unstructured data and containers
- metadata, quality, master data,
logs etc.
- Process training on changes made
to governance, quality, MDM,
metadata changes due to big
data
- Analysis training on issue logs,
interpreting them and working
with stakeholders to address data
issues
- Training on process changes due
to big data
- Training on new Training about
unstructured data and containers
- Training on process changes due
to big data
- Training on new tools used to
tag, profile, review big data
- Implement new tools used to tag,
profile, analyze, review big data
- Implement activity log analyzer,
automate and working with
stakeholders to address data
issues
- Training about unstructured data
and containers
Small Data - Structured data is highly organized information that uploads neatly into a relational database (schema on write), lives in fixed fields,
and is easily detectable via search operations or algorithms. Structured data is relatively simple to enter, store, query, and analyze, but it must
be strictly defined in terms of field name and type (e.g. alpha, numeric, date, currency).
Big Data - Unstructured data resides in files. It is increasingly available in the form of complex data sources, such as web logs, multimedia
content, email, customer service interactions, sales automation, and social media data. The fundamental challenge of unstructured data sources
is that they don’t follow a predefined schema, are difficult for nontechnical business users and data analysts alike to unbox, understand, and
prepare for analytic use. Beyond issues of structure, is the sheer volume of this type of data. Need additional contextual data to understand,
different tools to process/analyze, and more automation due to volume and variety.
#small-big-data

9. This architecture supports various data access patterns and governance.
ARCHITECTING A DATA LAKE WITH GOVERNANCE IN MIND - THE HOW?
#data-classification
#data-access-controls
#data-lineage
#data-semantics
#data-quality
#issue-log
9
Diagram Courtesy - “Enterprise Big Data Lake” a O’Reilly book by Alex Gorelik
#master-data
#people #process #tech

10. 10
SOME EXAMPLES OF DATA LAKE GOVERNANCE
Client: Fortune 50 Financial Services
Problem: CISO and Security Operation Center was able to
pinpoint sensitive data being accessed via security logs but had
no idea where it resided and who was accountable for it.
Solution: The Data Governance program had already classified
data by sensitivity, identified enterprise critical data, and had a
list of data stewards and custodians accountable for each. This
facilitated the root cause analysis exercise to get to address the
questions that the security team had.
Client: Fortune 100 Healthcare Client
Problem: Reports to CFO and corporate disclosures required
significant effort and impacted time-to-value due to the quality
of data acquired from upstream systems.
Solution: The Enterprise Data Quality group collaborated with
CFO’s group and the three key upstream system staff to define
data quality requirements for key data, agreed on the quality
dimensions, implemented a data certification process and
provided guidance on usage of open source data quality tools to
ensure consistent delivery of high quality data.
Client: Federal Government Civilian Agency
Problem: Internal Audit team wasn’t able to quickly pinpoint
issues with underlying data and how it could impact risk to the
organization.
Solution: The Enterprise Data Quality team had already
implemented a data quality framework that standardized the
data quality dimensions, data profiling process and tools, and a
consistent way to report this information. They collaborated
with the Internal Audit staff to train them on these capabilities,
so that they could independently validate data and process.
Client: Fortune 500 Hospitality Industry
Problem: Sales and Marketing campaigns weren’t delivering
the desired ROI and there was no way to determine the root
cause.
Solution: The Enterprise Data Governance team engaged the
Stewards and Custodians for Customer, Sales, and Rewards
system organizations to discuss the consistency and quality of
data based on their specific quality reports and subject matter
expertise and concluded that the lack of Customer 360 data and
inconsistencies in customer and sales data across three siloed
systems was the root cause. EDG facilitated sessions between
various teams to address these issues.
#data-lake-governance

11. 6-STEP GOVERNANCE PROCESS
1. Assess: Data Management Maturity Assessment focused on Governance, Quality and Master Data
Management.
2. Process: Apply our proprietary Scope, Process Automation, Ownership, Cross-functional
Engagement, and Human Intelligence (SPOCH) framework.
3. Agility: Use Agile Data Governance (DG), Data Quality (DQ) and Master Data Management (MDM)
processes.
4. Standards/Policies: Define and monitor compliance.
5. Alignment: Align Data Governance with IT governance.
6. Automation: Implement tools to automate DG processes, DQ profiling, Tagging, Discovery, etc.
#data-governance 11

12. APPENDIX
12

13. 1. Data Governance Demystified – Lessons From The Trenches
2. Bridging the Data Governance Chasm
3. You Think You Know Data? Think Again
4. 6 Reasons Why Big Data Investments Aren’t Paying Off For Some Organizations
5. 5 Reasons More Companies Don’t Have Data Quality Programs
6. What’s An Information Supply Chain and Why You Should Care?
7. The Dark Side of Big Data
8. Re-Thinking Information Security and Data Governance
SOME ARTICLES I’VE AUTHORED
(link to over 80 articles)
#thought-leadership 13