The Inforum, organized by the Records and Information Management Professionals Australasia will take place from 11th September to the 14th September 2016 at the Crown Perth in Perth, Australia. The conference will cover areas like offers excellent value for money with its diverse, relevant, informative and interesting program and optional workshops. This presentation was delivered as the Key Note by Jay Zaidi of AlyData.

1. Change your Organization's Culture to
Make Data and Information Quality a Part
of it’s DNA
inForum 2016
Perth, Australia
September 13, 2016
Jay Zaidi
Managing Partner

2. My Books, About Me, and Contact Details
Contact Details
Email – jayzaidi@alydata.com
LinkedIn - http://www.linkedin.com/in/javedzaidi
Web – http://www.alydata.com/
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My books on “Data-Driven Leadership” launched
worldwide on Amazon and Kindle in July 2016.
About Me
Founded AlyData after two decades in the industry.
In my last corporate role I reported directly for five
years to the Chief Data Officer of the largest financial
services company in the world. Worked for
PriceWaterCoopers LLC, Commerce One, and DOW
Chemical Company prior to that.

3. AGENDA
Part 1 – Age of Data
Part 2 – Quality is Job 1
Part 3 – There is a Trust Deficit that must be Overcome
Part 4 – Change Culture or Get Disrupted
Part 5 – Call to Action
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4. Our World’s Being Turned Upside Down
4
And You Should Think About What This Means to You!

5. The Fourth Industrial Revolution
“We stand on the brink of a technological revolution that will
fundamentally alter the way we live, work, and relate to one another. In
its scale, scope, and complexity, the transformation will be unlike
anything humankind has experienced before. This is the Fourth
Industrial Revolution or the digital revolution that has been occurring
since the middle of the last century. It is characterized by a fusion of
technologies that is blurring the lines between the physical, digital, and
biological spheres.” – Klaus Schwab, Executive Chairman of The World
Economic Forum
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I’ve labeled the Fourth Industrial Revolution the “Age of Data.”

6. Massive Disruption Is Happening In Every
Sector - Your Company May Be Next
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The Common Thread Across Disruptors are Data and Insights!
High Quality Data is required for best insights.

7. 5 Pillars of the New Business Model
1. Variety and Decentralization: Social, Mobile, Analytics, and Cloud
(SMAC) drive operations
2. Better Insights: Near real time insights for decision making, risk
management, and to gain competitive advantage
3. Agility: Transformation of the operating model from SDLC to Agile and
introduction of automated processes
4. Transparency: Sharing economy requires a sharing culture. Change in
team dynamics to become more transparent and share data and
algorithms.
5. Innovation: Innovate using data, people, algorithms, and process. New
areas such as artificial intelligence (AI), deep learning, intelligent
conversation engines, speech recognition, and image and pattern
recognition.
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The Intellectual Capital of this new world is Algorithms, Data, and People.

8. A New Leadership Paradigm -
Leadership 2.0
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Leaders and aspiring leaders must become “data savvy”
and pivot on “data” not IT.
Leadership 1.0 Leadership 2.0

9. Quality Is Job 1
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10. 10
Quality is a State of Mind and Has To Be Incorporated Into All
Data Processing Steps.
• In the 1980’s Jacques Nasser was CEO of Ford and introduced this
slogan
• Wanted to transform Ford into the leading consumer products
company
• This initiative changed not just the culture but the quality of the end
products
• Resulted in a US $300 Million in reduced scrap, rework, and non value
added activities. This is equivalent to $754 Million in today’s dollars –
a significant savings.

11. 11
https://www.youtube.com/watch?v=X3ecGr_-2vQ
https://www.youtube.com/watch?v=X3ecGr_-2vQ

12. 12
https://www.youtube.com/watch?v=Sx_6OoFmycohttps://www.youtube.com/watch?v=Sx_6OoFmyco
https://www.youtube.com/watch?v=Sx_6OoFmyco

13. 4 Eye Opening Facts
• Data Quality: At least 6% to 10% of IT operating budget wasted due to
re-work and inefficient processing
• Data Wrangling: 70% to 80% of data processing time and cost is
associated with data wrangling
• Dark Data (acquired but never used): 85% of data acquired isn’t used
for anything of value
• Metadata (context): Inability to find data, understand data semantics,
and data related rules results in massive inefficiency
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Companies need data quality founders, evangelists, and data quality
owners across all departments.

14. Companies Require Small and Big Data
To Succeed
Traditional (Small Data) Data-driven (Big Data)
Highly structured data Structured, Unstructured, Semi-structured
Pre-defined data schemas Flexible data schemas
Pre-defined data models (schema on write) Undefined data models (schema on read)
Relational database management systems Hadoop and NoSQL data stores
Silos of data Big Data Lakes (consolidated data sets)
Performance and scalability limitations Infinite scaling
Mostly on premise data Highly decentralized data (Cloud)
Data Mining and Business Intelligence Predictive, Prescriptive Analytics, Deep Learning
and Artificial Intelligence
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The introduction of Big Data into the “Data driven” business model
requires a culture change - new data quality and project management
skills, new execution capabilities, and agility at its core.

15. There Is A Trust Deficit That Must Be
Overcome
15

16. Trust Deficit
16
Business transactions are built on trust. Unfortunately, there
is a trust deficit today and it must be addressed.

17. Producers and Consumers Operate On
Trust
17
Government agencies define standards and policies that
producers must follow to ensure quality and transparency.
Labels
Food labels present nutritional and other information to help consumers
make safe and healthy food choices. Some labelling information is
mandatory, while others are voluntarily added by manufacturers.
Labelling must include a list of ingredients and food additives, as well as
any potential allergens. A nutrition panel outlining levels of key nutrients
is also required.
We use food labels:
• For health reasons
• To avoid particular ingredients or food additives
• For personal beliefs, such as avoiding genetically modified foods or
foods containing animal products or to buy items grown locally.
• Food labels must tell the truth and include:
• Name or description of the food
• Nutrition information panel
• Ingredient list
• Percentage labelling
• Food additives
• Country of origin
• Food recall information
• Directions for use and storage
• Information for allergy sufferers
• Legibility requirements
• Date marking.

18. When Was the Last Time You Were
Provided a Label with your Data Sets?
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Seems logical. But it’s never done in the industry. Shouldn’t it?
Data Set Labels
Data Set labels present information that describes the content of the data set and other information to help consumers understand what’s in it and to make
choices on its usage. Some labelling information should be mandatory, while others are voluntarily added by producers. Labelling must include a list of key
data ingredients and any data enrichment performed on it (additives), as well as any potential transformations (allergens). A data panel outlining levels of
sensitive and personally identifiable data should also be required.
We use data set labels:
• for business transaction reasons
• to be aware of particular sensitive or personally identifiable data so that we can handle them with care
Data labels must tell the truth and include:
• Name or description of the data elements in the data set
• Data consumption information panel
• Data element list
• Data quality labelling
• Any enrichments
• System(s) of origin
• Directions for use and storage
• Information for special handling
• Date marking.

19. 19
An Inspection Regime Is Needed
One must trust but verify quality. Companies must implement
an inspection regime to audit data quality at every step.

20. Data Quality Process Flow
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Repeatable Data Quality Processes must be Implemented.

21. Australian Bureau of Statistics Quality
Framework
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22. Suggested questions to assess institutional environment:
• Which organization(s) has supplied the data?
• What sort of organization is this (e.g., public, commercial, non-government organization)?
• Under what authority or legislation were the data collected?
• What procedures are in place to enable a need for a statistical product to be evaluated with
respect to its scope, detail or cost?
• To what extent are quality guidelines documented by the agency?
• Is statistical confidentiality guaranteed, and if so, under what authority?
• To what extent, and how quickly, are any identified errors in published statistics corrected and
publicized?
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Australian Bureau of Statistics
Recommendation
These questions can be tailored for companies and their
departments.

23. Change Culture or Get Disrupted
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24. “Culture eats strategy for breakfast,
technology for lunch, and products for dinner,
and soon thereafter everything else too.”
– Business Management Guru Peter Drucker
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This culture change is critical to winning with data. No amount of
strategizing will work otherwise. However, most companies are in
denial that there is a culture problem.

25. 25
“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.
Winning with data isn’t about Hadoop or new technology. It requires
you to develop a data culture that involves everyone.

26. Let’s Define Culture First
A culture is a way of life of a group of people--the behaviors, beliefs,
values, and symbols that they accept, generally without thinking
about them, and that are passed along by communication and
imitation from one generation to the next. Culture is symbolic
communication.
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27. Here Are 11 Characteristics of a Data Culture
1. Mission Alignment: Data’s role in the company’s overall mission and goals is clearly articulated. Openly discussing
strategies and innovation goals provides employees with a clear view of data’s role in the company’s overall mission and
reinforces their connection to the larger organization.
2. Data Quality Savvy: Management and staff that are data quality savvy – understand all the foundational elements of data
quality and why data is critical for success
3. Data Quality Processes: Define quality requirements, measure quality, and proactively address quality issues
4. Behaviors: Everyone makes evidence-based decisions (not based on gut)
5. Right Questions: Leaders and staff are empowered to ask the right questions such as – what is the system of record for
data?, what’s been done to it?, can I trust it?, who is accountable for specific data? etc.
6. Information Supply Chain: Departmental silos of information are the nemesis of thriving data cultures. To promote the
view of data as a flexible asset that’s usable by multiple departments, organizations need to educate employees on how
the data they use daily ripples through other parts of the organization. Employees need to see the big picture.
7. Rewards and Recognition: Data successes are shared and individuals and teams responsible for them are rewarded and
recognized
8. Right Incentives and Alignment: Cross-functional solution teams are completely aligned on goals and incentives between
IT, Data, and Business staff
9. Data Sharing: There is sharing of data and information between departments and total transparency – no data hoarding. A
thriving data culture depends on an environment in which everyone can share information without being perceived as
negative.
10. KPI Transparency: Availability and use of key data metrics and measures via comprehensive dashboard – data quality, data
issue management, data governance, data security and privacy, data lineage, etc
11. Robust Data Platform: A robust data platform has been built and it supports the types of analytics required to make
decisions, manage risk, and innovate
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28. The Culture Change Framework
Organizational
Culture Change
Business Case
For Change
Creating the
climate for
change
Implementing
and
Sustaining
Change
Engaging and
enabling
the
organization
1. Establishing a sense of urgency
2. Creating the guiding coalition
3. Development of a change vision
1. Communicating the vision for buy-in
2. Empowering broad-based action
3. Generating short term wins
1. Never letting up
2. Incorporating
changes into the
culture
1. Establishing the need for change
2. Tying culture change initiative to business priorities
3. Articulating the vision and tangible results

29. Team-Building Processes
Team building is a development process where the course is divided
into 4 phases plus a resolution phase.
Performance
Course of time
LowHigh
Orientation phase
(forming)1
Growth phase
(performing)4
Cooperation phase
(norming)3
Confrontation phase
(storming)2
Resolution phase
(adjourning)5

30. Call To Action
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31. Every great dream begins with a dreamer.
Always remember you have within you
the strength, the patience and the passion
to reach for the stars to change the world
Harriet Tubman, Abolitionist, Humanitarian & Spy (1822 – 1913)

32. “My biggest fear is not
crashing on a bike…
It’s sitting in a chair at 90
and saying,
‘I wish I had done more’.”
Graeme Obree

33. The Climb– It’s Tough But Helps You Win
1. Company leadership needs to elevate data quality as a top priority and tie it
to tangible customer, product, and employee benefits.
2. Data producers and consumers must agree an a mutual contract and deliver
on it. An independent audit regime checks adherence to contract terms.
3. Develop a roadmap for culture change and implement it.
4. Invest in training.
5. Build world class data quality process execution capabilities.

34. Appendix
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35. 17 Articles I’ve Authored On Data Quality
• 5 Reasons Why More Companies Don't Have Data Quality Processes In Place?
• Data Quality is Job 1 and Here's Why?
• Serious Implications of the Dark Side of Big Data
• What is this thing called "Data Quality"?
• Holistic Data Quality (HDQ) - A New Paradigm In Enterprise Data Quality Management
• The Holistic Data Quality Framework - Version 1.0
• High Quality Data + Analytics = Deep Insights
• Here's Why Your Data Doesn't Reconcile?
• 5 Data Quality Best Practices
• Should Users Switch From Office Productivity Tools To Commercial Data Quality Tools
• 7 Root Causes For the High Cost of Bad Data
• Bad Data Is Costing the U.S. At Least 6% of Its GDP
• 6 Core Components of a Data Quality Program
• 3 Actions Can Save Your Organization Millions
• Organization Can Save Millions By Applying This Data Rule
• 4 Ingredients For Producing Trustworthy Big Data
• Untrustworthy Data Is Spawning "Shadow IT and Data Ninjas"
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36. Here’s What’s Required To Win
1. Company leadership needs to elevate data quality as a top priority
• Everyone in the company should become data quality conscious and incorporate quality into every process step
• Benchmark the company’s data quality practices against best in class companies to identify gaps
• Assess maturity of data quality capabilities (i.e., people, process, technology and data) to identify areas of improvement
2. Data Producers and Consumers Must Agree On A Mutual Contract
• Contract provides transparency into data set, its contents, and its quality from producers
• Producers must certify data sets, based on pre-defined consumer expectations
• An objective third party should audit the contracts and the artifacts to ensure that producers and consumers are working in good faith
3. Develop a roadmap for culture change
• Galvanize leaders and associates to become “data quality focused” and to invest in data quality management
• Incrementally build data quality profiling and remediation capabilities in an opportunistic manner (with focus on business results)
• Influence peers to build a coalition for change to improve quality of data
4. Invest in training
• Train leaders and associates in change management
• Train leaders and associates on new frameworks, technologies, execution strategies for data quality
• Educate and raise awareness about new data quality capabilities amongst peers and leaders
5. Build World Class Execution
• Pick specific areas within your department where you can show tangible direct improvement to the bottom line by applying the new
execution model
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Becoming data conscious and implementing quality control requires culture change,
investments, and a long term view.