This document discusses strategies for effective data governance and data science. It argues that traditional data governance focuses only on symptoms and not root causes of data science failures. Effective data strategy requires understanding meaning in data, developing expert intuition over time, and allowing heterotopias for non-standard exploration. Data scientists must blend past and future methods during times of atemporality. Effective hiring strategies should sell mission, promote role crafting, and facilitate growth in a changing environment.

1. Strategy and Data
Governance of Data
Science
Edward Chenard

2. Welcome to the Dark Ages of Knowledge
According to Gartner, 73% of executives believe big data/ data
science will revolutionize their business – yet only 8% describe
their own big data/ data science projects as “successful.”
Believed common reasons for failed data science efforts are
more obvious and include:
• Cost: including tools, skills, infrastructure
• Dependence on legacy systems
• Siloed organizational information
• Lack of strong executive sponsorship
• Absence of clear business case
At the end of the day, data science has failed to live up to the promise. It never can deliver on that promise on its own.

3. Data Science and the One Hand Clap
Data science is a product, current focus is on process. Like any product, it
begins with a need and ends with something that provides clear utility.

4. Traditional Data Governance is Not Enough
Data
Governance
Risk
Mitigation
Data
Governance
Data Quality
Master Data
Management
Legal and
Ethics
Team Building
Data Strategy and Governance is about building a
single point of truth which allows:
- Reduce costs through better data quality
- Improve data quality
- Provides the foundation to support global business
processes and operations
- Harmonize business processes for mass onboarding
of data
“The typical data quality issues, as well as the typical impacts of poor data quality. The increased costs are
dramatic: between 8 and 12% in revenue and between 40-60% in expenses.” Thomas C Redman author of
“The Impact of Poor Data Quality”

5. Traditional Data Governance is Not Enough
O: Business Case / Situation
A: Analytics
B: Governance
C: Human Principles
Digital is already the past; the end of stupidity is the
next wave.
Our computers still can’t interpret the information it
is processing. Solving that problem is really the next
wave.
Traditional Data governance only focuses on the
symptoms and not the cause of the data science
failure.

6. Data Strategy and the Understanding of Drive
The real way to get success from data science is to help solve its short coming by applying other disciplines to problem.
Data Strategy
Identity
Expert
Intuition
Networks
(People)
Aspect-to-
Aspect
Transitions
Atemporality
Heterotopias
Connections and linked data, we are getting really good
at, but we are failing at understanding the meaning in
the data. It causes us to get good at knowing what is
wrong not at knowing what to do about it.
History is often called a study of humanity. Where as data
is a study of human activity

7. Expertise Governance (Expert Intuition)
We often don’t quantify our technology use. We just assume new is good.
There is little evidence that digital accounts for most our changes in business.
Regularity improves intuition intelligence. The rules of the
environment provide feedback that allows us to gain expert
level intuition with enough stimuli. Stock brokers don’t have
intuition due to the chaotic nature of the market. Short
success is attainable but never proven to work long term.
Intuition without expertise often come with the same level of
confidence as expert based intuition, but are often wrong.
Being a good data scientist is a lot like learning to be a grand chess master, it takes a lot of time and a lot of
learning of regularities to develop intuition.

8. Frequency Illusion
Leads of to believe a greater synchronicity than there actually is.
Our brains are patterns recognizing super
heroes. Combined with the recency effect
and confirmation bias, we are often fooled
into thinking something is important when in
fact it is not.
Expert Intuition is the only current method
to counter this illusion

9. Networks
The network does not respect history.
Extremely fluid, often poorly organized but seemingly persuasive even when wrong.
The networks we have today currently lack the ability to create a master narrative (maybe we will in a decade) but that
form is still yet undefined
Networks are human groups of knowledge all
Sharing
Networks have replaced traditional knowledge
sharing methods
We still lack a strong knowledge of how networks
influence our ability to solve problems

10. Diderot Effect
We often fall for this effect when it comes to data science, when a company starts on the journey and it catches on, all
of a sudden nothing from the past is good enough
Identity is believed to be uniformed and this
drives the Diderot Effect, which is often in
play in DS, the desire to out class takes over
decision making

11. Atemporatlity
One of the key characteristics of our time is the inability to define itself with a key set of intellectual ideas
Atemporality is an unmooring from historical methods and a transition period to find a new normal. We are in
such a time period now.
Increased chaos and an over abundance of information and view points have made it difficult for any area to truly stand the
test of contemporary ideas.
Atemporality allows for a blending of the past and potential future.
A clear vision is near impossible during Atemporality.
Too often teams are choosing between past and
present methods when in fact it is really about
blending to create something new entirely.

12. Aspect to Aspect Transition
Space is more important than action and time. Our culture is very goal oriented but aspect is often more important than
action when it comes to analysis work.
Space is often over looked as a component of data science. The environment often influences our decision making.
Our space is not created by the user but by networks or external players, our work is always limited by the way our space
is produced.
Being there over getting there.
Abaddon time for the exploration
of space

13. Heterotopias
Places that exist in a dynamic space of layers and meaning. Margin spaces to explore non-standard methods
- Norms are suspended
- Precise and determined function
- Always have a system of opening an closing, not always open to everyone
Heteroptopias are spaces that are required for data
science, it allows for the different view points and
methods of exploration to take place. Often a singular
view of the world is required by leadership, this
creates a lot of failure because discovery is about
finding the new, not repeating the known.

14. Identity
All things change in an dynamic environment, including self.
Increased chaos and an over abundance of information and view points have made it difficult for any discussion
Identity and space are now merging. The data scientist
is often defined by the space he or she works in.
Spaces like identity are constructed. Our abilities are
limited by the space in which it is produced.
How to define identity was as is still an important
question for any data science team. You can’t define
based off of old terms like stats and coding, but a new
definition is still being defined.
Heterotopias allow for the exploration of space and
identity to be refined.

15. From Concepts to Practical
Governance
Management
RunBuildPlan
MonitorDirect
Evaluate
Business Needs /
Strategy
Monitor
Data ScienceEngineeringDesign
IT
Architecture
Development

16. Wisdom
Collective application
of knowledge into
action
Knowledge
Experience, values, context
applied to a message
Information
A message meant to change receiver’s
perception
Data
Discrete, objective facts about an event
Experience
Grounded Truth
Complexity
Judgement
Heuristics
Values & Beliefs
Quantitative
Contextual
Evaluative
Qualitative
Intuitive
Informative
Quantitative
Connectivity
Transactions
Informative
Usefulness
Quantitative
Cost, Speed
Capacity
Timeliness
Relevance,
Clarity
Adding Value:
Action-oriented
Measurable efficiency
Wiser decisions
Adding Value:
Contextualized
Categorized
Calculated
Corrected
Condensed
Adding Value:
Comparison
Consequence
Connections
Conversations
Transitioning to emerging technologies
+
Human/Machine
=
Transformation
Data Philosophy

17. Applying to Hiring
Tips and Best Practices to Lure Unicorns:
01Sell your mission (your
passion!) and why it
matters (Aspect-to-
Aspect)
02Make the most of the
Heterotopias
05 Offer incentives –
money, prestige, flex
work, etc (Network)
03
Promote the ability to
define and craft role
(Identity) and to grow with
the company
04 Facilitate personal
development
(Atemporatlity)

18. Thank You!
Twitter: Echenard
Email: Edward@cyberiandata.com