Suggested audience: CIO, Enterprise Architects, Data Managers, Analytics Managers, Data Scientists IT is broken. Bad data assumptions, legacy technology, poor business decisions, and weak IT management have changed IT from a superstar to a second-rate department that struggles to maintain its seat at the CEO's table. With AI, personal data, & business ethics all in ascendence, the need for ethical IT policies has never been greater. Otherwise, companies risk building services and products that fall short of the ethics and trust that they have been given by employees. In this webinar, Amalgam Insights explores how current data, BI, analytics, and machine learning technologies threaten ethical IT and provides guidance based on other rules-based frameworks that derive business outcomes, such as the law and corporate legislation.

1. DATA AND ANALYTICS
STRATEGIES FOR
DEVELOPING ETHICAL IT
Hyoun Park
Founder and CEO, Amalgam Insights

2. AGENDA
• About Us
• Your Challenge for Ethical IT
• Why IT is Broken
• Identifying IT Issues
• Recommended Vendors
• Recommended Strategies

3. ABOUT AMALGAM INSIGHTS
• Advisory and Consulting Firm focused on Technology Consumption Management
• Bridging CFO and CIO challenges for managing technology at scale.
• Tactically includes
• IT Finance
• Machine Learning Prep
• Accounting and Audit Automation
• DevOps
• Enterprise Collaboration
• Training and Retention

4. HYOUN PARK
Founder and Enterprise Tech Whisperer, Amalgam
Insights, an advisory firm focused on Technology
Consumption Management
Previously:
Chief Research Officer at Blue Hill Research
IT Manager at Bose, Teradyne
Campaign Treasurer, Boston City Councilor
Fantasy Baseball Prognosticator

5. YOUR CHALLENGE

6. FIND JUST ONE IDEA FROM THIS
PRESENTATION THAT YOU CAN TAKE BACK
INTO YOUR ENTERPRISE

7. IDENTIFY THE ENTERPRISE ARCHITECT, CTO,
CIO, DATA, ANALYTICS, OR DATA SCIENCE
EXECUTIVE WHO CAN WORK ON THIS

8. USE THIS INFORMATION TO BECOME A MORE
ETHICAL IT ORGANIZATION

9. IT
IS BROKEN

10. In the beginning Now
Nature of IT Work Skilled Work Combo of outsourcing,
“cloud,” traditional IT,
science, and magic
Definition of IT
Work
Certification, Governed
and consistent
standards
Tech moves too fast for
certificates
Goal of IT Work Focus on building and
supporting tech
infrastructure
Bring Your Own
Everything, patchwork
security, reactive
governance
Funding for IT
Work
Big Budgets Shrinking Budgets

11. THESE CHANGES HAVE ALSO MADE IT
LESS ETHICAL

12. Is it the
Tech?
Tech allows us to
do things more
quickly, but not
inherently evil
Is it the
People?
Somewhat, but
more due to
inaction rather
than active evil
Is it the
Process?
Yes. IT has
adopted a variety
of unethical
practices.

13. ACROSS DATA, APPS, ANALYTICS, MACHINE
LEARNING, AND IT SUPPORT,
WE HAVE BEEN TRAINED TO DO THE WRONG
THING

14. BUT WE CAN FIX IT

15. BREAKING OUT THE PROBLEMS
• Traditional Technology and Do-It-Yourself IT
• IT-Business Alignment defined as Outcomes-Based efforts
• Big Data as an Opt-Out Process
• Machine Learning as Black Box Preferences
• Productivity and Growth as key IT goals

16. TRADITIONAL TECHNOLOGY AND DO-IT-
YOURSELF IT
• In the 90s and 2000s, we learned skills and thought they
would be careers.
• Financially, the focus was on maximizing Return on Assets
and minimizing Total Cost of Ownership

17. REALITY CHECK
• Reality Check: no skill lasts forever and dependence on any
technology represents fragility and a point of failure.
• Do It Yourself only works as long as the employee who built
it is still there. But it probably includes
• Ridiculous spaghetti code
• Lousy documentation
• Outdated skills

18. IT-BUSINESS ALIGNMENT IS A
PARTNERSHIP, NOT A SURRENDER
• The answer is often to use any tech that leads to the business outcome
• Business must also accept that IT must be repeatable to be truly successful
• Outcomes-based efforts lead to “Hero” IT rather than stable IT
• Align IT success to long-term growth, not short-term growth or immediate
fixes.

19. AVOIDING LEGACY IT FRAGILITY
• Shift to a Subscription mindset where new tech is a
negotiable partnership
• Focus on your Return on People, not Return on Assets
• Build repeatability and flexibility into IT: Bad things
happen when IT relies on Heroes.

20. BIG DATA AS AN OPT-OUT PROCESS
• In the early days of Hadoop and Big Data,
companies focused on ingestion.
• The assumption was that data would get sorted out
later or that it could just be used in aggregate

21. RESULTS OF BIG BAD DATA
• Result: Massive amounts of data collected without purpose,
leading to massive mistrust of companies and tech
• Also, companies often don’t know how to do the graph
analytics, machine learning, and data unification needed to
get value, anyways

22. SOLUTIONS FOR BIG DATA
• Use GDPR as guidance for
personal data
• Focus on collecting data that
serves a purpose and can be
analyzed by internal personnel.
• Build a philosophy for data
collection. Start with Why.

23. MACHINE LEARNING AS BLACK BOX ISSUES
• Unknown algorithms supercede human judgment and
stated goals
• Correlation and Causation are often confused.
• Businesses are complacent about Black Boxes as long as
they provide profit

24. BLACK BOX INCENTIVES FOR BUSINESSES
• In “Business Ethics,” people are taught to do what is right
for the business and is simplified to maximizing bottom-line
revenue.
• But maximizing short-term profit often leads to long-term
challenges
• Even worse: short-term profit based on unknown data and
analytic inputs

25. SOLVING THE BLACK BOX
• Document data and analytic models used in data science and
machine learning. Pure naïve and deep learning is of limited
value in today’s IT world
• Ask vendors to explain machine learning approaches
• Do not take software-provided recommendations for granted
• Get educated in algorithms and statistics: our new common
language in the digital era.

26. PRODUCTIVITY AND GROWTH AS KEY
• Productivity and Growth sound positive, but lead to
the need for speed
• Speed without control leads to mistakes and ethical
issues.
• Data, analytics, and computing are increasingly
differentiated by experience, not performance

27. IT FOR THE LONG HAUL
• Building for the long haul requires a focus on avoiding fragility, points
of failure, and uncontrolled growth and expansion
• Solution: focus on solving tech problems with process mapping first
rather than simply using brute-force processing and algorithms.
• Build room for new technologies, data sources, and APIs to provide
additional services within each IT function.

28. KEY TOOLS FOR ETHICAL IT
• Data Wrangling and Exploration
• Data Unification and Catalogs
• Expansive BI
• Data Science Preparation,
Workbenches, and Automation
• Guided Machine Learning

29. DATA WRANGLING AND EXPLORATION
• Your core data must
support both structured
and unstructured data
• Both data and metadata
must be available for
analysis
• A key bridge to managing
data across multiple
analytic silos

30. DATA UNIFICATION AND CATALOGING
To contextualize data, IT
needs
• Shared version of the truth
• Consisting of structured and
unstructured data
• That can be used for BI,
analytics, machine learning,
and application integration

31. EXPANSIVE BI
• Business Intelligence solutions best
suited for today’s data diversity must
• Work with other analytics and BI
solutions outside of their own
environment
• Support a wide variety of data
• Be extensible and API-friendly

32. DATA SCIENCE AND ANALYTICS
• In today’s marketing world, “analytics” solutions
are now “Data Science” solutions. But new
players have come into the world previously
owned by SAS and IBM SPSS.
• In general, larger vendors provide a portfolio of
tools and services while newer vendors provide
focused solutions.

33. MACHINE LEARNING
• Machine learning is a subset of data
science focused on algorithmic models.
• These terms can be used
interchangeably by laypeople asking for
results, which can be very confusing.
• Repeatable Machine Learning is best
done through Machine Learning
platforms rather than statistical
workflows

34. RECOMMENDATIONS
If a trend is
threatening your job,
learn it before it
replaces you.
Bring your structured
data and your unruly
data together so you
can at least see it all
from one place
Break out Black Boxes
Don’t define customers
and employees as data.
Data should be used to
help people and achieve
specific outcomes

35. TAKEAWAY
• What will you bring back to your office?
• Who will you speak to?
• How will your organization benefit from a
more ethical IT?

36. THANK YOU!
For more information on building the business case,
evaluating vendors, or working with Amalgam
Insights on technology consumption management,
please contact Amalgam Insights at:
Lisa@amalgaminsights.com
Phone: +1 (415) 754 9686
@AmalgamInsights