Chapter Outline
• Analytics, the Data Value Chain, and the
Analytics Roles
• Coming to a Common Definition of
Analytics
• The Major Analytics Job Families
• Decision Support Systems
• Analytics Defined
• The Analytics Competencies and the
Professional Maturity Model
• Analytics Skills and Competencies
• The Professional Maturity Model
• The DELTA+ Model and the
Organizational Model
• The DELTA+ Model
• The Organizational Maturity Model
Data Value Chain
• The data value chain describes the full
data lifecycle from collection to analysis
and usage.
• It categorizes all the various steps
required to transform raw data into useful
insights.
Data
• At this stage, data is created and
generated from its source. All of these are
collected by various people and
organizations, waiting to be extracted for
the value that they will provide.
Information
• The first transformation that data goes
through is when it is extracted from these
various sources and consolidated into a
single repository.
• Organizations will be able to answer the
question “What happened?”
Insights
• At this stage, since our single data
repository also contains historical
information, organizations can now see if
there are trends or patterns that will
emerge from all the stored information.
• Questions to Answer
•
•
Why did it happen?
What could likely happen next?
Imperatives
• At this stage, the value of data will not be
fully realized until organizations act on the
insights that emerge from the analysis that
has been done so far.
• Question to Answer
• What should be done next?
Common Analytics Terminologies
Data
•
•
•
•
Data Governance
Data Management
Data Security
Data Ethics
Information
• Data Engineering
• Data Warehousing
• Data Architecture
• Business Intelligence
• Descriptive Analytics
Insights
• Data Mining
• Algorithms
• Machine Learning
• Diagnostic Analytics
• Predictive Analytics
Imperatives
• Simulation
• Optimization
• Recommendation Engines
• Prescriptive Analytics
Major Analytics Job Families
•
•
•
•
•
Data Stewards
Data Engineers
Data Scientists
Functional Analysts
Analytics Managers
DATA
Data Stewards
• They develop, enforce, and maintain an
organization’s data governance process,
data usage, and data security policies to
ensure that data assets provide the
organization with high quality data.
• Expertise
✓ Business and Industry
Domains
• Related Job Titles
✓ Data Privacy Officer
✓ Data Security Officer
✓ Data Governance Manager
✓ Data Curator
✓ Data Librarian
INFORMATION
Data Engineers
• They design, construct, test, and maintain
data infrastructures including applications
that extract, clean, transform, and load
data from the data sources to centralized
data repositories.
• Expertise
✓ Information
Technology,
Information
Science,
Computer Science
• Related Job Titles
✓ ETL Developer
✓ Data Architect
✓ Data
Warehousing
Professional
✓ Big Data Engineer
INSIGHTS
Data Scientists
• They leverage statistical techniques and
create analytical models to derive new
insights from quantitative and qualitative
data.
• Expertise
✓ Mathematics and Statistics
• Related Job Titles
✓ Statistician
✓ Statistical Modeler
✓ Advance
Analytics
Professional
IMPERATIVES
Functional Analysts
• They utilize data and leverage on derived
insights to help organizations make better
decisions on a specific functional domain.
• Expertise
✓ Business and Industry
Domains
• Related Job Titles
✓ Research Analysts
✓ Human Resource Analyst
✓ Marketing Analysts
✓ Financial Analysts
✓ Operational Analysts
DATA, INFORMATION, INSIGHTS,
IMPERATIVES
Analytics Managers
• They develop and guide data-driven
projects – from initiation to planning,
execution to performance monitoring, to
closure.
• Expertise
✓ Project Management
• Related Job Titles
✓ Chief Data Officer
✓ Project Manager
✓ Data Engineering Manager
✓ Data Science Manager
✓ Analytics Translator
Decision Support Systems
• We still elect to use this term – decision
support – because while Analytics can
provide the end user with data,
information, insights, and prescribed
actions, we should maintain that the end
user can still choose to act upon them or
not.
• Analytics can only provide decision
support. We, the end user, will still have
the final say.
• Analytics is also about the provisioning
of data, information, and insights to drive
digitalized processes in an intelligent way.
Smart appliances, self-driving cars,
manufacturing robots have digital
processes that are supported by Analytics.
The Analytics Competencies
The Recommended APEC Analytics
Competencies
The following competencies apply to teams
comprising not only of data engineers and
data scientists but also to a new emerging
segment
of
Analytics
-enabled
professionals including data stewards,
functional
managers.
analysts,
and
analytics
Each of the competencies has a 3 -level
proficiency expectation as part of a toolkit.
Workspace Skills
• With the 21st Century Skills, there’s no 3level toolkit that can be applied given that
these are necessary skills not only in
Analytics but also in other fields as well. For
Analytics,
APEC
Project
DARE
recommended that Analytics professionals
should exhibit crosscutting skills essential
for Analytics at all levels, including but not
limited to:
✓ Critical Thinking: Demonstrating
the ability to apply critical thinking
skills to solve problems and make
effective decisions
✓ Communication:
Understanding
and communicating ideas
✓ Collaboration:
Working
with
others,
appreciation
of
multicultural differences
✓ Creativity and Attitude: Deliver
high quality work and focus on
result, initiative, intellectual risk
✓ Planning & Organizing: Planning
and prioritizing work to manage
time effectively and accomplish
assigned tasks
✓ Business Fundamentals: Having
fundamental knowledge of the
organization and the industry
✓ Customer Focus: Actively look for ways
to identify market demands and meet
customer or client needs
✓ Working with Tools & Technology:
Selecting, using, and maintaining
tools and technology to facilitate
work activity
✓ Dynamic
(Self-)
Re-Skilling:
Continuously monitor individual
knowledge and skills as shared
responsibility between employer
and employee, ability to adopt to
changes
✓ Professional
Network:
Involvement and contribution to
professional network activities
✓ Ethics: Adhere to high ethical and
professional norms, responsible
use of power data driven
technologies, avoid and disregard
un-ethical use of technologies and
biased data collection and
presentation.
The DELTA+ Model & the Organizational
Maturity Model