01 - The Data-Driven Enterprise and Fundamental Frameworks.pdf

The Data-Driven Enterprise and Fundamental Frameworks

1. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Assistant Professor Dr. Nattapong Kongprasert
Data Analytics and Governance for Business Decision

2. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Background
Assistant Professor Dr. Nattapong Kongprasert
Department of Industrial Engineering, Faculty of Engineering,
SrinakharinwirotUniversity
Email: [email protected]
Working Experiences:
2020-2024 Assistant to the President for Academic Affairs, SrinakharinwirotUniversity, THAILAND
2014 -PresentAssistant Professor, Department of Industrial Engineering, Faculty of Engineering, SrinakharinwirotUniversity, THAILAND
2014 -2020 Head, Department of Industrial Engineering, Faculty of Engineering, Srinakharinwirot University, THAILAND
2007 -2014 Lecturer, Department of Industrial Engineering, Faculty of Engineering, SrinakharinwirotUniversity, THAILAND
Education:
2007 –2010 Ph.D. in Industrial Engineering, Grenoble Institute of Technology (Grenoble INP), FRANCE
2002 -2005 M.Eng. in Production Engineering, King Mongkut’s Institute of Technology North Bangkok, THAILAND
1998 -2002 B.Eng. in Industrial Engineering, SrinakharinwirotUniversity, THAILAND

3. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Background
Research Areas: Brand Identity, Emotional Design, Sustainable Design, Manufacturing
Systems, Productivity Improvement
Professor in Industrial Engineering and industrial consultant with 14 years of
experience, specializing in lean manufacturing, product development, emotional
design (KANSEI engineering), and sustainable design. Expertise in improving
productivity in the manufacturing process and designing a new product to meet
customer requirements in more than 100 large and SME enterprises in Thailand.

4. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Data Analytics and Governance for Business Decision
Introduction to fundamental frameworks; data extraction techniques; data
presentation; creating business models from data for business decision- making;
utilizing and storing structured and unstructured data for business reports; business
forecast; data stewardship; data security and protection; data management; analyzing
through case studies and data ethics; security and privacy against mishandling or data
mismanagement in modern business.

5. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Course Syllabus
1.The Data-Driven Enterprise and Fundamental Frameworks
2.Data, Databases, and Data Quality
3.Data Extraction and Preparation
4.Data Presentation and Storytelling
5.Building Business Intelligence Dashboards
6.Business Forecasting
7.Creating Business Models from Data
8.Data Governance and Stewardship
9.Data Ethics, Security, and Privacy in a Global Context
10.Capstone Project

6. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Learning Outcomes
•Evaluatethe strategic importance of data analytics and a data-driven culture in a modern
international business.
•Applyfundamental frameworks to structure and solve business problems using data.
•Demonstrateproficiency in using common business tools to extract, clean, and visualize data for
effective storytelling.
•Developand interpret basic business models and forecasts to support strategic and operational
decision-making.
•Critiquethe role of data governance, stewardship, and management in maintaining data quality
and integrity.
•Analyzecomplex business scenarios involving data ethics, security, and privacy, and propose
solutions that align with global standards (e.g., GDPR, PDPA).

7. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
References

8. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Assistant Professor Dr. Nattapong Kongprasert
The Data-Driven Enterprise and Fundamental Frameworks

9. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Content
•Introduction of Data- Driven Enterprise
•DIKW Pyramid
•Business Analytics
•Analytics Frameworks

10. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
The Modern Business Challenge
"Without data, you're just another person with an opinion." -W. Edwards Deming.

11. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
What is a Data-Driven Enterprise?
A Data-Driven Enterprise is a business that uses data analysis to inform and drive all
aspects of its operations, from strategic planning to daily decision- making. Instead of
relying on intuition or gut feeling, these enterprises leverage data to optimize processes,
identify opportunities, and gain a competitive edge. They embed data analysis into
their core business processes, making it a fundamental part of their operations.
Then, an organization where decisions at all levels are informed by, and validated with, data.

12. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
The Strategic Importance of Data
Data's strategic importance lies in its ability to inform decision- making, drive
innovation, and enhance overall business performance. By effectively managing and
analyzing data, organizations can gain a competitive edge, optimize operations, and
adapt to changing market conditions.

13. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Data as a key business asset
Data is increasingly recognized as a key business asset, similar to financial capital or
physical resources. Its value lies in its potential to drive growth, innovation, and
competitive advantage.
Businesses are now leveraging data to gain deeper insights into customer behavior,
optimize operations, develop new products, and make more informed decisions.

14. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Examples of companies using data for a competitive edge

15. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Amazon
Personalized Recommendations:
Amazon analyzes customer behavior, browsing history, and purchase patterns to offer tailored product
suggestions, driving sales and customer engagement.
Supply Chain Optimization:
Real-time data analytics helps Amazon track inventory, optimize distribution, and minimize lead times,
leading to faster delivery and reduced costs, according to OPEN OCO.
Customer Service:
Amazon uses data to anticipate customer needs and provide personalized support through chatbots and
voice assistants, improving the customer experience.

16. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Netflix
Personalized Recommendations:
Netflix's recommendation engine, powered by user data, drives 80% of viewer activity, leading to
increased engagement and customer retention.
Content Creation:
Netflix uses data analytics to understand user preferences and predict the success of new content,
optimizing its original programming strategy.
Churn Reduction:
By analyzing user data, Netflix can identify at -risk subscribers and proactively offer personalized
recommendations or deals to retain them.

17. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Starbucks
Personalized Offers:
Starbucks uses data from its mobile app to understand customer preferences, offering tailored drink
recommendations and promotional deals, says BiGEVAL.
Targeted Marketing:
Starbucks leverages data to identify high-value customers and tailor marketing campaigns, optimizing its
promotional efforts.
Operational Efficiency:
Data analysis helps Starbucks optimize inventory management and staffing levels, ensuring efficient
operations.

18. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Content
•Introduction of Data- Driven Enterprise
•DIKW Pyramid
•Business Analytics
•Analytics Frameworks

19. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
DIKW pyramid
The DIKW pyramid, also known as the knowledge hierarchy, is a model that represents
the relationships between data, information, knowledge, and wisdom. It illustrates how
raw data is transformed into actionable insights and strategic guidance.
Source: https://medium.com/@89ataksinem/dikw-pyramid-data-storytelling-3059f82f86b3

20. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Breakdown of DIKW pyramid
•Data: Raw, unprocessed facts and figures. It lacks context and meaning on its own.
•Information: Data that has been processed, organized, and given context. It answers questions like
"who," "what," "where," and "when".
•Knowledge: Information that has been understood, interpreted, and applied to a specific context. It
answers questions like "how" and "why".
•Wisdom: The ability to apply knowledge to make sound judgments and decisions, often with
consideration for long-term consequences and ethical implications. It involves understanding the
underlying principles and potential impact of actions.
Data (raw facts) -> Information (contextualized) -> Knowledge (actionable insights) -> Wisdom (strategic application)

21. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Content
•Introduction of Data- Driven Enterprise
•DIKW Pyramid
•Business Analytics
•Analytics Frameworks

22. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
What is Business Analytics?
•Business Analytics is the systematic process of examining data sets to draw
conclusions about the information they contain.
•It involves collecting, organizing, processing, and analyzing data to uncover
meaningful patterns, correlations, and trends.
•The ultimate goal is to support business decision- making, improve efficiency,
and drive strategic outcomes.

23. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Types of Business Analytics
•Descriptive Analytics
•Diagnostic Analytics
•Predictive Analytics
•Prescriptive Analytics
Source: https://tommarch.com/2020/01/4- types-data-analytics-for-educators/

24. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Descriptive Analytics
The baseline and the place that all organizations should start is with Descriptive
Analytics. This type of analytics is when an assessment of data, often historical, is used to
answer the fundamental question, “What happened?”
What happened?
It looks at the events of the past and tries to identify specific patterns within the data.
When someone refers to traditional business intelligence, they are often describing
Descriptive Analytics.
Visualizations used for Description Analytics are pie charts, bar charts, tables, or line
graphs. It is the foundation of the other three tiers.
Source: https://iterationinsights.com/article/understanding- the-different-types-of-analytics/

25. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Diagnostic Analytics
Why did it happen?
Diagnostic analyticsis a form of advanced analytics that examines data or content to
answer the question, “Why did it happen?” It is described by techniques such as drill -
down, data discovery, data mining, and correlations.
This is the second step, as you must first understand what happened to be able to identify
why it happened. Once an organization achieves descriptive insights, it can apply
diagnostics.
Source: https://iterationinsights.com/article/understanding- the-different-types-of-analytics/

26. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Predictive Analytics
Source: https://iterationinsights.com/article/understanding- the-different-types-of-analytics/
What is likely to happen?
Once an organization can understand what occurred and why it happened, it can move up
to the next tier in analytics. Predictive Analytics is another type of advanced analytics
that looks to use data and information to answer the question, “What is likely to
happen?”
Predictive Analytics involves techniques. It includes regression analysis, forecasting,
multivariate statistics, pattern matching, predictive modelling, and forecasting.
These techniques are harder for organizations to achieve. This is because they need large
amounts of high- quality data. These techniques need a deep understanding of statistics
and programming languages such as R and Python.

27. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Prescriptive Analytics
Source: https://iterationinsights.com/article/understanding- the-different-types-of-analytics/
What should be done?
Prescriptive Analytics is a method of analytics that analyzes data to answer the
question, “What should be done?”
Prescriptive Analytics is characterized by techniques. These include graph analysis,
simulation, complex event processing, and neural networks.It also includes
recommendation engines, heuristics, and machine learning.
This is the most difficult level to achieve. To get an effective response from a prescriptive
analysis, the techniques required stem from how well an organization as has
accomplished each level of analytics.
The value that it brings is that an organization will be able to make decisions based
on analyzed facts rather than instinct.

28. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Summarize of Data Analytics
Type Focus Business Application Example
DescriptiveSummarizes historical dataMonthly sales report to identify best-selling products
DiagnosticExplains why something happenedRoot cause analysis of declining customer satisfaction
PredictiveForecasts future outcomesPredicting next quarter’s demand using past trends
PrescriptiveRecommends actions or strategies
Suggesting optimal pricing strategy based on customer
behavior

29. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Business Intelligence (BI)
Business intelligence (BI) focuses on descriptive analytics and reporting to understand
past and present business performance. It answers the question "what happened?" by
summarizing historical data and identifying trends, patterns, and insights.
BI utilizes tools and methodologies for data analysis, visualization, and reporting to
support informed decision- making.

30. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Data Analytics
Data analytics involves using data to answer questions, identify trends, and extract
insights to drive decision- making.Diagnostic analytics focuses on understanding the
"why" behind past events, while predictive analytics aims to forecast future outcomes.
Together, they help businesses understand past performance, identify potential
problems, and prepare for future scenarios.
Source: https://predikdata.com/what-is-predictive-analytics-understanding- the-basics-and-beyond-with-examples/

31. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Data Science
To achieve data science solutions with prescriptive analytics and machine learning,
focus on clearly defining the business problem, gathering relevant data, developing
and training machine learning models, and then deploying these models to generate
actionable recommendations. Prescriptive analytics builds upon descriptive and
predictive analytics by recommending specific actions to achieve desired outcomes.
Source: https://www.datasciencecentral.com/descriptive-predictive-prescriptive-analytics-will-fail-to-help/

32. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Analogy like driving a car
BIis the dashboardAnalyticsunderstands fuel consumptionData Science builds a self-driving car

33. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Content
•Introduction of Data- Driven Enterprise
•DIKW Pyramid
•Business Analytics
•Analytics Frameworks

34. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Analytics Frameworks
Analytics frameworks provide a structured approach to problem-solving, enhancing
efficiency and effectiveness by promoting repeatable processes, reducing the risk of
errors, and ensuring consistent results.
A structured problem-solving process, as often incorporated in these frameworks,
involves clearly defining the problem, gathering relevant data, analyzing it using a
predefined approach, developing solutions, and evaluating their impact.

35. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Why use an analytics framework?
Structure:
Frameworks break down complex problems into manageable steps, providing a clear
path for analysis and solution development.
Repeatability:
By establishing standardized processes, frameworks ensure that analyses can be
replicated with similar results, regardless of who performs them or when they are
conducted.
Reduced Risk:
Following a structured approach minimizes the chances of overlooking critical factors
or making errors in analysis, leading to more reliable insights and better decisions.

36. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Structured Problem-Solving Process
1. Define the Problem:
Clearly articulate the problem you are trying to solve, ensuring it is measurable and observable.
2. Gather Data:
Collect relevant information to understand the current situation and potential causes.
3. Analyze Data:
Utilize the chosen framework to examine the data, identify patterns, and generate hypotheses.
4. Develop Solutions:
Based on the analysis, create potential solutions that address the identified problem.
5. Evaluate Solutions:
Assess the effectiveness of the implemented solutions and make adjustments as needed.
A structured problem-solving process typically involves these key stages:

37. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
A popular framework for analytics
•CRISP-DM (Cross Industry Standard Process for Data Mining)
•KDD (Knowledge Discovery in Databases)
•Agile Analytics Framework
•Business Intelligence Lifecycle

38. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
CRISP-DM (Cross Industry Standard Process for Data Mining)
Source: https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
The CRossIndustry Standard Process for Data Mining (CRISP-DM) is a process model that
serves as the base for a data science process.
Business understanding – What does the business need?
Data understanding –What data do we have / need? Is it clean?
Data preparation –How do we organize the data for modeling?
Modeling–What modeling techniques should we apply?
Evaluation–Which model best meets the business objectives?
Deployment–How do stakeholders access the results?

39. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
CRISP-DM (Cross Industry Standard Process for Data Mining)
•Overview: Widely adopted, domain- agnostic framework for structuring data mining projects
•Phases:
1.Business Understanding –define objectives and success criteria
2.Data Understanding –collect, explore and assess data quality
3.Data Preparation –clean and format data for modeling
4.Modeling–apply analytical techniques
5.Evaluation–review outcomes for validity and business relevance
6.Deployment–implement insights into business processes
•Use Case: Ideal for structured analytics projects with clear deliverables

40. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
KDD (Knowledge Discovery in Databases)
Knowledge Discovery in Databases (KDD) refers to the complete process of uncovering
valuable knowledge from large datasets. It starts with the selection of relevant data,
followed by preprocessing to clean and organize it, transformation to prepare it for
analysis, data mining to uncover patterns and relationships, and concludes with the
evaluation and interpretation of results, ultimately producing valuable knowledge or
insights.
Source: https://www.geeksforgeeks.org/dbms/kdd-process- in-data-mining/

41. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
KDD (Knowledge Discovery in Databases)
•Overview: Focuses on uncovering patterns from large datasets
•Key Stages:
1.Selection –choose relevant data
2.Preprocessing–remove noise, correct inconsistencies
3.Transformation–convert data into suitable formats
4.Data Mining –apply algorithms to detect patterns
5.Interpretation/Evaluation –assess and interpret findings
Emphasis: Discovering novel and meaningful insights, often exploratory
Use Case: Research environments and advanced analytics

42. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Agile Analytics Framework
Agile Analytics is not a framework, not even a methodology; it's just a development
style that focuses on the client’s end goals to make better decisions using data-
driven prediction. Client satisfaction is the topmost priority for project delivery
achieved through Agile Analytics rapid delivery of usable predictions. These values
aim to create high- quality, high- value, working DW/BI systems.
Source: https://www.xenonstack.com/insights/what-is-agile-analytics

43. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Agile Analytics Framework
•Overview: Adapts Agile principles (iterative, incremental development) to data analytics
•Core Ideas:
1.Rapid prototyping of models
2.Frequent stakeholder feedback loops
3.Data products evolve over time with changing needs
4.Collaborative, cross- functional teams
•Benefits: Speed, flexibility, and adaptability for modern business challenges
•Use Case: Organizations needing fast and responsive analytics solutions

44. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Business Intelligence Lifecycle
The Business Intelligence Cycle is a continuous, iterative process that involves
collecting raw data from various sources, analyzing and transforming it into meaningful
insights, visualizing the results through dashboards or reports, and using these insights
to make informed, data- driven decisions that drive business growth and efficiency.
Source: https://theecmconsultant.com/what-is-the-business-intelligence-cycle/

45. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Business Intelligence Lifecycle
•Overview: End-to-end process for converting raw data into meaningful BI tools (dashboards,
reports)
•Steps:
1.Data Collection –collect and store from various sources
2.Data Processing –clean, organize and transform raw data into structured formats
3.Data Analysis – perform queries and analytics to uncover patterns, trends,
correlations, and insights that can inform strategic decision- making
4.Data Visualization–deliver insights via charts, graphs, dashboards and scorecards
5.Decision Making – guide strategic actions that drive business growth and success
•Use Case: Enterprises managing large volumes of business data across departments

46. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Comparison of the Success Frameworks
Framework Purpose Key Phases Best For Strengths Limitations
CRISP-DM
(Cross Industry
Standard Process for
Data Mining)
Structured
guidance for
data mining
projects
Business Understanding →
Data Understanding →
Preparation → Modeling →
Evaluation → Deployment
Cross-industry
analytics
projects
•Clear step-by-step
structure
•Well-documented
& flexible
Can feel linear; less
adaptive to evolving
business needs
KDD
(Knowledge Discovery
in Databases)
Discovering
patterns from
large datasets
Selection → Preprocessing →
Transformation → Data
Mining → Interpretation
Research &
exploratory
analysis
•Focuses on
uncovering hidden
insights
•Strong emphasis on
discovery
•Less emphasis on
deployment
•Limited
integration to
business strategy
Agile Analytics
Framework
Iterative,
incremental
development of
data solutions
Sprint Planning → Data
Exploration → Model
Development → Review →
Deployment
Dynamic,
evolving
business
environments
•Fast delivery
•Flexible to change
•Engages
stakeholders often
•Requires mature
agile culture
•Harder to
standardize results
Business Intelligence
Lifecycle
Converting data
into operational
insights
Data Collection → Data
Processing → Data Analysis →
Data Visualization → Decision Making
Enterprise BI & decision support
systems
•Strong integration
with reporting
tools
•Great for
dashboard creation
•May lack deep
analytics
•Slow to adapt in
fast-paced
markets

47. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Emphasize
The process is an iterative cycle, not a linear one.
BI LifecycleAgile Analytics FrameworkKDDCRISP-DM

48. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Summary & Key Takeaways
•Data is a strategic asset.
•Frameworks turn data projects from art to science.
•Your role as a leader is to ask the right business questions.

49. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Case Study #1

50. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Case Study #1
1. CRISP-DM (Cross Industry Standard Process for Data Mining)
Case Study: Financial Services –Northern European Bank
https://kodu.ut.ee/~dumas/downloads/rcis2021-crisp-dm.pdf
2. KDD (Knowledge Discovery in Databases)
Case Study: Industrial Construction –Labor Resource Optimization
https://www.scirp.org/pdf/JCC_2014031011341858.pdf
3. Agile Analytics Framework
Case Study: Retail –LoxonSolutions
https://www.knowledgehut.com/blog/agile/agile-case-study
4. Business Intelligence Lifecycle
Case Study: BI in Real-Life -Showcasing the Impact of Business Intelligence Services
https://datafortune.com/bi- in-real-life-case-studies-showcasing-the-impact-of-business- intelligence-services/

51. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Case Study #1
Break into small groups (4- 5 persons), assign each team two case studies.
Task:
1. Discuss the framework used
•Why does it fit the situation?
•What insights were gained?
2. Please summarize and make the presentation file. (one question one page)

52. Data Analytics and Governance for Business Decision -Assistant Professor Dr. Nattapong Kongprasert
Thank you for your attention