Transforming Toward Thailand 4.0 http://www.oie.go.th/content/OIE-Forum2560

1. Santisook Limpeeticharoenchot
MD.Stelligence
OIE Forum,Industrial Intelligence Information
24 July 2017, BITEC BangnaSource pic: https://www.linkedin.com/pulse/
big-data-startupsme-environment-nikunj-thakkar

2. What’s Big Data from industrial’s perspective?

3. WHAT IS DATA?

4. SME Standard reporting

5. What is Big Data definition?
• Compared to traditional data, the features of big data can be characterized by
5V; namely huge Volume, high Velocity, high Variety, low Veracity, and high
Value
• Volume: Large volume of data that either consume huge storage or consist of large
number of records (Exabyte, Zettabyte)
• Variety: Data generated from greater variety of sources and formats, and contain multi-
dimensional data fields
• Velocity: Frequency of data generation and/or frequency of data delivery data creation
like streaming and aggregation
• Value: The extent to which big data generates economically worthy insights and or
benefits through extraction and transformation
• Veracity: Inherent unpredictability of some data requires analysis of big data to gain
reliable prediction

6. Focus Has Been on Wrong Issues
Value ?
$

7. SMEs face multiple challenges to growth
• Competition from Enterprises & Franchisees
• Inability to invest in customer acquisition
• Inability to manage supply chain, distribution & sales force
• Inability to deliver large order size with short cycle times
• Lacks timely insights into market movements

8. © 2016 Alteryx, Inc. | Confidential Download a FREE Trial: alteryx.com/trial
PROCESS
TOOLS
COMPETENCIES
STRUCTURE
CULTURE
POLITICS
ECONOMY
CUSTOMERS
SUPPLIERS
REGULATION
COMPETITORS
BRAND
MARKET
CHALLENGES
ORGANIZATIONAL
CHALLENGES
TEAM
CHALLENGES
Sources of Challenges for Business Analytic
Source: The value of business analytics,Evan Stubbs,Wiley and SAS.

9. Why (big)data analytic ?
• (Big) Data analytic is a new paradigm shift for SMEs.
• If it is implemented correctly, it will increase…
• Competitiveness
• Profitability
• Productivity
• Responsiveness
• Capturing blind spots and making better
decisions
• New Revenue Stream

10. Big Data Challenges for SMEs
• Lack of understanding
• Shortage of in-house IT and data analytic expertise
• Shortage of useful and adoption consulting and
business analytics services
• Cultural barriers and intrinsic conservatism
• Lack of management and organizational models
• Concern on data security
Source : An Overview of Big Data for Growth in SMEs

11. Process of data analytic (CRISP-DM)

12. “Even within large enterprises, you don’t need mountains of data to
gain insight from it: you simply need to be asking the right questions,
and smaller companies are just as capable of asking intelligent
questions as big companies.”
Matt Assay, Vice-President of Corporate Strategy at 10gen
Become data-driven organization…

13. What’s source of Data for SME?
• Customer’s Data
• Firm’s Financial data
• Supply Chain data
• Marketing and Social media
• Economic Data, Gov-OpenData
• HR Data
• Sensors, IOT data.

14. Type of analytic

15. Common Type of Analytics for Business
1. Reporting : summarize historical data
2. Trending : identify pattern in time series data
3. Segmentation : identify similarities within data
4. Predictive Modeling : prediction future of events
Source: The value of business analytics,Evan Stubbs,Wiley and SAS.

16. Benefit of Big Data Analytic to SME..
• key components for SME’s growth.
Customer acquisition Sale Forecasting
Customer
relationship
manage
supply chain
Sale Force
& Distribution
Journal Paper: An Overview of Big Data for Growth in SMEs

17. Data Preparation and Blending
www.alteryx.com

18. Data Analytic recommendation for SME
1. Data exploration -> to understand its main characteristics and decide on
the best approach
2. Time-series analysis -> collection of values obtained from sequential
measurements over time
3. Clustering -> process of grouping together data points
4. Association rule -> discovering hidden relationship within data set
5. Regression -> discover factors and forecast target value.
Descriptive Predictive

19. Simple Tools on Excel
• Data exploration -> Pivot, V-lookup , graph

20. Interactive dashboards

21. Simple Tools on Excel
2. Time-series analysis (exploration)

22. Simple Tools on Excel
2. Time-series analysis

23. Simple Tools on Excel
3. Clustering

24. Simple Tools on Excel
4. Association rule

25. Simple Tools on Excel
4. Association rule

26. Simple Tools on Excel
5. Regression

27. “Cloud” and “Self-Service” solves challenges SMEs face, in
adopting traditional IT based solutions
IT Solutions to Business challenges
▪ Integration of value chain
▪ Tools for Management Control
▪ Tools for Analytics & Decision support
▪ Tools Collaboration with peers & partners
Challenges to adoption
▪ Initial Investments
▪ Internal knowhow
▪ Cost of operations
How “Cloud” solves these Challenges
▪ Inexpensive, as resources are provided by the service provider.
▪ No need for expensive in house IT manpower or Knowhow
▪ Cost effective as only pay for services used
Cloud is about Economics, not Technology
Source:NetApp

28. Taxonomy of Big Data Companies
Your Data Other Data
Sell Data Compute + Sell
Data-driven
Product
Twitter, NYSE Bit.ly, Hunch Zynga, Amazon
DataSift, Yipit Metamarkets,
PlaceIQ
RecordedFuture,
BillGuard
Data Products
Source: IA-venture

29. Common type of Business in Thailand
Manufacturing Services Trading

30. How to monetize data?
What questions we are going to
ask?
How to present data?
What chart to visualize
our data?
How to create ABC
reports?
World of wizard
Let’s gather business
requirement
How can I use data to introduce
measurable business benefit?
Transition from insight to action
Let’s brainstorm
Source: Tableau

31. Data Business in Thailand
Data Provider BizModel New S-Curve Startup/platform
- Social monitoring

32. Data Business in Thailand
User Generate Content, Data Processing Data Processing, Ecommerce

33. Thank you, Q&A
We are Data Analytic Provider for SMEs and Enterprise
Facebook.com/stelligence
www.stelligence.com