(if you want to watch the webinar recording associated to this presentation, go to: https://hubs.ly/H0g03_z0) Businesses are collecting more data than ever. Being able to collect, collate and analyse this information is critical as organisations use this to improve efficiency and effectiveness. Yet how do you deliver meaningful outcomes? How do you separate the hype surrounding predictive analytics from the reality? We will explore the myths and realities of digital transformation with Machine Learning: WHAT can be delivered, WHY you should be analysing this data, and most importantly... WHO in your organisation already possesses many of the right skills ...and what we reveal should both surprise you and excite every data-driven professional. Learn the realities of this opportunity now.

1. FROM UNICORNS TO RACEHORSES:
Taking Predictive Analytics with Machine Learning
from Myth to Business Reality

2. Powerful statistical
software everyone can
use.
Integrated project tools
and reporting to manage
Continuous Improvement.
Online learning solution to
master statistics and Minitab®
anytime, anywhere.
Powerful data mining with
machine learning and
predictive analytics.
What Minitab offers
PRODUCTS:
SERVICES:
Training
Public courses or onsite training
matched to your requirements.
Statistical
Consulting
Personalized help with your statistical
and analysis challenges from experts.
Support
Assisting your use of the software,
installation and implementation.

3. © 2019 Minitab, Inc.
Throughout her career, Gillian has
been applying statistical analysis to
guide informed management
decisions on business opportunities
or problems.
Gillian has a Master's Degree in
Probability and Statistics from
Sheffield University.
Meet the Presenter:
Gillian Groom
Technical Training Specialist, Minitab

4. © 2019 Minitab, Inc.
The Digital Transformation Race
Real-time data
Smart Factory
Internet of Things
Industry 4.0
IT Infrastructure
Analytic talent
Predictive Analytics
Machine Learning
Project management
The Winning Post
Fully Integrated Digital Transformation

5. © 2019 Minitab, Inc.
How many years do you think it will take your overall
industry to realize the potential of the Digital
Transformation ?

6. © 2019 Minitab, Inc.
Where Machine Learning Fits?
Machine Learning is one of many Predictive Modelling Techniques
Data
Information
Machine
Learning
Actionable
Decisions

7. © 2019 Minitab, Inc.
The majority of manufacturing operations do not
currently use machine learning in operations, asset
management or field operations.

8. © 2019 Minitab, Inc.
Myth #1 : Machine Learning is new
(selected methods)
Logistic Regression
1958
Support Vector Machine
1963
K-nearest neighbors
1967
Artificial Neural Networks
1975
Decision Tree
1986
Random Forest
1995

9. © 2019 Minitab, Inc.
Fact #1: Technology Delivers the Opportunity to use Machine Learning
(selected technology)
Minitab
1972
SPM
1983
Windows PC
1985
World Wide Web
1991
Cloud Networks
2009
Google – 30 Trillion Pages
2018

10. © 2019 Minitab, Inc.
What changed is the volume of data available now due to digitalization
 Computer power and storage has become a commodity with faster access
 More data is being collected
 With the emergence of AI/AR use in commercial applications, the need to
convert data into information & decisions is growing exponentially
 This will continue to grow as more data is stored in digital form

11. © 2019 Minitab, Inc.
©Copyright Minitab® and Market Strategy Group, 2018

12. © 2019 Minitab, Inc.
Big Data, Talent, Cloud are Top Present Challenges
©Copyright Minitab® and Market Strategy Group, 2018

13. © 2019 Minitab, Inc.
Myth #2
Predictive Analytics
Requires Big Data

14. © 2019 Minitab, Inc.
Fact #2: Suitable Analytic Tools Exist for All Data Types & Sizes
Problem Definition + Data Available + Right Analytic Technique
=
Evidence based Decision Making

15. © 2019 Minitab, Inc.
Myth #3:
Machine Learning is the
Complete Tool for
Quality Improvement
Data Data

16. © 2019 Minitab, Inc.
Fact #3: Analytics are Part of the Framework for Driving Improvement
 As is true for all problem solving initiatives, teams benefit from
structured methodology:
 CRISP-DM
 DMAIC
 SEMMA
These Frameworks define the problem, the data and analytics

17. © 2019 Minitab, Inc.
Big Data, Talent, Cloud are Top Present Challenges
©Copyright Minitab® and Market Strategy Group, 2018

18. © 2019 Minitab, Inc.
Myth #4: You Need a Data Scientist to Deliver Predictive Analytics
Data science is a powerful combination of various disciplines.
Computer Science Skills
 Programming
 Big data technologies
Math and Statistics Knowledge
 Machine Learning
 Ensemble models
 Anomaly detection
Domain Expertise
 Business knowledge
 Expert systems
 User testing
Data
Science

19. © 2019 Minitab, Inc.
Where to Find this Ideal Data Scientist

20. © 2019 Minitab, Inc.
Fact #4: With the Right Process, Tools and Training
Data science is the process of extracting information, understanding and learning
from raw data to inform decision making in a proactive and systematic fashion that
can be generalized.
A key aspect of data science is the utilization of the scientific method to form and
challenge hypotheses to validate conclusions about data.
You already have Racehorses to run the Digitalisation Race

21. © 2019 Minitab, Inc.
Computer Science Skills
 Programming
 Big data technologies
Math and Statistics Knowledge
 Machine Learning
 Ensemble models
 Anomaly detection
Domain Expertise
 Business knowledge
 Expert systems
 User testing
Data
Science
Myth #5 : Data Scientists Can Quickly Acquire Domain Expertise

22. © 2019 Minitab, Inc.
Fact #5: Process Owners know the Problems that Need Resolving
 Address in-field reliability for smart components
 Automatic temperature adjustment in a production process
 To understand which areas of an airline wing behave differently
than the other parts in terms of vacuum levels to improve
performance and to ultimately optimize the # of sensors needed
 Root Cause Analysis for leakage in a pressure valve
A data scientist, will require time to develop this process expertise

23. © 2019 Minitab, Inc.
Myth #6:
Predictive Analytics
with Machine
Learning Requires
Programming Skills

24. © 2019 Minitab, Inc.
Fact #6: Minitab & SPM Designed For Business Users
to Deliver Predictive Analytics
Focus Time & Effort on Interpreting Results not Number Crunching

25. © 2019 Minitab, Inc.
What do these Digitalisation
Winning Racehorses Look Like?
Brain – Logical, organised,
computer literate, analytical
problem solvers
Shoulders – Broad, good at
taking responsibility and leading
Legs – Tireless at getting
to the root cause of
problems and keeping
projects moving
Heart – Passionate about
improving the business
performance
Muscles – Able to absorb new
ideas and continually train and
adapt to the changing environment
Feet – Steady, reliable but agile, taking
sensible size steps to reach the goal

26. © 2019 Minitab, Inc.
Digitalisation Race Winners
• It will not be achieved solely with technology
• It will not be done solely with predictive analytics and machine learning
• It will not be addressed solely with trained resources
Organisations need to determine the balance and cadence of these factors,
within the strategy to execute.

27. © 2019 Minitab, Inc.
Take away – We are all at various stages of the digitalization race
 This truly is a journey, no one is there yet
 Assess the current state, define goals and plan a series of well-placed
projects to propel the journey forward
 The Operations Excellence groups provide a head start for the digital
industrial transformation because they are experts in project selection,
project execution, data analysis and change management

28. Taking Predictive Analytics with Machine Learning
from Myth to Business Reality
AN ORGANIZATION’S JOURNEY
Gillian Groom
Racehorse Trainer, Minitab Ltd

29.  TRAINING: Enhance your skills with Minitab’s industry training courses
 FREE CONSULTATION: Which analysis software is right for you?
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