This brief presentation provides a high level perspective of the benefits of machine learning, its impact on unstructured and structured data, and how the aspects of language are important

1. Object-Oriented Data Governance
Overview
Global IT Solutions
Intuitive, Cost Effective, Data-Centric,
Scalable Solutions
Global IT Solutions (GITS) Presents:
Machine Learning and Language
Global IT Solutions
Intuitive, Cost-Effective, Scalable Solutions

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Machine Learning
and the importance of Language

3. Genetics
Pharmaceuticals
Medicine
Hematology Anatomy
Different Business Areas have a
Common Denominator
Solving a common puzzle…
Question:
What do the above areas of science have in common?
Pathology

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Answer is - Untapped Potential!
Like other industries, 80% of information is Unstructured, and
buried in Artifacts:
o Journals
o Websites
o Publications
Scientists create Artifacts to share knowledge
If Knowledge is power and Information fuels Knowledge - then
logic dictates that vast opportunities are being missed
So you’ve digitized/scanned your documents…
You’ve provided document-to-document links on the Web…
Information stored in documents (unstructured data) is still
‘buried’ – you can’t link it to structured/geospatial data
Thus, you’re still not getting the results you were looking for…
The Problem

5. It doesn’t have to be this way…
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You can build a Roadmap that leverages both
Structured and Unstructured Data Strategies
You can forge Interoperability/Collaboration, Globally
You can enlist Machines to exceed the limits of
humans..
The Future is here
The contents of Unstructured documents from various
scientists can be shared and linked in a meaningful way
Can you measure the benefits of your improvements?
Do your plans include building and implementing the
framework that is necessary to sustain 'Machine
Learning'?
Let's talk a little bit about Machine Learning benefits and
hurdles...

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You may not know that much about Machine Learning (ML) …
But you know enough to know you don’t want what's behind Doors #2 and #3…
You also know that nothing is as easy as they say it is
Question: So, who’s right?
The Experts say that ‘Machine Learning’ can achieve your objectives….

7. The Answer: You both are (you and the experts)
You can ‘teach the Machine’ to learn and help:
Discover patterns and similarities across millions of Artifacts
Impart Knowledge contained in Unstructured Text and Structured Data
Make Inferences and Extrapolations on what you provide
Aid in making decisions
Exceed the limits of humans
But you are also right, there will be hurdles…
The hurdles are rooted in both the Machine and Humans
GITS uses the term ‘hurdles’ deliberately – the following items are not
‘problems’, they are just realities that have to be addressed
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Machine Learning

8. Hurdle #1: Machine Learning
is a gradual process…
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Reality #1 – When teaching new concepts to the
Machine, assume it thinks like a Child
Reality #2 -- You also must think like a Child, to
understand the ML process
Reality #3 – You can’t assume the Machine has
grasped a concept, you have to prove it
Reality #4 - Machine Learning Maturity is obtained
through trial-and-error – you need to conduct
‘experiments’
Reality #5 – You don’t need to be a genius to
conduct experiments, for trial-and-error ML
Reality #6 – You do need to keep track of your
experiments to determine how the Machine has
matured.

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Reality #8 -- People work in Silos. You can't change it. People like their Silos.
Within a given Silo, as Unstructured/Structured Data is captured, Reality #7 is
not a problem
In an Integrated Environment, Reality #7 is a problem
Reality #7 -- There is a ‘Vernacular’, collectively - among Colleagues; within
Global Regions, independently - amongst Authors
When individuals speak, it is common to use Synonyms, Homonyms and
Homographs
Hurdle #2: The Human Language
is fluid..
Reality #9 -- Enterprises rarely understand the importance of having
Ontologies/Taxonomies – until they witness the benefits

10. Taxonomy Example
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The GITS Methodology:
Provides visual representations of Taxonomies (e.g.,
Venn, Hierarchy) specific to the language of the business
Stores Taxonomies as Meta-Data
Provides the ability to link
Unstructured Data
Structured Data
Geospatial Data

11. GITS is realistic about the hurdles…
GITS doesn’t attempt to change these Realities, our Methodology accommodates them
Before you can teach the machine, GITS can show you how to manage the language
GITS will develop a Framework that can sustain Machine Learning
GITS will help you to ‘Practice what you teach’ the Machines
If you manage the language properly, you can exceed your expectations
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12. • The GITS Methodology:
– Mitigates ‘Untapped Potential’
– Uses Ontologies/Taxonomies (as diagrams and Metadata)
– Links meaningful content from Unstructured Documents to
Structured Data/Geospatial Information
– Creates an environment amenable to efficient Machine
Learning
– Facilitates Machine Learning
• GITS understands how to:
– Use Machines to exceed the limits of humans
– Provide Cost-Effective, Data-Centric Solutions
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GITS provides The Solution

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Are the following part of your
Solutions Framework?
Bi-Temporal Time Series Solutions
Interoperability, Collaboration and Operational Efficiency
Unstructured/Structured Data Analytics
Multifaceted Business Intelligence (i.e.,
Unstructured/Structured Data, Geospatial)
Leveraging Social Media and Big Data
Ontology/Taxonomy Management and Implementation
Data Architecture/Data Science
Cost-Based Data Governance
Preparation for and usage of Machine Learning
If not, discover why they should be – contact us for a free
Consultation Session
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Visit our website or contact us
for additional information
Global IT Solutions
info@globalitsolutionscorp.com
www.globaliltsolutionscorp.com
732-356-0835