The world is changing at a rapid pace. New varieties of data continue to spring up and be made available for integration and knowledge improvement across many domains. Knowledge engineering, using advanced techniques in data science, is therefore moving to the forefront of technology and IT concerns at many companies. We see this in the expansion of cloud technologies, semantic technologies, data analytics, and the construction of Data Lakes. Understanding one’s data and being able to derive complex patterns of interest from across a multitude of different data sources (public and private) should be of paramount concern for companies in the pharmaceutical, crop science and life science industries. Companies who embrace knowledge engineering practices will possess a distinct advantage in the coming years due to their ability to integrate and use data to their advantage. This talk will discuss recent trends in data science and will highlight some of the main points to consider for taking advantage of these new technologies and approaches. We will also cover certain lessons learned from real-world industry use cases to highlight how people are using these technologies for improved business benefits.

1. Eric Little, PhD
Chief Data Officer
eric.little@osthus.com
Innolabs Summit London 2017
Why Data Is Becoming the Most
Valuable Asset Companies Possess

2. Slide 2
The Current Problems Most Companies Face
Data Silos
Incompatible instruments/systems
Proprietary data formats
Legacy architectures/tools
SME knowledge resides in people’s
heads
Little common vocabulary
Data schemas are not explicitly
understood (or non-existent)
Lack of common vision between
business units and scientists
2

3. Slide 3
Handling Complexity
3
Scientific data is incredibly complex
 Because the underlying science is incredibly complex
 But do we use the right tools?

4. Slide 4
User experience must be highly
simplified – consumer
expectations
Underlying tech is highly
complex
Complexity Does Not Mean For the User
VS.

5. Slide 5
Metadata is key element for:
 Data Management practices that
drive successful organizations
• Data Governance (#1 use)
• Data Stewardship
• Data Warehousing
• Master Data Management
• Enterprise Resource Management
• Customer Relationship
Management
• Business Intelligence
• Analytics
2/3 of recent survey respondents
 Metadata is more important than
10 years ago
The Increasing Importance of Metadata
Source: “Emerging Trends in Metadata Management” Dataversity 2016 Report
By Donna Burbank and Charles Roe

6. Slide 6
Align disparate data
 Human Concepts (Lenses)
 Context
Search is more important than ever
 The Google Effect
Machine Readability
 Increasingly useful
 Automation
Standards
Business Users Are Increasing
 Legacy approaches are too “techie”
Why Metadata Matters So Much

7. Slide 7
A New Approach to Data Science

8. Slide 8
Data Lakes are a rising trend
 Preserves raw data
 Reduces ETL processing
 Longer shelf life
Internet of Things (IoT)
 Machine-to-Machine communication
 Relies on context
Data Variety continues to grow
 Descriptions are critical
 Link to Data Veracity (statistics)
Metadata Matters for Big Data

9. Slide 9
AT OSTHUS LAB DATA SCIENCE IS CALLED
BIG ANALYSIS
STATISTICAL
SEMANTICS
MACHINE
LEARNING
REASONING

10. Slide 10
Why Big Data Is Moving To Big Analysis
It’s not about the IT problems anymore
 IT is rapidly changing (moving beyond its traditional boundaries)
Human-centered Design is increasingly more important
Technology is dominating strategic business priorities (Deloitte “Tech Trends 2017”)
It’s about being able to use data, not simply retain it
 Past RDB tech was built on storage techniques, not always retrieval
 Data Warehouses – hard to repurpose the transforms
 Data Lakes – often hard to do Schema-on-Read
Big Analysis combines metadata + statistics to build advanced lab informatics

11. Connecting data, people and organisations
Learn more by visiting BigAnalysis.com