Big Data Innovation Summit 2014 Toronto: Day 2 Highlights
Tags: Analytics, Big Data, Conference, Graph Analytics, Healthcare, IE Group, Innovation, Toronto-Canada
Highlights from the presentations by Big Data leaders from Aviva, Canadian Imperial Bank, Royal College of Physicians and Surgeons of Canada, and University Health Network on day 2 of Big Data Innovation Summit 2014.

We provide here a summary of selected talks along with the key takeaways.
Highlights from Day 1.
Here are highlights from Day 2 (Thursday, June 5, 2014):

Masoud elaborated on a framework that helps find focus areas in big data, and demonstrated that the problem is not information overload; it's filter failure. Citing research and surveys, they stated that market is currently bullish on Big Data.
Shirin stated that there are 2 approaches in Analytics, which she referred to as "Stone Age" approach and "Information Age" approach. She shared the following anonymous quote:
“The Stone Age was marked by the clever use of crude tools;
the Information Age has been marked by the crude use of clever tools”
The "Stone Age" approach in Analytics uses Basic Tools + Clever People. Quite a few companies follow this approach which does hold true to some extent - as far as you are looking for the "known unknowns" only. Whereas, the "Information Age" approach uses Clever Tools + Basic People. By putting the majority of technical expertise in the tools, such organizations have a better distribution of departments with analytical capabilities. Also, this approach is better suited for Big Data, where you look for a signal in huge amount of noise, and are often looking for the "unknown unknowns".It is very important to identify the focus areas and ask the right questions. The focus should be on the biggest and highest value opportunities, and within each opportunity, start with questions, not data. Early focus should be on areas that involve no more than first or second degree inferential analysis.
Finally, by walking through a practical example, they outlined the following suggestions for Analytics processes:
- Quantify questions
- Explore validity
- Keep track of surprises
- Focus on finding differentiators
- Explore segments
- Explore relationships

He asked: Is Big Data really the answer or are we just taking bad data creating massive analytics? As a result, are we taking small problems and turning them into BIG ones? Addressing the ultimate question: "how to generate true business value?", he shared some tools, techniques and thinking models, to help the audience understand various perspectives when it comes to analytics and the roles they play in decision-making.

He outlined the Big Data opportunities in Healthcare as: Quality, Safety, Access, Efficiency, Cost, Outcomes (reporting and optimization), Analytics (personalized medicine, clinical decision support) and R&D. The new sources of health data include genomic data (gene sequencing data), streamed data (home monitoring, tele-health, bio-sensors) and clinical data (80% unstructured documents, images, clinical or transcribed notes). He highlighted the following major challenges:
- Collecting data (structured vs. unstructured, data quality)
- Aggregating data (interoperability, privacy, ownership)
- Analyzing data (data discovery, interpreting results)
- Consuming data (alignment of payers and providers)
Most legacy systems were not initially designed for clinical care. They were focused on visits rather than patients. Those systems had complicated work-flows and poor inter-operability. Currently, the healthcare industry is facing major challenges around standards, inter-operability, customization, marketplace fragmentation, regulation and user adoption. Big challenges require big resources, and thus, need for the power of community (eg. World Computing Grid). In conclusion, he recommended eHealth initiatives to focus on: Design (empower patients to boost self-efficacy, connect providers to reduce medical errors), Technology (leverage economies of scale, support & fund innovation), and Governance (national standards & strategy, international comparison & benchmarking).

Graphs are a very powerful tool for dealing with more complex data. He defined data complexity through the following equation:
Data Complexity = f (connectedness, size, structure)
The benefits of a graph database include "miutes to milliseconds" performance, fit for the domain and business responsiveness (easy to evolve). Then, he walked through several examples such as geographical routes graph, internet networking graph, friendship graph, e-commerce graph (buying patterns & relationship among customers).A graph database is one that uses graph structures with nodes, edges, and properties to represent and store data. Graph databases provide index-free adjacency. Examples of popular graph database: Neo4j, FlockDB, AllegroGraph, InfiniteGraph, OrientDB, etc. Neo4j is a NOSQL graph database with powerful traversal framework. It works with the Cypher query language over HTTP. Cypher is a human readable language that was purpose built for working with graph data (with inspiration from SQL syntax). It's a primary tool for building graph applications. Finally, he suggested that Graph Theory is particularly useful when we first want to gain some insight into a new domain and understand insights to extract from a domain.
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