In the July 10, 2012 Peer Incite, Brigham Hyde, Ph.D., managing director of Relay Technology Management, Inc.; Sid Probstein, CTO of Attivio; and the Wikibon community discussed best practices for deriving business value from Big Data analytics. The community agreed that the keys to deriving maximum insights from big data analysis are methods and tools to combine structured and unstructured data sources. Relay Technology has leveraged technology from Attivio to analyze high-quality, unstructured data from the patent office, from research papers in medical journals, and from SEC documents, to create insightful linkages to the structured data, including drug usage and pricing.
If the goal of the Big Data initiative, however, is deriving business value, it is just as important to build a data analytics team made up of both analytics specialists and content or business experts. Smaller teams of higher quality experts that include domain-specific business experts will create more business value for organizations than large teams of data analysis professionals who lack an understanding of the language of the domain. Domain experts provide the language, and data scientists provide the structure, and domain-specific linguistic knowledge is vital when building ontologies. These ontologies should be driven by business or domain experts, rather than data scientists and analytics specialists.
Relay was founded by individuals with deep expertise not only in pharmacology but also in investment decision making. By combining their domain expertise with the expertise of the data scientists, Relay is helping investors and drug companies make better drug development decisions for both efficacy and potential for return on investment.
Action Item: Organizations that wish to create maximum business value from big data analytics projects should start with small, even two-person, teams, including a data scientist and a business expert. Together, they can develop and curate relevant sources for unstructured data that can be combined with structured data sources, create ontologies for big data analysis that makes sense to the business decision makers, and together derive maximum business value to the organization.