Databases Reference
In-Depth Information
Chapter 7
Closing Thoughts
I started this topic with a deinition of Big Data using the four V's: Velocity,
Volume, Variety, and Veracity. Big Data growth can be attributed to three
market forces: sophisticated consumers, product and process automation,
and data monetization. I discussed a number of emerging use cases, includ-
ing location-based services, micro-segmentation, next best action, Product
Knowledge Hub, Social Media Command Center, infrastructure and opera-
tion improvement, and risk management. The solution includes a number of
architecture components. Massively parallel platforms provide capabilities
for data integration, storage, and analytics. Unstructured text analytics
complements traditional quantitative modeling. Big data enhances the creation
of customer and product MDM. Real-time adaptive analytics provides high-
velocity analytics while changing its modeling parameters based on sophisticated
predictive modeling of historical data.
I discussed data privacy issues and how some of the data can be masked to
limit exposure. These components can be organized in a three-layer architecture,
with a conversation layer that uses real-time analytics to provide low-latency
decisions and an orchestration layer that synthesizes entities, controls the
conversation, and offers visibility to business users via a command center.
The supporting discovery is provided by unstructured and structured analysis.
Last, I discussed implementation approaches, data governance, roadmap develop-
ment, and maturity models.
By calling it “Big Data,” our attention obviously goes irst to the volume
dimension. With data sizes in exabytes, the analytics requires special tools
capable of scaling to such big volumes. We saw how massively parallel
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