At last week’s TDWI Conference in Orlando, FL there was a general buzz about how to derive value from Big Data for competitive advantage. That makes perfect sense, given that the general conference theme was to “Get Smarter with Big Data.”
Naturally, attendees – largely data scientists, business analysts, architects, and developers — were on hand to learn more about which technologies to recommend back at headquarters for their Big Data initiatives. We at HP Vertica were busy addressing a range of questions at our booth. However, the conversations went beyond features, benefits, and how we complement BI, ETL, and technologies like Hadoop. Attendees were also generally interested in learning from their peers and presenters how to:
- Build a Business Case
- Define the Meaning and Value of Big Data
- Adopt Common Use Cases
One attendee from a Fortune 100 company could understand how his company could gain greater analytics performance from a columnar database (such as HP Vertica Analytics Platform). However, he acknowledged that his company needed to step back and learn how the insight from Big Data analytics would address top-line objectives: reduce costs, generate revenue, differentiate, and ultimately improve customer satisfaction.
My colleague, Chris Selland, participated in a panel (Business Value: The Fourth V of Big Data) that answered a range of questions from the audience. It was clear that attendees still struggled with a clear common definition of Big Data. The three V analogy (Velocity, Volume, and Variety) seemed to provide more clarity. However, the fourth V (Value) underscored how Big Data analytics is supporting overall business benefits. For an example, check out our recently published case study — Cardlytics Serves Up Success with HP Vertica.
There were some interesting lunch-time discussions centered on how Big Data analytics could address common use cases by industry. In health care, for example, health care providers are using analytics to ultimately provide improved preventive care based on family history, test results, etc. A large logistics company is using analytics for route optimization in reducing fuel costs and overall emissions. Clickstream analytics, fraud detection, inventory management, and a myriad of use cases are emerging as companies gain greater insight from their Big Data, once they have removed the technology barriers (performance, scalability, and overall low TCO).
How does your organization plan to derive value from Big Data? We’d love to hear your use case.