The other key issue imo. is turning data sets from tribal phenomena to enterprise assets... registries, meta information, lineage, provenance, and the like? thoughts?
Topic: Data Science? What is the role and who are the people and skills needed?
What are the data governance and security implications for self-service analytics? Should business users be let loose on any data source they like? Where does IT fit in this equation?
Decisions made at rt contextual level. No need to include excess granularity. Limit data to relevance, but provide rich view with diverse sources.
Question: is the age of Big Data infrastructure on decline, to be replaced by rise of end user tools?
How has the evolution of visualization techniques and technologies changed the game? Hard to see a pattern in a Billion of anything. What's the next big step here?
Just bcuz the data is available does'nt mean it should be used pervasively. Right Data for rt process still an imperative. Variety & source for richer decisions, not only more quantity
defines a framework for decision validation. represents interactive process of understanding decision process. 3G of with
Hammer & Nail scenario. All HDFS needs to look like SQL today for tools to be able to provide value. Extracts circumvent the problem, but wastes the computing pwr.