I don't feel statistics and analytics can't be treated differently to extent. For analytics, statistics is the backbone.
So I would suggest you to find a good problem statement in this domain and that would be a great topic for your research.
I don't feel so it will good for PhD point of view.
I would suggest you to find-out some more domain centric or application centric problems.
you have to think top-down. First, find an application domain of interest, then define a problem that related to Big Data that the domain you have chosen suffers from, think for nontrivial solutions to save processing resources, and getting better performance rather than the literature.
Abdulaziz Darwiesh,
To illustrate further, attached is the progress vs. time curve.
In this curve, the x-axis represents the time it takes to make progress, while the y-axis is the amount of progress achieved.
You must choose a domain or topic of interest that is relatively new and lies in region 1 of the curve.
For the same amount of time t1=t2, the achieved progress P1 is much higher than P2.
This means that for all applications in region 1, you can achieve good progress in a short period.
Keep searching for a region 1 related application :)
Minimal data sets, i.e. learning more from less data in a reliable fashion. One body of thought perceives a challenge to big data analytics as not having enough data. If there were 'minimal' data analytical techniques that were reliable, it would be helpful.
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