Data analysis is incredibly useful for all kinds of businesses and also has academic and hobbyist applications. Nonetheless, it’s still possible to fall into numerous traps when trying to accurately interpret your data. That’s why we’re giving you a list of the top 8 common data analysis mistakes to avoid at all costs. Our first expert Jitin Narang, CMO at TechAHead contributed the following five top data mistakes to avoid:

This is part of our series of blog posts on recent enhancements to Impala. The entire collection is available here.

Here at Cloudera, we’ve seen many large organizations struggle to meet ever-changing and ever-growing business demands. We see it everywhere. Traditional on-premise architectures, which create a fixed, finite set of resources, forces every business request for new insight to be a crazy resource balancing act, coupled with long wait times, or a straight-up no, it cannot be done.

One of the things that I'm really passionate about is great design. Design is important in all aspects of our lives and it's really important for analytics as well. When you're the recipient of bad design, you know it immediately. Have you ever seen those emails that have been completely misaligned or sat through a PowerPoint where everything is in the wrong colors and fonts? How does it make you feel?

User feedback is a key element to our success and directly influences our roadmap and where we invest and innovate. This feedback fuels our drive to deliver truly impactful capabilities – ones that accelerate business value, drive ROI and lower TCO.