Ok, full disclosure, this really has nothing to do with quitting your job. On the contrary, if you work in the field of analytics it has everything to do with how you do your job. In particular, how you decide
These days you can buy some really great analytics software. In the past ten years it has improved dramatically, and today it enables rapid report development, easy dashboarding and robust ad-hoc data analysis. You can get it hosted or installed
Before you go and start a new analytics project, consider these sobering statistics.
As far back as 2005 Gartner began ringing the warning bells. “More than 50 percent of data warehouse projects will have limited acceptance, or will be outright
(This is the final part of a three-part series illuminating the challenges of data analytics and describing a proven solution to the problem.)
In part one, Measuring the Cost of Doing Business With Insufficient Analytics, we learned that, on
In part one of this series, Measuring the Cost of Doing Business with Insufficient Analytics, I referenced a series of studies conducted over the past five years showing that data analysts spend about 80% of their time organizing data.
A recent survey conducted by CrowdFlower and summarized on Forbes found data scientists spend most of their time massaging rather than modeling or mining data for insights. It seems 79% of their time is spent either accessing or preparing data,
The “data lake”, a catchy new buzzword in analytics circles, has many people wondering if they still need a data warehouse. You may have heard that you can run analysis directly against the data lake, and that’s true. This quickly
Some of the most challenging data warehousing situations come in the form of external data mashups. Because the term “data mashup” has taken on a number of meanings over the past few years, I’ll clarify how the term is used
Anyone who has been involved with data warehousing knows that there are plenty of things that can go wrong. Mistakes can be made when researching the data sources, collecting requirements, designing a dimensional model, etc… Assuming all of the analyst
Data warehousing has a clear set of objectives such as data persistence, single easy to navigate data model, fast query performance, etc… While it is not the role of the data warehouse to mimic the data in source systems, the