Dimensional modeling (DM) places and emphasis on the end user’s experience. The goal is to create a data model that performs well and is simple to query. In many cases a star schema does a great job of accomplishing these
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
At LeapFrogBI we use the term data solution to refer to the portion of the overall analytics system that acquires data and makes it report-ready. The data solution (not the reporting software) is the most important factor in determining what
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
A resilient ETL process deals with data quality issues without causing a process failure while also meeting business requirements. One issue that should be anticipated is the early arriving fact (aka late arriving dimension) situation. As the name implies an
When using several of the LeapFrogBI component types, configuration information is stored in Excel. This architecture is used in cases where it is not feasible to upload or input data into a web browser efficiently. Examples include; multi file stage,