When developing a reporting solution there are a variety of possible solution architectures to choose from. The available architectures range in complexity and purpose, and
There are two kinds of companies, those that get analytics right, and those that fail. Failure may not come overnight, but in a competitive marketplace
Your choice of data solution helps define your overall solution architecture as well as the specific needs that must be filled by data wrangling and
Reporting tools have become pretty good, and companies buy them with the expectation that doing so will solve their reporting challenges. But is this really the case? Are they really up to the job? This is a conversation we have frequently with clients and we hope it may benefit you.
Many organizations have built data warehouses successfully, and some have failed. There’s no reason at all for you to learn the hard way. In this
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
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.
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
(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,
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