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. You can learn more about […]
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 video I’m going to tell you what I believe are the top three reasons for data warehouse failure. Stick around. Reason number one for data warehouse failure; not implementing a […]
A data warehouse offers the benefits of fact-based decision making, and these days nearly everyone agrees on their value. But data warehouse project have an alarmingly high failure rate. In this video we explain why and offer a way you can succeed where others have failed. You can find our article on data lakes here: […]
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 what to work on, and when to stop working on any particular analytics problem. It […]
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 on-premise, and run it in virtually any environment on any database. In addition to all […]
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 failures, as a result of a lack of attention to data quality issues” they reported. […]
(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 average, data analysts spend 4 days a week organizing data, leaving only one day per week to conduct […]
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. They devote only one day per week to analyzing data and generating reports, suggesting that their job function […]
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, leaving only 21% for everything else. Far from being a new problem, this same issue has […]
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 leads to the question, why build a data warehouse when you can have a data […]
Metadata has the power to make or break a data warehouse. Like most innovation, a metadata driven approach to data warehousing solves common challenges by taking the process to a higher level.
Data warehousing success depends on properly designed ETL. In this short video we walk though the foundation design pattern step-by-step.