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(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 […]
Under Construction… We are in the process of publishing our roadmap. For now, below is a list of the most commonly requested features. Got a request? Send it to email@example.com. We keep track of all requests, and we do our best to get them added to a upcoming release.
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 […]
What is a dimensional model? What is a data warehouse? This video introduces dimensional modeling while setting the stage for the series of dimensional modeling training videos to follow.
Components are typically unique and apply only to a particular unit of work. The component’s source contains a unique set of outputs and the component exposes a transformed set of output. However, there are some cases where a component can be applied to numerous data flows with only minor differences. Consider a data warehousing effort […]
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 early arriving fact is a record that is bound for a fact table which references […]
This quick tip video demonstrates how to create a new LeapFrogBI project. http://t.co/QG9icobhWw
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, and rest API collection. Excel workbooks include the option to automatically “Calculate Workbook” when the […]