As organizations move to data lakes from the enterprise data warehouse, denigrating schema on write in favor of schema on read approaches, I get the impression that many see the data lake as the lazy man’s lasagna of data storage
We live in a world where larger and larger volumes of varied data types are coming at us in ever increasing speeds, i.e. we live in a world of big data. In order to make sense of big data, we have turned to data science. Data Science is a tool employed by the transliterate to transform data into information.
From Data Literacy to Transliteracy Understanding necessary skills for data democratization.
Using the principle of Occam's razor to optimize your supervised learning model.
Supervised machine learning is inferring a function which will map input variables to an output variable. Let’s unpack this definition a bit with an example. Say that we are a bank that wants to determine to whom we should give a loan. The objective, therefore, is to infer a function that examines a set … Continue reading Overfitting / Underfitting – How Well Does Your Model Fit?
Business Intelligence is corporate fat. When you go to the butcher do you look for a steak with fat? Think about it, because that is what BI is.
The world in which Dodo birds existed changed with the arrival of humans and they, the birds, did not adapt. The world in which business intelligence (BI) exists is changing, changing pretty drastically. Will BI go the way of the Dodo bird?
Everyone is talking about data science. One study found that 96% of companies believe that data science is integral to the success of their business. Yet, most of these organizations (70%) are not realizing its full potential. They cite such factors as poor data quality, lack of talent, and access to proper tools and technology. … Continue reading Open Data Science