Blogs
Data Architecture 101, Part 4: Ontology-Driven Development is Lean
In software-development & data architecture nirvana, the business analysts, database technologists, and application developers all speak the same language. Everyone agrees about what each user story means.
Blogs
Data Architecture 101, Part 3: Dimensions
Data marts, data warehouses, and some operational datastores use dimension tables. A dimension table categorizes a fact table that joins to the dimension. At query time one filters the facts by values in the dimension table, and uses those values to label the query results
Blogs
Data Architecture 101, Part 2: Relational Architectures
This post uses those concepts to survey the main types of relational architectures. These divide fundamentally into two types, the second having four sub-types: OLTP & BI.
Blogs
The Role of Industry Experience in Data Science
In this post we explain why the assumption about industry experience is outdated—why often industry experience detracts from the best possible application of data science.
Blogs
Data Science Design Pattern #5: Combining Source Variables
Variable selection is perhaps the most challenging activity in the data science lifecycle. Our blog highlights a repeatable approach to variable engineering.
White Papers
Predicting Employee Churn
This white paper examines a machine learning approach to predicting employee churn and optimizing for retention.
Blogs
Data Science Design Pattern #4: Transformations of Individual Variables
n this post we describe some common ways to transform individual variables, and explore how doing so may benefit an analysis.
Blogs
The Executive Role in a Data-Driven Organization
Our blog post examines the role of an executive in a data-driven organization.
Blogs
Data Science Design Pattern #3: Handling Null Values
Most data science algorithms do not tolerate nulls (missing values). So, one must do something to eliminate them, before or while analyzing a data set.