data preparation

11月 282016
 

One aspect of high-quality information is consistency. We often think about consistency in terms of consistent values. A large portion of the effort expended on “data quality dimensions” essentially focuses on data value consistency. For example, when we describe accuracy, what we often mean is consistency with a defined source […]

The post Harmonizing semantics for consistency in interpreting analytical results appeared first on The Data Roundtable.

11月 162016
 

It's that time of year again where almost 50 million Americans travel home for Thanksgiving. We'll share a smorgasbord of turkey, stuffing and vegetables and discuss fun political topics, all to celebrate the ironic friendship between colonists and Native Americans. Being part Italian, my family augments the 20-pound turkey with pasta – […]

The post 3 Thanksgiving lessons about data warehouses, Hadoop and self-service data prep appeared first on The Data Roundtable.

11月 072016
 

Most enterprises employ multiple analytical models in their business intelligence applications and decision-making processes. These analytical models include descriptive analytics that help the organization understand what has happened and what is happening now, predictive analytics that determine the probability of what will happen next, and prescriptive analytics that focus on […]

The post Why analytical models are better with better data appeared first on The Data Roundtable.

9月 142016
 

In Part 1 of this two-part series, I defined data preparation and data wrangling, then raised some questions about requirements gathering in a governed environment (i.e., ODS and/or data warehouse). Now – all of us very-managed people are looking at the horizon, and we see the data lake. How do […]

The post Data preparation and data wrangling, Part 2 (yippee, bring your lasso) appeared first on The Data Roundtable.