Want to become a better data quality practitioner? Change your lens
How do you become a better data quality professional? @dataqualitypro offers advice.
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Big Data and analytics: which is the bigger trend?
.@philsimon on the data behind Big Data.
Use a no brown M&M's clause
Jim Harris (@ocdqblog) explains why you should channel your inner David Lee Roth and include a No Brown M&M's Clause.
Considering success factors for data warehouse consolidation
The last thing you want to result from a data warehouse consolidation project is the creation of yet another siloed data asset that must be populated and managed with respect to the requirements of the downstream users. To really benefit from a consolidation project, your newly-created consolidated warehouse should replace
Decision-making and the invisible information chain
Business leaders and managers at all levels within an organisation make hundreds of important decisions throughout the year and nearly all of them are driven by data.
Big Data: get a little bit pregnant
I understand big things. Some of my favorite songs exceed 20 minutes. Many of my favorite books push 500 pages. Heat is one of my favorite movies and it clocks in at nearly three hours. Sometimes big ideas just can't be compressed into small packages.
Cargo cult data science
Last week, Phil Simon blogged about being wary of snake oil salesman who claim to be data scientists. In this post, I want to explore a related concept, namely being wary of thinking that you are performing data science by mimicking what data scientists do.
Narrowing the lead on big data early adopters
A new SAS survey uncovered a big data disconnect, with only 12 percent of organizations on board. Why weren’t more of the organizations surveyed taking steps towards a big data pay off? Without a doubt those that are implementing big data strategies will see a competitive advantage. And the longer they
Reporting and analytics modernization through data warehouse consolidation
At this point in time, many medium to large organizations have a history of developing systems for reporting and analysis. Simultaneously, these organizations have evolved a set of basic processes that “pump out” a series of data marts used by specific business functions for generating reports.
Data quality: one habit at a time
I started learning about data quality management back in 1992. Back then there were no conferences, limited publications and if you received an email via the internet the excitement lasted for hours.