Category: IT
-
Three things about “Interview with a Data Scientist”
Recently, I posted “Interview with a Data Scientist” at my company’s blog site. In it, my friend and colleague Yan Li answers four questions about being a data scientist and what it takes to become one. In my view Yan’s responses provide a bracing reminder that data science is something truly new, but that it rests on…
-
To SQL or to NoSQL?
Recently there was a great post at Dzone recounting how one “tech savvy startup” moved away from its NoSQL database management system to a relational one. The writer, Matt Butcher, plays out the reasons under these main points: Our data is relational We need better querying We have access to better resources Summing up: “The bottom…
-
DIY Data Dictionary: ODBC Reporting from the ERwin Metamodel
Application developers and business people accessing relational databases need data dictionaries in order to properly load or query a database. The data dictionary provides a source of information about the model for those without model access, including entity/table and attribute/column definitions, datatypes, primary keys, relationships among tables, and so on. The data dictionary also provides…
-
Technical Interviewers: Seek Opinions Not Facts
Asking fact questions in technical interviews is like eating a donut, feels great at the time but not so satisfying later. Let’s say the interview consists of facts like this “softball question”: “What is the default port number for SQL Server?” The linked list of questions is a really good high level study guide for…
-
How To Do Well in Your Next Job Interview
Recently I read an editorial about job interviews. It was breezy and funny, but not very helpful. Given that millions are out there looking for work, I want to help by giving my perspective on how to “win” the interview. I do a lot of interviewing, from both sides of the desk. As a consultant…
-
Guiding Principles for Data Enrichment
The data integration process is traditionally thought of in three steps: extract, transform, and load (ETL). Putting aside the often-discussed order of their execution, “extract” is pulling data out of a source system, “transform” means validating the source data and converting it to the desired standard (e.g. yards to meters), and load means storing the…
-
Get the Big Picture: Effective High-Level Diagrams
I believe that early, effective big picture diagrams are key to application development project success. According to the old saw, no project succeeds without a catchy acronym. Maybe so, but I’d say no project succeeds without a good big picture diagram. The question: what constitutes a good one? To me good high-level diagrams have four key characteristics:…
-
Thoughts on Healthcare Data Quality
The well-publicized problems with healthcare.gov are disturbing, especially when we remember they might result in many continuing without health insurance. But it seemed a step in the right direction when recent a news report differentiated between “front end” and “back end” problems. The back end problems were data issues, like a married applicant with two…
-
Business Intelligence Requirements: The Payoff’s in the Details
A technique for reporting requirements has emerged as the de facto standard in the business intelligence community. The technique, which emerged in the mid-2000s, is new enough to be as yet unacknowledged by the requirements analysis powers that be. David Loshin describes how it works in this 2007 post: Start with a business question about…
-
The ERwin Model Mart Access Problem
On two successive client assignments as a data modeler I’ve waited while client technicians wrestled with getting access to the ERwin Model Mart. In short, clicking on File, Mart, Connection, and logging in to the Model Mart failed every time, with various error messages. In both cases the teams lost literally months, in spite of…