Stop Poor Data Quality STOP

Mar 27, 2013 by Jim Harris

In the 19th century, the harnessing of electricity brought about the means to transmit signals via electrical telegraph.  The term STOP was used in telegrams to mark the end of a sentence because punctuation cost extra.  Therefore, a telegram requesting an end to poor data quality would literally have been sent as “Stop Poor Data Quality STOP” — and if you think data quality wasn’t an issue in telegraphy, stop, and think again.

2012 Quarterly Review (Part 4)

Jan 02, 2013 by Jim Harris

Welcome to the unofficial 2012 quarterly review that I have decided to perform on the Data Roundtable. In this four-part series, I will select and summarize my favorite posts published on this blog during each quarter of this year, selecting one post per contributor per month.

Part 1 of this series covered blog…

2012 Quarterly Review (Part 3)

Dec 19, 2012 by Jim Harris

Welcome to the unofficial 2012 quarterly review that I have decided to perform on the Data Roundtable. In this four-part series, I will select and summarize my favorite posts published on this blog during each quarter of this year, selecting one post per contributor per month.

2012 Quarterly Review (Part 2)

Dec 12, 2012 by Jim Harris

Welcome to the unofficial 2012 quarterly review that I have decided to perform on the Data Roundtable. In this four-part series, I will select and summarize my favorite posts published on this blog during each quarter of this year, selecting one post per contributor per month.

Part 1 of this series covered blog…

2012 Quarterly Review (Part 1)

Dec 05, 2012 by Jim Harris

Welcome to the unofficial 2012 quarterly review that I have decided to perform on the Data Roundtable. In this four-part series, I will select and summarize my favorite posts published on this blog during each quarter of this year, selecting one post per contributor per month.

Part 1 covers blog posts from January,

When and Where to Cleanse in Data Migration

Jul 06, 2012 by Dylan Jones

Data migration projects are an excellent vehicle for helping companies shine a huge, megawatt-sized lantern on where their data quality has historically been ignored.

Often the target system has been acquired and only during the migration project itself are all those wonders of legacy computing unravelled. This is why I always plead with companies to get started with data profiling and discovery even before the project is formally signed off because you never have enough time to do the job completely. If you want to create a Zero Defect Data Migration however, you’ll be glad of that extra analysis.

Solving the Data Migration Target Puzzle [Part 2/2]

May 04, 2012 by Dylan Jones

In my last post I introduced the concept of the Data Migration Target Puzzle, the challenging situation where you’re tasked with delivering a data migration strategy to a target environment that isn’t fully implemented.

In this post I want to discuss why this is is such a common scenario and what you can do to help mitigate the problem.

Solving the Data Migration Target Puzzle [Part 1/2]

Apr 20, 2012 by Dylan Jones

Congratulations on the sign-off of for the design of your all new customer management project. In 12 months you will have the most advanced customer engagement, tracking and marketing platform on the planet. Of course, that’s once it’s loaded with all that legacy data that’s currently sitting in a plethora of legacy customer databases, marketing spreadsheets and a rack of 3rd party sales performance desktop databases.

Why Doesn’t My Data Listen to Me?

Apr 19, 2012 by Phil Simon

One of my favorite Porcupine Tree songs is “I Drive The Hearse.” Here are some of the song’s haunting lyrics: