Topic "Data Quality"
We all know this story. Salesperson X logs into Salesforce. Notices a new lead assigned to her. Picks up the phone to call and speak with this lead and hears, “You have the wrong number” or “That person doesn’t work here anymore” or “The number you have reached is not in service”. The example could... more
So as I sit here contemplating life, I realize that change is not only hard but damn’ hard. Not so much for me (of course;-) but for everybody else who does not run into and advise a new company every other week including a wide variety of personalities and business models, aka canonized modes of... more
A consultant, a data architect and a call center manager walk into a bar in some Asian metropolis (figuratively speaking). The data architect says, “Can you help me figure out how to convince the business to invest in upgrading our data management environment?” The consultant says, “Sure, let me chat with your call center manager... more
Did you spell out the heading of this post correctly? I bet you did not. I am talking about “opjuqzsdof bube vs da** ma*****”. Let’s move ahead and we will revisit it again. The main agenda of this post is to understand the intricacy of this jumbled and starred raw text. In the data security... more
Much has been written about the monetary impacts of poor data quality. A familiar and easy to understand illustration of this is the cost of bad postal address data: The US Postal Service (USPS) estimated in 2013 that there were approximately 6.8 billion pieces of mail that could not be delivered as addressed. Beyond the fact... more
Nine years ago when I started in the data integration and quality space, data quality was all about algorithms and cleansing technology. Data went in, and the “best” solution was the one that could do the best job of fuzzy matching the data and cleaning more data than the other products. Of course, not data... more