Data Quality: Reducing Friction

Data quality (DQ) is a buzzing concern in our industry. Quality is the demarcating line which separates you from rest of the strata. Comprehensively, businesses are all about stake holders. Driving forces are always demand and supply. If we view both these aspects pertaining to our industry we find that on one side of the table, there are suppliers of data who have a unique mix of validation checks and data refining techniques aiming at providing quality products to the customers. One other side, there are clients, who demand different levels of quality in terms of comprehensiveness, conciseness and moreover finesse in data.

The major friction between the two remains there in form of Data Quality. To analyze and to find a single or rather a linear answer to this friction is a looming issue as root cause of this friction at times gets beyond the barbwires of calls, difference of opinions, stances and approaches to data cleansing. So, every time there is a friction on quality fronts, either sides lands into different zones every other time. If we do really want to sort it out we got to understand the issue first.

What is the basic contributing factor to this issue? A part of the picture is we have at times not defined the DQ in itself. As at times even a small dent can ruin the entire picture. Minor things like a spacing error, incorrect spelling or improper grammar can contribute to bad data in case of our industry. Like really!! Yeah you have read it right.

Different answers will be expected when you ask about DQ from different stakeholders in the industry. So, if we can define properly and precisely on either sides of the table (not entirely, but to a certain extent) the contributors good data and the differences in bad data we may reduce the friction to a certain extent.