With a lack of executive confidence and high cost of poor data, it lends the question of how bad is your data quality and even more importantly how can it be quantified?
The estimated annual cost of data quality problems for US businesses is $611 Billion. Less than 33 % of companies are confident in their data quality.
Therefore, understanding the quality of organizational data is extremely important. See our previous post on determining the cost of bad data.
In this post, we share a quick way to help you measure the quality of your organizational data.
To measure data quality, we recommend using the Friday Afternoon Measurement (FAM) method. This method allows managers to measure data quality (DQ) with a score.
The FAM method has provided some astonishing insights into data quality across organizations. According to insights published by the Harvard Business Review, 47% of newly created data records have at least one critical (work impacting) error. Even more staggering was that only 3% of the DQ scores HBR reported were rated as “acceptable” using the loosest possible standards. These poor data quality scores were consistent across all business sectors, private and public.
How to use the FAM Method
There are four simple steps an organization can take to apply the FAM method in order to obtain a DQ score.[1]
Step 1
Assemble the last 100 data records that your group used, such as setting up a customer account or delivering a product.
Step 2
Ask two or three people with knowledge of the data to join you for a two-hour meeting.
Step 3
Working record by record, instruct your colleagues to mark obvious errors. For most records, this step will go very quickly. Your team members will either spot errors — the misspelled customer name or information that’s been placed in the wrong column — or they won’t. In some cases, you will engage in detailed discussions about whether an item is truly incorrect, but usually, you will spend no more than 30 seconds on a record.
Step 4
Summarize the results in a spreadsheet. First, add a “record perfect” column to your spreadsheet. Mark it “yes” if there aren’t any errors, otherwise enter “no”.
To interpret the data simply extrapolate the errors of perfect or not. For example, if only 40 of the 100 analyzed were without error then you can infer that you have a 40% DQ score and a 60% error rate.
This rate can be quantified by using the rule of 10, based on an observation that it costs 10 times as much to complete a unit of work when the input data is defective as it does when it is perfect.
As a simple example, suppose your work team must complete 100 units per day and each unit costs $1.00 when the data is perfect. If everything is perfect, a day’s work costs $100 (100 units at $1.00 each). But with only 40 perfect:
Total cost = (40 x $1.00) + (60 x $1.00 x 10) = $40 + $600 = $640
As you can see, the total cost for the day is over six times higher than the cost when the DQ score is 100%. Consider how much your organization can save by eliminating errors. For example, if 50% of the errors are eliminated in this scenario then the organization will benefit from a 42% reduction in daily cost.
[1] Thomas Redman, Harvard Business Review, Assess Whether You Have a Data Quality Problem