Frightening Quality Assurance Nightmares
- Quality Assurance
- BY tracy
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With the right information in hand, really good things can happen in your call center(s). Things like better decision-making, improved efficiency, new marketing opportunities and ultimately, higher revenue. But what happens when data goes bad? According to research firm SiriusDecisions, the average company is hopelessly holding on to 25% of bad data in most of their systems. What’s more,
With the right information in hand, really good things can happen in your call center(s). Things like better decision-making, improved efficiency, new marketing opportunities and ultimately, higher revenue.
But what happens when data goes bad? According to research firm SiriusDecisions, the average company is hopelessly holding on to 25% of bad data in most of their systems. What’s more, data goes bad fast; so fast it decays at an average rate of 2% every month. Under normal circumstances, you can expect 25-30% of your data to decay within a year.
More than that, without the right data at the right time, disastrous consequences are sure to follow.
The following data-related QA horror stories are here just in time for Halloween, but more importantly, these hard QA lessons are yours to digest and consider so you don’t have to follow in their footsteps!
- Egg on the Face
After a certain prototype had been completed in just 2 weeks, the project team asked for six more months to move into production. Six months!? What was the problem?
Apparently the development staff felt the design was incorrectly applied even though the user was ecstatic with the solution. So they changed much of the underlying technology to meet their IT standards and literally broke away from what the client had agreed to, setting off a series of emergency rework sessions. Needless to say, the lack of business collaboration and understanding of the client’s needs was by far the biggest nightmare for all involved.
This horror could have easily been avoided if the technical team had included the business semantics in their plan from the beginning. This way, as they recreated the requirements to match the needs of the clients, the desired functionality would have matched the outlined criteria throughout the various phases of development, giving the user exactly what they were looking for.
- 17,000 Pregnant Men
Then there’s the story of the incorrectly entered medical codes at British hospitals. Perhaps more frightening than the previous horror story, this one is about thousands of men who went to the hospital to check their health only to end up scheduled for prenatal and obstetric exams.
A few misplaced key strokes here and there led to disastrous billing, claims and regulatory compliance. However, if the hospitals had a quality assurance team to check on their performance and highlight the weakest links in their data systems, the whole problem could have easily been avoided.
- Big Trouble
Then there’s the company that invested 18 full months and a few million dollars to design, develop and launch a huge new data warehouse only to discover that it didn’t have any of the data that the users needed. What’s even scarier is the fact that most of the data required by the user was for compliance with critical financial and industry regulations- yikes!
Integrating data can be a horrific experience, especially when there are no clear data definitions. However, if the company had a data integration plan, it would have been easier to manage the complexities involved, streamline the connections and make it easy to deliver data to any system.
- “Dear Idiot”
When customer service agents at a large financial institution dealt with angry customers, they started entering phrases into the salutation field such as, “what an idiot this customer is.” So when the marketing department decided to release a marketing campaign using the customer data base, emails went out as, “Dear Idiot Customer John Doe.”
You can clearly imagine the damage inflicted on both the company’s reputation and its future relationships with customers. If the customer service center had in place human analysts checking out all the customer interaction points, this catastrophe could have easily been caught and avoided.
To sum up these terrifying tales of “invisible” quality assurance programs, it’s important to understand the use of data in driving decisions and measuring performance in the call center. With that being said, relatively few companies have a clear and concise understanding of it being an integral piece of every successful business.
Most companies are now moving away from policy-centric views and are now thinking in terms of its customers. Just imagine what type of chaos might follow if there wasn’t any clearly defined data to cater to your customer’s?
These nightmares are examples of the most common killers of quality assurance project success. And while these cases may sound extreme, the lessons are clear; what appear like simple errors can quickly get out of hand, harming your brand’s reputation and ruining customer relationships.
So while you’re munching on your kids Halloween candy this year or scaring away the trick or treater’s, remember that without a viable QA program that utilizes smart data and a customer centered approach, you could be on next year’s list of frightening QA horror stories!