Beyond Six Sigma Data Quality: The KFR Inc. Story

KFR Services went beyond Six Sigma levels of Data Quality to create an unbeatable competitive advantage in the telecoms sector

KFR Services went beyond Six Sigma levels of Data Quality to create an unbeatable competitive advantage in the telecoms sector

Beyond Six Sigma Data Quality

The KFR Inc. Story

Anyone that sells products are aware that customer returns due to defects are not good for long-term business.

If you supply information as your core business then the data quality levels have to be extremely high or else your customers will walk briskly into your competitor’s arms.

Tele-Tech Services, a division of KFR Services Inc., took the quest for perfect quality data beyond Six Sigma levels and demonstrated that delivering world-class data quality is good for business, customers and the workforce.

I caught up with co-president Stephanie Fetchen to find out how they did it and what our members can take from their experience.

You can also find an interesting account of their obsession with data quality in the PDF they publish on their website:

UPDATE: Tele-Tech have been Alfred P.Sloan award winners for Business Excellence and Flexibility in the workplace over the last 3 years, a further testament to the benefits of adopting quality principles in their organisation.

A Business Model Built on Data Quality

Tele-Tech provide telecoms organisations with local calling data to help them perform accurate billing of their customers and other carriers.

Their business is built on supplying clients with accurate data and for three decades they had consistently been regarded within the industry as providers of data with the highest levels of quality.

Co-president, Kimberly Russo, realised that quality can often be a buzzword, many can claim it but few can actually prove it.

“Quality can be preached by people with decades of experience, or heralded on the web site of a company that’s only been around for a few days. But, saying you’re quality focused doesn’t necessarily make it so.”

Kimberly Russo, Co

Kimberly raised the question of whether data accuracy could be measured in their particular environment and set about reviewing their current measurements to establish an initial set of metrics.

Data Accuracy – Good news, Bad news

Initial findings were positive, 99.75% of their data files were accurate.

Most companies would be delighted with this result but the news was even better. This was the figure of errors before they were resolved prior to shipping the data. So in effect, they actually had closer to 100% as the data left the building.

In 2001, Tele-Tech was the only company providing any statistical information on the quality of their data.

However, what Tele-Tech lacked were accurate stats on the amount of customer complaints raised due to data quality issues slipping through their quality control and being passed onto customers.

Kimberly recognised a flaw in the process as there simply was not enough data to derive end-to-end accuracy metrics where it really mattered, with the customers.

“In most cases the data defects were minor however there were some that became a big impact. Rate and bill data can cause a huge downstream issue. One customer made the news and got negative press over a billing error”

– Stephanie Fetchen

Tele-Tech began tracking each rework and recalculated their statistics to show the number of errors reported by customers each month versus the number of actual files that were changed that month. This was a more stringent metric than calculating errors against all files but a more accurate measure of quality.

Buoyed with what appeared to be solid progress, Kimberly proposed the introduction of Service Level Agreements (SLA’s) to guarantee the supply of quality data.

At this point Tele-Tech realised they needed help.

Step Forward Tom “Data Doc” Redman

Tom Redman is one of the early pioneers of data quality and as president of Navesink and author of several data quality publications has helped literally thousands of individuals and organisations on their road to data quality improvement.

Tele-Tech chose Tom by searching for data quality specialists on the internet and realised he not only had the data quality credentials but a proven track record in the telecoms sector from his work in running the AT&T Bell Laboratories Data Quality Lab in the 80’s and 90’s.

Tom quickly advised a stringent improvement process that would take their data quality levels beyond Six Sigma, already widely regarded as the pinnacle of quality metrics.

The initial suggestion of SLA’s was rejected by Tom:

“Tom said SLA’s were irrelevant. He explained that customers are not concerned with receiving discounts or penalty rewards, they simply want the data to be right first time.”

– Stephanie Fetchen

Tele-Tech workers soon eased into the process for several reasons:

“It was surprisingly smooth because we had management support from the start and we did it gradually, this was very important. We set goals that were stretches but that we knew were attainable.”

– Stephanie Fetchen

If all this seems overkill, Stephanie recalled a situation from the late 90’s that typifies the costs of poor quality data:

“One of our biggest customers perceived that we had DQ issues, they subsequently forced us to take a 20% drop in fees when a new contract was negotiated. Reducing customer churn and maintaining our fee levels was a key driver for pushing ahead with the data quality improvement and measurement programme.”

– Stephanie Fetchen

Tele-Tech recognised that the improvements required were a combination of people and technology innovations. Knowledge workers were actively involved in the process and most suggestions for improvement came from the workforce.

Data Quality Improvement (Without Expensive Technology)

What is encouraging for organisations with limited budgets is that Tele-Tech achieved all this without expensive data quality software:

“We focused on implementing personnel training programmes but rejected the use of additional quality tools. It doesn’t have to be that complicated, our focus is now on prevention at the point of entry as opposed to downstream cleansing and improvement”

– Stephanie Fetchen

Key to success was the knowledge workers who are motivated and incentivised to improve data quality through a simple bonus scheme.

With the scheme we award a bonus if one of our staff reaches a data quality goal. We try and tie their goals to the overall company goal. So if our corporate goal was a 50% reduction in defects we would make the individuals goal to be cutting their own recorded defects in half. This motivates the team as no-one wants to see a month where defects occur, it definitely helps the entire team to keep the figures up 

– Stephanie Fetchen

Simple Innovations Implemented

So how did Tele-Tech make the improvements that resulted in near perfect data quality? Here are some of the changes they implemented:

  • Adding more quality control measures to their delivery procedures
  • Undertaking a thorough post-mortem review when errors were found
  • Creating a plan to eliminate tariff interpretation errors by providing additional training that included continuous weekly staff training sessions, a six month review of the procedures manual and continuous mock exercises to challenge the data researchers to find ever more defect types
  • Reviewing the type of errors for ideas to eliminate human error and increase automation
  • Incentivising customers to report faults in return for cookies (yes, cookies!)

In Summary

The fact that they now report monthly accuracy figures on their website means current and prospective customers have a simple means of differentiating Tele-Tech with their competitors and this has paid off:

We know that we have won companies from our competitors because of our data quality. Companies have also measured our data against that of our competitors and chosen us. Reducing customer churn and attracting new clients was one of our main goals and we have certainly been successful in achieving this.

The Tele-Tech story is a model for how small companies with limited budgets can make dramatic improvements to their competitive advantage, bottom line and workforce morale by tackling data quality head-on and going beyond customer expectations.


Do you or your business supply data to customers? Why not review the recommendations below and identify where your data supplier processes could be improved. 

This list is by no means exhaustive so please add your own suggestions and send them to us.

  • Demonstrable data quality is a competitive differentiator. Most markets are crowded so being the best at any anything is a powerful differentiator and if your business is data then it’s a no-brainer to differentiate on data quality.
  • Don’t rest on your laurels. Competition is never far away and quality is never static. Consider how a reported data quality metric can fend off the competition and keep your business focused on improvement.
  • Measure the right data.Tele-tech didn’t rely on annual customer satisfaction surveys or random spot-checks instead they implemented rigorous data quality measurement on defects that really mattered to the customer.
  • Get external help where required.Recognise that you don’t know it all and a second opinion is always beneficial in vetting your approach and giving you a fresh perspective.
  • Reject the status quo and create a sense of urgency.“That’s not how we do things here” is the mantra for poor performance, recognise where it exists and identify how to change it.
  • Implement change management.Tele-tech recognised the need for change and together the whole team created a sense of urgency, vision and ability to move forward.
  • Make it easy for customers to complain.There is a classic tail of an American airline receiving regular poor performance in customer surveys so they simply took away the feedback slips on their planes. Can your customers get feedback to you easily? Can you see the results every minute of every day? Do you provide cookies for customer error reports like Tele-Tech!
  • Focus on people improvement.Tele-tech clearly had a total commitment to increasing the skills of their staff and ensuring their processes were defined and continuously improved.
  • Make it fun and involve everyone. The latest “Corporate Quality Drive” passed down as an edict from above rarely works. Get the knowledge-workers integrated and involved in the whole improvement process as they know the process and customers better than anyone and will have boundless energy and creativity, if they’re engaged.
  • Be transparent with your quality reporting.Reporting accuracy figures every month was a bold step for Tele-Tech but it paid off because they had the workforce, process and sponsorship to deliver.
  • Don’t just settle on quality data. Tele-tech realised that having 100% accurate data is only one part of the picture. They are now extending the same quality principles to the entire customer service chain. This is critical and often overlooked – the customer is the final arbiter of quality, find out what they need and deliver beyond their expectations.
  • Reward, don’t punish.Notice how Tele-Tech rewards both the workers (with bonuses) and the customers (with cookies). Try and find a positive situation for all parties and eliminate punitive actions in your quest for data quality excellence.
  • Quality is continuous.Tele-Tech have demonstrated that regular targets, continual education and a culture of constant improvement are vital in delivering first-class business services that delight your customers. Don’t settle for annual cleanups or random survey. Your business never stops delivering services so a data quality strategy has to be ongoing.