Over the course of many years in this field, I can say without reservation that data quality is one of the cornerstones to a solid sales compensation process. Due to the nature of this beast, it is often a hidden aspect to the process and often rears its head only when the quality of your data is poor. Think of it, if you are one of those fortunate enough to have a relatively high quality pool of performance data, when was the last time someone appreciated that fact and all the work that went into creating that high quality information? The alternative is those who struggle with issues related to data quality and quite frankly there are few things that can hamstring your sales performance improvement efforts more than poor data quality. When this occurs, the two steps forward, three steps back grind overtakes your process affecting everyone from sales reps to analysts, and all the senior managers to whom these producers report.

World at Work with Open Symmetry recently performed a Sales Performance and Technology Survey which shed some light on this issue. Over 460 survey participants from a broad swath of industries were represented.  An interesting perspective brought by this survey is its emphasis on the administration of the Sales Incentive Management process. This perspective yields insights from those who need to confront and address any process shortcomings firsthand on a daily basis.

One of the top complaints highlighhted in the survey involved data quality, whats interesting is how widespread data issues are amongst your peers!

• Those participants who had made technology investments to improve matters within the last 12 months (122 of them in all) were asked to rank the top three things they wished they could do over.  
  • Spending more time on data quality (28%)
  • Streamlining work processes (29%)
  • Spending more time defining reporting requirements (29%).
• When asked ‘What is the single biggest challenge for providing sales and performance pay reports to your plan participants at this time, the top two challenges listed were;
  • Time to generate reports – 30%
  •  Data quality – 29%.
• Four major issues headed the list of items identified as the biggest challenges to the administration of sales compensation; two of them involved data;
  • Too many manual adjustments – 57%
  • Program & tool complexity – 47%
  • Data problems – 37%
  • Data volume – 34%

What does low data quality look like from a Sales Operations perspective? Of course, this is a highly subjective matter that is unique to each business.  Here are three examples of common Data Quality issues;

• Data Entry issues;
  • Quality issues may affect the process right at the start of the data trail; order entry. We’re not only talking about manual errors at this juncture (although studies have shown human error rates can run as high as 3-5%) but also the timeliness of the reference data used during order entry. Errors can involve credit assignment, territory designation, strategic account flags, etc.
• In-consistent data definitions across multiple source systems.
  • Growing companies are constantly struggling with data synchronization across systems.
• Plans whch require data that is not being fully captured.
  • Example; strategic account team that rewards based on design wins and follow on sales, but channel selling to manufacturing sites where volume sales are occurring use different part numbers than manufacturer. Compensation must the be extrapolated based on several secondary interpretations of aggregate revenue numbers.

So what Best Practice Advice can we suggest to anyone struggling with data quality issues and who has decided to fix the problem?

• Gain cross functional (and in some cases cross divisional/SBU) buy-in that a harmful business condition exists.
  • This may involve executive briefings, case write-ups and damage assessments. Be sure to recognize early that data quality is usually not just an ’IT issue’ - Escalate the issue!
• Build consensus and commitment to a broadly supported improvement effort.
  • This may or may not involve IT.
  • Be sure the organization has a formalized team based quality improvement process (ask NetCommissions for recommendations) based on a practical, systemic, proven process improvement methodology.
• Fix the problem.
• Best practice is an iterative improvement effort that is continuous in nature. Take the early wins but keep working to improve.
• The end result of this effort should be certifiable data produced through verifiable and repeatable processes.