Customer Data Quality: A Sales and Marketing Leadership Issue

Poor data quality can impact an organization in a myriad of ways: Employees wasting time checking and rechecking their work, the unnecessary cost of redoing mundane tasks and dissatisfied customers screaming at the other end of the phone are just a few of the data quality challenges that customer-facing managers must deal with every day.

With the proliferation of customer data silos and the increased demands that customers are placing on organizations, every manager instinctively knows that data quality is a serious business issue. Sadly, despite these challenges, customer data quality issues are often left unaddressed.

Top Priority

The cross-functional nature of the problem makes it a hard one to fix, but easy enough to pass the blame elsewhere (i.e., "if only those guys in IT could sort out this data mess!"). However, leading companies have realized that customer data quality is a business issue and, as such, have made data quality issues a top priority.

New Aberdeen research reveals that 70 percent of top-performing companies have identified competitive and growth pressures as the leading driver for customer data quality initiatives. Granted, most companies are concerned about growth and profitability, but organizations identified in the study as Best-in-Class are significantly more likely than all others to consider data quality initiatives a top priority (80 percent vs. roughly 50 percent).

On the whole, the study found that leading organizations that deploy, refine and benchmark customer data quality initiatives are four times more likely than lagging organizations to report gains in customer, organizational and revenue key performance indicators (KPIs). Specifically, the Best-in-Class reported far better results than all others in operational metrics such as improvement in customer data integrity (94 percent), usability of customer data (95 percent) and time it takes to preparing customer data for business use (89 percent).

To achieve these superior results, the Best-in-Class take a systematic and organizational approach to customer data quality initiatives. Not surprisingly, virtually all of the Best-in-Class invest in a data process management (98 percent) and data collection, cleansing and analysis tools (100 percent).

However, the Best-in-Class are also focused on breaking down the organizational silos that can hinder data quality initiatives. For instance, more than eight out of 10 (83 percent) have assigned accountability for data quality initiatives to a data steward or named data manager and have a process in place to get cross-functional consensus on data quality goals, priorities and actions (79 percent).

Another significant differentiator between the Best-in-Class and all other organizations is the propensity measure operational KPI's (79 percent), as well as link customer (89 percent) and revenue (84 percent) KPI's to data quality initiatives.

That is not to say that the Best-in-Class foolishly rush into their data quality initiatives. In fact, leading companies are more than three times more likely than Laggards to have implemented a pilot and benchmark evaluation programs.

Suggested Steps

There are several steps that companies can take to begin to achieve the same success as the study's Best-in-Class:

• Customer data quality is a learning-by-doing endeavor. Firms without an established, cross-functional, data quality program should start with a pilot program and establish benchmarks and quantifiable success criteria to iteratively refine the process.
• Best-in-class firms can link successful customer data quality programs to improvements in both financial and customer KPIs. Organizations that are just starting out with these types of initiatives must begin to measure these KPIs as a basis for calculating return on investment for their data quality programs.
• Customer data quality is a cross-functional problem. As such, someone within the organization must have both the mandate and authority to cross organizational boundaries, as well as negotiate and manage program priorities.
• Lastly, the Best-in-Class are more likely to invest in specific data quality technology and process enablers such as functionally specific solutions for data collection, cleansing and analysis and archival and storage. Organizations wishing to achieve Best-in-Class status must invest in these technologies, or plan for these investments in the next budget cycle.

Plans to Implement Solutions

Sales and marketing leaders should understand that data quality is a business issue, and getting it right can be a huge competitive differentiator. Unfortunately for laggards, the study findings indicate the Best-in-Class status is a moving target.

Within the next two years, nine out of 10 of the Best-in-Class are planning on implementing enterprise data management solutions, and 74 percent are planning on implementing formal master data management programs. With the adoption of increasingly sophisticated customer data quality solutions, it is critical to invest in the basics and put the right structure in place before the gap between the Best-in-Class and laggard companies becomes insurmountable.

The Aberdeen report -- "Customer Data Quality: a Foundation for Customer and Revenue Growth" -- is available to download for free for a limited time.

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Andrew Boyd is senior vice president and research director for the Aberdeen Group, where he is responsible for customer relationship and experience management practice. Currently, his team's research agenda focuses on customer-centric business models, the optimization of market-to-order processes and understanding the online customer experience. He can be reached at andrew.boyd@aberdeen.com.

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