Have you ever been hiking and caught your foot on a hidden tree root, stumbled and twisted your ankle? Or been fishing and snagged your line on something below the surface only to lose both your favorite lure and the fish?Having poor quality data in your company’s systems is the business equivalent of that thwarted hiking and fishing trip; it undermines the purpose of the activity, results in unintended consequences, and disappointing results. But, instead of a twisted ankle, or the one that got away, companies deal with customer churn, revenue loss, and decreased profitability.Unlike the great outdoors, though, telcos can take some simple steps to mitigate the impact of poor data quality on their customer retention and engagement programs. Data quality management tools enable telecommunications companies to cleanse customer data on an on-going basis and avoid the pitfalls of current data quality management practices.Current data quality best practices, while a good start, are compromised by three common mistakes.
1. Keep data quality management as a continual process
Good data is not something that only needs to be done once and can be considered complete. While everyone on the project may breathe a huge sigh of relief once the last entry in the CRM has been reconciled, the truth is this is just the beginning of a data quality program. After all, people change their phone numbers and move regularly, and those changes in data need to be entered into the system and reconciled across departments within the business structure.2. Develop a strategic plan, deploying internal resourcesData quality management programs are a marathon, not a sprint, and therefore require detailed planning to meet the intended goal. Without precision planning, a data quality management programs often fail because of resource shortages.3. Make sure the plan is implemented company wideAnd the final mistake is to treat data quality as siloed activity – where it is accomplished in one department or business segment and the success is not replicated organization wide.So, what is the new gold standard for data quality management? Navin Sharma, Global Portfolio Director, Enterprise Data Management at Pitney Bowes Software has laid out his thoughts in a recent podcast. The starting point for Navin is that data is a business asset that needs to managed on an on-going basis just like any other physical asset or intellectual property.