Some of the best practices have been identified for customer data management. First of all, one needs to avoid excess data centralization. This is because excess concentration at one place could lead to formation of silos. On the other hand, much fragmentation of the same will lead to problems during data warehousing. This will further compound the matters when trying to deduce insights from incomplete data. Specific focus now has to be maintained on data governance. Established standards are also available for the same. There must also be clear goals established on what kind of data is to be collected. Otherwise, teams end up collecting reams of it, a lot of it of little strategic value. Data management involves first of the validation of the data to be collected, stored or disbursed. The unnecessary bits need to be cleansed out. One needs to monitor the quality of the data collected, else the business intelligence the warehouse is supposed to provide will be compromised. Data security and subsequent privacy are aspects customers are deeply concerned about at present. Access to sensitive information in particular needs to be restricted.

Source:https://www.martechadvisor.com/articles/data-management/5-best-practice-for-customer-data-management/

Uploaded Date:05 November 2018