The abundance of data generated by online consumers is both a boon and a bane for marketers. While it presents a rich source of information for finding and targeting consumers, it can also be difficult to handle properly.
Mismanaged data is all too common. A recent survey by Demand Gen Report revealed that more than 62% of organisations rely on marketing/prospect data that is 20 to 40% incomplete or inaccurate. Poor data management, quality and access can hinder marketers from realizing significant returns on investments into marketing automation platforms.
Effective automation cannot come about without accurate data. When data is relevant, complete, accurate, timely, consistent, meaningful, and usable, it can propel an organisation to great success. Mismanaged data, on the other hand, is worthless and can be harmful.
Data management programs (DMPs) can help in the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. A DMP can offer a centralised platform of customer data, which can then be segmented so that brands can target select audiences via other martech or adtech engines, such as Demand Side Platform (DSP).
Accurate data leads to efficiency. Marketing automation has the potential to make life much easier for marketers. Using specialised software to automate repetitive tasks such as emails, social media and other website actions, marketers can simplify tasks and significantly reduce the time spent on them. Automation is heavily dependent upon the accuracy, completeness, and validity of the data that the technology is run on.
To better manage data, marketers need to work with IT to create a data quality compliance process for source data. This means ensuring that the data entering corporate and marketing systems and processes meets required standards for accuracy, completeness, and validity.
It’s important to assess the quality of existing data and determine what measures need to be taken to remedy them. Converting rules into a common format and embedding automatic validation and correction of data can help maintain a high level of data quality and consistency.
Remember, data management is a never-ending process that must be taken seriously if you want to reap the benefits of accurate data management.
