Data clean-up is one of the most crucial steps to ensure B2B marketing teams work on relevant and accurate data. But while cleaning unstructured data, organizations need to ensure they do not lose its valuable wealth of information.
Businesses today generate a huge amount of unstructured marketing data from multiple sources, which have valuable insights that can scale the business exponentially. But unstructured data is not always usable for extraction of market insights, so clean up is inevitable. Though Data cleaning is one of the essential steps to allow for efficient data analytics, enterprises need to assure that the cleanup process should not cause the deep insights that are concealed in it. CMOs should set up the right practices to ensure they make the most out of the data gathered.
Here are a few ways to ensure data does not lose valuable insights during clean-up:
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Developing a business case for strategic data cleanup process
Bad marketing data will have a catastrophic impact on the presales campaigns, yet many businesses have not realized the influence of data quality on business results. B2B marketing teams should understand the long-term and short-term business goals to spot the relevant data that makes sense for their organization. Furthermore, the CMOs should consider setting the right Key Performance Indicators (KPIs) to measure the performance of the data cleansing techniques. Businesses also need to evaluate the financial impact of cleaning unstructured marketing data; if the results are not great, valuable insights are probably lost during the clean-up.
Adopt the right data clean up strategies
To ensure the B2B marketing unstructured data does not lose its value during clean-up, it is crucial to implement the right data cleaning techniques and tools. Marketing teams need to understand the type of unstructured data which is being collected and how the data will be utilized to accomplish business goals. CMOs should consider evaluating each data set thoroughly to understand how to efficiently clean data without losing its quality. Evaluating the most crucial data fields to get valuable insights from the unstructured data and ways to standardize or normalize similar data from various sources, will assist in maintaining data quality even after data cleaning.
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Organize and validate unstructured data before cleaning up
Data needs to be classified and categorized to be ready for analysis, and this is a key step to avoiding marketing data lakes from becoming data swamps. One technique to organize the unstructured data is to give it a structure as per the business marketing use case that the organization is planning to optimize. As there are multiple sources and data types, the marketers need to understand the context of collecting it to be analyzed effectively.
Invalidated data provides B2B marketing teams with limited insights. Hence CMOs should consider collecting data from reliable sources and setting integrity checks before the unstructured data enters the data lake. Establishing data integrity in the early stages of the data collection process will ensure valuable insights and have a higher ROI.
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