3 Biggest Challenges to Maintaining Data Health
Posted: Sun Jan 19, 2025 5:06 am
Data is essential for businesses to grow and create engaging customer experiences. However, many companies struggle to keep their CRMs and other databases consistent and clean — leading to misalignment between marketing, sales, and customer success initiatives. Poor data health can also affect brand perception, resulting in lost leads and opportunities.
Data quality can be challenging to maintain due list of antigua and barbuda consumer email to several factors, including duplicate records, siloed data sources, and a lack of visibility into the data.
In this blog post, we'll discuss the top challenges resulting in low-quality data and provide helpful tools to get your valuable data back on track.
Challenge #1: Lack of Standardization
One of the main reasons data quality suffers is a lack of standardization. Poor standardization practices include:
inconsistent data entry,
poorly designed data capture processes, and
a lack of governance around manual data entry into the CRM.
Poor data hygiene practices lead to duplicate and inaccurate records, potentially propagating throughout the data ecosystem and creating even more chaos.
To combat poor-quality data, establish clear guidelines for data entry and put a system in place to enforce those standards. The solution could be as simple as creating drop-down menus for certain fields to ensure consistent data entry or setting up field-level validation to catch any errors. Additionally, having a dedicated data team or administrator to oversee the data quality process can help identify and fix any emerging issues.
Data quality can be challenging to maintain due list of antigua and barbuda consumer email to several factors, including duplicate records, siloed data sources, and a lack of visibility into the data.
In this blog post, we'll discuss the top challenges resulting in low-quality data and provide helpful tools to get your valuable data back on track.
Challenge #1: Lack of Standardization
One of the main reasons data quality suffers is a lack of standardization. Poor standardization practices include:
inconsistent data entry,
poorly designed data capture processes, and
a lack of governance around manual data entry into the CRM.
Poor data hygiene practices lead to duplicate and inaccurate records, potentially propagating throughout the data ecosystem and creating even more chaos.
To combat poor-quality data, establish clear guidelines for data entry and put a system in place to enforce those standards. The solution could be as simple as creating drop-down menus for certain fields to ensure consistent data entry or setting up field-level validation to catch any errors. Additionally, having a dedicated data team or administrator to oversee the data quality process can help identify and fix any emerging issues.