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When CX issues arise, identifying and resolving errors is more manageable when the product data is well-maintained and clean. Businesses can develop more accurate strategic plans, and managing multiple channel needs becomes faster and more feasible.
Cleansing data silos and lakes eliminate errors.
Lost Sales No More: The Power of Data Cleansing to Boost eCommerce Success
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These can arise through various means, such as data migration, data exchange via integrations, third-party connectors, manual entries, and batch product data imports. Failure to deduplicate may result in:
-Poor workflow and data retrieval efficiency
-Substandard software adoption due to inaccessible data
-Reduced ROI in CRM and marketing automation
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Outdated data sets may be years old, irrelevant, incorrect, and in non-compatible formats, making them useless. Yet, why do such data persist?
-Important stakeholders enter or leave
-rebranding and acquisitions
-Legacy systems evolving from earlier versions constantly can render data incompatible.
Today's digital ecosystem undergoes constant changes, so product information must be updated and current before being used for insights, syndication, or decision-making.
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Data security and privacy laws are evolving due to technological progress, digital shopping dominance and geopolitical shifts. In a consumer-focused business environment, insecure data is seen as the most damaging form of dirty data regarding the company's potential financial and reputational costs.
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Data is considered incomplete if it lacks key fields necessary for processing before sales and marketing action. Missing key feature fields in new or existing product data can't be included in catalogues or eCommerce sites, leading to missed revenue opportunities.
The main issue with incorrect data is incorrect storage (e.g. a text field holding a numerical value). In contrast, inaccurate data is a field filled with wrong information (e.g. incorrect product dimensions).
Incorrect data can lead to inaccurate targeting and segmentation, non-personalized messaging, and storage/display problems.
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A common challenge in PIM implementation projects is finding multiple versions of the same data elements across different databases, leading to inconsistent, non-standardized data.
Some companies simply dislike data culling, which leads to
-Slower data exchange
-Inflated record counts
-Non-compliance with storage limits.
Maintaining "lean" databases is essential for achieving complete product data hygiene and organizational agility.
Data cleansing methodology addresses the challenges of cleaning product data, which has unique attributes, no set syntax, and limited standards across selling and content-providing organizations.
To clean product data effectively, with its multitude of variables, methods often utilize the following approaches:
The necessary business reasons for data cleansing are:
Verifying incoming data at the point of entry to standardize it before entering the database simplifies duplicate detection. Implementing a standard operating procedure ensures that only quality data is accepted into PIM, CRM, ERP, etc., at the point of entry.
Begin data profiling early and regularly once the scope of the product data management solution's business case has been defined. Establish a benchmark of the initial quality level before cleansing to objectively show the impact of poor-quality data on business value and justify continued funding.
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