Understanding common data challenges

Not optimizing data carries risk. Another major risk? Unsuccessfully navigating the challenges along your journey to data transformation. A comprehensive approach encompassing transformation, modernization, governance and analytics can assist you in conquering these major roadblocks.

Understanding common data challenges

Legacy system limitations

Legacy systems' outdated architecture and integration issues make data retrieval difficult. Their complex and inconsistent data formats create bottlenecks that slow down operations and limit your ability to extract timely insights.

Compliance risks and inconsistent governance

A lack of data management standardization and inconsistent data usage lead to governance and compliance risks. This makes it challenging to have consistent data governance frameworks and exposes the enterprise to potential regulatory penalties.

Data is often fragmented and stored in silos across multiple databases handled by different business units. This poor quality contributes to difficulty in trusting the data.

Data quality and trust issues

Skill gaps and fragmented data

Your teams may lack advanced data knowledge that could support data transformation. The data itself is often scattered among applications across business units. This siloed data inhibits enhancing quality and usage.

Wonder what data transformation looks like? Download our Data readiness for AI guide for details on the powerful connection between AI and data and strategies for advancing your organization through four data-readiness levels.