It’s no secret that organizations face multiple challenges when embarking on a Cloud transformation journey. This journey often involves the transition from a legacy or on-premises ERP to a cloud-based SaaS ERP system. A key element that is often overlooked or underestimated during this transition is the migration and retention of historical data.
a key element that is often overlooked or underestimated during this transition is the migration and retention of historical data
Typically, organizations gather and maintain several years of data in their application systems. In case of an on-premises ERP system, it is not uncommon to see over 20 years of transactional data in many organizations. Employee data in HR/HCM and Payroll systems can be of even longer duration, especially in case of long-term employees who have been with an organization for decades. All this data poses several challenges during a cloud transformation project.
Challenges during historical data migration and retention planning:
First, since it is not cost-effective to use a cloud-based SaaS ERP system as a data repository it is often recommended as best practice to only convert and migrate data that is required to operate the business efficiently post go-live. In addition, an extended data conversion and migration cycle are often dreaded and shunned by project managers since it creates an unnecessary drag on the cloud transformation project.
developing and implementing an Enterprise Data Management strategy which involves data modernization, data analytics and data monetization components
The second challenge is developing and implementing an Enterprise Data Management strategy which involves data modernization, data analytics and data monetization components. It is a known fact that data is the foundation for actionable insights for any organization, so it is important to get a 360-degree view of the business by eliminating data silos.
Organizations need to uncover potential opportunities and business risks by using predictive analytics to analyze trends and build models that can forecast future outcomes. Using technologies such as robotic process automation (RPA) augmented with artificial intelligence (AI) and machine-learning (ML) help to uncover the hidden value within an organization’s historical data.
the organization’s legal department becomes a key stakeholder in data retention strategy and needs to be involved in the planning stages of the cloud transformation journey
Third, there is a regulatory and compliance aspect where government authorities mandate the requirement for organizations to retain historical financial data for a certain period. In North America the mandate is typically 7 years. However, this can be a challenge for organizations that operate globally and have business units dealing with various governments and multiple tax jurisdictions.
The fourth challenge is the requirement to retain data due to contractual obligations with contractors, suppliers, customers and other third parties. Which means that an organization may need to retain historical data for period of 10 years or more. In this case the organization’s legal department becomes a key stakeholder in their data retention strategy and needs to be involved in the planning stages of the cloud transformation journey.
how historical data is handled is a critical component of an organization’s cloud transformation journey and needs to be addressed at the initial planning stages of the project
What this means is that deciding how historical data is handled is a critical component of an organization’s cloud transformation journey and needs to be addressed at the initial planning stages of the project. Organizations need to acknowledge that historical business data will need to be retained in a cost-effective repository or application system where it is accessible and usable as and when required.
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