Though data migration facilitates the transition to superior technology, it entails more than what it reveals to the eye.

A process as crucial as data migration does not come without its own set of challenges. Data migration as a process with several concerns and potential pain points.

Data Security Concerns

For any business organization, data is the most crucial resource. It may consist of business-centric data along with other related data critical for its existence. Any compromise or threat to its security is a risk that businesses would not want to undertake. The same notion spills into migrating data to the cloud. A small hint suggests that the cloud is not secure will make organizations develop cold feet towards migration. Any cloud infrastructure will comprise of such elements as vulnerabilities or defects of a co-subscriber’s code could substantially affect other applications. To tackle this concern, many cloud vendors are performing ‘onboarding audits’ to reassure prospective customers that their level of security is appopriate. Nonetheless, its level of conviction still needs confirmation.

Poor Knowledge of Source Data

The existence of poor knowledge of the source data is a general trend already observed over several data migration processes across industries. Issues such as duplicates, spelling errors, and erroneous data are always a hindrance to ensuring complete and proper data migration. Often, organizations become complacent and tend to assume that they can configure their data without any complications. However, any mismatch could mean nothing else but the failure of the data migration process.

Vendor management

From the perspective of businesses, the process of data migration requires businesses to trust their vendor. Concerns exist whether technical issues on the vendor’s side could affect data security on the cloud. It is therefore imperative that data migration vendors provide SLAs that prioritize the concerns of their clients. Since cloud computing offers a standardized, multi-tenant infrastructure, cloud vendors may not offer the same level of SLAs as IT managers are accustomed to.

Lack of technical integration

Data migration often involves various kinds of technologies and data platforms.

This lack of parity may lead to failure in data transfer between the multiple phases of data migration :

1- Analysis

2- Development

3- Testing

4- Implementation

Such failures not only cause financial repercussions but also compel businesses to re-engage time in the migration of missing data, leading to a loss of precious man-hours.

Cumbersome Data Cleansing Process

Data cleansing refers to the process of altering data intended for migration. The mechanism takes into consideration incomplete data, data relevance, data accuracy, and data duplication as factors of validation. It focuses on maximizing data accuracy in a system.

Additionally, it uses parsing or other relevant methods to omit syntax errors and typographical errors in records. Despite there being cases where data cleansing leads to an increase in response time and hampers efficiency, its significance in fruitful data migration is second to none.

The critical of data migration is such that one cannot take any of the pain points mentioned above for granted. Any form of deviation in data matching and verification will lead to failure in migrating the data.