Insight into Data Migration
In the digital age, big data is the fuel that powers a company. In a time when data collection and analytics never stops, successful organizations streamline data integration processes. Whether information migrates from an external source to a company database or moves internally, data migration should be a continuous and simple process.
If businesses fail to enact a set of strategies to migrate data, they tend to overspend and extract unreliable insights. This can decrease operational effectiveness and result in lost profits. Business leaders can optimize data integration by fully understanding how to migrate data with the correct tools and procedures.
What is Data Migration?
What exactly is data migration? Migration occurs when someone or something moves from one location to another. In the business world, the transfer of data is a common practice. As organizations collect large quantities of big data, they can't always keep all of it in the same system.
Business leaders invest in new technologies and update existing processes to maintain a competitive edge. When this occurs, the IT department must begin the migration process to transfer data out of an old storage system into a target system or migration database.
A migration project typically requires migration software to optimize the Extract Transform Load (ETL) steps. This means that engineers need to clean, prepare, and remove duplicate information to ensure data integrity, data quality, and data security. Once the migrated data is in a target database, users can easily access it. The goal is to improve the accuracy and access to data so users can extract valuable insights and make better decisions.
Data migration also occurs when an organization needs to-
- Rebuild an entire system
- Upgrade an old system
- Create an entirely new data warehouse
- Merge source data from an acquisition or alternate source system
- Enact another system in conjunction with an existing data center application
Importance of Data Migration Strategy
Regardless of the reason for a data migration project, an organization needs a set of best practices to ensure a successful outcome. Companies don't enact a migration plan for no reason. The purpose is to always improve each business process so the company can maintain a competitive edge. But without the correct data migration software and set of strategies, the entire data migration process is ineffective.
Poor data migrations lead to poor data quality, duplicate information, and data loss. This can occur even if original data sources are functional and sufficient. Furthermore, any concerns with the original data source or source-target system will be exaggerated in more advanced storage systems. This is why it's so critical to establish an effective data migration strategy before transferring data.
Good migration involves a set of experienced engineers who give the project the proper attention. This will prevent migration challenges and poor data quality in a new system that the migration project is supposed to fix. Incomplete strategies also result in missed timelines, strapped budgets, and failed outcomes. Teams must prioritize the data transfer project instead of deemphasizing it in place of other projects.
Effective migration projects include the following-
- Knowledge of Migration Data - Before teams move data, they should perform an audit on all original data sources. If they fail to do so, there may be unforeseen problems that arise later.
- Clean Data - Once the team pinpoints any problems with the original real-time data, they need to fix them. This typically necessitates the use of a migration tool or ETL tools along with an outside migration application. Because the process requires expertise and a lot of work, many organizations need to hire a third-party specialist. It's also critical to perform data backup to prevent any data loss during the migration process. While this isn't required, most companies choose to do so as a safety precaution.
- Maintain and Protect Data - After a certain period, data quality can degenerate. This makes the information inaccurate and unusable. Teams need to enact quality control measures to protect and secure all data.
- Data Governance - A good data management policy will make sure to prioritize data governance. In governance, teams track and report on the quality of data to gain a full understanding of the integrity of data. This is not a one-time event, but part of an ongoing strategy to optimize data quality. Users can't gain valuable insights from information that is unreliable and inaccurate.
- Incorporate the Correct Tools - Teams need to invest in and utilize the proper migration tools when migrating data. A big data migration plan cannot be successful if an organization does not take the time and money to use the proper applications.
Key Takeaways of Data Migration
In conclusion, here is what to know about data integration and moving data-
- Data migration occurs when an organization rebuilds a system, invests in a new system, upgrades one storage system, creates a new data warehouse, merges data, or enacts a new system to run alongside an old system.
- Data migration projects require a set of strategies and data migration tools to ensure a successful outcome.
- It's critical to thoroughly understand all open-source data before data migrations. This requires an audit. It's also important to clean, transform, load (ETL) all information to maintain data quality and prevent data loss. A full backup is a preferable option.
- Teams need to enact quality control measures to prevent the deterioration of process data. They should also prioritize the tracking and reporting of information to understand data integrity. It's also critical to use the correct migration software to migrate data from one system into a new database.