Data is arguably the cornerstone of every organization’s success. It enhances customer experience, corporate strategy and operational efficiency. Statistics from AnalyticsWeek highlight the following:
- Data is growing at a rapid rate, in fact, by next year, close to 1.7 megabytes of new information will be created every second for every human being on the planet.Nearly 34 percent of all data will go through the cloud by 2020.
- Studies suggest by leveraging big data, the healthcare sector, for instance, could save as much as $300 billion a year. In other words, this will mean bringing down costs by $1000 a year for every man, woman, and child.
- Figures project enterprises could increase their operating margins by more than 60 percent by harnessing the full force of big data.
- The market for Hadoop (an open source distributed processing framework that manages data processing and storage for big data applications) is expected to surpass $1 billion by 2020. Notably, 94 percent of organizations that have adopted the Hadoop platform perform analytics on large volumes of data not possible before; 88 percent of users analyze data in greater detail while 82 percent can now retain more of their data.
As digital transformation takes root, enterprises far and wide will need to take on a data migration project at some point. However, this can present some unique and fairly complex challenges for most organizations especially when you consider that source and target systems often have different formats and rules requirements.
Let’s have a detailed look at data migration and how to develop a solid data migration strategy.
Introducing Data Migration
Data migration refers to the process of moving data from one system to another. It may involve the replacement of one or more legacy systems or the deployment of an additional system that will sit alongside the existing applications.
Enterprises spend so much money in data migration; yet nearly three quarter of new systems fail to meet expectations. This is largely due to flaws in the data migration process which normally result in inaccurate data and increased exposure to risk.
So what factors do enterprises need to consider when developing a data migration strategy?
1. Classify and manage data
Organizations need to classify and manage their data as this can be a key factor for the data migration strategy they adopt. And with new and more stringent regulations such as general data protection regulation (GDPR) and the recent Cambridge Analytica scandal, organizations need to carefully manage their data.
They should address the following questions:
- What kind of data will be handled and processed in the new system?
- Has the data already been classified, for example, sensitive PII, PII, confidential, etc.?
- Where will it be stored?
- What about the data flow?
- How would they ensure privacy of customer data, and compliance to legal and regulatory requirements?
- Do they have a data lifecycle management process within the organization?
- Will there be a Data Loss Prevention solution in place?
2. Understand data quality issues
Numbers from a research conducted by Experian note that 44 percent of organizations say that issues related to data quality are the biggest challenges when it comes to data migration. The takeaway here is that data quality is crucial to a successful data migration.
When companies decide to move data to new systems, they often encounter data quality issues that they didn’t notice in their older systems thanks to workarounds or system shortcomings. As a result, this drags the whole migration process and in most cases, poor data will be rejected by new systems as they have stricter standards in regards to the quality of data being integrated.
3. Use the right tools and gather metrics
Often, most organizations use tools that lack data quality capabilities. They need to address this and choose tools that can help make their migration as seamless as possible. There are a number of tools designed specifically for data migrations such as extract, load, transform (ELT) tools as well as data quality software that can complement migration software.
Better yet, if an organization is new to the cloud and is planning to move its data to the cloud, for instance, the best choice is to partner with a dedicated team of cloud specialists with expertise in cloud technologies. These teams can connect a company to the right cloud solutions and then create, deploy and sometimes operate an organization’s IT infrastructure through the cloud.
In addition, organizations should measure network bandwidth needs before moving their data to new systems. Once an organization is clear on how much bandwidth should be allocated to the data migration process and when it will be available, the bandwidth can be managed with tools (think optimization technologies, replication optimizers, and traffic shapers).
4. Have the right workforce
Studies show that insufficient labor is a major hurdle for some organizations and thus, results to significant delays when undertaking data migration.
Data migration involves moving millions of records and often requires a lot of manpower. Yet most enterprises heavily depend on manual processes when moving their data to new systems. A case in point would be teams of SQL experts coding queries to extract all the existing data for migration without even having an understanding of the business context or the quality of data.
The point is for a successful data migration process, organizations need to bring together the necessary IT talent and other stakeholders across the business to help process the data.
5. Improve on communication
As with most projects, poor communication among the stakeholders in a data migration can prove to be a challenge for most organizations and hence increase delays in the process. The IT department should, at all stages of the migration process, involve the business team as this will ensure the new system is practical and useful for the enterprise at large.
There is no denying data migration is a crucial IT process for enterprises across the globe. However, in order to ensure a successful data migration process, enterprises need a strategy that will deliver accurate data that supports the needs of the organization. They have to develop a solid strategy that will enable them to fully plan and effectively implement their data migration needs.