Data Migration Problems in the Digital Workplace
While digital workplaces have made it easier for information workers to access data, problems remain. Particularly, companies are struggling to move their data into accessible siloes — and even the cloud.
The issue is so widespread that vendors are jumping at the chance to launch products to help. In December, for example, Microsoft introduced Azure Storage Mover, a cloud migration tool that seeks to help system administrators plan, initiate and start migrations. What Azure hopes to solve, wrote Azure VP Jurgen Willis, is not so much a problem with moving files themselves but more about moving the data they contain.
“File storage is a critical part of any organization’s on-premises IT infrastructure. As organizations migrate more of their applications and user shares to the cloud, they often face challenges in migrating the associated file data,” he wrote.
Data management tools provider Komprise also announced a new migration tool designed to speed up the process of data migration. Hypertransfer for Elastic Data Migration is a solution that, according to the company, can make cloud data transfers 25 times faster.
Komprise and Microsoft aren't, of course, the only ones coming up with products that can help. But the problem they aim to solve for is the same: support organizations in their digital transformation strategies by helping them keep, maintain and reuse data that is stored in legacy systems.
Data Mobility, Portability and Security
Organizations are moving away from paper and disparate services to take advantage of cloud offerings, which can simplify storage and afford a larger array of data queries than what was available a decade ago. In this context, migration to the cloud isn't the problem. Rather, it is how to migrate this data to an easily accessible and protected place.
Patrick Kopins, COO of data governance consultancy Accscient, said the need for effective migration and migration tools has never been greater. “With the increase in remote/hybrid work models and the growing use of cloud-based tech amid digital transformation for enterprises, data migration/mobility and portability are increasingly vital in enterprise environments,” he said.
Even the parameters of migration have changed. Before beginning the migration process, Kopins said, companies need to delineate between data mobility — i.e., being able to access to data wherever it is needed — and data portability, which in the context of cloud services is a term that describes data that can be moved from one service to another. “These terms are not mutually exclusive, but both have different meanings and considerations attached to them,” Kopins said.
Data mobility supports remote employees as well as customers by giving them access to the data they need regardless of their physical location or the medium used to access that data. By using services like high availability, data synchronization and data duplication, the infrastructure facilitates information sharing to remove delays and increase interoperability.
Data portability means that end-users and organizations are not tied to one vendor and the data format is interoperable with other services. Kopins said it is common for vendors to lock in customers contractually and functionally by using proprietary data formats. Even with the ability to export data, the use of proprietary or atypical data can prevent a successful migration.
The objective of data portability is to standardize data formats between services so that the data owner can manage that information at their discretion. But there are other considerations, too. Maintaining data security is also critical. It begins with knowing what data is stored and assigning it the appropriate classification to determine which data elements can be shared and which should be restricted.
“A digital transformation plan, data mobility and data portability can support each other. People, applications and businesses interact with historical and current data,” Kopins said. “Here is where data mobility and data portability are considerations which shape the steps taken and technologies chosen. It is very much a new way for organizations to think about data management."
Related Article: What You Need to Build a Cloud Strategy for the Digital Workplace
Learning Opportunities
Data Cleaning Remains a Priority
There is another issue that is important: converting and keeping data clean. Inga Broerman, VP of marketing at BluLogix, said organizations often have issues where data isn't connected or lacks necessary external data links, both of which can obstruct migration plans.
According to Broerman, the problem is that the data is gathered and assembled from a variety of sources, including old databases and spreadsheets, so some of the original links may not work after the migration.
“Because of insufficient and out-of-date metadata and subsequent problematic data conversion, data cleansing is essential," she said. “The complex legacy system's business rules must be understood, and extensive data analysis, data profiling, data structuring and data purification must precede a successful rollout.”
There are other problems of this ilk, too. “It's just as frustrating and time-consuming to deal with duplicate entries as it is with incomplete data, binary data or encrypted data,” Broerman said. “Unnecessary, unneeded, obsolete, outdated, duplicate or inaccurate information will make up the bulk of the project's focus.”
Data quality matters when it comes to data migration and designing business operations. Data errors can significantly impact a company's bottom line and reputation, said Ben Johnson, founder and CEO of Freya Systems, a data analytics company in the defense and space manufacturing sector.
“Garbage in, garbage out can also apply to predictive algorithms," he said. "On the other hand, clean data sets help automate processes, optimize operations and make informed decisions."
While data cleaning can be an arduous process for manual entry, organizations can use automated data cleansing tools to reduce data errors and streamline the data management process. These tools also help identify data inconsistencies that could lead to inaccurate reporting or wrong decisions in the future.
“The number one focus for improving algorithm performance is to clean or somehow manipulate data to get better performance as the algorithm learns. Improvements can be made through data cleaning, data augmentation or a more precise labeling process,” Johnson said.
Related Article: Why Your Digital Transformation Project Is Failing — and What You Can Do About It
About the Author
David is a European-based journalist of 35 years who has spent the last 15 following the development of workplace technologies, from the early days of document management, enterprise content management and content services. Now, with the development of new remote and hybrid work models, he covers the evolution of technologies that enable collaboration, communications and work and has recently spent a great deal of time exploring the far reaches of AI, generative AI and General AI.
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