Automation Is the Answer to Big Data Migration Obstacles

Summary

Managing data migration of petabytes of information is becoming a necessary practice within businesses looking to extract value from unstructured data. In order to gain insights via machine learning and artificial intelligence (AI), businesses are searching for ways to modernize their data migration and management processes. They need to be efficient and fast in routing data sets to inform analytics or support product development. One key to making this a manageable activity is automating data migration tasks.
 
Data management continues to be a major hurdle for businesses. While they see the value of data in achieving customer insights and engagement, they are struggling with how to make that happen. A PwC survey of CIOs, CTOs and technology leaders found that the top two challenges cited were data and the current state of systems and processes. While almost half of respondents cited data platforms as the key to business growth, modernization, including data migration and management, remains a challenge.
 

From manual to automation         

The volume of unstructured data now flowing and, in part, the result of GenAI, prompts a new approach to migration and management. Manual migration, the legacy approach, is a resource intensive, inefficient approach to copying data. It requires an administrator to schedule and maintain custom scripts to migrate large data sets. Uploading the data to a cloud, or on-premises location also requires additional scripting.
 
Manual migration cannot keep up with the timeframe businesses need to move data to the cloud in order to have the benefit of real-time replication. It can take more than 100 days to migrate one petabyte at 1 Gbps. In terms of customer experience, for example, no business can afford to wait a lengthy period of time to receive customer data that may dictate changes in service, product pricing or promotional campaigns.
 
Besides the loss of a competitive edge with slow migration, there is the issue of data consistency. With large datasets, changes to the source data during migration will occur. Without an automated solution that can respond to those data changes throughout data movement, businesses run the risk of inaccurate data arriving at the cloud or an on-premises location.
 
Manual migration approaches are ill equipped to support replication at the scale of data being generated today in a business. Using an automated approach enables validation of data consistency so that wherever data changes occur, the person using that data for analytics or other tasks can feel confident they have the most up-to-date, accurate information.
 

Avoiding disruption         

During a big data migration, a manual approach can also require a costly disruption of on-premises applications. If it involves changing datasets a business may find it is not able to meet its critical service level agreements (SLAs) for consistency and availability of workloads, among other standards. That results in loss of employee productivity, customer satisfaction and increased help desk tickets.
 
Automating migration and data changes can avoid downtime while providing continuity of operation of on-premises workloads, consistency of the data, and the ability to fulfill SLAs.
 

Economic upside

Businesses, pressured to become more digitally competitive, have invested in cloud providers, cloud storage and modernized datacenters. The advent of GenAI and large data sets has spurred new investments in AI, analytics and the technology and people needed to realize the power of unstructured data. What businesses can’t afford is inefficient, costly methods like having IT people spend their valuable time in manual data migration.
 
A way to conserve resources, human and financial, is to use automation to control costs related to migration and replication of large data sets. This method can enable data migration at scale, data changes without disruption and can protect IT budgets to support new initiatives.
 

Making it happen

As the PwC survey notes, getting from a desire to better leverage data and actualization will take some organizational changes. “For CIOs focused on data modernization, it’s about much more than the technology they implement. Foundational issues around governance, privacy and cybersecurity are critical to bridge organizational silos and give the business an enterprise-wide view of data,” the report says.
 
When it comes to implementing a modern, automated data migration and management solution, these organizational dynamics also apply. Some issues to consider are:

• The current state of your data systems. Have they been modernized to support the volume of data now being generated by your business? Hadoop has gained popularity for its ability to support big data, including unstructured data, by enabling tasks to be split and processed across distributed servers. The improved processing speed helps businesses get faster analysis results, but it can require significant maintenance and capital costs for expansion. Modern data platforms may provide better alternatives.
• The extent of cross-enterprise collaboration. Data migration, AI use and multi-team support will be more successful if the siloed approach to thinking is replaced with collaboration. This can include the CIO, CDO, finance, technical and sales personnel, all of whom may have conflicting budget priorities and input to which data-driven development initiatives will reap the greatest return.
• The clarity of your vision in investing in modern data migration, GenAI and analytics. As petabytes flow daily through businesses, creating and actualizing a clear vision will make data use a rational experience rather than a daily catchup struggle. It will help evaluate opportunities that arise by putting them through the vision filter.

Automation will enable a more efficient, consistent use of data as it flows from the cloud to datacenters and back again. With a modernized data system, team consensus on priorities and an overall vision of achievement, businesses can conquer data hurdles.