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4 Issues in Data Migration from Legacy Systems to Avoid

By July 9, 2020 No Comments
4 Issues Data Migration from Legacy Systems Avoid

The Risk of Avoiding Full Data Migration to a Cloud Data Platform

The scales have finally tipped! According to a Flexera survey, 93% of organizations have a multi-cloud strategy and 53% are now operating with advanced cloud maturity. For those who are now behind the bell curve, it’s a reminder that keeping your data architecture in an on-premises solution is detrimental to remaining competitive. On-prem architecture restricts your performance and the overall growth and complexity of your analytics. Here are some of the setbacks of remaining on-prem and the benefits of data migration from legacy systems.

Greater Decentralization

For most organizations, data architecture did not grow out of an intentional process. Many on-prem storage systems developed from a variety of events ranging from M&A activity and business expansion to vertical-specific database initiatives and rogue implementations. As a result, they’re often riddled with data silos that prevent comprehensive analysis from a single source of truth.

When organizations conduct reporting or analysis with these limitations, they are at best only able to find out what happened – not predict what will happen or narrow down what they should do. The predictive analytics and prescriptive analytics that organizations with high analytical maturity are able to conduct is only possible if there’s a consolidated and comprehensive data architecture.

Though you can create a single source of data with an on-prem setup, a cloud-based data storage platform is more likely to prevent future silos. When authorized users can access all of the data from a centralized cloud hub – either through a specific access layer or the whole repository – they are less likely to create offshoot data implementations.

Slower Query Performance

The insights from analytics are only useful if they are timely. Some reports are evergreen, so a few hours, days, or even a week doesn’t alter the actionability of the insight all that much. On the other hand, real-time analytics or streaming analytics requires the ability to process high-volume data at low latency, a difficult feat for on-prem data architecture to achieve without enterprise level funding. Even mid-sized businesses are unable to justify the expense – even though they need the insight available through streaming analysis to keep from falling behind larger industry competitors.

Using cloud-based data architecture enables organizations to access much faster querying. The scalability of these resources allows organizations of all sizes to ask questions and receive answers at a faster rate – regardless of whether it’s real-time or a little less urgent.

Plus, those organizations that end up working with a data migration services partner can even take advantage of solution accelerators developed through proven methods and experience. Experienced partners are better at avoiding unnecessary pipeline or dashboard inefficiencies since they’ve developed effective frameworks for implementing these types of solutions.

More Expensive Server Costs

On-prem data architecture is far more expensive than cloud-based data solutions of equal capacity. When you opt for on-prem, you always need to prepare and pay for the maximum capacity. Even if the majority of your users are conducting nothing more complicated than sales or expense reporting, your organization still needs the storage and computational power to handle data science opportunities as they arise.

All of that unused server capacity is expensive to implement and maintain when the full payoff isn’t continually realized. Also, on-prem data architecture requires ongoing updates, maintenance, and integration to ensure that analytics programs will function to the fullest when they are initiated.

Cloud-based data architecture is far more scalable and providers only charge you for the capacity you use during a given cycle. Plus, it is their responsibility to optimize the performance of your data pipeline and data storage architecture – letting you reap the full benefits without all of the domain expertise and effort.

Hindered Business Continuity

There’s a renewed focus on business continuity. The recent pandemic has illuminated the actual level of continuity preparedness worldwide. Of the organizations that were ready to respond to equipment failure or damage to their physical buildings, few were ready to have their entire workforce telecommuting. Those with their data architecture already situated in the cloud fared much better and more seamlessly transitioned to conducting analytics remotely.

The aforementioned accessibility of cloud-based solutions gives organizations a greater advantage over traditional on-prem data architecture. There is limited latency when organizations need to adapt to property damage, natural disasters, pandemic outbreaks, or other watershed events. Plus, the centralized nature of this type of data analytics architecture prevents unplanned losses that might occur if data is stored in disparate systems on-site. Resiliency is at the heart of cloud-based analytics.

It’s time to embrace data migration from legacy systems in your business. Aptitive can help! We’re experienced with migration legacy implementations to Azure Data Factory and other cloud-based solutions.

Let’s Start Your Data Migration