How to Use Data Modeling When Migrating to the Cloud

Over the past decade or so, cloud providers have steadily expanded their portfolio of offerings. This has complicated the process of migrating systems to the cloud. What was once a fairly straightforward matter of finding the right price for cloud storage or compute capacity has evolved into a more complex undertaking. Many more choices exist for companies moving some of their computing environment to the cloud. 

The basic advantages that first attracted customers to the cloud still exists. Organizations essentially rent capacity from cloud providers, eliminating the need for capital hardware expenditures. Using the vast resources that cloud vendors bring to the table, enterprises can easily scale up as their needs grow. With the right agreement in place, they can also scale back if necessary to save money. 

A wide array of cloud offerings has been developed as vendors seek to provide more value to their customers and create incentives to spur migration. In some cases, they support a simple “lift and shift” method of moving systems to the cloud intact. Many organizations have adopted a hybrid approach employing a mix of cloud and on-premises resources. These solutions can offer a path toward modernizing the computing environment and extracting more value from enterprise data assets. 

A Simple Migration May Not be Efficient

Migrating legacy systems intact is the quickest way to get them into the cloud. It’s usually just a matter of obtaining the cloud resources and moving some data to its new home. But multiple issues can impact the success of this type of migration strategy including:

  • A limited ability to introduce improvements or extend the solution;
  • Difficulty accurately estimating migration and post-migration costs;
  • Performance degradation due to latency introduced by new connectivity methods; 
  • Legacy systems may ignore the current and emerging data requirements. 

These issues need to be considered when planning how to best use an IT environment to achieve business goals. There may be better ways to address the needs of enterprise data consumers by modernizing systems and processes during migration. 

Taking Advantage of Modernization Opportunities

One of the defining factors of modern business is its data-driven nature. Information resources are available from a dizzying array of diverse and incompatible data streams. Data consumers are everywhere from executive offices to the shipping department. Addressing the specific needs of these very different groups of data consumers is one of the main challenges of integrating enterprise data resources. 

Many legacy systems cannot, even with major enhancements, handle the current needs of enterprise data consumers. That implies they also will not be able to keep up with the evolution of new forms of information and innovative ways to use them. The initial planning stage of a cloud migration should address these limitations head-on and search for ways to modernize the systems rather than simply shifting their location to the cloud.

Answering some questions about the systems under consideration for migration and their users can help determine if the process would benefit from a modernization effort.

  • Who are the data consumers and what do they need from the solution?
  • Are new data sources available that can enhance the delivery of legacy systems?
  • Is data coming from less controlled sources that pose security risks?
  • Is sensitive or personal data contained in your data resources?

Finding the answers to these questions requires data awareness which can be accomplished with the help of data models. The major components of data awareness are:

  • Business process modeling including documenting and sharing models across the organization;
  • Data discovery to identify the scope of enterprise data resources including where sensitive data resides so it can be protected;
  • Managing metadata allowing visibility into data definitions to address the needs of various consumer groups;
  • Data pipeline management to understand data lineage and how information flows through the enterprise;
  • Database structure that is required to efficiently represent the information.

Once these questions can be answered, you can make an informed decision about whether to migrate legacy systems or take advantage of new cloud offerings that modernize your IT environment and improve business processes.

Creating Enterprise Data Models

An IDERA Geek Sync Webcast presented by David Loshin provides viewers with an in-depth discussion of modernizing systems when migrating them to the cloud. David brings a wealth of experience in the IT field to the discussion and walks viewers through the differences between simply migrating or modernizing when moving to the cloud. The webcast wraps up with a demonstration of ER/Studio Data Architect, highlighting its use as a data modeling tool.  

With ER/Studio, organizations can gain a better understanding of data resources so they can be used productively. The tool enables the easy creation of entity-relationship diagrams to model and understand enterprise data. These models can be used to obtain details of data lineage and uncover relationships that allow information to be used more effectively. Platform migration is facilitated by creating logical models that act as a bridge between different physical models.

When migrating to the cloud, the easiest path is not always the one that leads to your desired destination. Look at the big picture and see if it’s time to modernize your systems when taking advantage of cloud resources. 

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