When deploying your data estate to Azure, automation is the key
Imagine that your boss asks you to move your on-premises analytics data architecture to the cloud and have it completed within a week. What would your reaction be? Would you find yourself immediately overwhelmed with the sheer size of such a project? Or would you simply click a button and let your boss know the task is well underway to being completed?
Employees want instant access to data when they need it, and management and IT want to oblige, but often have significant, legitimate concerns about governance, security, privacy and cost. Furthermore, organizations likely understand that they should take advantage of deploying their data estate to the cloud, but might not be sure of the best way to proceed.
As we know, the benefits of cloud computing are numerous: Saving time and money on maintaining systems, only paying for technology that you use, working anywhere without disruptive access delays, and more. While that all seems nice in theory, the thought of mapping out a cloud data management strategy that encompasses systems responsible for critical financial, operational, and customer data can sound overwhelming. It doesn't have to be. The answer to easing the journey to deploying data in the cloud can be summed up with one word: automation.
Data warehouse migration typically requires manual migration of schemas, data, ETL processes, metadata, users and applications. Companies looking to leverage a public cloud service like Microsoft Azure for their modern data estate should look for tools to simplify and accelerate the work of delivering a data estate that better serves your organization. Why is this important? Because time matters and time-to-data matters. Time-to-data boils down to the time between when someone has an idea about a new way of using data and when the data for that idea hits the BI front-end or analytics tool being used.
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