Council Post: Four Myths About Digital Transformation And How To Debunk Them By Modernizing At The Data Layer

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Council Post: Four Myths About Digital Transformation And How To Debunk Them By Modernizing At The Data Layer
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The reality is that advanced data management techniques can integrate compliance seamlessly into processes and workflows across the business.

All of the above myths are self-limiting because they underestimate the possibilities today for unlocking and connecting data throughout the enterprise. Fortunately, transformation teams now have a growing range of options to choose from in making the needed pivot toward a more dynamic and connected data ecosystem across the entire IT estate for better visibility, availability and agility.

Since then, a range of more agile data frameworks—including data lakes, data mesh and data fabric architectures—have evolved to exploit the potential of modern cloud deployments and the flexibility of API-driven, app-centric DevOps that can happen in the cloud.represents one of the earliest leaps beyond the traditional DW by eliminating the need to cleanse, format and transform data before it’s loaded.

The result is more flexibility and more ability to innovate with cloud-native DevOps, but the trade-off is that sophisticated data engineering is still required to run a data mesh architecture.architecture is essentially a data mesh with an added “abstraction layer” that virtualizes all data into a centralized platform. The benefit is a single pane of glass for all data, virtualized and contextualized for a broader range of business users to work with.

All three approaches remain represented in today’s marketplace for organizations to choose from. And while the calculus for making the choice will vary for each company based on their DX goals and level of technical expertise, a common ingredient to success is to prioritize scalable and repeatable processes through automation and low-code wherever possible.

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