What are the pillars of data-centric architecture

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What are the pillars of data-centric architecture? Next, we show you how the data-centric architecture works based on the pillars that support this model. Follow us! Security Data security is a rapidly growing topic as data becomes more available and the world becomes increasingly digital. During the Covid-19 pandemic, cyber attacks on companies have increased , given the greater vulnerability of systems. In this context, it is clear why security is one of the pillars of data-centric architecture. As we noted above, this model should guarantee greater protection of corporate data against improper access. Unification Data-centric architecture seeks to unify data to unleash its full potential.

Instead of creating data silos and multiple infrastructures Telegram Number Data that make access and analysis difficult, the intention is to achieve a continuous data architecture that can tame big data. In an analogy with real estate architecture, this model allows you to create a well-integrated house internally, instead of random buildings and disconnected rooms. Agility Another pillar that supports data-centric architecture is agility. AI technologies require real-time access to data to provide an agile response to users. In this sense, unified infrastructure and enhanced processing power should enable instant access to data by applications. This ensures agility for organizations to respond appropriately to the speed of the digital age.




Accessibility Data-centric architecture must be democratic . Any person, regardless of their experience or technical knowledge, must access the data to make the best decisions and optimize the quality and speed of the processes. Therefore, this model must also prioritize user accessibility, considering the different levels of access permissions. How to implement data-centric architecture? Implementation would be much easier if the organization could focus on data from the beginning of its operations. However, the reality is that companies are already dealing with various applications and tools, have already gone through mergers and acquisitions, and are facing a confusing data landscape.