Building A Modern Data Architecture to Support Your Business Goals
With a lot of noise in the data analytics industry about modernization, enterprises are now gradually shifting to building a modern data architecture that helps them eradicate data silos. Over the past few years, organizations are frequently deploying technologies against legacy systems to leverage market-driven innovations. In 2018, 81% of CEOs admitted to prioritizing technology initiatives that can equip them with advanced analytics. In such an arrangement, it becomes difficult for businesses to pace up with the increasing scale of data. It also makes it impossible to derive valuable insights from unstructured raw data due to incoming data flows from disparate sources.
Modernization enables an organization to manage increasing amounts of data while getting rid of data silos. For enterprises to maintain a competitive edge within the market, they need a scalable architecture in place.
Shifts Your Business Needs to Create a Modern Data Architecture
In recent years, companies have been observed working towards a few foundational shifts to make their data architecture blueprints rapid delivery enabled. They are focused on various major data activities, such as data acquisition, processing, storage, and analysis. Even though enterprises can adopt the required shifts while leaving their core technology stack as it is, many would need to re-engineer their data platform architecture to support legacy as well as advanced technologies.
What is modern data architecture? Why should you consider it?
Data architecture is the framework that supports an enterprise’s data assets, data strategy, and data management resources. But with proactive advances in the technologies, the architectures that have dominated the IT market for decades are no longer capable of handling the enormous workloads of today’s enterprises. In such a technologically advanced world, organizations need a scalable, flexible architecture that can anticipate complex data needs.
Before we jump into the benefits of modern architecture, let’s look at the modern data architecture principles that will help you strategize in an efficient manner:
View data as a shared asset: Extending a complete view of the company across all the involved stakeholders instead of department-wise data silos helps enterprises outperform their competitors in a short period of time.
Security and access control simultaneously: With the introduction of unified data platforms like Snowflake, it’s important that the organization must enforce data policies to architect security and provide self-service access to raw data and information without compromising access controls.
Get the right interfaces for users to consume data: Easy accessibility for users to consume data is as important as offering data as a shared asset. To achieve the vision of becoming a data-driven enterprise, you need to have the right interface in place.
Having a common vocabulary: Establishing a shared vocabulary for standard data items helps in building a pattern regardless of how consumers analyze data.
Curating data: With no proper data curation, users can perceive data differently, impacting the real value of data.
Eliminating data copies and movement: Data movement costs in wasting of time, accuracy, and effort. By eliminating this additional step, enterprises can optimize the overall agility of the organization.
Learn why to use modern enterprise data architecture. Take a look at all the advantages.
Your modern data architecture blueprint
By implementing a modern data architecture blueprint, you can expect a significant increase in return on investment. Here are the shifts that your organization has been long waiting for:
Shifting from legacy or on-premises arrangement to cloud data architecture: Cloud offers a competitive advantage for businesses to rapidly scale their AI tools and capabilities while pacing up with continuous technological advancements. You get access to serverless data platforms and containerized data solutions.
Batch to real-time processing: Since real-time data processing and streaming capabilities have become affordable, businesses are leaving batch processing behind to get the most out of advanced analytics, alerting platforms, and more.
Point-to-point to decoupled data access: Decoupled data access offers seamless collaboration among the team members. For instance, enabling an API gateway will allow developers to search for existing data interfaces and reuse them for faster delivery.
For starting your data architecture modernization journey, it’s important to understand what your business needs and how is it going to benefit you in the longer run.
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