As companies re-evaluate current IT infrastructures and processes with the goal of creating more efficient, resilient, and intuitive enterprise systems, one thing has become very clear: traditional data warehousing architectures that separate data storage from usage are pretty much obsolete.
The basic structure of current data platforms inhibits strategic outcomes by creating data silos and inconsistencies in dashboards and reports across an organization. As a result, the quality of the data is often questionable since it is an amalgam of information from multiple sources. The design disadvantages and limitations of these platforms include:
- Redundant layers in the architecture and multiple data marts that result in increased processing time and data latency.
- Complexity in the data flow and the existing processing layers that make it cost-prohibitive to optimize or scale the performance of the hardware and software that comprise these systems.
- The need for multiple data marts and independent data repositories due to the absence of a single user-based consumption layer.
- An inability to support enabling data science technologies such as predictive modeling, AI, and machine learning that are future drivers of digital transformation.
- Antiquated identity, security, and audit controls that escalate risks to the enterprise.
Since data is an anchor point for the digital transformation efforts at every company, it makes sense to create a modern data platform that can support real-time processing and enabling technologies like AI, while offering a future-proof architecture that can deliver actionable business intelligence to achieve an organization’s goals.
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(Insider Story)
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Source: News