Strategic Partners

Steps To Build A Winning Data Architecture

Modern data architecture supports agility and resilience. Khaled AlShami, Director, Solution Consulting, Middle East & Africa, Infor, shares seven traits to steps to ensure your data architecture is primed for the digital age

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Khaled AlShami, Infor
Khaled AlShami, Infor

Today’s global economic upheaval underscores the critical need for enterprises to be agile and resilient. In most industries, change is constant and dramatic. It can also be debilitating for organisations lacking a flexible infrastructure and tools needed to make decisions quickly and accurately. As businesses worldwide adopt new go-to-market models, heightened supply chain expectations, and flexible workforce policies, their enterprise data architectures should also be examined and updated.

Here are some key guidelines to help distinguish a modern data solution from an outdated one. Ensuring your data architecture embraces these principles will help your enterprise to be agile, resilient and ready to meet the challenges of the digital age.

  1. Results-driven. A modern data architecture is business centric, not IT focused. It isn’t about technology for the sake of technology. It is about driving better business outcomes. It is focused on the needs of the business over the technology that enables business success.
  2. Automated. Next, a modern data architecture leverages automation. It seeks to augment and automate the most manual tasks to ensure we are not building brittle processes.
  3. Flexible and elastic. A modern system should be flexible enough to address the use cases, which aren’t even envisioned yet. A modern data architecture is also elastic, leveraging the power of cloud computing to provide instant, on-demand scalability, ensuring that capacity is always available.
  4. Adaptable. The modern solution should be able to adapt to the changing needs of the business and the changing landscape of the enterprise. With a semantic layer that can be updated, the company can add definitions and parameters as the needs of the company expand. This means the company isn’t locked into the way work is being done today.
  5. Smart. Today’s solutions should leverage the power of artificial intelligence (AI) and machine learning (ML) to operationalise automated insights. AI-driven functionality can help users discover previously undetected insights about data, spotting trends and patterns that can easily be missed by humans.
  6. Secure. Modern solutions, of course, must be secure, ensuring governance across the entire information supply chain. The systems should not only protect from outside infiltration but should control internal access. Users only should be allowed to access and use the information that is appropriate to their roles.
  7. Collaborative. A modern data architecture should be collaborative, supporting sharing of information across organisation boundaries, departments, or even outside the enterprise, ensuring everyone is working from the same data.

Enterprises today need to unify complex data across their organisations and bring it into a single integrated view of the business. Reporting needs to be fast and easy so the organisation can manage the ever-changing needs of their industry and customers. Agility is essential today, and resilience is just as important. Only modern data architectures can offer companies the insights they need to adapt and stay relevant.

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