Machines learn best with the right information architecture. Did you get it right?

What’s bogging you down? The scout for the right information architecture? Of course, there cannot be Artificial Intelligence (AI) without Information Architecture (IA).

As Artificial Intelligence springs up, the need would be for right information architecture. The development of AI environment requires some prerequisites to support AI. AI calls for its sub field machine learning, machine learning calls for analytics and analytics calls for data, along with right information architecture.

Businesses today are betting big on emerging technologies in-order to showcase competitive capabilities and agility. And the first and foremost priority remains to deploy AI capabilities. As per a research by IDC, vendor revenue from cognitive software and cognitive server infrastructure will rise to $10 billion and $9 billion, respectively, by 2021.

However, a plethora of challenges lies in the deployment of AI. About 77% of businesses face limitations in the deployment in the absence of right architecture in place. Modernising data architectures to get the right information architecture remains a major challenge faced by many organisations today. Updating the legacy architecture to get the best technology with the goal of optimizing operational efficiency and analytical effectiveness has become indispensable.

Some of the key questions revolve around integration and cost challenges while modernizing data architecture.

To know more about modernization of data architecture and to understand how tech solutions and tech-minded, data-driven professionals hold the keys to updating legacy architecture, attend the webinar ‘Decoding IA Challenges’.

Key Takeaways

• How to get rid of legacy architecture
• Developing AI with right information architecture in place
• Tackling integration, cost challenges
• Optimizing operational efficiency with apt tech solutions