How a Data Cloud Unlocks AI/ML Innovation at Scale

Enterprises and governments around the world have entered an era of “compressed digital transformation (DX).” In this scenario, organizations are forced to innovate using AI and ML to create new data products and applications in weeks and months, not years and thus can leap ahead.

Advancements in machine learning, natural language processing, and computer vision AI are at the forefront of AI software innovations and transformative customer and employee experiences. Quality AI solutions require data diversity, and the full transformative impact of AI can be realized by using a wide range of data types: structured, semi-structured and unstructured in batch and/or real-time and from on-premises to edge to cloud. 

AI solutions offer more than operational efficiencies. In IDC’s AI StrategiesView 2022 study – a global survey of 2,053 organizations, with IT and LOB decision makers and influencers as respondents the following trends were revealed:

  • Early adopters report 35% improvement in innovation and 33% improvement in sustainability by investing in AI over the past three years. Customer and employee retention are reported to each have 32% improvement.
  • Secure availability and accessibility of data is critical to scaling of AI initiatives.
  • To democratize AI and realize the full potential of AI/ML, organizations worldwide are challenged to:
    • – Optimize costs of development and deployment (i.e., hardware accelerators and compute resources)
    • – Address the talent gap
    • – Contend with the lack of machine learning operations tools and technologies
    • – Eliminate data silos and enable access to adequate volumes and quality of data
    • – Ensure trust and governance of AI systems

To address the data-related and development challenges, organizations will need to formulate and execute on a comprehensive AI build strategy that encompasses data, development costs and talent.

Crafting a Winning AI Build Strategy 

  • Data 
    • – Get employee buy-in and trust for the data strategy with inclusivity and transparency.
    • – Create a workflow for bringing in third-party and/or net-new data sources into the organization, including testing, buying, and seamless integration with existing internal data sets and processes.
    • – Ensure the process is cross-functional across IT, procurement, legal, compliance, and security.
    • – Select a secure and governed data platform with support for all data types including streaming data pipelines to support the entire AI/ML life-cycle workflow; and a platform for today’s organizations to unite siloed data, discover and securely share data, and execute diverse analytics workloads.
    • – Ensure platform support for open-source table formats such as Apache Iceberg that brings the reliability and simplicity of SQL tables to big data, while providing flexibility to run multiple processing engines to safely work with the same tables, at the same time.
    • – Embrace an intelligent data grid that helps:
      • – Automate and enforce universal data access and usage policies across on-premises to edge to multicloud ecosystems.
      • – Automate how data is discovered, cataloged, and enriched for users.
      • – Eliminate the need of doing any data movement or replication.
  • Cost of development
    • – Examine usage-based pricing technology and elastic infrastructure where you only pay for what you use
    • – Streamline processing architecture with support of multiple languages in one engine
    • – Explore flexibility of compute with both CPU and memory optimized processing environment
  • Talent
    • – Build a talent pool of industry domain and technical experts like data engineers, data scientists, and machine learning engineers
    • – Ensure you leverage your pool of existing SQL skills
    • – Upskill your workforce to Python, an easy to learn language with many prebuilt components supported by community developers

By leveraging these varied benefits, an organization can accelerate its DX journey and help realize superior business outcomes across the top-line, bottom-line and green-line.

As with most innovations, AI/ML solution development and deployment is just a starting point. The ability to intelligently engage, collaborate, and resolve customer urgent needs will be the true differentiator for forward-thinking organizations.

IDC believes that the AI/ML market will help organizations future proof their business. To learn more, read the IDC White Paper, Scaling AI/ML Initiatives: The Critical Role of Data, sponsored by Snowflake Inc.


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