Building the AI-Ready Enterprise: A Knowledge-First Approach/data-science-insights/building-the-ai-ready-enterprise-a-knowledge-first-approach

Building the AI-Ready Enterprise: A Knowledge-First Approach

Building the AI-Ready Enterprise: A Knowledge-First Approach

The field of data science has undergone significant evolution in recent times, transforming from a loosely defined discipline into a more structured and integral part of business strategy. Corporate leaders now harbor a strong desire to harness the potential of generative and other practical yet somewhat challenging forms of artificial intelligence. This increasing demand presents an opening for influencers and forward-thinking individuals within organizations to advocate for the establishment of AI-ready data foundations.

Therefore, the demand for professionals proficient in data science skills is higher than ever now. In fact, according to the US Bureau of Labor Statistics, the demand for these professionals will grow by 35.8% by 2031.

These foundations would support a blend of hybrid AI, combining knowledge graphs with statistical machine learning, and existing machine learning initiatives. Without such a foundation, companies may struggle to expand their AI endeavors.

The Need for Articulating Roles in AI-Enabled Enterprises

Data scientist's knowledge is primarily occupied with creating and fine-tuning statistical machine learning models, alongside the essential data preparation tasks to derive meaningful insights. On the other hand, data engineers focus on constructing data pipelines and ensuring access to resources required by data scientists and others. These specialists are often deeply immersed in their specific domains, leaving little room to contemplate the intricacies of semantic knowledge graphs and broader architectural transformations.

Clearly, additional roles need to be introduced or updated to complement the functions of data scientists and data engineers. If, as Andrew Ng emphasizes, superior data surpasses superior algorithms, organizations must prioritize the development of findable, accessible, interoperable, and reusable (FAIR) data, specially tailored for large-scale AI initiatives. Furthermore, knowledge engineers, architects, ontologists, taxonomists, and data stewards are critical for establishing ownership and facilitating the sharing of diverse FAIR data, which is best managed and expanded using a knowledge graph.

A Missing Piece: The Data Foundation for Enterprise-Wide AI

It is evident that many organizations neglect the importance of establishing a robust data foundation for their enterprise-wide AI endeavors. Often, they overlook the significance of scaling their AI initiatives, assuming that cloud computing inherently provides AI readiness. The reality, however, is that public Software as a Service (SaaS) platforms primarily prioritize the data interests of cloud providers. Enterprises should assert more control over their data territory to counter this trend and mitigate the fragmentation stemming from numerous SaaS subscriptions. Achieving this involves adopting a data-centric, rather than an application-centric architectural approach and engaging data architects to guide genuine AI scaling transformations.

Embracing Multi-Purpose Architecture for Enhanced Flexibility

Consider the perspective of a building architect when contemplating the concept of multi-purpose commercial structures. Just as contemporary buildings often commence as versatile structures, AI implementation should be equally flexible. Avoiding the inefficiencies of building new foundations for every distinct AI application is imperative.

Modern building architects strive to design structures capable of accommodating various purposes, such as residential, retail, and office spaces. This approach has become the norm, with single-purpose buildings becoming exceptions rather than the rule.

Similarly, an AI-ready enterprise necessitates a flexible foundational structure. With a well-structured, interoperable data foundation, facilitated by a robust knowledge graph, an organization can customize its data, rules, and processes to meet specific AI requirements as they evolve. This is an important data science skill to learn for all data science professionals in creating an advanced AI infrastructure.

Building a Multi-Purpose Data Foundation for Hybrid, Neurosymbolic AI

Knowledge graphs have been available for over a decade, offering mature and adaptable technology. However, while numerous Fortune 50 companies have implemented knowledge graphs, few have integrated these graphs as a foundational element for broader AI initiatives. A notable exception is Montefiore Health, a hospital chain in the New York area, which has leveraged its knowledge graph, developed with the assistance of Franz, provider of the AllegroGraph database.

The Montefiore knowledge graph unites diverse external and internal data sources, enabling advanced analytics and machine learning methods to benefit from this comprehensive network. As a result, the Patient-Centered Analytics Learning Machine (PALM) at Montefiore can predict and prevent specific health incidents, such as sepsis and respiratory failure.

Present-day AI initiatives call for a more dependable data foundation that instills users' trust and certainty. The concept of "neurosymbolic AI," as championed by AI centers at the University of South Carolina and Kansas State University, aptly captures the synergy between neural networks (statistical deep learning) and symbolic AI (represented by semantic knowledge graphs). Building awareness of the potential of these two technologies working in concert is essential for achieving AI success beyond mere algorithmic or interface magic.

To sum up!

The evolving landscape of data science and AI demands a fundamental shift in how organizations approach their data foundations. The critical need for AI-ready data structures, supported by knowledge graphs and flexible architectures, cannot be overstated. As AI continues to gain prominence, businesses must recognize the significance of creating accessible, interoperable, and reusable data assets to propel their large-scale AI efforts. By embracing the concept of multi-purpose data foundations and fostering awareness of the power of neurosymbolic AI, organizations can unlock the full potential of AI, moving beyond mere algorithms to harness the true transformative power of data-driven intelligence.

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