Data + AI Strategy: Focus on the Platform

The key to good artificial intelligence (AI) is having great data. As AI adoption grows rapidly, the data platform has become the most important component of any company’s technology infrastructure. It is increasingly clear that generative AI systems will not be monolithic, but rather a combination of multiple components that need to work together. And while data is a crucial piece, there are many other functions needed for companies to actually deploy models in the real world.

Data Intelligence Platform
When companies look to build the foundational platform that will underpin their data and AI needs, they must consider three key pillars: data collection, data governance, and creating value from data.

Companies are realizing that significant positive results are possible when each of these pillars is managed through a single platform, called a Data Intelligence Platform (DI Platform). This platform will allow companies to operationalize their data, access any commercial or open source AI model, query information as if using a search engine, and integrate data from partners to quickly visualize the resulting insights.

Consolidation
In today’s enterprises, the critical tasks of storing, overseeing, and using data are often divided among many different tools. According to recent research from MIT Technology Review and Databricks, 81% of large organizations, or those with more than $10 billion in annual revenue, currently operate 10 or more data and AI systems. Relying on so many different technologies is not only expensive, but also a nightmare for data unification and governance. That’s why, in addition to future-proofing their IT infrastructure, companies are trying to consolidate the number of tools they use.

Unifying data with the right controls helps significantly reduce IT complexity. With the entire enterprise operating on a single platform, managing the underlying data becomes easier, eliminating common questions like: “Where is the latest supply chain data?” or “What are the latest supply chain business rules?”

Data Governance
Intellectual property data leaks, security concerns and misuse of corporate information are common fears among executives. With increasing pressure from governments to protect customer data, companies are concerned that any mistakes will attract the attention of regulators. In addition to data compliance, companies need to worry about AI compliance. Soon, they will need to explain how they are training their models, what data they are using, and how the model arrived at its results. Some industries, such as insurance companies or financial service providers, are already required to prove to regulators that the technology they use does not harm consumers.

Building to Scale
Launching a new AI solution involves three main steps: preparing the data, fine-tuning the model, and deploying the final application. First, companies must identify relevant, timely data and get it into the hands of the right experts. Next, AI models need to be continually evaluated and fine-tuned to ensure they are producing accurate and useful results while protecting data.

Finally, AI is only useful if it is actually used. This means that companies need to hide all the complexity of model development and execution with a consumer-friendly application, allowing developers and other end users to start building instantly. Tracking each of these steps separately adds enormous complexity to the process. Instead, a Data Intelligence Platform that can handle the entire model development cycle, from data discovery to final application, as well as providing the monitoring tools necessary to continually improve the model, is essential.

While the underlying platform is important, it is just one step in the process. Check out our previous blog for insights on how to prepare your employees and culture for the future of AI.

Artycs is positioned to help its customers adopt and optimize Data Intelligence Platforms, as discussed in this article. We offer expertise in custom integration and implementation, enabling companies to build and operationalize their data and AI infrastructures efficiently. With our approach focused on data unification, robust governance and value creation through advanced analytics, we help companies consolidate their IT tools and address challenges such as data security and regulatory compliance. Our comprehensive support ensures organizations are prepared to scale their AI initiatives sustainably and effectively.

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