Data + AI Strategy: Platform Focus

The key to good artificial intelligence (AI) is having great data. As AI adoption rapidly grows, the data platform has become the most important component of any enterprise’s technology stack. It’s increasingly clear that generative AI systems won’t be monolithic but rather a combination of many components that need to work together. And while data is a crucial piece, many other functions are necessary for businesses to deploy models in the real world effectively.

Data Intelligence Platform

When companies aim to build the foundational platform that will support their data and AI needs, they should consider three main pillars: data collection, data governance, and value creation from data.

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

Consolidation

In today’s enterprises, critical tasks of storing, overseeing, and using data are often split across many different tools. According to a recent survey by MIT Technology Review and Databricks, 81% of large organizations, or those with over $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. This is why, alongside future-proofing their IT infrastructure, companies are trying to consolidate the number of tools they use.

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

Data Governance

Data IP leakage, security concerns, and the misuse of corporate information are common fears among executives. With increasing government pressure to protect customer data, businesses are rightly concerned that any misstep could attract the attention of regulators. Beyond data compliance, companies also need to worry about AI compliance. They will soon 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 insurers or financial service providers, are already required to prove to regulators that the technology they use is not harmful to consumers.

Building to Scale

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

Finally, AI is only useful if it is actually used. This means companies need to hide the complexity of developing and running the model with a consumer-friendly application, enabling 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 the final application, and provide the monitoring tools needed 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 AI future.

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

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