It’s hard not to speak in cliches when we talk about artificial intelligence (AI). Today, AI seems to be all around us. And whatever its cultural impact, its rapid evolution is leading to widespread adoption across industries. Much of the discourse focuses on what machine intelligence can do to enrich our lives and businesses. But less has been said about data, and how every AI system relies on it to operate.
The Importance of Database Health in Artificial Intelligence
In an AI-driven economy, it’s impossible to overstate the importance of databases and database health. Unlike human intelligence, which can be pinpointed to a single instance of the human brain, few examples of artificial intelligence can be traced to a single database or a single piece of technology. The quantities of data required for AI systems are colossal. All that data must reside somewhere, be it on-premises, in the cloud, or in a hybrid solution.
Storage solutions for the data used in AI must be able to handle extremely large volumes of data and provide high throughput and low latency—all while ensuring data durability and availability. It turns out cloud storage is particularly well-suited for AI workloads, mostly thanks to its seamless data access, scalability, and flexibility. More than anything else, databases need to work as efficiently as possible. Tolerance for underperforming applications is generally low. Tolerance for underperforming AI is close to zero.
Your Data Estate
When we talk about your data estate, we sometimes refer to the infrastructure used to store data. In a broader sense, your data estate encompasses the approach and initiatives your company takes to acquire, handle, and store data used to feed your applications and ensure the continued success of your business. Most companies considering AI will use existing databases and their corporate data together with AI. Many will use specially created datasets.
If you’re considering adopting AI in your company, there are a few key points to bear in mind. The first is data quality. No matter how it’s handled and where it’s stored, the quality and quantity of data are crucial for creating reliable AI systems. Poor data quality adversely affects AI models and analytics. Where AI is used for decision-making processes, inaccurate or incomplete data can lead to flawed predictions and suboptimal insights.
Next comes data management. This is a very broad topic, but in essence, good data management is about safely collecting, storing, and using data. Having well-defined policies and standards will go a long way to ensuring data consistency and security.
Finally, there’s database performance. No matter how good your data—or how stringent your data governance—if your databases aren’t working properly, the risks can be serious. Delays in processing queries, retrieving data, and executing transactions can slow down applications, causing frustration among users and poor customer experience. System crashes, unscheduled downtime, and poor application responsiveness can lead to lost revenue opportunities. Inefficient database operations can increase operational costs. Data inconsistencies, errors, or loss can compromise data integrity and expose the company to legal and regulatory compliance risks.
It All Starts With Data
When the emphasis is so often placed on the output from AI systems rather than the input, the connection between artificial intelligence and databases isn't always clear. Machine learning depends on optimized databases tuned to the requirements of some of the most demanding systems in computer science. Beyond AI, the list of problems created by poorly performing databases is endless, making the case for database monitoring and management tools self-evident.
