In this rapidly changing, and evolving, digital landscape, artificial intelligence (AI) is becoming a key tool in databases and in database administration. It is being used to enhance the performance of our database systems – both in efficiency and effectiveness. It is being deployed in areas such as predictive analytics through to automated optimisation. AI is delivering real, tangible business value.
We have looked into the 4 main ways it is delivering real value today:
1. Automating Routine Database Tasks
As in other industries, one of the main benefits of AI in databases is in the realm of automation. Database administrators (DBAs) have traditionally spent a large proportion of their time on repetitive maintenance tasks, such as performance tuning, indexing and anomaly detection. AI can now handle much of this grunt work: Machine learning models can analyse workloads constantly, in real time, and adjust various database settings, indexes and query plans automatically. This not only improves performance, it also makes the databases more robust.
2. Intelligent Query Optimisation
AI-driven query optimisers use historical query execution data to predict and select the most efficient execution paths. Unlike traditional optimisers that rely on heuristics or static rules, AI models can adapt to changes in data distribution, schema modifications, and query patterns. This results in faster query performance and lower computational costs. Reduced response times and a better ROI give a real, tangible benefit for business.
3. Predictive Maintenance and Anomaly Detection
With AI, databases can predict failures before they occur. Machine learning algorithms can monitor logs, usage patterns, and system metrics to detect unusual behaviour. These anomalies, often early indicators of issues such as hardware degradation or security breaches can then trigger alerts or automated responses. This proactive approach reduces downtime and enhances security.
4. Smarter Data Insights
AI enables databases to go beyond storage and retrieval, allowing them to derive insights directly. For example, natural language processing (NLP) can turn raw data into human-readable summaries, and embedded AI can detect trends, forecast future values, or identify hidden correlations. This transforms databases from passive repositories into active decision-making tools.
We will write more on AI data insights in a subsequent article. It is a big, interesting and expanding topic.
