Integrating AI and machine learning into a data warehouse can improve the speed, efficiency and quality of data and information management.
Data warehouses often form the basis of BI. They play a vital role in business environments, which can become complex and difficult to manage if poorly managed. Think of a data warehouse like the ocean: it’s where all the data resides and all the rivers flow in and out of it. Keeping these pathways open and easily accessible is key to powering rapid analytics and delivering insights at scale.
A data warehouse is a central repository of data. It can store data from various sources, such as relational databases or transactional systems. It can organize data based on predefined schemas, which sets it apart from other data storage systems.
This ability to extract data from many sources and then sort and store it in one place makes the data warehouse a great combination for BI applications and data analysis tools. Data warehouses allow these applications and tools to quickly access the structured data they need to perform analytics, ad hoc queries, visualizations, and reports.
One of the key benefits of a data warehouse is that it can serve as a single source of truth for an organization. It collects data from every department in an organization and stores it in one place, creating a comprehensive database with a clearly defined architecture that makes it easy to use. Data warehouses are also powerful enough to provide the large amounts of data that AI and ML applications need to perform optimally.
The role of AI and ML in a data warehouse
Modern data warehouses can power AI and ML capabilities, but AI and ML technology can also integrate into a data warehouse to perform and improve certain functions.
Computer science
AI and ML excel at analyzing large amounts of data. Data warehouses must quickly sort and retrieve data based on a query. AI and ML are ideally suited to improving data processing use cases. IT administrators can program AI to retrieve data based on simple, common queries, while ML algorithms can be trained to handle more complex queries. Using both can improve data processing speed and enable data warehouses to analyze more complex and larger volumes of data.
Automation
AI is ideal for automate tedious and intensive data tasks in a data warehouse. Administrators can program AI to automate several different processes, such as data integration, performance monitoring, and data cleaning and validation. Data integration helps ensure seamless connections between data sources and warehouse pipelines. Performance monitoring ensures that no data connections are interrupted and verifies that all processes are active and working as expected. Data cleaning and validation verifies that all data elements are completed, accurate, and correct. Automating all of these critical business processes allows humans to focus on other tasks.
Schema management
Data schema can become incredibly complex in an enterprise environment and a mistake in the schema upstream can lead to huge problems downstream. Managing a pattern can be tedious for humans, but AI can manage a pattern itself, if properly trained, by flagging or mitigating problems. ML can analyze warehouse schema usage to determine the most effective strategies and architectures for schema types.
Identifying patterns and trends
ML is particularly effective for analyze patterns. It can identify trends in stored data that human analysts might overlook. For example, it can be trained to examine query performance and find that certain processes are being bottlenecked by a particular data task repeatedly. Discovering this information can lead to optimizations that improve query performance. ML can also predict outcomes based on trends in historical data, enabling better decisions.
Scalability
AI and ML can work together to help improve data quality and consistency while optimizing data warehouse architecture. This can result in a much leaner data warehouse that can handle data requests in real time, store larger volumes of data, and remain more organized and efficient. An AI and ML-enriched data warehouse can scale faster and more easily as the organization grows, even as the technology landscape evolves and data processes become more demanding.
5 Ways Data Warehouses Benefit from AI and ML
Data warehouses can reap many benefits from AI and ML, including more efficient, faster, and more cost-effective operations.
Improved efficiency
Using AI and ML to optimize data storage frees data teams from tedious tasks, such as data validation. They are then able to focus on higher priority responsibilities that can improve the organization’s financial results. AI and ML algorithms can resolve data inconsistencies and handle repetitive and tedious tasks, such as extraction, transformation, and loading, on their own. This improves overall efficiency within the data warehouse.
Boosted speeds
ML algorithms that monitor query process performance can automatically identify opportunities for improvement and make adjustments that can increase speed and accuracy. Automating data ingestion and delivery allows users to act on information faster. Data is often more valuable when it is accessible in real time. Improved speed can result in faster and more effective decision-making.
Improved use of data for all skill levels
AI and ML can improve data quality and the accuracy and speed of data queries, which can enable more users to take advantage of business intelligence applications. regardless of their level of technical skill. A user without data literacy skills can simply enter a command in natural language and receive information in easy-to-understand formats, including simplified visualizations. When employees across the business can use data from a single source of truth, it can drive more aligned decision-making based on the same database.
More accurate forecasting capabilities
The predictive capabilities of ML can give data warehouses a competitive advantage. ML can predict trends and proactively identify and resolve issues. Predictive models and anomaly detection can also help a data warehouse stay ahead of customer demand as well as issues that could cause downtime or inaccuracies. The more predictions an algorithm makes, the more it improves over time, further improving the accuracy of the model and enabling better insights.
Reduced data storage costs
AI and ML can analyze data usage and determine the best ways to optimize data storage. For example, AI can identify duplicate or redundant data and automatically delete it, freeing up space. ML algorithms can streamline schema and data architecture, introducing efficiencies that can reduce operational costs in the data warehouse. As the organization scales, improved efficiency makes it easier to store, consolidate and process more data.
Jacob Roundy is an independent writer and editor with over a decade of experience specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change, and sustainability. His writing focuses on demystifying technology, tracking industry trends, and providing practical advice to IT managers and administrators.
