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5 reasons to use AI and machine learning in a data warehouse
Using AI and ML in a data warehouse gives the whole organization a single source of truth that can align decision making and foster better insights.
Integrating AI and machine learning in a data warehouse can improve the speed, efficiency and quality of data management and insights.
Data warehouses often form the foundation of BI. They play a critical role in enterprise environments, which can become complex and difficult to navigate if managed improperly. Think of a data warehouse like the ocean: it's where all data resides and all rivers flow into and out of it. Keeping those pathways open and easily accessible is the key to powering rapid analytics and delivering insights at scale.
A data warehouse is a central repository of data. It can store data from a variety of sources, such as relational databases or transactional systems. It can organize data based on predefined schemas, which is what separates it from other data storage systems.
This ability to pull in data from many sources, then sort and store it in one place makes the data warehouse a great pairing for BI applications and data analytics tools. Data warehouses enable these applications and tools to quickly access the structured data they need to perform analyses, ad hoc querying, visualizations and reporting.
One of the major 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 deliver the large amounts of data that AI and ML applications need to function 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 enhance certain functions.
AI vs ML for BI
AI enables machines to simulate humans' ability to use logic to make decisions and solve problems based on data. AI can automate tedious or complex tasks, optimize processes, oversee detail-oriented jobs, and perform data-heavy tasks on its own.
ML is a subset of AI. It's often an algorithm, and it enables machines to simulate humans' ability to learn. When ML is fed data, the ML algorithm decides based on what it learns from that data. It then analyzes and evaluates the results to fine-tune its next decision, with the goal of incrementally improving its accuracy.
ML can train on massive datasets and make decisions without being programmed to do so. AI typically needs explicit instructions on what actions to take, whereas an ML algorithm can act based on what it learns over time. ML's learning capabilities make it suitable for predictive analytics and data classification.
Data processing
Both AI and ML excel at parsing large amounts of data. Data warehouses must quickly sort through and retrieve data based on a query. AI and ML are a great fit for enhancing data processing use cases. IT administrators can program AI to retrieve data based on simple, common queries whereas ML algorithms can be trained to handle more complex queries. Using both can improve the speed of data processing and enable data warehouses to parse more complex and larger volumes of data.
Automation
AI is ideal for automating tedious, intensive data tasks in a data warehouse. Admins can program AI to automate several different processes, such as data integration, performance monitoring and data cleansing and validation Data integrations helps ensure smooth connections from data sources to warehouse pipelines. Performance monitoring ensures that no data connections are broken as well as double-checks that all processes are active and functioning as expected. Data cleansing and validation verifies that all data elements are filled out, accurate and correct. Automating all these critical business processes enables humans to focus on other tasks.
Schema management
Data schema can get incredibly complex within an enterprise environment and one error in schema upstream can cause huge issues downstream. Managing schema can be tedious for humans, but AI can manage schema on its own, if trained properly, by flagging or mitigating issues. ML can analyze warehouse schema usage to determine the most efficient strategies and architectures for schema types.
Patterns and trends identification
ML is particularly efficient at analyzing patterns. It can identify trends in stored data that human analysts might overlook. For example, it can be trained to review query performance and might find that certain processes are bottlenecked by a particular data task repeatedly. Uncovering this information can lead to optimizations that boost query performance. ML can also forecast outcomes based on historical data trends, leading to 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 process data requests in real time, store larger volumes of data, and stay more organized and efficient. A data warehouse augmented with AI and ML can scale more quickly and easily as an organization grows, even as the technology landscape evolves and data processes become more demanding.
5 ways data warehouses benefit from AI, ML
Data warehouses can gain a variety of benefits from AI and ML, including more efficient, faster and cost-effective operations.
Improved efficiency
Using AI and ML to optimize data storage frees data teams from time-consuming tasks, such as data validation. Then, they're able to focus on higher priority responsibilities that can improve the organization's bottom line. AI and ML algorithms can address data inconsistencies and handle repetitive and tedious tasks, such as extraction, transformation and loading, on their own. This boosts overall efficiency within the data warehouse.
Boosted speeds
ML algorithms that monitor the performance of the query process can automatically identify opportunities for improvement and make adjustments that can boost speeds and accuracy. Automating data ingestion and delivery enables users to take action on insights faster. Data is often most valuable when accessed in real time. Improved speed can translate to more prompt and more effective decision-making.
Enhanced data use for all skill levels
AI and ML can improve data quality, as well as the accuracy and speed of data queries, which can enable more users to take advantage of business intelligence applications regardless of their technical skill level. A user who lacks data literacy skills can simply input a natural language command and receive insights in easy-to-understand formats, including simplified visualizations. When employees across the enterprise can make use of data from a single source of truth, it can foster better-aligned decision-making based on the same data foundation.
More accurate forecasting capabilities
The predictive capabilities of ML can give data warehouses a competitive edge. ML can foresee trends and proactively identify and address issues. Predictive models and anomaly detection can also empower a data warehouse to stay one step ahead of customer demand as well as issues that might cause downtime or inaccuracies. The more forecasting an algorithm does, the better it gets over time, which further enhances model accuracy and empowers 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 duplicated 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 across the data warehouse. As the organization scales, improved efficiency makes it easier to store, consolidate and process more data.
Jacob Roundy is a freelance writer and editor with more than a decade of experience with specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.
