Data management can be a daunting task. It requires significant time and resources to collect, process, and analyze large volumes of information. To address these challenges, approximately 44% of companies are planning to invest in artificial intelligence (AI) to streamline their data warehousing processes and improve the accuracy of their insights.
AI is a powerful tool that goes beyond traditional data analytics. It identifies patterns and trends often missed by manual methods, leading to increased efficiency and improved accuracy. The current use of AI in data warehousing is just the beginning, and the possibilities for new applications are endless.
AI and Data Warehousing: Trends
AI is already shaping the processes surrounding data warehousing in many ways. By implementing AI-powered trends, companies have experienced dramatic improvements in the speed and accuracy of data processing. This has led to better decision-making and increased efficiency.
AI-assisted ETL Processes
One of the trends seen in data warehousing is the use of AI to assist in the extract, transform, and load (ETL) process. AI-powered ETL tools can automate repetitive tasks, optimize performance, and reduce the potential for human error.
By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing data models, training machine learning algorithms, and creating data visualizations. For instance, Coca-Cola uses AI-powered ETL tools to automate data integration tasks across its global supply chain to optimize procurement and sourcing processes.
Smart Data Modeling
Another trend in data warehousing is the use of AI-powered tools for smart data modeling. AI can analyze data sources and automatically generate data models, considering relationships between data points.
This saves time and resources for data scientists who would otherwise need to spend hours manually creating data models. Additionally, AI-powered data modeling can improve data accuracy and completeness.
For instance, Walmart uses AI-powered smart data modeling techniques for specific use cases, such as supply chain management and customer analytics. Data warehouse optimization enables Walmart to quickly and accurately identify trends in customer behavior, as well as forecast demand for specific products to ensure a smooth customer shopping experience.
Automated Data Cleansing
AI is increasingly being used to automate the process of data cleansing in data warehousing. Automated data cleansing, or data preparation, involves using AI to detect and remove inaccuracies, inconsistencies, errors, and missing information from a data warehouse, ensuring that the data is accurate and reliable.
With AI-powered data cleansing tools, organizations can leverage advanced algorithms and robust computing power to efficiently process and clean massive amounts of data. The ability to handle diverse data types allows for comprehensive data cleansing, resulting in high-quality data for analysis and decision-making.
For example, GE Healthcare leverages AI-powered data cleansing tools to improve the quality of data in its electronic medical records, reducing the risk of errors in patient diagnosis and treatment.
Continuous Data Quality Monitoring
According to Gartner, poor data quality cost enterprises an average of $15 million per year. With continuous data quality monitoring, businesses can avoid the costly and time-consuming process of identifying and correcting errors after the fact.
Continuous data quality monitoring is a new trend in data warehousing, transforming the way businesses manage their data in the future. Unlike traditional data management approaches that involve periodic data quality checks, continuous monitoring involves real-time monitoring of data quality.
With AI technology, it ensures that data is always clean, accurate, and up to date by automatically detecting anomalies and errors as they occur, streamlining the data management process.
For example, Airbnb has implemented AI-powered data quality monitoring tools to identify and correct data quality issues in real-time, leading to more accurate search results and pricing algorithms.
AI and Data Warehousing: Predictions
As artificial intelligence continues to rapidly advance, its potential applications are constantly expanding. In the realm of data warehousing, AI is predicted to play a significant role in the future.
Automated Schema Design
AI-powered schema design tools will analyze data sources and suggest the best schema design that fits the data, resulting in more efficient and accurate data warehousing. This technology will greatly benefit businesses dealing with large and complex data sets, such as financial institutions, healthcare organizations, and e-commerce companies.
For example, an e-commerce company could use an AI-powered schema design tool to optimize their data warehouse schema for different types of products, enabling them to easily add new product categories as they expand their offerings.
AI-Driven Data Curation
With a rise in big data, manual curation has become a time-consuming and tedious task. AI-powered data curation tools automate data cleaning and organization, enabling businesses to derive mission-critical insights.
Moreover, AI will automatically classify data using machine learning algorithms based on criteria like keywords, metadata, or content type, ensuring consistency and saving time. Safe to assume, the future of data curation with AI is bright, and it will undoubtedly pave the way for more accurate and insightful data-driven decision-making.
For example, a healthcare organization could leverage predictive analytics and use an AI-powered data curation tool to analyze patient data and identify trends or correlations between certain symptoms and diagnoses, leading to improved patient care and outcomes.
Intelligent Data Discovery
As data warehousing becomes increasingly complex, Intelligent Data Discovery (IDD) will become a crucial trend in business intelligence. With the ability to automatically identify patterns, trends, and relationships in large datasets, IDD systems offer real-time data analysis, providing instant insights that can help businesses make informed decisions.
For example, a transportation company could use an IDD system to analyze customer data and identify patterns in travel habits, leading to new service offerings or pricing models. Additionally, IDD systems can help businesses better understand their customers and improve their products or services by analyzing customer feedback and sentiment.
AI will play an increasingly important role in the future of data warehousing. The use of machine learning models, natural language processing, and other advanced data science techniques will enable data warehousing systems to become more intelligent and efficient at analyzing complex data sets.
A successful AI-enabled data warehousing tool should possess features such as advanced data mapping and transformation capabilities, automated code generation, support for multiple data formats, seamless integration with data lakes, and real-time learning capabilities.
These intelligent and autonomous data warehousing systems will be able to identify patterns and trends that are not immediately apparent and provide insights and recommendations that can help businesses stay ahead of the curve.
As we move forward, we can expect to see more innovative solutions that continue to push the boundaries of what is possible in the world of data warehousing. This will help businesses of all sizes unlock the full potential of their data.