Data warehousing is a term and practice being increasingly adopted by enterprises for some time now, which has become an important resource for businesses for storing, managing, and analyzing data. However, recent innovations have made it possible to integrate Artificial Intelligence (AI) and Machine Learning (ML) in data warehouses, paving the way for new opportunities to gain a competitive advantage and lead innovation.
In this post, we will cover a short overview on data warehouse, AI and ML, how AI and ML work within a data warehouse, and what are the best data warehousing practices that can help you draw more from this technology.
What is a Data Warehouse?
Data warehouse refers to a type of data management system designed to offer support to important Business Intelligence (BI) activities, and most importantly, to analytics. Data warehouses contain a huge amount of historical data, performing queries and analysis to provide the right output.
Application log files and transaction apps are a couple of sources through which the data within a warehouse is derived.
With a data warehouse, businesses can extract valuable insights from the gathered data to improve their decision-making process. The ways of implementing data warehouses can change, but AI tools and Machine Learning allow users to easily craft and run analytics without being dependent on IT teams.
Working of AI and ML within a Data Warehouse
AI and ML might have been applied to datasets as a part of testing. Still, recent innovations have opened avenues to allow AI and ML to be completely integrated into a warehouse to foster new capabilities in the process.
With AI and ML integrated into a data warehouse, the data warehouse implementation can transform business operations by offering a way for predictive analytics to play a role.
Here’s how:
- Better Data Processing: AI and ML models can handle large data volumes with high efficiency. By integrating AI and ML, a data warehouse can process difficult queries, make data retrieval more efficient, and analyze statistics quicker than legacy methods.
- Tailored Recommendations: By using AI and ML, a data warehouse can create tailored recommendations for its customers. This proves to be more than capable for e-commerce platforms, multiple streaming services, as well as any industry where understanding user preferences plays an important part.
- Predictive Models: AI and ML facilitate the creation of predictive models that can forecast outcomes based on historical data in a data warehouse. These models can be used by businesses to understand market trends, customer behavior, and other critical factors, helping make better decisions.
- Automated Cleansing: For accurate analytics, data quality plays a crucial role. AI-enabled warehouses automate the data cleansing process, ensuring that the derived insights are reliable and accurate.
Data Warehousing Best Practices
With a basic understanding of what a data warehouse is and how it works with AI and ML, here are a few best practices that can help you in your data warehouse development:
1. Start with the Right Master Data Management
Data accuracy is important to establish proper data-driven decisions in an organization. To ensure this happens, it’s important to have a proper master data management plan. It’s a system that develops a process to ensure that the data warehouse only has accurate master data in place.
If rightly implemented, MDM cuts down most of the effort in populating those warehouses.
2. Analyze the Frequency of Data Uploads
Identifying the frequency of loading data into the warehouse will also help you in defining the right cases for your enterprise. With batch processing, a large amount of data can be processed at once, and because it doesn’t require any special skills, the process reduces the operational costs of the company.
3. ELT Instead of ETL
ELT and ETL are two of the most popular data integration methods used when it comes to data warehouses. ELT means Extract, Load, and Transform, while ETL refers to Extract, Transform, and Load.
While both methods gather data from disparate sources and put it in a data warehouse, ELT offers more convenience when it comes to storing new and unorganized data.
4. Consider Using Cloud
The cloud has opened new opportunities for IT companies. Creating a data warehouse in the cloud can give you better flexibility and convenience to manage your data warehouse remotely instead of on-premises.
You can also make use of cloud data lakes which offer more storage for general purposes before it gets utilized for analytical use in a data warehouse.
Conclusion
Setting up requirements in advance and following these best practices will ensure that the data warehouse implementation you are planning is effective and provides you with the desired output. If you are looking for robust and customized data warehousing solutions, you can consult the experts at Vertisystem by clicking here.
With more than a decade of experience in AI and ML solutions, they could be the perfect match for you.